Fauzia Yasmeen - Higher Education...

GENETIC DIVERSITY OF MINERAL CONTENTS, NUTRITIONAL TRAITS AND HIGH MOLECULAR

GLUTENIN SUBUNITS IN BREAD WHEAT (TRITICUM AESTIVUM)

Fauzia Yasmeen

QUAID-I-AZAM UNIVERSITY

ISLAMABAD

2013

Genetic diversity of mineral contents, nutritional traits and

high molecular glutenin subunits in bread wheat

(Triticum aestivum)

Fauzia Yasmeen

Submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biological Sciences

with specialization in Biochemistry at the Department of Biochemistry,

Quaid-i-Azam University, Islamabad.

January 2013

DEDICATED

TO

THOSE WHO ARE SOURCE OF

INSPIRATION

FOR ME

ACKNOWLEDGMENT

First of all, praise is due to almighty ALLAH with His compassion and

Mercifulness for giving me strength and ability to complete this study. Allah's peace

and blessing be upon our Beloved Prophet Muhammad (Sallah-ho-Alai-hay-

Wassalam) who was a mercy unto us from Allah whose character and nobility none

has seen before or after Him (PBUH). May Allah give us all the ability to learn the

Seerah, the Shamail and instill within our hearts the love for His Prophet such that our

desire is to apply the Sunnah in every given instance.

The writing of a dissertation can be a lonely and isolating experience, yet it is

obviously not possible without the personal and practical support of numerous people.

Thus my sincere gratitude goes to my parents, all my friends, and my companions for

their love, support, and patience over the last few years.

I gratefully acknowledge my supervisor, Dr. Abdul Ghafoor, for his advice,

supervision, and crucial contribution, which made him a backbone of this research

and so to this thesis. His involvement with his originality has triggered and nourished

my intellectual maturity that I will benefit from, for a long time to come. Sir, I am

grateful in every possible way.

This thesis would not have been possible without the expert guidance of my

esteemed co-advisor, Dr. Muhammad Rashid Khan Not only was he readily available

for me, as he so generously is for all of his students, but he always read and responded

to the drafts of each chapter of my work more quickly than I could have hoped. His

oral and written comments are always extremely perceptive, helpful, and appropriate.

Prof. Wasim Ahmad of Quaid-e-Azam University has also played an

extremely important role in my research who stepped in as my Chair late in the

process after my original Chair retired, and helped push me through the last chapter.

Her flexibility in scheduling, gentle encouragement and relaxed demeanor made the

impetus for me to finish. Thank You Sir.

I am very grateful to the Dr. Yasmeen Ahmed and Dr. Javed Iqbal Mirza.

They‘ve been motivating, encouraging, and enlightening and supported me a lot in

maintain my greenhouse experiments in Murree. I am forever indebted to Dr. Ejaz

Rafique, not only for the countless ways he contributed to this particular project, but

also for the myriad ways he supported me in all of my goals.

This dissertation could not have been written without the support of Dr.

Tabassum. His patience, flexibility, genuine caring and concern, and faith in me

during the dissertation process enabled me to attend to life while also earning my

Ph.D.

Many people on the faculty and staff of the NARC assisted and

encouraged me in various ways during my course of studies. I am especially grateful

to Mr. Muhammad Hayat, Mr. Zulfiqar Ali, Mr. Muhammad Audil, Mr. Naveed and

Mr. Jahanzeib. The faculty and staff at NARC are the most dedicated and generous

people that I have ever met and I feel honored to have worked with them. Their

support has served me well and I owe them my heartfelt appreciation. During the

course of my research, I also worked with one student, Haris Khurshid, and thankful

to him for all of his conscientious work on this project. I alone remain responsible for

the content of the following, including any errors or omissions which may unwittingly

remain.

Where would I be without my family? My parents deserve special mention for

their inseparable support and prayers. My Father, Dr. Muhammad Aslam Asghar, in

the first place is the person who put the fundament my learning character, showing me

the joy of intellectual pursuit ever since I was a child. My Mother is the one who

sincerely raised me with her caring and love. Words fail me to express my

appreciation to my parents whose dedication, love and persistent confidence in me,

has taken the load off my shoulder. Finally, I would like to thank everybody who was

important to the successful realization of thesis, as well as expressing my apology that

I could not mention personally one by one.

Words fail me to express my appreciation to my husband Ali Bahadur whose

dedication, love and persistent confidence in me, has taken the load off my shoulder. I

owe him for being unselfishly let his intelligence, passions, and ambitions collide with

mine.

Fauzia Yasmeen

ABSTRACT

Plant genetic diversity is a key element in any agriculture. Wheat is an annual plant

that belongs to the grass family Poaceae Wheat contains carbohydrates, essential

amino acids, vitamins, protein and minerals. Institute of Agri-Biotechnology and

Genetic Resources (IABGR), Islamabad is a good source of wheat germplasm

collected from all over the country. Rust caused by Puccinia spp. cause considerable

worldwide damage to wheat production. There are three types of wheat rust viz, stripe

rust, stem rust and leaf rust .For the assessment of genetic variability in germplasm

collections biochemical markers, such as storage proteins, have received more

attention in recent years. High molecular weight glutenin subunits, encoded by Glu-

A1, Glu-B1, and Glu-D1 loci located on long arms of the homologous group 1

chromosomes of wheat, play a vital role in determining the bread making quality of

wheat. Phenotypic identification based on morphological characteristics has been

successfully used for genetic diversity analysis. However, morphological traits have a

number of limitations, including low polymorphism, low heritability, late expression

and may be controlled by epistatic and pleiotropic gene effects) while protein

markers, like seed storage proteins, reflect with more accuracy the genotypes,

independently from the environmental effects. Single seed was ground to fine powder

with the help of mortar and pestle. Protein extraction buffer (400µl) was added to

0.01g of seed flour in eppendorf tube and mixed. The samples were mixed thoroughly

by vortexing and centrifuged at 13,000 rpm for 10 min. Electrophoresis was carried

out at 100 mA until a blue line of Bromophenol blue reached the bottom of the gel

(approximately three and half hour). Then staining and destaining was carried out.

139 accessions of wheat germplasm were evaluated for nutritional characteristics.

The experiment was carried out at Grain Quality Testing Laboratory, National

Agricultural Research Centre, Islamabad. Fibre, oil, moisture, ash and protein were

studied following the standard methods of AOAC (2005). Determination of Minerals

Contents was carried out by dry ashing (Boron), wet digestion(Zinc, Copper,

Manganese, Iron, Sodium, Potassium and Phosphorus) and Kjeldahl method

(Nitrogen). Seed characteristics studied included seed length (By vernier caliper),

seed width (By vernier caliper), 100 seed weight, seed colour, seed size and degree of

seed shriveling. For the screening of stem rust, plants were inoculated with 09077.

Inoculums in the form of uredial suspension in soltor-170 (eight weight

non-phototoxic mineral oil) was sprayed uniformly with a sprayer having five nozzle.

The seedlings were left in open air for 1-2 hours to evaporate mineral oil and shifted

afterwards to a humidity chamber for 24 hours, after which they were transferred to

green house at 18-22oC. After ten days infection types were recorded. Summary

statistic showed that fibre ranged from 0.64 to 1.87 %, oil from 1.18 to 2.49 %,

moisture from 6.00 to 8.50 %, ash from 0.77 to 6.86 %, protein from 7.12 to 16.92 %,

Nitrogen from 1.25 to 2.97 %, Phosphorus from 0.10 to 0.44 %, Potassium from 0.30

to 0.88 %, Boron from 0.48 to 3.78 ppm, Zinc from 13.50 to 54. 00 ppm, Copper from

1.00 to 9.00 ppm, Manganese from 7.80 to 41.60 ppm, Iron from 8.20 to 300.0 ppm,

Sodium from 0.02 to 0.08 %, seed length from 3.36 to 7.43 mm, seed width from

1.67 to 3.15 and 100 seed weight from 2.20 to 5.36 g.

Regarding nutritional traits, PC1 contributed 23.7% and PC2 contributed

23.4% to the genetic variance of wheat germplasm constituting 139 accessions

belonging to Punjab and Baluchistan. Moisture (0.736) and ash (0.505) contributed

more positively to PC1 while oil (0.717) and protein (0.679) imparted maximum

genetic variance to PC2. First four principal components contributed 62.3% of the

total variation as far as mineral contents are concerned. PC1 contributed 21.1%, PC2

15.4%, PC3 13.8% and PC4 contributed 11.8% to the total variation shown by the

wheat germplasm. Nitrogen (0.571), Phosphorus (0.581), Zinc (0.729) and Copper

(0.616) imparted maximum genetic variance to PC1, Potassium (0.718) and Iron

(0.643) to PC2, Boron (0.532) to PC3 and Manganese (0.768) and Sodium (0.675)

contributed more positively to PC4. The seed characteristics that contributed more

positively to PC1 included seed length (0.745) and sized width (0.741). To PC2 seed

size (0.514) contributed more positively while seed width (0.597) and seed color

(0.659) imparted maximum genetic variance to PC3.Regarding combined traits of

Punjab and Baluchistan the characteristics which imparted maximum genetic variance

to PC1 included protein (0.828), Nitrogen (0.831) and Zinc (0.687). Moisture (0.638),

Phosphorus (0.611) and Boron (0.656) contributed were positively to PC3, Iron

(0.533) to PC4, Sodium (0.539) to PC5, and ash (0.589) contributed more positively to

PC7.Oil was found to be positively correlated with Zinc whereas moisture showed

positive association with Phosphorus and Boron. Protein exhibited positively

association with Nitrogen and Zinc. P exhibited positive correlation with Boron and

Manganese. Zinc showed positive association with Manganese and Iron. Seed length

was observed to be positively associated with seed width, and seed width showed

positive correlation with 100 seed weight.

Wheat germplasm was subjected to sodium dodecyl sulphate polyacrylamide

gel electrophoresis (SDS-PAGE) to predict the genetic variability on the basis of high

molecular weight glutenin sub-units. In Punjab accessions three allelic variants (Null,

1 and 2*) were found at Glu-A1 locus. Glu-B1 locus was observed to be highly

polymorphic. 19 sub-unit or sub-unit pairs were found at Glu-B1 as 16,(14*+9), (9,

17+18), 17+18, 7**+8, 7**, 7**+8*, 7, 7+8, 7(7**), (6, 7), 7*+9, 7*+8, (8, 13+160,

13+16,9, 7+9, 6+9 AND (7*, 7**+8). Glu-D1 locus consisted of four allelic sub-units

or subunit pairs i.e. 12, 2+12, 4, 5+10.At the Glu-A1 locus, four allelic variants (Null,

1, 2* and 2’) were observed in 122 wheat accessions belonging to Baluchistan region.

Glu-B1 locus was found to be highly polymorphic. 30 sub-unit pairs or sub-units were

found at this locus as 7*+8, 7*+8(8**), 7+8, 7+9, 7(7*)+9, 8*, 7+8*, 7+8**, 7**,

7**+9, 7**+8, 7(7**)+9, 13, 7**+8**, 7(7**), 17+18, 8**(17+18), 14+15, (6,

14+15), (7, 14+15), 20, 9, 7*+9, 7(7*)+8, 13+16, (8*, 7+9), 8*(7*+9), (6, 17+18) and

17. Glu-D1 locus was comprised of nine allelic subunits or sub-unit pairs i.e. 2+12,

3+12, 2+12*, 10, 12*, 12, 5+10, 5+12*, 5+12. In commercial varieties three allelic

variants (Null, 2 and 2*) were observed at the Glu-A1 locus. The Glu-B1 locus was

found to be highly polymorphic. Out of fourteen allelic variants detected, ten sub-unit

pairs or subunits were found at this locus as 7+9, 7*+9, 7**+9, 17+18, 13+16, 7+8,

&*+8, 7+8(8*), 14 and 7* (13+16). Glu-D1 locus was comprised of two allelic sub-

unit pairs i.e. 5+10 and 2+12.

Total of 192 accessions/commercial varieties were screened against stem rust

and stripe rust including eighty seven accessions of Baluchistan, 37 accessions of

Punjab and 68 commercial varieties. For stem rust resistance was recorded as

resistant, moderately resistant and susceptible. Regarding stem rust, 153

accessions/commercial varieties were recorded to be resistant. While 16

accessions/commercial varieties were found to be susceptible. The data regarding

resistance against stripe rust was recorded as resistant, moderately resistant,

moderately susceptible and susceptible. Nine accessions of Punjab, 21 Baluchistan

accessions and 34 commercial varieties were identified to be resistant. None of the

accessions or commercial varieties was found to be susceptible.

vii

TABLE OF CONTENTS

1 INTRODUCTION 1

1.1 Wheat 1

1.2 Plant Genetic Diversity 2

1.2.1 Genetic Diversity in Wheat 3

1.2.1.1 Nutritional Traits 3

1.2.1.2 Mineral Contents 4

1.2.1.3 Seed Characteristics 5

1.2.1.4 High Molecular Glutenin Subunits 5

1.2.1.5 Rust 6

1.3 Statement of the Problem 7

1.4 Objectives of the Study 7

2 REVIEW OF LITERATURE 8

2.1 Germplasm 8

2.1.1 Germplasm Conservation and Evaluation 9

2.2 Genetic Diversity/Erosion 10

2.2.1 Nutritional Traits 10

2.2.2 Minerals 12

2.2.3 High Molecular Glutenin Subunits (HMW-GS) 15

2.2.3.1 HMW-GS and Quality Scores 17

2.2.4 Rust in Wheat 18

3 MATERIALS AND METHODS 23

3.1 Germplasm Collection 23

3.2 Experimental Material 23

3.3 Determination of Nutritional Traits 28

3.3.1 Fibre 28

3.3.2 Oil 28

3.3.3 Moisture 29

3.3.4 Ash 29

3.3.5 Protein 29

3.4 Determination of Minerals Contents 30

3.4.1 Dry Ashing 30

3.4.1.1 Boron 30

3.4.2 Wet digestion 31

3.4.2.1 Zinc, Copper, Manganese and Iron 31

viii

3.4.2.2 Sodium and Potassium 31

3.4.2.3 Phosphorus 32

3.5 Seed Characteristics 32

3.6 Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE)

33

3.6.1 Extraction of Protein 33

3.6.2 Preparation of Electrophoretic Gel 33

3.6.3 Electrophoresis 34

3.6.4 Staining, Destaining and data scoring 34

3.7 Data Analysis 34

3.8 Cluster Analysis 35

3.9 Principal Component Analysis 35

3.10 Rust 35

3.10.1 Stem Rust 36

3.10.2 Stripe Rust 36

4 RESULTS 37

4.1 Genetic Diversity Based on Geographic Pattern 37

4.1.1 Germplasm collected from Punjab 37

4.1.2 Germplasm collected from Baluchistan 37

4.2 Frequency Distribution 40

4.2.1 Nutritional Traits 40

4.2.2 Mineral Contents 40

4.2.3 Seed Characteristics 44

4.3 Principal Component Analysis based on Geographic Pattern 47

4.3.1.1 Punjab 47

4.3.1.2 Baluchistan 59

4.4 Genetic diversity in wheat germplasm collected from Punjab and

Baluchistan provinces 66

4.5 Cluster Analysis 72

4.5.1 Based on Geographic Pattern for the germplasm collected from Punjab ..

................................................................................................................72

4.5.2 Based on Geographic Pattern for the germplasm collected from

Baluchistan 85

4.6 Correlation Analysis among Various Traits Based on Geographic Pattern 103

4.7 High Molecular Glutenin Subunits (HMW-GS) for the germplasm collected

from Punjab province 103

ix

4.7.1 Patterns of Allelic Distribution 107

4.8 Screening of Rust 122

4.8.1 Effect of Rust on Combined Traits 122

5 DISCUSSION 126

5.1 Nutritional Traits 126

5.2 Mineral Contents 128

5.3 Seed Characteristics 131

5.4 Coefficient of Correlation 132

5.5 Genetic Diversity based on Multivariate Analyses 134

5.6 High Molecular Weight Glutenin Subunits (HMW-GS) 135

5.7 Screening of Rust 140

5.8 Conclusions 143

5.9 Recommendations 145

6 REFERENCES 146

7 APPENDICES 184

x

LIST OF TABLES

Table

No.

Title Page

No.

2.1 Allelic variation of HMW-GS in wheat 19

3.1 Brief view of the experiments conducted at NARC, Islamabad 24

3.2 List of wheat accessions collected from Baluchistan 25

3.3 List of wheat accessions collected from Punjab 26

3.4 List of commercial varieties of Pakistani wheat 27

4.1 Basic statistics for nutritional traits, mineral contents and seed

characteristics of wheat accessions collected from Punjab

38

4.2 Basic statistics for nutritional traits, mineral contents and seed

characteristics of wheat accessions collected from Baluchistan

39

4.3 Promising accessions selected on the basis of combined traits in

wheat accessions collected from Punjab and Baluchistan

42

4.4 Principal components based on nutritional traits of wheat accessions

collected from Punjab

50

4.5 Principal components based on mineral contents of wheat accessions


53

4.6 Principal components based on seed characteristics of wheat

accessions collected from Punjab

55

4.7 Principal components based on combined traits of wheat accessions


57

4.8 Principal components based on nutritional traits of wheat accessions

collected from Baluchistan

60



62


accessions collected from Baluchistan

64



67

4.12 Principal components based on nutritional triats of wheat accessions

collected from Punjab and Baluchistan (Combined)

69

xi



71


accessions collected from Punjab and Baluchistan (Combined)

74



76

4.16 Clusters based on linkage distance for nutritional characteristics of

wheat germplasm collected from Punjab accessions

80

4.17 Mean and standard deviation within clusters for nutritional traits in

wheat accessions collected from Punjab

81

4.18 Clusters based on linkage distance for mineral contents in accessions

of wheat germplasm collected from Punjab

83

4.19 Mean and standard deviation within clusters for mineral contents in


84

4.20 Clusters based on linkage distance for seed characteristics in

accessions of wheat germplasm collected from Punjab

87

4.21 Mean and standard deviation within clusters for seed characteristics

in wheat accessions collected from Punjab

88

4.22 Clusters based on linkage distance combined traits in accessions of

wheat germplasm collected from Punjab

90

4.23 Clusters based on linkage distance for nutritional characteristics in

accessions of wheat germplasm collected from Baluchistan

92

4.24 Mean and standard deviation within clusters for nutritional traits in

wheat accessions collected from Baluchistan

93

4.25 Clusters based on linkage distance for mineral contents in wheat

germplasm collected from Baluchistan

96

4.26 Mean and standard deviation within clusters for mineral contents in


97

4.27 Clusters based on linkage distance for seed characteristics in


99

4.28 Mean and standard deviation within clusters for seed characteristics

in wheat accessions collected from Baluchistan

100

4.29 Clusters based on linkage for combined traits in accessions of wheat 102

xii


4.30 Coefficient correlation for combined traits in wheat accessions


104

4.31 Coefficient of correlation for combined traits in wheat accessions

from Baluchistan

105

4.32 Coefficient of correlation for combined traits in wheat accessions


106

4.33 Allelic frequency of three high molecular glutenin in wheat


108

4.34 Allelic summary in quality score in wheat accessions collected from

Punjab

111

4.35 Allelic frequency of high molecular glutenin subunits in wheat


113

4.36 Allelic summary and quality score in wheat accessions collected

from Baluchistan

115

4.37 Allelic frequency of three high molecular weight glutenin in

commercial wheat varieties

117

4.38 Allelic summary and quality score in commercial wheat variety of

Pakistan

120

4.39 Allelic frequency of high molecular glutenin subunits in wheat

accessions and commercial varieties

121

4.40 Screening of wheat germplasm for stripe rust 123

4.41 Screening of wheat germplasm for stem rust 124

4.42 Effect of yellow rust and stem rust on combined traits 125

xiii

LIST OF FIGURES

Figure

No.

Title Page

No.

4.1 Frequency distribution for fibre and oil in wheat germplasm 41

4.2 Frequency distribution for moisture and ash in wheat

germplam

41

4.3 Frequency distribution for protein and Nitrogen in wheat

germplasm

43

4.4 Frequency distribution for Phosphorus and Potassium in wheat

germplasm

43

4.5 Frequency distribution for Boron and Zinc in wheat germplam 45

4.6 Frequency distribution for Copper and Manganese in wheat

germplasm

45

4.7 Frequency distribution for Iron and Sodium in wheat

germplasm

46

4.8 Frequency distribution for seed length and seed width in wheat

germplam

46

4.9 Frequency distribution for 100 seed weight in wheat

germplasm and seed size in Punjab

48

4.10 Frequency distribution for seed color and seed shriveling in

Punjab

48

4.11 Frequency distribution for seed size and seed color in

Balochistan

49

4.12 Frequency distribution for seed shriveling in Baluchistan 49

4.13 Scattered diagram of first two PCs for nutritional traits in


51

4.14 Scattered diagram of first two PCs for mineral contents in


54

4.15 Scattered diagram of first and third PC for mineral contents in


54

4.16 Scattered diagram of first two PCs for seed characteristics in


56

xiv

4.17 Scattered diagram of first two PCs for combined traits in wheat


58

4.18 Scattered diagram of first and third PC for combined traits in


58



61

4.20 Scattered diagram of first and third PC for nutritional traits in

wheat accessions collected from Balochistan

61



63



63



65

4.24 Scattered diagram of first and third PC for seed characteristics

in wheat accessions collected from Baluchistan

65



68



68


wheat germplasm collected from Punjab and Baluchistan

(combined)

70



(combined)

73



(combined)

73



(combined)

75

4.31 Scattered diagram of first and third PC for seed characteristics

in wheat germplasm collected from Punjab and Baluchistan

75

xv

(combined)


germplasm collected from Punjab and Baluchistan (combined)

77



(combined)

77

4.34 Phenogram for 46 accessions of wheat germplaasm collected

from Punjab based on nutrition traits

78

4.35 Phenogram for 46 accessions of wheat germplasm collectrd

from Punjab based on mineral contents

82

4.36 Phenogram for 46 accessions of wheat germplasm collected

from Punjab on seed characteristics

86

4.37 Phenogram for 46 accessions of wheat germplam collected

from Punjab based combined traits

89


from Baluchistan based on nutritional traits

91


from Baluchistan based on mineral contents

95


from Baluchistan based on seed characteristics

98


from Baluchistan based on combined traits

101

4.42 Gel photograph of SDS-PAGE indicating HMW-GS in wheat

accessions and varieties alongwith checks

109

4.43 Dendogram for wheat accessions collected from Punjab based

on high molecular glutenin subunits

110

4.44 Dendogram for wheat accessions collected from Baluchistan

based on high molecular glutenin subunits

114

4.45 Dendogram for commercial wheat varieties based on high

molecular glutenin subunits

119

1

1 INTRODUCTION

1.1 Wheat

Wheat (Triticum aestivum) is an annual plant belonging to Poaceae (Grass

family), tribe Triticeae and subtribe Triticinae. It originated in southwestern Asia and

some of the earliest wheat fossils were discovered in Turkey, Jordan and Syria.

Archeological findings reflect that in England, China and India bread wheat was

cultivated about 5,000 B.C. (Gibson and Benson, 2002). Wheat roots are of two types,

i.e., 1) nodal roots and 2) seminal roots. Shoot is composed of phytomers and each

phytomer has the potential to bear a node, leaf, internodes and bud. From each basal

leaf axil tillers originate. Leaf is comprised of wrapping sheath and lamina. Ligules

and auricle arise at the junction of lamina and sheath. Leaf base becomes swollen to

form pulvinus (Kirby, 2006). Kerby et al. (1990) mentioned that Sakamura (1918) and

Kihara (1924) were the first who identified that three-level ploidy series including

diploid (2n=14), tetraploid (2n=28) and hexaploid (2n=42) is the characteristic of

Triticum. Cultivated forms predominates the hexaploid wheats such as bread wheat or

common wheat (Triticum aestivum). From commercial point of view T. aestivum

(AABBDD) is the most important wheat (Hilu, 1987). Three genomes, which show

close genetic relationship, combine together resulting in its formation (Li et al., 2000).

The A, B and D genome contributes seven pairs of chromosomes to wheat total

genome (Dvořák et al., 1998).

Global wheat production occupies third position, among the crops following

corn and rice (Roper, 2011). Wheat can resist harsh climatic conditions as compared

to rice and corn, which gives the best production at intermediate temperatures (Gibson

and Benson, 2002). Production of wheat in 2010 was 682.6 million tons (Food

Outlook Global Market Analysis, 2010). Pakistan is the 7th

largest wheat producer

(Food Outlook Global Market Analysis, 2009) and contributes 3.52 % of the world

wheat production (Food Outlook Global Market Analysis, 2010). Pakistan was the

first country in Asia to achieve self-sufficiency in wheat as a consequence of Green

Revolution (Hussain and Qamar, 2007). In Pakistan, total wheat production during

2008-09 was 23.4 million tons (GOP, 2009). Wheat is cultivated in wide agro

ecological areas of Pakistan covering 50% of the areas under cultivation (Agricultural

Census Organization, 2003). Wheat is grown in all provinces of Pakistan (Mujahid,

2010) but mainly in the plains of Sindh and Punjab (Husain, 2010). Wheat is the

2

staple food of masses and holds a distinct position in Pakistani diet and it contributes

80 % of the total dietary intake, 60 % of the total protein including calorie

requirements (Bostan and Naeem, 2002). Bread wheat is considered to be a major

food crop since its domestication (Curtis, 2002). In Pakistan, the most commonly

consumed wheat product is flat bread locally known as chapatti. Moreover, wheat is

used for other bakery products like bread, cakes, cookies, buns, pastries (Mahmood et

al., 2004), biscuits, crackers, roles, waffles, pancakes, doughnuts, muffins, pie crusts,

macaroni, ice cream cones, spaghetti, pizza, puddings etc. It is commonly used as

thickener in gravies, sauces and soups. Bran, malt and germ are additional sort of

wheat products. Wheat serves as livestock and poultry feed. For bedding of livestock,

wheat straw is commonly used. At industrial level, wheat germ is used for the

production of gluten, oil, alcohol and starch for paste. Paperboard, newsprint and

some other products are prepared from wheat straw (Gibson and Benson, 2002).

Pakistan is among the main centres of diversity of various cultivated crops

including wheat (Vavilov, 1951; Hirano et al., 2008). Hawkes et al. (2000) observed

that in the germplasm collected from various regions of Pakistan it was difficult to

find older bread wheat varieties which were commonly cultivated three decades ago.

During the last three decades the area under modern high yielding varieties has been

increasing that caused genetic erosion and threatened the genepool of wheat land

races. Therefore the need to collect and conserve wheat landraces for future utilization

was highlighted. The Institute of Agri-Biotechnology and Genetic Resources

(IABGR), Islamabad has collected and conserved 2814 (bread wheat), 207 (durum

wheat) and 150 wild wheats that has been collected from all over the country. For

wheat breeding this material can best be utilized if the information regarding the

extent and distribution of genetic diversity becomes available (Wikipedia, 2012).

Pakistani wheat breeders had much concern about wheat breeding for higher

yield potential in the past, but recently research for the improvement of wheat quality,

breeding resistant culitivars, monitoring of diseases and gene analysis has gained

popularity among the researchers (Mujahid, 2010).

1.2 Plant Genetic Diversity

Genetic diversity is the sum total of genetic makeup of a species that could be

analyzed through numerical measures. In any agricultural production system genetic

variability either existing or created plays vital role and interdisciplinary approach

3

involving technological, socioeconomic and environmentalists can make the best

usage of genetic diversity for crop improvement (Hirano et al., 2008). Biological

diversity is being rapidly lost by human interventions (Hawkes et al., 2000) including

breeding high yielding cultivars, use of fertilizers, irrigation and monoculture of plant

cultivar (Moghaddam et al., 1997). Since 1970s, the naturalists have been informing

the public about conservation of germplasm and techniques for conservation so that

landraces could be maintained in the absence of their habitats. To maintain diversity

patterns of the crop germplasm collection has been carried out in local as well as in

global gene banks for conservation of genetic resources of major crops (Hirano et al.,

2008).The extent of genetic diversity results in the development of disease resistant

cultivars.

1.2.1 Genetic Diversity in Wheat

As mentioned, the knowledge of genetic diversity in crop germplasm has

linear relationship with improvement of crop species (Moose and Mumm, 2008) either

through heterosis or generation of productive recombinants (Saleem et al., 2009).

Wheat is a rich source of carbohydrates and contains essential amino acids, vitamins,

protein and minerals (Khan and Zeb, 2007; Iskander and Murad, 1986). Genetic

diversity in wheat has been well evaluated using morphological, protein (Caballero et

al., 2004a) and molecular markers (Marshall and Brown, 1975; Xu et al., 2008; Zhang

et al., 2008). Phenotypic identification based on morphological characteristics has

been successfully used for genetic diversity analysis (Cox et al., 1985; Tolbert et al.,

1979; Ceccarelli et al., 1987; Daâloul et al., 1998; Fakhfak et al., 1998). However,

there are some limitations of morphological characteristics including low heritability,

low polymorphism, delayed expression and pleiotropic or epistatic gene effects

(Nakamura, 2001), whereas protein markers reflect more accuracy and are

independent of environmental effects (Brown and Weir, 1983; Jana and Pietrzak,

1988; Nevo, 1988; Nevo et al., 1988; Asfaw, 1989; Gepts, 1989; Autran et al., 1995).

1.2.1.1 Nutritional Traits

Nutritional composition of wheat varies widely due to huge number of

varieties and their cultivation under different conditions (Wallington, 1997). Major

components of wheat are protein (7 to18%), ash (1.5 to 2 %), fat (1.5 to 2%), crude

fibre (2 to 2.5%) and moisture contents (8 to 18 %.) as described by Samuel (1991).

Fibre consumption is associated with the reduction of diabetes incidence and diseases

4

related to heart, consequently, it is desirable to develop foods with higher dietary fibre

contents (Wang et al., 2002). Although diversification of micronutrient rich traditional

foods is one of the solutions to these challenges, but most of the people of the world

cannot access the variety of food which is rich in nutrition (Graham et al., 2001). For

long time Pakistan has been a food deficit country and crop improvement programs

were based on yield-orientation and less attention has been paid to quality of grain.

Presently wheat breeders in Pakistan are paying more attention to evolve new varieties

with improved yield potential coupled with superior quality (Zeb et al., 2006).

Pakistani wheat varieties are cultivated over a wide agro-climatic range and are

expected to exhibit diversity in yield and quality (Chaudhry et al., 1995).

1.2.1.2 Mineral Contents

Deficiency of certain minerals in human diet is a major health associated

problem throughout world, especially in the developing countries (Pinstrup-Andersen,

2000; Bouis, 2003). Malnutrition causes high social and economic costs (Sanchez and

Swaminathan, 2005). Micronutrient malnutrition ‘Hidden Hunger’ affects over 2 to 3

billion people worldwide (Stoltzfus and Dreyfuss, 1998; Stoltzfus, 2001;Bouis, 2007;

White and Broadley, 2009). High rates of chronic diseases, increase in mortality and

morbidity, permanent retardation of mental abilities of infants born to micronutrient-

deficient mothers, poor health and low worker productivity are all consequences of

micronutrient malnutrition (Cakmak et al., 2002; Caballero, 2002; Demment et al.,

2003; Hotz and Brown, 2004; Sanchez and Swaminathan, 2005). The two minerals of

prior importance regarding micronutrient deficiency include Zn and Fe (Welch and

Graham, 1999). During the reproductive development of wheat, boron deficiency and

cold temperatures cause failure of grain to set (Subedi et al., 1998).

One approach to increase the concentration of zinc and iron in seeds is through

applying fertilizers to plants through foliar sprays or soils, however zinc or iron

concentration cannot be improved to the desired levels (Bouis et al., 2000). Moreover

farmers did not feel motivated to apply fertilizers for the improvement of nutritional

status of seeds, although the crop yields may be improved. The second approach is to

exploit through plant breeding the genetic diversity in micronutrients in seeds (Bouis

et al, 2000; Poletti et al., 2004; Ghandilyan et al., 2006; Distelfeld et al., 2007; Ortiz-

Monasterio et al., 2007; Cakmak et al., 2000). Since recent past, not a single instance

was found where plants were bred for improvement of their nutritional contents and if

5

it has occurred, it was totally by chance (Lindsay, 2002; Welch and Graham, 2002).

1.2.1.3 Seed Characteristics

The grain characteristics including seed texture, color, size, shape and many

other traits have been the major selection criterion for wheat breeding in most of the

countries (Milligan et al., 2004). In wheat grain, pericarp and seed coat are fused and

cannot be separated that ultimately enhance the flour quality due to dietary fibres

(Kumar et al., 2011) Wheat grain has a length of about 5mm, the kernel has somewhat

vaulted shape with the germ or embryo at one end, and a bundle of hairs at the other

end. In cross section, endosperm is clearly visible that is rich in starch and has protein

that helps in dough making by forming gluten. Wheat grains are either light, yellowish

color (known as white wheats) or dark, orange-brown appearance (red wheats). There

was a time when white-wheats were preferred by many countries, but this preference

has gradually disappeared in Europe, whereas mainly red-colored wheats are

cultivated nowadays (Beiderok et al., 2000). The main reason for this change is that

the red wheats are more resistant to pre-harvest sprouting as compared to white

wheats. White wheat varieties are grown particularly in Australia and South Asia,

though these are also found in Canada and USA (Belderok et al., 2000).

1.2.1.4 High Molecular Glutenin Subunits

Mature grain of wheat has 8-20% protein in which gluten accounts for 80-85%

of total flour protein (Li et al., 2009). The seed storage proteins of common wheat

“gluten” are composed of ethanol-soluble monomeric gliadins and alcohol-insoluble

polymeric glutenins (Shewry, 2003). The glutenins of wheat seed consist of two major

categories of subunits categorized on the basis of their molecular weight, i.e.,one with

the molecular weight of 30-51 KDa is named as low molecular weight glutenin

subunits (LMW-GS) and the other with the molecular weight of 90-150 KDa is called

high molecular weight glutenin subunits (HMW-GS) as referred by Niwa et al. (2008)

and these subunits accounts for 80% and 20% of the glutenin, respectively (Šramková

et al., 2010). These subunits are required for processing of wheat flour for making

chapatti, pasta or bread (Shewry et al., 1989). The HMW-GS cause dough elasticity

and its rise, by trapping air bubbles formed by yeast (Hamer, 2003). Separation of

glutenin can be carried out by sodium dodecyl sulphate polyacrylamide gel

electrophoresis (Lawrence and Shepherd, 1980; Miflin et al., 1983; Singh and

Shepherd, 1988; Pogna et al., 1990; Madgwick et al., 1992; Shewry, 1996; Ciaffi et

6

al., 1999; Shewry, 2000). Characterizations of germplasm collections through

biochemical markers need minimal space and time (Ruiz et al., 2002). Moreover, the

results are not affected by the environment fluctuations (Gras et al., 2001).

To evaluate the genetic diversity in wheat variation of alleles at Glu-A1, Glu-

B1and Glu-D1 coding for HMW-GS has been used alone or along with other molecular

markers (Margiotta et al., 1993; Bushehri et al., 2006). The HMW-GS are highly

polymorphic (Pflügeret al., 2001b; Degaonkar et al., 2005) and their expression is

strictly under genetic control (Margiotta et al., 1988; Ciaffi et al., 1993). For the

determination of bread making quality of wheat, HMW-GS plays a significant role

(Lawrence et al., 1987; Randall et al., 1992; Ahmad et al., 1998; Rodriguez-Quijano

et al., 2001; De Bustos et al., 2001), hence the knowledge of the HMW-GS in the

germplasm could help to find out the effect of Glu-A1, Glu-B1and Glu-D1on particular

traits associated with quality (Gregováet al., 1999; Li et al., 2009).

1.2.1.5 Rust

Although wheat occupies top position among cereals in Pakistan, yet a

considerable loss in yield is observed due to various diseases including rust that may

cause more than 90% yield loss in case of susceptible variety (Johnston and Miller,

1934).Three types of rust in wheat are known namely, stem rust, leaf rust and stripe

rust. Stem rust is caused by Puccinia graminis sp. Triticithat was once known to be

the disease causing most fear among wheat growers worldwide.Singh et al.(2006)

mentioned that first time the detailed account of stem rust in wheat was described by

Tozzetti and Fontana in 1767 (Tozzetti, 1767; Fontana, 1767).The rust caused by

Puccinia results in a considerable damage to wheat production and cultural practices

or chemical treatment cannot provide 100 % protection against disease causing

organisms (Curtis et al., 2002). For controlling rust, cultivation of resistant varieties is

the most effective, economic and environment friendly approach (Line and Chen,

1995). Though resistant varieties provide relief for some time, but as the pathogens

keep on undergoing mutation or evolving them, this solution did not prove to be a

long-term relief (Friesen et al., 2006).A wheat cultivar (Inqilab 90) resistant to stripe

rust resulted in the spread of stripe rust in the region later on, therefore continuous

identification of rust resistant sources are vital for sustainable varietal development

programme (Chen and Ashraf, 2011).

7

1.3 Statement of the Problem

Pakistan is a country where malnutrition exists among poor masses who

mainly depends upon wheat, hence investigation on nutritional status of wheat

germplasm is imperative. The use of wheat genetic resources will likely be able to

develop cultivars with better yield potential coupled with higher nutritive and mineral

value to cope with the hidden hunger of poor masses, not only in Pakistan but in the

region. The study on high molecular glutenin subunits of wheat has direct linkage to

have better combinations of HMW-GS to screen huge germplasm accessions as well as

the breeding population. Due to constant threat of rust in wheat, the data on

germplasm to be used in future is important, hence was included in the study. The

material used in the present study will enrich the database being maintained by the

Institute of Agri-Biotechnology and Genetic Resources (IABGR) and germplasm

access is available for research and development (www.parc.gov.pk).

1.4 Objectives of the Study

The overall goal of the study was to provide the base material to broaden the

wheat improvement horizon, especially for development of high quality wheat that

has an emerging demand worldwide. To achieve this, the following specific objectives

were outlined as:

1. Evaluation of wheat germplasm predominantly collected from Baluchistan and

from Punjab provinces for diversity on the basis of nutritional traits, seed

characteristics and mineral contents.

2. To analyze wheat germplasm for HMW-GS for determining wheat quality and

its practical utilization in screening wheat germplasm and breeding population

for quality.

3. To know the rust status of indigenous wheat germplasm and identification of

resistant sources.

4. Identification elite lines for future exploitation and utilization in wheat

breeding programme.

http://www.parc.gov.pk/

8

2 REVIEW OF LITERATURE

Knowledge of genetic diversity among elite germplasm or varieties is helpful

for the classification of accessions/varieties for desirable characteristics (Singh et al.,

2010), determination of genetic erosion caused by breeding programs (Hammer and

Laghetti, 2005), and evaluation of genetic differentiation by different breeding

programs (Marchelli and Gallo, 2001). Genetic diversity in wheat has been evaluated

using morphological traits, biochemical markers and genetic markers (Nisar et al.,

2011). Recently researchers have taken interest in breeding quality wheat (Graham et

al., 1999) and due to ‘hidden hunger’ caused by micronutrient malnutrition,

improvement in nutritional quality of wheat is a big challenge for wheat breeders

(Bouis, 2002). Moreover, onset of stem and stripe rust of wheat in various regions of

the world has emphasized the screening of germplasm for the production of resistant

wheat cultivars.

2.1 Germplasm

The sum total of all the genes present in a crop and its related species

constitutes its germplasm. It provides the breeders with the raw material for the

development of cultivars. So collection, conservation and evaluation of germplasm are

greatly emphasized by scientists (Singh and Singh, 2010). Plant breeding in modern

terminology has been referred as the induced evolution under human guidance and the

genes of economic importance are being scattered in the genome, and it is the skill of

a breeder to combine vital traits in a single cultivars. Although the yields of crops are

increasing day by day, but it is causing genetic erosion of food crops because farmers

now do not cultivate the traditional varieties that were having high genetic variability

(Reif et al., 2005). Germplasm collections were made by agronomists and genetists to

prevent genetic vulnerability in crops (Adham and Van Sloten, 1990) to provide

genetic resources for the development of high yielding plants (Ayana and Bekele,

1998) resistant to diseases (Hahn, 1978), insects (Khush, 1977), poor soils and

climatic extremes (Plucknett et al., 1983). Ex situ plant conservation includes three

ways 1) in vitro storage, 2) fields gene banks and 3) seed banks. To maintain genetic

integrity of the accessions present in gene bank, they must be handled carefully

regarding storage, regeneration and passport data (Steiner et al., 1997; Börner et al.,

2000). The basic objective of exploration of crop genetic potential is to make right

decisions regarding conservation of crops in the gene bank (Virchow, 1999; Hammer,

9

2003). Conservation, collection and exchange of germplasm are guided by three

principles: Firstly, when an accession is collected a sample is left in the country of

origin for national use. Secondly bonafide workers must have free access to

germplasm and thirdly, duplication and maintenance of long-term collections must be

carried at some other location (Zohrabian and Traxler, 1999).

The information of the gene bank should be easy to access and understand, so

that it could serve a useful purpose for plant breeders (Hayward and Breese, 1993).

Generally number of cereals dominates in overall accessions found in the gene bank

(ICARDA, 2012). The number of accessions in a gene bank provides a rough

measure of the relative importance of a peculiar crop (Plucknett et al., 1983).

Throughout the world, more than 1300 gene banks possess more than six million

acquisitions and with the passage of time increase in the quality of materials have

occurred (FAO, 1998). Moreover technology regarding storage conditions has

tremendously improved, yet optimization of their management is still a challenge

(Marshall, 1989; Koo et al., 2004). Gene banks get great importance when they have

such material which has vanished elsewhere (Plucknett et al., 1983). The importance

of germplasn regarding crop improvement have been well recognized however

developing countries do not use germplasm collections properly and the germplasm

without evaluation is of little value.

2.1.1 Germplasm Conservation and Evaluation

Germplasm refers to the explanation of material in a collection that includes

reception of new material, its multiplication, characterization, evaluation and

documentation (Dias, 2001). Preliminary evaluation of germplasm includes planting

data, leaf characters, floral characters, fruit characters and seed characters, whereas

further evaluation involves description of those agronomic traits that identify

usefulness of an accession for particular purpose in particular conditions (Hashmi et

al., 1982). It specially includes quality traits, resistant to pests and diseases, and stress

tolerance (Upadhyaya, 2011). The genebank collections becomes useful and properly

managed if the germplasm is evaluated for its genetic variability (Frankel, 1970;

Duvick, 1984; Williams, 1991; Hailu et al., 2010). These assessments usually rely on

the identification of distinctive agronomic, physiological and morphological features

(Tolbert et al., 1979; Konishi, 1987). Field evaluation of plants is often costly,

laborious and time consuming especially when number of accessions to be analyzed is

10

very large (Annicchiarico et al., 2000). Therefore it is quite common now a days to

use molecular markers including proteins and isozymes for the evaluation of genetic

diversity in the germplasm (Nevo et al., 1988; Asfaw, 1989; Perry et al., 1991;

Felsenburg et al., 1991; van Hintum and Elings, 1991; Ciaffi et al., 1993; Salem et al.,

2008; Zhang et al., 2008).

2.2 Genetic Diversity/Erosion

Genetic erosion is a process that threatens the genetic integrity of crops

(Guarino, 1995) by eradication of wild accessions and traditional varieties (Reif et al.,

2005) along with the changes in cropping system (Gao, 2003). It emphasizes that the

use of high-yielding varieties must be reduced (Dalrymple, 1985; Miller and

Tanksley, 1990) and provocative to reduce the progression of modern technology in

agriculture (Gregová et al., 1997). Whereas contrarily, Brush (1992) observed that

high-yielding varieties do not completely replace the local cultivars even in cradle

areas. Genetic erosion implies that change regarding normal disappearance and

addition of genetic variability occurs in a population in such a way that the net

alteration in genetic diversity is negative (Gregová et al., 1997). Genetic diversity

among individuals of a variety or population refers to the amount of genetic variability

in terms of differences in morphological traits, physiological properties, biochemical

characteristics and DNA sequence (Perry and McIntosh, 1991). The study of genetic

diversity is crucial for cultivar identification and purity maintenance for

implementation of protection rights of a cultivar and its export. Genetic diversity can

be measured through morphological characteristics, analysis of pedigree or molecular

markers (Pejic et al., 1998). However pedigree analysis provides unrealistic estimates

of diversity (Fufa et al., 2005). Similarly morphological characteristics are very

limited and are affected by environmental conditions (Maric et al., 2004).

Biochemical markers, however, serve this purpose in a much better way as they do not

need previous information of pedigree and are abundant (Bohn et al., 1999).

2.2.1 Nutritional Traits

Wheat has all the elementary compounds required for humans, and its nutritive

value is high (Bojňaská and Frančáková, 2002). Spelt wheat contains valuable

nutritional potential because of its lipid content, protein, crude fibre (Wieser, 2001;

Abdel-Aal and Hucl, 2002; Pruska-Kedzior et al., 2008), vitamins and mineral

contents (Ranhorta et al., 1995). The genome of bread wheat (Triticum aestivum L.)

11

and spelt wheat (Triticum aestivum subsp. Spelta) is same, though there exist a few

differences between them (Campbell, 1997; Onishi et al., 2006). Dietary fibre is

defined as polysaccharide along with lignin components of plants that are not

digestible with the help of enzymes in human digestive system (Bermink, 1994).

Wheat endosperm contains minor amounts of fibre and whole wheat flour are

somewhat better, but are not superior sources of dietary fibre (Cummings and Englyst,

1987). Wang et al. (1993) determined that total dietary fibre was higher (55%) in raw

wheat bran as compared to raw whole wheat (14%). In both cases most of the fibre

was insoluble and the soluble dietary fibre of raw whole wheat and wheat bran did not

show significant differences. Bjorck et al. (1984) determined that total dietary fibre

increased slightly after extruding white and whole meal wheat flour. Lipids found in

wheat kernels are free fatty acids, tocopherols, simple glycerides, wax esters, sterol

lipids glactosylglycerides, carotenoids, phosphoglycerides, sphingolipids, diol lipids

and hydrocarbons. Significant amounts of cholesterol have occasionally been reported

(Samuel, 1991). White wheat contains 2.0% total lipid, 0.30% saturated fatty acid,

1.14% unsaturated fatty acids and 0.95% poly-unsaturated fatty acid (Lockhart and

Nesheim, 1978).

The total protein of wheat kernel is not a well-balanced nutrient so far as the

human diet is concerned although its protein quality is within the same range as most

other cereals (Samuel, 1991). Marconi et al. (1999) reported 14.3-18.4% protein in

five spelt cultivars, Loje et al., (2003) 15.4% and Marconi et al. (2002) observed 11.4-

13.7% proteins. Zieliński et al. (2008) detected 7.5%-10.8% proteins in spelt wheat,

Bojňaská and Frančáková, (2002) reported 12.49-19.48% proteins in five spelt

varieties. Protein content can significantly be affected by agronomic technique and

location (Marconi et al., 1999). Bioavailability of wheat minerals must be considered

in any nutritional evaluation of the grain and Zieliński et al. (2008) determined 0.43%

to 0.61% ash content in spelt wheat. In the study by Bojňaska, and Frančáková (2002)

the ash contents were found to range from 1.79 % to 2.36 %. Ruibal-Mendieta et al.

(2005) determined lower ash content in bread wheat (1.49%) as compared to dehulled

spelt (1.83%). Moisture and temperature are principle influences in safe storage and

wheat can be stored for a year or more at a moisture level of 13%, whereas hard wheat

with a moisture content of 14% can be stored with minimal deterioration (Samuel,

1991). Ranhorta et al. (1994) reported protein, ash, fat and soluble fibre in wheat and

12

detected low fibre for the production of bakery products as compared to original bran.

Hussain et al. (2010) carried out biochemical analysis of Bangladeshi wheat, and

determined that in wheat moisture was 13.42%, proteins 12.23%, fat 1.63%, ash

1.52% and fibre 1.43%.

2.2.2 Minerals

Minerals are categorized into two types on the basis of the amount humans

need per day, i.e., if human need 100 mg (1/50 of a teaspoon) or more per day of

mineral, it is known as major mineral, otherwise it is considered a trace mineral.

Based on these criteria, sodium (Na), potassium (K) and phosphorus (P) are major

minerals and zinc (Zn), copper (Cu), manganese (Mn), iron (Fe) and boron b are trace

minerals (Wardlaw, 1999). Sodium (Na) is a key factor for retaining body water and it

also participates in absorption of other nutrients in the small intestine, and in

conduction of nerve impulses, whereas potassium (K) maintains fluid balance and

nerve impulse transmission and phosphorus (P) plays many roles in the body and it is

a component of deoxyribonucleic acid, ribonucleic acid, enzymes, cell membranes

and bones (Ganong, 1998). Copper (Cu) increases iron absorption and participates in

brain development, blood clotting, immune system function, cell maturation of red

and white blood, bone strength, and anabolism and catabolism of cholesterol and

glucose (Linder and Hazegh-Azam, 1996). Manganese (Mn) acts as a cofactor for

certain enzymes used in carbohydrate metabolism (Greger and Malecki, 1997). It is

also important in bone formation (Wardlaw, 1999). Boron b is involved in the

metabolism of steroid hormones, and is likely a basic nutrient component (Nielsen,

1996). Nitrogen (N) is directly involved in the chlorophyll formation in plants, and is

key component of proteins and enzymes that promotes cell division, whereas P

ensures vigorous early seedling growth, promotes reproduction and seed formation,

improves water use efficiency and uniformity of crop maturity. The potassium (K)

controls plant respiration, reduces plant lodging, develops disease resistance and

regulates many enzyme reactions. Zn affects plant height, and controls the use of other

elements in plants and also required for growth hormone, and production of seed and

grain. Cu acts as oxidizer in plant processes and is required for intercellular

metabolism, the Mn helps in photosynthesis and the synthesis of chlorophyll,

accelerates germination of seed and plant maturity, and aids in respiration and

oxidation processes of plants, whereas Fe is required for synthesis of chlorophyll,

13

oxidation reactions and plant metabolism. Boron b is required in the formation of

seed and nodules in the leguminous plants and helps in formation of terminal bud and

calcium uptake, and sugar transfer (Linhart Analysis Services, 2010).

Since the green revolution, dramatic increase in grain yields of cereals has

been observed worldwide (Borlaug, 1983; Slafer and Peltonen-Sainio, 2001; Abeledo

et al., 2003), but the food systems are not resulting in the production of sufficient

micronutrients (Welch and Graham, 2002), thus micronutrient deficiencies prevails at

an increased rate (Welch, 2002). The ancestral wild wheat allele encoded a NAC

transcription factor (NAM-B1) that increases the remobilization of nutrients from

photosynthetic plant organ to grains, whereas modern wheat contains NAM-B1 allele

which is not functional and results in the reduction of mineral contents by 30% (Uauy

et al., 2006). Among the staple food crops, cereals contain twice the level of

micronutrients than detected in commonly grown varieties that is required to use in

crop improvemet programme (Welch and Graham, 1999). Most nutrients are less than

0.1 % of the dry weight of food that represent that it is impractical to significantly

increase the levels of micronutrients (DellaPenna, 1999).

Wheat grain consists of 1.6% mineral contents (Fujino et al., 1996), but

modern hexaploid wheats had much lower and less variable concentration of zinc and

iron in seeds as compared to wild tetraploid and diploid wheat (Calderini and Ortiz-

Monasterio, 2003a; Gómez-Becerra et al., 2010). The decreased level might be

attributed to increased grain yield which caused a dilution of nutrients in seeds, but

Graham et al. (1999) were not of this view point. They concluded that there was not

always a negative association between concentration of micronutrients and yield

capacity. Moreover Deckard et al. (1996) observed that nitrogen concentration could

be increased in modern wheat by transferring those genes from wild wheat (tetraploid)

which have no relation with yield of grain. Bio-fortification of cereal crops with

micronutrients using transgenic strategies and/or plant breeding has gained attention

of the scientists recently (Hurrell et al., 1992; Chen, 2004; Šramkova et al., 2009). In

order to enhance the levels of micronutrients in wheat by using conventional breeding,

it is necessary to identify the genetic resources with high levels of targeted compound

(Ortiz-Monasterio et al., 2007). Several wheat varieties were identified by CIMMYT

which contained 25% to 30% higher iron and zinc contents in grain and some of the

highest concentrations of zinc and iron were identified in wild relatives of wheat,

14

therefore backcrossing could result in highly nutritious cultivars with better yield

potential (Ozturk et al., 2006; Peleg et al., 2008a). Variability of mineral

concentrations in wheat has been studied by many researchers (El Gindy et al., 1957;

Kleese et al., 1968; White et al., 1981; Dikeman et al., 1982; Pomeranz and Dikeman,

1983; Wolnick et al., 1983).

Zinc (Zn) deficiency is occurring in humans as well as in crops (Welch and

Graham, 2004). With reference to report of WHO on risk factors causing illness and

diseases, Zn deficiency ranks 11th

among the 20 most important factors in the world.

Hotz and Brown (2004) found that deficiency of Zn is the problem of 33% population

of the world mainly belonging to South Asian Subcontinent, North Africa and West

Asia (CIMMYT, 2004). Zn deficiency results in several severe health problems, e.g.,

impairment of immune system, learning and physical growth, increased risk of DNA

damage, infections and cancer (Gibson, 2006). In plants deficiency of Zn reduces the

crop yield (Graham and Welch, 1996; Cakmak, 2008). The Fe deficiency causes a

high risk of tissue hypoxia (Viteri, 1998) and maternal mortality during birth and

reduces both physical performance and work productivity. Children having low Fe in

their bodies have impaired motor skills, poor attention spans and less capacity of the

memory (Walter et al., 1997). The average Zn and Fe concentration in whole wheat

grain in many countries ranges from 25 ppm to 35 ppm (Rengel et al., 1999; Cakmak ,

2004), while in soils deficient in zinc, Zn concentration is 10 ppm (Kalayci et al.,

1999).

Wolnik et al. (1983) reported that range of iron, zinc and copper was 24-61

µg/g, 13-68 µg/g and 2-9 µg/g respectively in wheat belonging to United States.

Monasterio and Graham (2000) screened 505 lines of wheat including landraces, wild

species, durum wheat, high yielding bread wheat and triticale. The concentration of

iron ranged from 25 ppm to 56 ppm, whereas zinc ranged from 25 to 65 ppm. The

order of importance regarding higher levels of Zn and Fe were observed to be, wild

relatives of wheat, landraces, bread wheat, triticale and durum wheat. Zook et al.,

(1970) found that the concentration of Mn, Cu and Zn in wheat grain was 4.40 ppm,

1.39 ppm and 6.15 ppm, respectively. Zhang et al. (2010) reported concentration of

minerals in Chinese wheat cultivars as Fe (39.2 ppm), Zn (32.3 ppm), Mn (48.8 ppm),

Cu (7.39 ppm), K (48.47 ppm) and P (41.79 ppm). Zinc content of grain of a genotype

depicts its efficiency regarding uptake from soil, mobilization from

15

various plant organs, and finally loading in the grain (Pearson et al., 1995; Genc et al.,

2006).

McDonald et al. (2008) reported high level of variation in zinc among the

introduced germplasm collections and Hussain (2009) reported the concentration of

K, P & B was 0.48%, 0.33% and 0.96%, respectively in hard red spring wheat,

whereas in hard red winter the concentration of K, P & B was 0.37%, 0.27% and

0.99%, respectively. Chatzav et al. (2010) observed two-fold greater Zn, Fe and

proteins contents in wild emmer wheat than in the domesticated genotypes. Based on

the importance of micronutrients for coping malnutrition, a bio-fortification challenge

program was developed by the Consultative Group on International Agricultural

Research (CGIAR) to produce crops with high micronutrient concentration through

the techniques of plant breeding (Bouis et al., 2000). Concentration of micronutrients

is affected by genetic factors, but environmental and management factors have greater

influence (Peterson et al., 1986), hence the breeding methodologies should be

designed in such a way to have maximum knowledge on genotype-environment

interaction. In the recent past, the development of grain crops with high levels of

micronutrients has gained attention of scientists (White and Broadly, 2005).

2.2.3 High Molecular Glutenin Subunits (HMW-GS)

Wheat breeders have described the allelic diversity of HMW-GS for quality

improvement of the crop (Lawrence and Shepherd, 1980). Allelic variability at Glu-

A1, Glu-B1and Glu-D1 is the cause of differences in bread wheat quality (Afshan and

Naqvi, 2011). The SDS-PAGE is effective and simple technique for genetic diversity

related to HMW-GS in wheat for bread making quality (Ahmed et al., 2010; Abdel –

Aal et al., 1996; Shuaib et al., 2007). Protein markers are useful tools in identifying

cultivars (Wagner and Maier, 1982), registration of new varieties, classification of

crop species (Galili and Feldman, 1983b) and in studying genetic diversity, in turn

efficiency of wheat breeders is improved (Gianibelli et al., 2001). Based on solubility,

proteins are classified into four classes (Osborne, 1924; Loponen et al., 2004), i.e.,

albumins, globulins, prolamins and glutenins. Gluten is composed of glutenins and

gliadins and has been extensively studied for genetics and biochemistry (Magdalena et

al., 2002; Starovicova et al., 2003; Picard et al., 2005). The gliadins give extensibility

to dough, whereas glutenins confer elasticity to dough (Payne et al., 1981a;

MacRitchie, 1984; Branlard and Dadervent, 1985; Kasarda, 1989; Dong et al., 1991;

16

MacRitchie, 1992; Shewry et al., 1995; MacRitchie and Lafiandra, 1997; Shewry and

Tatham, 1997; Shewry et al., 2003a; Peña et al., 2005; Cornish et al., 2006; Shah et

al., 2008).

Gliadins and glutenins serve as a store of carbon, nitrogen and sulpher that is

used during germination of the seedling, therefore these proteins are known as storage

proteins. The genetics of glutenins complies with conventional breeding and their

characteristic feature is multiple allelism. The variation at Glu-1 loci could be

exploited as complementary marker for variety identification and pedigree analysis

(Bahraei et al., 2004). Payne and Lawrence (1983) published the catalogue of Glu-1

alleles and reported three alleles (Null, 1 and 2*) at Glu-A1locus, 11 alleles (7, 20, 21,

22, 7+8, 7+9, 6+8, 13+16, 13+19, 14+15 and 17+18) at Glu-B1locus, and 6 alleles

(2+12, 3+12, 4+12, 5+10, 2+10 and 2.2+12) at Glu-D1 locus. Later on, more alleles

were detected mostly at Glu-B1 locus (Pogna et al., 1990; McIntosh et al., 2003).

The SDS-PAGE is efficient and advantageous as single lane of gel can assess

variations of alleles at multiple loci (Radovanovic and Cloutier, 2003) and by its

application; it is possible to identify novel alleles of HMW-GS even in landraces

(Juhász et al., 2001; Gregová et al., 2004). The Glu-A1, Glu-B1, and Glu-D1 loci

encode HMW-GS (Payne, 1987) and in the gel, the central portion of the glutenin

fraction is occupied by subunits controlled by chromosome 1B, whereas lower and

upper portions are occupied by subunits controlled by chromosome 1D and 1A. The

gliadins occupy the central part of the gel and ranges from 50 KD to 68 KD (Galili

and Feldmen, 1983a). To carry out breeding by combining glutenin subunits, it

prerequisite to investigate genetic resources, and the research related to genetic

diversity could facilitate to separate accessions with various alleles and allelic

combinations (Gregová et al., 2007). Allelic variations for HMW-GS in common

wheat have been evaluated in wheat producing countries as summarized in the Table

2.1. In hexaploid wheat, European germplasm has been well studied, particularly from

Spain (Xu et al., 2009). At Glu-A1, the most frequent allele was 1, the allelic

combination 13+16 was frequent at Glu-B1 locus, and at Glu-D1, 2+12 was the most

abundant (Caballero et al., 2001; Cabellero et al., 2004b). An et al. (2005) observed

that the frequency of 13+16 had decreased and the frequency of 6+8 had increased in

European hexaploid wheat, than the Spanish spelta wheat (Rodriguez-Quijano, 1990).

In compactum wheat, alleles 21 or 7 or 13+16 were thought to predominated alleles at

17

Glu-B1 locus, whereas allele 7+8 had lower frequencies (Rayfuse and Jones, 1993).

Wei et al. (2002) reported that common wheat contained two alleles more frequently

and the difference of the two alleles, 7+9 and 17+18, among several countries, such as

Russia (Morgunov et al., 1990), Australia (Lawrence, 1986), China (Nakamura,

2000), Pakistan (Sultana et al., 2007) and USA (Shan et al., 2007) have been reported.

The differences in the frequency were related to respective criteria of a region for

artificial selection of parameters affecting quality of wheat. The similar findings were

reported when specific Glu-D1f allele was compared between Chinese and Japanese

wheats by Nakamura and Fujimaki (2002). In Chinese endemic wheats, the alleles

Null, 7+8 and 2+12 were frequent (Liu et al., 2007). Wei et al. (2001) identified six

alleles in nine Tibetan wheat accessions, whereas Wang et al. (2005) determined five

Glu-A1 alleles in 24 Tibetan wheat accessions. The subunit 8** was reported by Liu

et al. (2007) in Chinese landrace from Hubei Province, whereas Zhang et al. (2002)

and Wei et al.(2000) reported that in Chinese landraces the frequently occurring

alleles at Glu-1 loci were Null, 7+8 and 2+12.

2.2.3.1 HMW-GS and Quality Scores

Strong association has been observed between HMW-GS and bread making

quality of wheat (Tanaka et al., 2005; Eagles et al., 2006; Todorov, 2006; Obreht et

al., 2008). Kolster et al. (1991) detected that the HMW-GS, 5+10 combinations are

more important than others for predicting quality of bread making. At Glu-A1 locus

the subunits 1 and 2* have positive effect on bread-making quality as compared to

null, whereas at Glu-B1 locus the positive effects hold true for the combinations, 7+8

and 17+18 compared to 7+9, 6+8 and 7. The subunit pair 5+10 had better effect on

quality than 2+12 at Glu-D1 (He et al., 2004; He et al., 2005). The Japanese wheat

varieties quality score ranged from 5 to 9 (Nakamura et al., 1999). Zhong-hu et al.

(1992) observed that the quality scores of 183 Chinese wheat varieties ranged from 3

to 10 with an average of score of 6.7. Due to development of wheat for quality, the

genetic diversity of glutenin alleles has been decreased (Todorov et al., 2006;

Atanasova et al., 2009).

The HMW-GS are made easier for quality in wheat due to the availability of a

simplified nomenclature system of the individual alleles and subunits (Gianibelli et

al., 2001). Chinese wheat had average quality scores lower than the well known

quality wheats from Canada, Australia, USA and Russia (Khan et al., 1989; Ng et al.,

18

1989; Graybosch et al., 1990), but they are higher than wheats from Great Britain,

Denmark and Germany (Payne et al., 1987; Lukow et al., 1989; Rogers et al., 1989).

Spanish wheats, however exhibited average quality scores close to the Chinese

wheats. Trethowan et al., (2001) reported research on 100 genotypes developed at

CIMMYT that most frequent spectrum has 2*, 7+9 and 5+10 alleles. This was not

coincidental since these breeding centres carry out long time breeding for high grain

quality. Šramková et al.,(2010) determined the composition of HMW-GS in 84

cultivars of common wheat originating from eight European countries and registered

in Slovakia. Among these the most frequent combinations were Null, 7+9 and 5+10.

Branlard et al., (2001) worked to understand genetic and biochemical basis of the

bread making quality of 162 wheat varieties registered in the French or European

Wheat Catalogue for twelve main storage protein loci. At Glu-A1, three alleles were

identified, at Glu-B1 locus, six alleles were detected, and at Glu-D1 locus, four alleles

were observed and at least two loci encoding HMW-GS affect the variation of quality

parameters.

Sultana et al. (2007) studied 121 Pakistani wheat varieties and landraces, and

concluded that the novel alleles, 2**+12', detected in their study were the same as

observed in germplasm from Afghanistan according to the description of Cross and

Guo (1993) and Lagudha et al. (1987). Polymorphism in the composition of HMW-GS

has been intensively studied but limited research work have been carried out to

determine the polymorphism in landraces of the Near East (Mir Ali et al., 1999), India

and Pakistan (Masood et al., 2004). It is important to study lines from these regions

in more detail as they are close to the region of the origin of common wheat and so

may exhibit unique genetic diversity (Niwa et al., 2008).

2.2.4 Rust in Wheat

The rust in wheat is a serious threat worldwide and has been extensively

investigated for various aspects by many researchers (Stakman and Harrar, 1957; Zhi-

bin et al., 2005; Fang et al., 2008; McNeil et al., 2008; Yang et al., 2008; Jin et al.,

2009; Yue et al., 2010). Translocation lines of wheat-rye have been developed with

rye segment, Sr31 and SrR which have no effect on the quality of dough (Rogowsky

et al., 1991; Lukaszewski, 2003; Dundas et al., 2004), whereas Sr24, Sr26 from

Agropyron has been transferred to wheat (Dundas and Shepherd, 1994; Dundas and

Shepherd, 1996).

19

Table 2.1Allelic variation of HMW-GS in wheat

Country Number of

genotypes

Main bands observed for HMW-GS Brief conclusion Reference

Glu-A1 Glu-B1 Glu-D1

Bulgaria 73 Null, 1 and 2* 7+9, 7+8*, 6+8 5+10, 2+12, 5+12 2* , 7+9 and 5+10 were in the

highest proportion

Tsenov et al. (2009)

Bulgaria Null, 1 and 2* 7+9, 7+8*, 6+8 5+10, 2+12, 5+12 2* , 7+9 and 5+10 were in the

highest proportion

Ivanov et al. (2000)

Europe Null, 1 and 2* 7+9, 7+8*, 6+8 5+10, 2+12, 5+12 2* , 7+9 and 5+10 were in the

highest proportion, then N, 7+9 and

5+10

Tohver, 2007

China 615 Null, 1 and 2* 6+8, 13+16, 17+18 & 6+9*

were detected in cultivars and

23+22, 7, 6+16, 7+22, 8 &

6*+8 in landraces,

5+10, 2+12 & 4+12 The most frequent alleles in

cultivars and landraces were (1,

14+15, 7+9 & 5+10 ), and (Null,

7+8 & 2+12), respectively

Li et al. (2009)

Mexico 14 Null, 1, and 2* 7+8, 17+18 2+12 followed by 5+10 3*,7, 7+9, 6+8, 13*+16, 7+17 , 12,

2+12*, 2+12', 2'+12, 2''+10 & 2''+12

were rare

Caballero et al.

(2010)

Iran and Europe 310 Null, 1 and 2* 13+16, 6.1+22.1, 17+18, 7+8,

7, 7+9, 6+8, 13*+19+, 13+22*,

6.1+Null, 13+22.1 and 14*+15

2+12, 5+10, 12, 3+12, 4+12 &

2+10.

2.1*at Glu-A1 and, 2.1'+12 at Glu-

D1 were very rare

An et al. (2005)

Japan 174 Null, 1 and 2* 7+8, 20, 7+9, 17+18 6+8 & 7 2+12 followed by 4+12 Null was frequent and

5+10 was rare

Nakamura (2001)

China 98 Null and 1 7+8 & 8 2+11, 5+11, 2+12 & 2+10 Two novel subunits, 1.5* and 12.2*,

were at Glu-D1 locus

Guo et al. (2010)

Iran 43 Null, 1 and 2* 7, 7+8, 7+9, 17+18, 13+16,

14+15. 20 and Null

2+12, 5+10 and 2***+12' Bahraei et al.

(2004)

Afghanistan, Iran

and Pakistan

475 Null, 1 and 2* 7, 7+8, 7+9, 6+8, 20, 13+16.

17+18, 8 and Null

2+10, 2+12, 5+10, 4+12, 3+12,

5+12, 2, 12, 10, 2+12* &

2.8+12

Terasawa et al.

(2009)

China 66 Null and 1 7+8 and 6+8 2+12 Null was most frequent and novel

subunit pairs 7**+8, 7+8**, 4+12 ,

2+12* & 2 were detected

Fang et al. (2009)

China 111 Null and 1 7+8, 14+15, 17+18 2+12, Novel alleles 7*, 8*, 8** & 4 Liu et al. (2007)

China 251 Null, 1 and 2* 7+8, 7+9, 14+15 2+12, 5+10, 4+12 6+8, 17+18, 20, 7*+8 , 13+16, 7 &

3+12 were rare

Liu et al. (2005)

China 274 Null, 1 and 2* 7, 7+8, 7+9, 6+8, 20,

2+12, 3+12, 145KD+12 was least Nakamura

20

When the Mendel’s laws were rediscovered, Biffen (1905) reported that the

resistance to stripe rust of wheat followed Mendel’s laws. Stakman and Piemeisel

(1917) reported various types of stem rust pathogen while studying devastating

epidemics of North America (1904 and 1916). Researchers of USA, Canada, Australia

and Europe give strong emphasis to detect stem rust resistance and then breed those

cultivars. Epidemiology and evolution of wheat rust was also understood

simultaneously. As a result barberry eradication programme and formulation of

genetic control strategies were carried out in North America and Europe including

collaboration among global wheat breeders to find out resistance against stem rust

(Singh et al., 2006).

A series of resistant genes, i.e., Yr1 - Yr28, have been detected (Lupton and

Macer, 1962; Chen et al., 1998c) and incorporated into commercial cultivars (Allan

and Purdy, 1967; Allan et al., 1993; McIntosh et al., 1995). Stripe rust resistance

involves seedling resistance that can be determined at seedling level that is most

effective at high temperatures in the adult plants (Qayoum and Line, 1985; Chen and

Line, 1995a; Chen and Line, 1995b). Cultivars with seedling resistance conferred by

a single gene often become susceptible within a few years after their release races

appear which are virulent and overcome plants’ resistance (Chen et al., 2002). High-

temperature adult-plant resistance is not race specific, hence difficult to incorporate

(Chen and Line, 1995a). In 1950, Bayles and Rodenhiser initiated “International

Spring Wheat Rust Nursery Program” with the theme to detect new gene/s in wheat

resistant to rust at the global level. The CIMMYT and several other centres started

using this method for evaluation of germplasm performance for agronomic and

disease resistance attributes (Singh et al., 2006).

Fifty genes for resistance against stem rust were catalogued and many of them

have been incorporated in wheat from its relatives (McIntosh et al., 1998). Many of

them are ineffective now because of the corresponding virulence in the pathogen (Bux

et al., 2011). During 1950, the cultivar Yaqui 50, released in Mexico stabilized the

situation against stem rust in Mexico and several other countries. Another variety

“Sonalika” with the gene Sr2 was released in Indian subcontinent in 1960 and

remained resistant to stem rust. The Sr2 gene if present alone can confer slow rusting

but cannot tolerate heavy pressures of rust disease, this gene along with some minor

genes, provides adequate resistance. Population of the pathogen stopped evolving

21

since the ‘Green Revolution’ and most of the wheat cultivars developed later were

resistant at global level, but it was mainly because of inadequate disease pressure,

non-prevalence of disease, or presence of races which did not possess virulence

capability against resistance genes of the germplasm found at CIMMYT (Singh,

1991).

The threat of agricultural bio-terrorism is one of the emerging concerns of the

present time (Hugh-Jones, 2002) because it can reduce production of staple food to a

devastating level (Leonard, 2001), hence there is a dire need to have information on

rust status of the indigenous wheat genetic resources. Rust pathogens migrated to

South Asia from eastern Africa in about ten years, causing sever epidemics during its

passage from various countries (Singh et al., 2004). Epidemics occur when host plants

are susceptible to a particular pathogen over long distances (Singh et al., 2006). Mago

et al. (2005) developed robust PCR markers for SrR, Sr31, Sr26 and Sr24 which could

be applied to a large number of germplasm. Yan et al. (2003) tested Yr5 line and T.

spelta album with eight races of P.s. tritica (PST-17, PST-25, PST-29, PST-37, PST-

43, PST-45, PST-58 and PST-59) and found them resistant with either infection type 0

or 1 and the Yr5 proved itself to be the excellent gene in breeding resistant against

stripe rust. Chen et al. (1998b) used a technique called resistance gene-analog

polymorphism to detect polymorphism in wheat that is highly efficient for

identification of biochemical markers of the genes resistant to disease.

Detection and spread of Ug99, race of black stem rust in East Africa once

again shake off the complacency from past successes (BGRI, 2009; Pretorius et al.,

2000). Hence during 2008-09 cropping season dissemination of Ug99 resistant

varieties and seed multiplication were started in Afghanistan, Iran, Bangladesh,

Ethiopia, Egypt, Nepal, Pakistan and India (Joshi et al., 2010b). In Pakistan, since last

ten years, the valiety “Inqalab 91” dominated the area of wheat cultivation (Aqil and

Mumtaz, 2004; Joshi et al., 2007) that is susceptible to Ug99; hence identification of

resistant sources is crucial for food security. Stripe rust spreads in wet and cool

environments so it occurs in Middle East, Northern Europe and Mediterranean region,

New Zealand, Western United States, China, East African highlands, Australia, Indian

subcontinent and Andean regions of South America (Mamluk et al., 1996). Stripe rust

also occurs in tropical regions of higher altitude like Himalayan foothills of India and

Pakistan, North African countries and Mexico (McIntosh, 1980). Yellow wheat rust

22

has been described in more than sixty countries till now and Antarctica was the only

continent where it has not been reported. In Pakistan, high yielding cultivars of wheat

have been developed and adopted, but due to unidirectional selection criterion the

genetic base has been narrowed down, and cultivation of these varieties over a larger

area can be risky. It has resulted in the onset of new pathotypes, e.g., Yr9 and Yr7

making the variety susceptible. Yellow rust spread in Baluchistan during 1991-92 for

three consecutive years and caused significant losses. Plains and foothills of Northern

Punjab and Khyber Pakhtunkhwa suffered from a loss of two billion rupees during

1994-95, and almost similar losses were recorded during 1995-96 (Ahmad, 2000).

23

3 MATERIALS AND METHODS

The research work for the present study includes investigation on genetic

diversity for nutritive traits, mineral contents, seed traits and High Molecular Glutenin

Subunits, whereas the rust status of the germplasm was also observed. The

experimentations for nutritive traits, mineral contents, seed traits and High Molecular

Glutenin Subunits were conducted at National Agricultural Research Centre (NARC),

Islamabad, whereas screening against rust was carried out at the Crop Disease

Research Institute, Murree, Pakistan. The Table 3.1 gives the details of experiments

conducted. The following methods were used to achieve the objectives of this

research.

3.1 Germplasm Collection

Wheat is a very important crop of Pakistan and the local germplasm has good

nutritional quality, highly adaptable to different climatic conditions and can resist

abiotic and biotic stresses. Germpasm collection was started during 1976 and is

continued till now so as to counteract the threat of genetic erosion which is occurring

because of the production of improved cultivars. At present more than 3000

accessions of wheat including bread wheat, durum wheat and wild wheats have been

collected and conserved in the genebank, IABGR, NARC from where the germpalsm

was obtained for present study. This germplasm was multiplied at NARC under same

environmental conditions so as to minimize environmental effects.

3.2 Experimental Material

Wheat germplasm preserved in the genebank has been characterized partially for

various agronomic traits but the material involved in the present study was not studied

before. Further there is no report on Pakistani wheat germplasm for nutritive traits and

mineral contents. One hundred and thirty nine accessions were evaluated for

nutritional traits, mineral contents, seed characteristics, HMW-GS and rust. The

material was obtained from the gene bank of Institute of Agri-biotechnology and

Genetic Resources, National Agricultural Research Centre, Islamabad. Accessions, 46

were collected from Punjab, whereas 93 were from Baluchistan province that

exhibited high genetic diversity for most of the crops including wheat (Asif et al.,

2010). The accession numbers of wheat collected from Baluchistan and Punjab are

given in Table 3.2 and 3.3 resprctively. The list of commercial varieties is given in

Table 3.4.

24

Table 3.1 Brief view of the experiments conducted at National Agricultural Research

Centre (NARC), Islamabad

Experiment Experimental

condition Laboratory/site

Nutritional traits Laboratory Grain Quality Testing Laboratory, NARC

Mineral contents Laboratory Land Resources Research Institute, NARC

Seed characters Laboratory Institute of Agri-biotechnology and Genetic

Resources, NARC

SDS-PAGE Laboratory Institute of Agri-biotechnology and Genetic

Resources, NARC

Rust Green house Crop Disease Research Institute (Murree

station), NARC

25

Table 3.2 List of wheat accession collected from Baluchistan

S. No. Accession No. S. No. Accession No. S. No. Accession No.

1 11145 32 11220 63 11288

2 11150 33 11221 64 11293

3 11154 34 11224 65 11294

4 11155 35 11226 66 11295

5 11156 36 11229 67 11296

6 11160 37 11231 68 11298

7 11162 38 11233 69 11299

8 11164 39 11235 70 11300

9 11167 40 11236 71 11302

10 11170 41 11237 72 11303

11 11171 42 11238 73 11304

12 11174 43 11239 74 11305

13 11177 44 11240 75 11307

14 11178 45 11242 76 11308

15 11183 46 11243 77 11309

16 11184 47 11244 78 11310

17 11185 48 11246 79 11311

18 11186 49 11248 80 11312

19 11187 50 11255 81 11315

20 11188 51 11259 82 11325

21 11190 52 11261 83 11328

22 11193 53 11262 84 11333

23 11194 54 11263 85 11334

24 11195 55 11265 86 11335

25 11198 56 11267 87 11344

26 11199 57 11272 88 11527

27 11200 58 11278 89 11528

28 11202 59 11280 90 11531

29 11210 60 11281 91 11534

30 11211 61 11283 92 11536

31 11214 62 11284 93 11538

26

Table 3.3. List of wheat accessions collected from Punjab

S. No. Accession No. S. No. Accession No.

1 11348 24 18679

2 11349 25 18680

3 11350 26 18681

4 11351 27 18682

5 11352 28 18683

6 11353 29 18685

7 11355 30 18687

8 11356 31 18688

9 11359 32 18689

10 11360 33 18690

11 11361 34 18692

12 11362 35 18693

13 11363 36 18694

14 11364 37 18695

15 18669 38 18696

16 18670 39 18698

17 18672 40 18699

18 18673 41 18701

19 18674 42 18702

20 18675 43 18703

21 18676 44 18705

22 18677 45 18707

23 18678 46 18708

27

Table 3.4. List of commercial varities of Pakistani wheat

S.No. Variety name S. No. Variety name

1 Bakhtawar 92 ++36 Bahawalpur-2000

2 Blue silver 37 Bahkhar-2002

3 Chakwal 86 38 Fakhr-e-Sarhad

4 Sind-81 39 Mehran-89

5 Zarghoon 40 Tatara

6 Faisalabad 83 41 Takbeer

7 Faisalabad 85 42 AS-2002

8 Inqilab 91 43 Iqbal 2000

9 Kaghan 93 44 Auqab-2000

10 Morocco 45 Chakwal-97

11 Kirin 95 46 Watan 94

12 Kohinoor 83 47 Moomal 2002

13 LU-26 48 Zarlashata

14 Nowshehra 96 49 GA-2002

15 Parwaz 94 50 Wafaq-01

16 Pasban 90 51 Margalla-99

17 Mexipak 65 52 Manthar-3

18 Punjab 96/97 53 Saleem 2000

19 Sariab-92 54 Khyber 87

20 Sarsabz 55 Pirsabak 2004

21 Shaheen 94 56 Pirsabak 2005

22 Shahkar 95 57 Punjnad-1

23 Soughat 90 58 Darawar-97

24 Tadojam 83 59 V-87094

25 SH-2002 60 Shafaq 2006

26 Pak 81 61 Sehar 2006

27 Bahawalpur-97 62 Chakwal -50

28 MH-97 63 Saussi

29 Kohistan 97 64 Lasani-08

30 Kohsar 95 65 Meraj-08

31 Rohtas 90 66 Fareed-06

32 Suleman 96 67 Faisalabad-08

33 WL 711 68 Bathoor

34 Zardana 69 Raskooh

35 Abadgar 93

28

3.3 Determination of Nutritional Traits

The experiment was carried out at Grain Quality Testing Laboratory, National

Agricultural Research Centre, Islamabad. Fibre, oil, moisture, ash and protein were

studied following the standard methods of AOAC (2005).

3.3.1 Fibre

Crude fibre consists largely of the cellulose contents together with a

proportion of hemicellulose and lignin. The digested material is then filtered, washed

with hot water and then ignited. The loss in weight after ignition is called crude fibre

(Williams and Starkey, 1982). Two gram of sample was weighed in a 500mL beaker.

200 mL sulphuric acid (6.88%) was added in the beaker. The beaker was boiled for 30

min under reflux condensation. Beaker was removed from heat and 10 mL NaOH was

added and again boiled for 30 min. Then beaker was filtered through filtration unit.

Residues were washed with hot water to remove access alkali. Crucible was dried at

110oC for 30 min, cooled in dessicator for 20 min. Residue was put in the crucible

and crucible along with the residue was dried at 110oC for one hour, cooled in

dessicater for 20 min and weighed (W1). Residue was ignited at 600oC for overnight.

Ignite was cooled in dessicator for 20 min and weighed (W2).

Crude fibre was calculated by using following formula:

3.3.2 Oil

Two grams of sample was folded in a tissue paper and it was inserted into

thimble. Beaker was dried in an oven at 110oC for 30 min and cooled in desiccator for

20 min. Then beaker was weighed (W1) and half filled with the solvent, i.e., hexane.

Thimbles and beakers were set in the soxtherm extraction system at 120oC. The tap

water was opened on the heating system. Knobs were set in boiling for 45 min. Then

knobs were set in the rinsing position for 35 min. Then extraction outlet was blocked

for 30 minutes. Beakers were removed and dried in oven at 110oC for 30 min. Then

beakers were cooled in desiccator and weighed (W2). Crude oil was calculated by

using following formula:

Crude oil (%) =

Weight of sample

W1-W

2

x 100

Crude fibre (%) = Weight of sample

W1-W

2

x 100

29

3.3.3 Moisture

The principle is that a weighed amount of sample is dried in an oven at 130 o

C

for constant weight, i.e., a time after which no loss is weight is observed. For this

purpose beaker was dried in an oven at 130oC for half an hour, and then was cooled in

a desiccator containing silica gel for 20 min. Five gram of sample was added in the

beaker. The beaker along with the sample was weighed (W1). The beaker with sample

was placed in an oven at 130oC for one hour and twenty min. Then beaker was placed

in a desiccator for 20 min was weighed again (W2).

Moisture was calculated by using following formula:

( )

3.3.4 Ash

All carbon compounds (organic), after ignition at high temperature, i.e., 450-

600oC are burnt out as carbondioxide. The remaining part is inorganic (minerals) in

nature is called ash. Crucible was dried in oven at 110oC for 30 min. Crucible was

cooled in dessicator for 20 min and then weighed (W1). One gram of sample was

added in the crucible and crucible along with the sample was weighed (W2). Sample

was ignited at 600oC for overnight. Crucible along with the ash (minerals) was

cooled in desiccator and weighed again (W3).

Ash was calculated by using following formula:

3.3.5 Protein

The protein was determined by Kjeldahl method. Most of the organic

compounds are detected by this method. The principle of this method is that the

organic compound is digested with the help of sulphuric acid and other catalysts. As a

result conversion of nitrogen into ammonium acid sulphate occurs and the reaction

mixture becomes alkaline. Ammonia is liberated. The removal of ammonia is carried

out through steam distillation. Then it is collected and titrated.

Total N in plant tissue was determined by Kjeldahl method (Lynch et al.,

1997; Searle, 1974; Helrich, 1995). 0.25g plant sample, 3.5g catalyst (K2SO4+Se)

Ash (%) = W

1

W2-W

3

x 100

30

and 10 mL H2S04 were added to a digestion tubes, and digested in a digestion

chamber at 360-420oC for one hour, and allowed to cool. Then 50 ml.of distilled

water was added to the digestion tube. The digested samples were inserted in Buchi

Auto Kjeldhl 370. Boric acid solution was introduced into the titration cell. The

sample in the digestion tube was diluted with water followed by NaOH addition.

Steam distillation and titration occur simultaneously. Percent protein was calculated

and printed. The titration and distillation flasks were automatically drained, and the

analyzer was ready for the next sample. Analysis was carried out in 3 to 10 min. For

determination of protein, the reading of nitrogen was multiplied with factor 5.7 (FAO

Corporate Document Repository, 2003).

3.4 Determination of Minerals Contents

The minerals studied include boron, zinc, copper, manganese, iron, sodium,

potassium, phosphorus and nitrogen. These were analyzed at Land Resources

Research Institute, NARC, Islamabad. For determination, the standard protocols were

used as described under:

3.4.1 Dry Ashing

3.4.1.1 Boron

In the wheat grain, Boron (B) was measured by dry ashing according to the

protocol by Gaines and Mitchell (1979), and subsequent measurement of B was

carried out by calorimetry using azomethine–H (Keren, 1996). For dry ashing, 0.5g

dry ground grain material was put in a 30mL porcelain crucible and placed in a muffle

furnace. The material was ignited in furnace by slowly raising the temperature to

600oC. After attaining 600

oC, ashing was continued for 6 hr.

Five mililitre of 0.36N sulphuric acid solution was added into the crucible. It

was allowed to stand for 1 hr at room temperature. It was filtered through Whatman

No.42 filter paper and put in storage bottles which were already washed with double

distilled water. Buffer solution was prepared by dissolving 250g ammonium acetate

and 15g ethylendiamine tetraacetic acid, disodium salt (EDTA-disodium) in 400mL

distilled water. 125mL of glacial acetic acid was added slowly and mixed well.

Preparation of Azomethine-H reagent was carried out by dissolving 0.45g

Azomethine-H in 1% L-ascorbic acid solution (1g L-ascorbic acid dissolved in

100mL double distilled water). Fresh reagent solution was prepared weekly and

31

stored in refrigerator.

One mililitre of sample solution was transferred into 10mL polypropylene

tube. 2mL buffer solution and 2mL azomethine-H reagent was added into the tube and

mixed well. After 30 min, color intensity was read at 430mm by using

spectrophotometer. Reagent solution was weakly prepared and stored in refrigerator.

The concentration of boron was calculated by using formula: Factor mean x dilution

factor x spectrometer reading of the sample. Factor mean was calculated, i.e.,

concentration of standard / Absorption reading and then the mean value of six

standards was calculated. Standards included the concentration of 0.5 ppm, 1.0 ppm,

1.5 ppm, 2.0 ppm, 2.5 ppm and 3.0 ppm. Dilution factor was calculated by dividing

total volume (5mL) with weight of the sample (0.5g). Finally concentration of B was

measured in ppm (parts per million).

3.4.2 Wet digestion

Seed sample was grinded and 0.25g was placed in 50mL digestion flask. To

this, 10mL of acid mixture was added. Acid mixture was composed of nitric acid and

perchloric acid with the ratio of 2:1. The flask was placed on hot plate in a digestion

chamber and temperature was gradually increased upto 300oC. After production of

brown NO2 fumes, dense white fumes of perchloric acid appeared in the flask. The

contents were further digested till the liquid became colorless. Volume was made upto

50mL by using distilled water. Then the solution was filtered and stored in storage

bottles. This solution was used for the determination of Zn, Cu, Mn, Fe, Na, K and P

(Ryan et al., 2001).

3.4.2.1 Zinc, Copper, Manganese and Iron

The determination of Zn, Cu, Mn and Fe in grain digests was determined with

the help of atomic absorption spectroscopy (Wright and Stuczynski, 1996). The

concentration of Zn, Cu, Mn and Fe (in ppm) was calculated by using formula:

(Absorption reading – reading of the blank) x dilution factor

Dilution factor was calculated by dividing volume (50mL) by sample weight (0.25g).

3.4.2.2 Sodium and Potassium

Grain digest volume of 1mL of was taken in a test tube, then 4mL distilled

water and 5mL lithium chloride was added in it. Concentration of Na and K was read

on a flame photometer (Wright and Struczynski, 1996). The concentration of Na and

32

K was calculated by formula:

Blank was passed through all the steps of wet digestion and chemical additions

but excluding addition of grain sample.

3.4.2.3 Phosphorus

22.5g of ammonium heptamolybdate was dissolved in 400mL distilled water.

1.25g of ammonium vanadate was mixed separately in 300mL boiling distilled water.

Then vanadate solution was added to molybdate solution and cooled to room

temperature. To the mixture, 250mL of the concentrated nitric acid was added slowly

and diluted to IL with distilled water. Five mililitre of the digest was transferred into

a glass tube. Then 5mL of ammonium vandomolybdate reagent was added to each

tube. Color intensity was read at 410nm after 30 min with the help of

spectrophotometer (Ryan et al., 2001). The concentration of P (in %age) was

calculated by using formula:

Mean factor x dilution factor x Absorption reading

10,000

The concentrations of the standards were 2.5 ppm, 5.0 ppm, 7.5 ppm, 10.0

ppm, 12.5 ppm and 15.0 ppm, and mean factor was calculated as described in

determination of B. The dilution factor was 200, i.e., 50mL/0.25g. The nitrogen was

determined by Kjeldahl method which is described previously in section 3.3.5

(protein) of this chapter.

3.5 Seed Characteristics

The seed characteristics of 139 accessions studied included quantitative (seed

length, seed width and seed weight) and qualitative (seed color, seed size and degree

of seed shriveling) traits. Seed length and seed width were measured in mm by using

vernier caliper. The 100 seeds were weighed to find out 100 seed weight in grams,

whereas the seed color was observed as white, creamy white, red or purple and seed

size was observed qualitatively as small, intermediate, large or very large.The degree

of seed shriveling based on appearance of seeds were observed as plump, intermediate

or shriveled.

10,000

(Absorption reading x Dilution factor x 10 Reading of the blank) _

33

3.6 Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE)

The SDS-PAGE was performed for 51 wheat accessions collected from Punjab,

122 accessions collected from Baluchistan and 69 commercial varieties. The nutritive

traits and mineral contents were not performed for commercial varieties, because this

information is pre-requisite for varietal developmemt/approval program, whereas the

SDS-PAGE data is available on few varieties only, hence most of the varieties

developed till date were included for this analysis in the present study.

3.6.1 Extraction of Protein

Single seed was ground to fine powder with the help of mortar and pestle.

Protein extraction buffer (400µl) was added to 0.01g of seed flour in eppendorf tube

and mixed thoroughly with a needle. The protein extraction buffer was composed of

tris (0.6057g), sodium dodecyl sulphate (0.2g), urea (30.3g), 2-mercaptoethanol

(1ml), bromophenol blue (little bit), distilled water (70mL to make total volume of

100mL) and HCl (adjusted to pH 8.0). Bromophenol blue serve as tracking dye to

watch the movement of protein in the gel. The samples were mixed thoroughly by

vortexing and centrifuged at 13,000 rpm for 10 min. The samples were stored in

refrigerator till electrophoresis, but not more than two weeks.

3.6.2 Preparation of Electrophoretic Gel

Eighty percent ethanol was applied to kimwipe to clean glates plates. Seal

gasket was placed on glass plates with spacer. Double clips were applied for the

fixation of a set of glass plates. For the preparation of 11.5% separation gel, 5ml of

solution A (Tris – 36.3g, SDS 0.4g, Distilled water 70 mL,HCl, Adjusted to pH 8.8)

was mixed with 7.6mL of solution C (Acrylamide – 30g, Bis-acrylamide – 0.8g,

Distilled water – Added to make volume 100mL), which was followed by the addition

of 7.4mL distilled water. At the end 200µL of 10% Ammonium persulphate (APS,

0.1g dissolved in 1000µL) and 15µL N, N, N΄, N΄- tetra methylethylenediamine

(TEMED) were added. The separation gel solution was poured into space between

two glass plates (upto 2cm from the top). Gentle addition of distilled water (120µL)

was carried out on the separation gel. The gel was polymerized in 30 minutes. The

stacking gel (4.5%) was prepared by mixing 2.5mL of solution B (Tris, 5.98%, SDS,

0.4g, Distilled water, 80mL, HCl, Adjusted to pH 7.0, and total volume was made

100mL) with 1.5µL of above mentioned solution C. Then 70µL of 10% APS and

17µL TEMED were added. Removal of distilled water from the top of the separation

34

gel was followed by the pouring of stacking gel solution on separation gel. Thereafter

comb clips were fixed and the gels left for polymerization.

3.6.3 Electrophoresis

Electrode buffer solution [Tris, 3.0g, Glycine 14.4g, SDS 1.25g and distilled

water to make total volume IL) was poured in the bottom tank of the apparatus. The

comb and gasket seal were removed and the gel plates were fixed in te apperatus with

care not to allow the formation of air bubbles at the bottom of the tank. The electrode

buffer solution was poured in the upper tank. Six micro litre of protein supernatant

was loaded into the wells of the gel by micropipette. Positive (red) and negative

(black) electrode of power supply were connected to the apparatus. Current was

maintained at 100 mA and electrophoresis was performed till Bromophenol blue

(Blue line) reached the bottom of the gel (approximately three and half hour).

3.6.4 Staining, Destaining and data scoring

After the electrophoresis, the gels were separated from the glass plates with

the help of spatula. After the removal of stacking gel, the gel was shifted to a box

having staining solution [Coomassie brillant blue R250, 2.25g, Methanol, 440mL,

acetic acid 60mL and distilled water 500mL]. The box containing gels was shaken

gently at the speed of 25 side wise movement per min for 25min. Then destaining was

carried out in the destaining solution which is composed of methanol (200mL), acetic

acid (50ml) and distilled water (750mL). Kimwipes were put in the box to absorb

stain and, removing the old one. Box was shaken gently until the color of background

disappeared and electrophoretic bands were clearly visible. Destained gel was kept in

distilled water in a refrigerator and used for data recording. The gels were scanned

with the help of scanner and images were obtained as ‘jpg’ files in the computer. For

scanning, gel was placed on a transparency sheet that was folded to cover the gel.

Water bubbles were removed with the help of tissue paper and then scotch tape was

applied on the open sides of the gel. Later on the gel was scanned and data for the

absence or presence of bands were taken.

3.7 Data Analysis

For recording gel data, presence of band was scored as ‘1’ and absence of

band as ‘0’ in a binary fashion. The data regarding nutritional traits, mineral contents

and seed characteristics were analyzed for simple statistics (mean, standard deviation,

variance), cluster analysis using ward’s method (Ward, 1963) and principal

35

component analysis with the help of computer software ‘STATISTICA’ and ‘SPSS’.

To avoid the effect due to difference in scale, means of each trait were standardized

prior to cluster analysis for quantitative data. The data of high molecular glutenin

subunits was analyzed by cluster analysis using UPGMA method, whereas association

of protein markers with the nutritional traits, minerals contents and seed

characteristics were determined using t statistics with the help of SPSS for Windows.

3.8 Cluster Analysis

A group of multivariate techniques with ultimate objective to form groups of

individuals on the basis of their characteristics in such a way that each cluster carries

individuals with same descriptions is known as cluster analysis. As a result high

homogeneity within cluster and high heterogeneity between clusters (Hair et al.,

1995) exists. Moreover, they can be represented graphically by dendogram

(Mohammadi and Prasanna, 2003). Difference between two entities determined on the

basis of variations in alleles is referred as genetic distance (Nei, 1973). More

comprehensively it is the genetic difference measured quantitatively between

populations, species or individuals at the sequence or allelic frequency level

(Beaumont et al., 1998).

3.9 Principal Component Analysis

Clarification of the relationships between multiple traits, and the formation of

non-associated and limited number of variables from the total variance detected by

original traits is known as principal component analysis (Wiley, 1981). The principal

components (PCs) are the result of data reduction in which variables are linearly

transformed into a set of variables which are uncorrelated. Most of the variability is

summarized by first PC, most variability next to first PC is summarized by second PC

(Jolliffe, 1986). By using PCA, components, cumulative in nature, are formed by the

breakdown of total variation found in data (Muhammadi and Prasanna, 2003). The

scattered plot of individuals (2 or 3 dimensional) is derived by using PCA and the

geometrical distances in such a scattered plot show the genetic distances among the

individuals with little distortion. Sets of genetically similar individuals are indicated

by the clustering of the individual in the scatter plot.

3.10 Rust

One ninety two accessions/commercial varieties were screened against stem

rust and stripe rust in green house at Murree station of Crop Disease Research

36

Institute. The material comprised of 37 accessions collected from Punjab, 87 from

Baluchistan and 68 commercial varieties.

3.10.1 Stem Rust

The material to be analyzed for stem rust and checks were cultivated in

30x23x7cm plastic trays on 16-04-2010 and the seedlings were raised in the

greenhouse. After ten days, at three leaf stage, the plants were inoculated with

accession number 09077. Inoculums in the form of uredial suspension in soltor-170

were sprayed uniformly with a sprayer with a fine nozzle. The seedlings were left in

open air for 1-2 hours to evaporate mineral oil and shifted afterwards to a humidity

chamber for 24 hours, after which they were transferred to green house at 18-22oC.

After ten days, infection types were recorded by following the method suggested by

Long and Kolmer (1989). According to which resistant plants exhibit no uredinia, few

faint flecks, small uredinia often surrounded by a necrosis and small to medium

uredinia often surrounded by chlorosis whereas susceptible plants have medium-sized

or large uredinia without necrosis and chlorosis. Moderately resistant plants possess

uredinia having variable sizes and distributed randomly.

3.10.2 Stripe Rust

Accessions, commercial varieties were sown in plastic trays on 11-03-2010

under greenhouse condition. After seven days plants were inoculated with bulk 2010

with the help of a fine nozzle. Non phototoxic isoparafinnic oils (liquid paraffin:

petroleum ether, 30%:70%) were used as carriers which are recommended especially

for green house inoculation (Andres and Wilcoxson, 1984; Rowell, 1957; Rowell and

Olien, 1957). The conducive conditions were provided for the establishment of

disease. Plants were placed in 5000 lux light for one hour, and then they were shifted

to growth room, where adequate moisture was provided, for 48 hours. From the

growth chamber, plants were transferred to green house at 12±1ºC. Notes were taken

20 days after planting. The standard scoring system for stripe rust, as suggested by

McNeal et al. (1971) was followed which classifies major infection types into

resistant (No visible uredia, necrotic flecks, necrotic areas without sporulation,

necrotic and chlorotic areas with restricted sporulation),moderately resistant

(Moderate sporulation with necrosis and chlorosis), moderately susceptible

(Sporulation with chlorosis) and susceptible(Abundant sporulation without chlorosis).

37

4 RESULTS

4.1 Genetic Diversity Based on Geographic Pattern

4.1.1 Germplasm collected from Punjab

Summary statistics for nutritional characteristics, viz., fibre, oil, moisture, ash,

and protein presented in the Table 4.1 revealed low variance that may restrict

improvement of these traits in the germplasm collected from Punjab and used in the

present study. Fibre contents ranged from 0.94% to 1.87% with a mean value of 1.40

±0.05%, oil exhibited mean value of 1.88 ± 0.014% and it ranged from 1.38% to

2.49%. The mean value for moisture was 7.38 ± 0.07% and its range was from 6.1%

to 8.5%. The ash, with the mean value of 1.51 ± 0.09% ranged from 1.17% to 5.51%.

Protein exhibited mean value of 12.61 ± 0.16% and it ranged from 9.26% to 14.87%.

The highest mean value (42.71 ± 4.10 ppm) was observed for iron with the

highest variance (Table 4.1). Other minerals with higher mean values included, Zn

(29.58 ± 0.80 ppm) and Mn (27.95±1.07ppm) that had 99.45% and 187.38% variance,

respectively. The mean values for N, P, K, B, Cu and Na were low (<3). Although

the variance for most of the nutritional characteristics ranged from low to medium but

some of the accessions exhibited higher value for particular trait (Table 4.1 ). Low

variance was observed for N, P, K and Na. Nitrogen ranged from 1.62% to 2.6%, P

from 0.18% to 0.44%, K from 0.30% to 0.62%, B from 0.65ppm to 3.78ppm, Zn from

20.0 ppm to 46.4 ppm, Cu from 1.2 ppm to 7.0 ppm, Mn from 10.4 ppm to 41.6ppm,

Fe from 8.2ppm to 144.2ppm, and Na from 0.02% to 0.08%.

Basic statistics for seed characteristics showed that the mean values for seed

length, seed width and 100 seed weight were found to be 5.76±0.08 mm, 2.53±0.03

mm and 3.78 ±0.09 g, respectively. Low variance was observed for all the three traits.

Seed length ranged from 3.36mm to 6.54 mm, seed width from 1.89 mm to 3.15 mm,

and 100 seed weight ranged from 2.44g to 4.8g.

4.1.2 Germplasm collected from Baluchistan

Wheat germplasm collected from Baluchistan evaluated and the results are

presented in the Table 4.2. The mean value for fibre was 1.29 ±0.03 that ranged from

0.64% to 1.87%. Oil contents ranged from 1.18% to 2.34% with a mean value of

38

Table 4.1 Basic statistics for nutritional traits, mineral contents and seed

characteristics of wheat accessions collected from Punjab.

Mean±SE σ σ

2 in percent

of means Minimum Maximum

Fibre (%) 1.40±0.05 0.35 8.55 0.94 1.87

Oil (%) 1.88±0.04 0.25 3.38 1.38 2.49

Moisture (%) 7.38±0.07 0.45 2.70 6.10 8.50

Ash (%) 1.51±0.10 0.67 29.92 1.17 5.52

Protein (%) 12.61±0.16 1.06 8.85 9.26 14.87

Nitrogen (%) 2.22±0.03 0.19 1.61 1.62 2.60

Phosphorus (%) 0.30±0.01 0.06 1.09 0.18 0.44

Potassium (%) 0.44±0.01 0.08 1.53 0.30 0.62

Boron (ppm) 2.16±0.13 0.90 37.24 0.65 3.78

Zinc (ppm) 29.58±0.80 5.42 99.45 20.00 46.40

Copper (ppm) 2.80±0.16 1.12 44.55 1.20 7.00

Manganese (ppm) 27.95±1.07 7.24 187.38 10.40 41.60

Iron (ppm) 42.71±4.10 27.80 1809.57 8.20 144.20

Sodium (%) 0.04±0.00 0.02 1.17 0.02 0.08

Seed length (mm) 5.76±0.08 0.52 4.67 3.36 6.54

Seed width (mm) 2.53±0.03 0.23 2.14 1.89 3.15

100 seed weight (g) 3.78±0.09 0.59 9.35 2.44 4.80

σ - Standard error, σ 2

- Variance expressed as percent of means

39

Table 4.2 Basic statistics for nutritional traits, mineral contents and seed

characteristics of wheat accessions collected from Baluchistan

Mean±SE Σ σ

2 in percent

of means Minimum Maximum

Fibre (%) 1.29±0.03 0.31 7.57 0.64 1.87

Oil (%) 1.81±0.03 0.27 4.09 1.18 2.35

Moisture (%) 7.35±0.04 0.42 2.43 6.00 8.40

Ash (%) 1.69±0.09 0.90 48.10 0.77 6.86

Protein (%) 12.29±0.18 1.77 25.64 7.12 16.92

Nitrogen (%) 2.16±0.03 0.32 4.74 1.25 2.97

Phosphorus (%) 0.27±0.01 0.09 2.87 0.10 0.44

Potassium (%) 0.61±0.01 0.13 2.65 0.30 0.88

Boron (ppm) 2.14±0.09 0.87 35.67 0.48 3.58

Zinc (ppm) 31.05±0.92 8.90 255.14 13.50 54.00

Copper (ppm) 3.41±0.19 1.80 94.87 1.00 9.00

Manganese (ppm) 25.80±0.67 6.45 161.37 7.80 39.60

Iron (ppm) 79.69±7.27 70.07 6160.48 17.00 300.00

Na (%) 0.04±0.00 0.02 1.08 0.02 0.08

Seed length (mm) 5.73±0.05 0.49 4.22 4.41 7.43

Seed width (mm) 2.48±0.03 0.33 4.47 1.67 3.09

100 seed weight (g) 3.65±0.07 0.65 11.64 2.20 5.36

σ - Standard error, σ 2

- Variance expressed as percent of means

40

1.81±0.03%, moisture with mean value of 7.35%±0.04%, ranged from 6.0% to 8.4%,

whereas ash, with the range from 0.77% to 6.85%, showed the mean value of

1.69±0.09. Protein showed the highest mean value of 12.29±0.18 with the range from

7.12% to 16.92%. Fe (79.69±7.27ppm), Zn (31.05±0.92ppm) and Mn (25.80±0.67)

showed higher mean values with higher levels of variance, whereas N, P, K and Na

exhibited low variance. It was evident that the material collected from Baluchistan

presented higher degree of variance than the material collected from Punjab indicating

the presence of landraces in the area. Basic statistics of seed traits indicated the mean

value for seed length (5.73±0.05 mm), for seed width (2.48±0.03mm) and for 100

seed weight (3.65± 0.07g). All the seed characteristics showed low variance.

4.2 Frequency Distribution

4.2.1 Nutritional Traits

For fibre contents, maximum accessions (63) which were 45.32% of the

population, had the fibre contents ranged from 1.25% to 1.54% that was followed by

42 accessions that exhibited the range from 0.95% to 1.24% (Fig. 4.1). Twenty three

accessions were identified on the basis of higher fibre concentration (Table 4.3).

Sixty five accessions which were 46.76% of the total exhibited oil ranging from

1.83% to 2.14% and it was followed by 48 accessions having oil ranging from 1.51%

to 1.82%. The frequency distribution showed that the maximum number of

accessions (82) which were 58.99% of the population possessed 7.25% to 7.86%

moisture that was followed by the range of 6.63% to 7.24% where 35 accessions were

observed (Fig. 4.2). One hundred and thirty accessions (93.52%) produced < 2.29%

ash, whereas five accessions (11308, 11246, 11259, 11312 and 11255) < 1.00% ash

and hence were marked for low ash content. Regarding protein content, the maximum

accessions (72) which were 51.79% of which had the range of 9.58% to 12.02% (Fig.

4.3). The accessions (18699, 11238, 18696, 11281, 11199, 11304, 11309, 11211,

11280, 11261, 11263 and 11229) were identified for higher protein contents, hence

could be utilized in development of quality wheat in Pakistan.

4.2.2 Mineral Contents

The maximum accessions (70) which were 50.35% of the total had 2.12% to

2.54% N, and it was followed by the range of 1.69% to 2.11% with the frequency

value of 53 accessions (Fig. 4.3). Forty nine accessions ranging from 0.27% to 0.34%

for P contents were followed by 41 accessions in the range from 0.19% to 0.26%

41

Fig.4.1 Frequency distribution for fibre (left) and oil (right) in wheat germplasm

Fig. 4.2 Frequency distribution for moisture (left) and ash (right) in wheat germplasm

10

42

63

24

0

10

20

30

40

50

60

70

< 0.94 0.95-1.24 1.25-1.54 > 1.54

13

48

65

13

0

10

20

30

40

50

60

70

< 1.50 1.51-1.82 1.83-2.14 > 2.14

9

35

82

13

0

10

20

30

40

50

60

70

80

90

< 6.62 6.63-7.24 7.25-7.86 > 7.86

130

4 2 3

0

20

40

60

80

100

120

140

< 2.29 2.30-3.81 3.82-5.33 > 5.33

42

Table 4.3 Promising accessions selected on the basis of combined traits in wheat

accession collected from Punjab (P) and Baluchistan (B)

Trait Range criteria Accessions

Fibre > 1.8% 11527(B), 11255(B), 11167(B), 11224(B), 11150(B), 11177(B), 11184(B), 11202(B), 11231(B),

18673(P), 11335(B), 11344(B), 18703(P), 11351(P), 11363(P), 18677(P), 18683(P), 18690(P),

18693(P), 11352(P), 11355(P), 11531(B), 18698(P)

Oil > 2.10% 11335(B), 11312(B), 11248(B), 11350(P), 18701(P), 11325(B), 11309(B), 11334(B), 11307(B),

18702(P), 11315(B), 11278(B), 11298(B), 11177(B), 11154(B), 11361(P), 18690(P), 18703(P),

18689(P)

Moisture > 7.8% 11267(B), 11305(B), 11311(B), 11538(B), 11351(P), 18669(P), 18681(P), 18696(P), 11231(B),

11233(B), 11259(B), 11272(B), 11283(B), 11298(B), 11531(B), 18679(P), 11220(B), 18703(P),

11261(B), 11284(B), 18682(P)

Ash < 1.0% 11308(B), 11246(B), 11259(P), 11312(B), 11255(B)

Protein > 14.0% 18699(P), 11238(B), 18696(P), 11281(B), 11199(B), 11304(B), 11309(B), 11211(B), 11280(B),

11261(B), 11263(B), 11229(B)

N > 2.6% 18696(P), 18707(P), 11281(B), 11199(B), 11304(B), 11309(B), 11211(B), 11156(B), 11280(B),

11261(B), 11263(B)

P > 0.38% 11243(B), 11283(B), 11305(B), 11315(B), 11265(B), 11284(B), 11353(P), 18708(P), 11294(B),

11295(B), 11200(B), 11272(B), 11360(P), 11259(B), 11267(B), 11304(B), 11255(B),

18696(P)

K > 0.8% 11198(B), 11202(B), 11294(B), 11295(B), 11145(B), 11178(B), 11296(B), 11311(B), 11171(B)

(B) > 3.2ppm 11303(B), 11267(B), 18682(P), 11214((B), 11344(B), 18674(P), 18680(P), 11299(B), 11259(B),

11310(B), 11231(B), 18687(P), 11325(B), 11334(B), 11528(B), 11362(P), 11237(B), 11281(B),

18683(P), 18698(P)

Zn >40.0 ppm 11170(B), 11296(B), 11334(B), 11363(P), 11156(B), 11308(B), 11298(B), 11238(B), 11200(B),

11534(B), 11304(B), 11309(B), 11199(B), 18708(P), 11211(B), 11272(B), 11229(B),

11280(B)

Cu > 5.0 ppm 11255(B), 11278(B), 11283(B), 11310(B), 11315(B), 11294(B), 11309(B), 11281(B), 11296(B),

11248(B), 18694(P), , 1200(B), 11272(B), 11299(B), 11263(B), 11308(B), 11265(B)

Mn >35.0ppm 11534(B), 11311(B), 18689(P), 11335(B), 11235(B), 11349(P), 11214(B), 11220(B), 11348(P),

11262(B), 11356(P), 18688(P), 11359(P), 11210(B), 11355(P), 11360(P), 18690(P), 11280(B),

18696(P)

Fe >100.0ppm 11193(B), 11309(B), 11237(B), 11195(B), 11335(B), 11199(B), 18692(P), 11310(B), 11155(B),

11185(B), 11233(B), 11238(B), 11235(B), 11298(B), 11315(B), 11311(B), 11272(B), 11154(B),

11194(B)

Na > 0.08% 11160(B), 11195(B), 11198(B), 11202(B), 11210(B), 11226(B), 11303(B), 11315(B), 11349(P),

11362(P), 18672(P), 18676(P), 18687(P)

Seed

length

> 6.5mm 18669(P), 11255(B), 11288(B), 11171(B), 11164(B)

Seed width > 2.85mm 11255(B), 11293(B), 11349(P), 11239(B), 11164(B), 11362(P), 11538(B), 11178(B), 11300(B),

11226(B), 11283(B), 11214(B), 11278(B), 11171(B), 11246(B), 11248(B), 18675(P)

100 seed

weight

> 4.5g 18703(P), 11352(P), 11221(B), 11164(B), 18693(P), 11362(P), 18672(P), 11194(B), 11237(B),

11171(B), 11170(B)

43

Fig. 4.3 Frequency distribution for protein (left) and Nitrogen (right) in wheat

germplasm

Fig. 4.4 Frequency distribution for Phosphorus (left) and Potassium (right) in wheat

germplasm

4

53

72

10

0

10

20

30

40

50

60

70

80

< 9.57 9.58-12.02 12.03-14.47 > 14.47

4

53

70

12

0

10

20

30

40

50

60

70

80

< 1.68 1.69-2.11 2.12-2.54 > 2.54

21

41

49

28

0

10

20

30

40

50

60

< 0.18 0.19-0.26 0.27-0.34 > 0.34

31

59

28

21

0

10

20

30

40

50

60

70

< 0.44 0.45-0.58 0.59-0.72 > 0.72

44

(Fig. 4.4). Fifty nine (42.44%) accessions exhibited 0.45% to 0.58% K, while only

nine accessions (11198, 11202, 11294, 11295, 11145, 11178, 11296, 11311, and

11171) produced K > 0.8%. As shown in Fig. 4.5, forty two accessions contained B

in the range of 2.13ppm to 2.94ppm; which was followed by 40 accessions that

contained B 1.31 ppm to 2.12 ppm.

Frequency distribution regarding Zn concentration indicates that sixty seven

accessions were in the range from 23.63 ppm to 33.74 ppm Zn contents and it was

followed by the range from 33.75 ppm to 43.86 ppm (34 accessions). Ten accessions

were with Zn contents more than 43.86 ppm (Fig. 4.5). The frequency distribution of

Cu is presented in Fig. 4.6. Maximum accessions (80) which were 57.55% of the

population exhibited less than 3.00 ppm Cu, while six accessions (11200, 11272,

11299, 11263, 11308 and 11265) contained Cu more than 7.00 ppm. Sixty accessions

had Mn in the range from 24.71 ppm to 33.15ppm and it was followed by 47

accessions with 16.26 ppm to 24.70 ppm Mn. On the basis of Mn contents, 19

accessions were selected with Mn content, i.e., >35ppm (Table 4.3). As depicted in

the Fig 4.7, forty seven accessions had Fe in the range of 25.1ppm to 50.0ppm. This

was followed by 42 accessions with Fe contents in the range of 50.1ppm to 75.0ppm.

Nineteen accessions were selected on the basis of high Fe content (>100ppm). Less

than 0.035% Na contents were observed in the maximum accessions (70). On the

basis of higher Na content (>0.08%), thirteen accessions (11160, 11195, 11198,

11202, 11210, 11226, 11303, 11315, 11349, 11362, 18672, 18676 and 18687) were

identified (Table 4.3), whereas 70 accessions were with low Sodium (<0.02 %)

4.2.3 Seed Characteristics

Frequency distribution presented in Fig. 4.8 indicates that maximum

accessions (103) which were 74.10% of the total population possessed seed length in

the range of 5.39 cm to 6.39cm whereas only one accession (11348) exhibited seed

length less than 4.37. Five accessions (18669, 11255, 11288, 11171 and 11164) were

selected on the basis of greater seed length (>6.5 cm). As far as seed width is

concerned, maximum accessions (60) which were 43.16% of the population exhibited

seed width in the range of 2.39 cm to 2.75 cm. It was followed by 36 accessions

which had seed width ranging from 2.03cm to 2.39 cm. Seventeen accessions were

selected which contained seed width greater than 2.85 cm. Maximum accessions (55)

which were 39.56% of the population were having 3.79g to 4.57g seed weight as

45

Fig. 4.5 Frequency distribution for Boron (left) and Zinc (right)in wheat germplasm

Fig. 4.6 Frequency distribution for Copper (left) and Manganese (right) in wheat

germplasm

26

40 42

31

0

5

10

15

20

25

30

35

40

45

< 1.30 1.31-2.12 2.13-2.94 > 2.94

28

67

34

10

0

10

20

30

40

50

60

70

80

< 23.62 23.63-33.74 33.75-43.86 > 43.86

80

46

7 6

0

10

20

30

40

50

60

70

80

90

< 3.0 3.1-5.0 5.1-7.0 > 7.0

9

47

60

23

0

10

20

30

40

50

60

70

< 16.25 16.26-24.70 24.71-33.15 > 33.15

46

Fig. 4.7 Frequency distribution for Iron (left) and Sodium (right) in wheat germplasm

Fig. 4.8 Frequency distribution for seed length (left) and seed width (right) in wheat

germplasm

21

47

42

10

19

0

5

10

15

20

25

30

35

40

45

50

< 25.0 25.1-50.0 50.1-75.0 75.1-100 > 100

70

35

21

13

0

10

20

30

40

50

60

70

80

< 0.035 0.036-0.050 0.051-0.065 > 0.065

1

27

103

8

0

20

40

60

80

100

120

< 4.37 4.38-5.38 5.39-6.39 > 6.39

12

36

60

31

0

10

20

30

40

50

60

70

< 2.034 2.035-2.394 2.395-2.754 > 2.754

47

depicted in Fig. 4.9. Eleven accessions (18703, 11352, 11221, 11164, 18693, 11362,

18672, 11194, 11237, 11171 and 11170) were selected on the basis of high seed

weight (>4.5g) as shown in Table 4.3.

Out of 46 accessions of wheat germplasm, 44 accessions (95.65% of the

population) were possessed intermediate seed size, whereas only two accessions were

with small seeds, whereas none of the accession was in large or very large category

(Fig.4.9). On the basis of seed color, 18 accessions were red and seventeen

accessions were observed creamy white (Fig. 4.10). Regarding degree of seed

shriveling, data were recorded as plump, intermediate and shriveled. Forty two seeds

(91.30%) were intermediate regarding degree of shriveling whereas only one seed was

found shriveled.

Among 93 accessions collected from Baluchistan, 77 had intermediate seed size,

two were large and fourteen were small (Fig. 4.11). Regarding seed color 47

accessions (50.54%) were red, 32 (34.41%) were creamy white, and 14 (15.05%)

were white whereas purple seed color was missing in the indigenous germplasm (Fig.

4.11). Seventy seven accessions out of 93 had intermediate degree of seed shriveling,

whereas 8 accessions were plump and rest of the eight (8.60%) were shriveled (Fig.

4.12).

4.3 Principal Component Analysis based on Geographic Pattern

4.3.1.1 Punjab

The measured variance partitioned by PCA for five nutritional traits indicated

Eigenvalues for the first two components greater than unity (>1.0), that contributed

more than half of the variation amongst 46 accessions of wheat collected from Punjab.

The PC1 showed 29.3% of the total variation, and PC2 had 23.8% of the total

variation. Characters which contributed more positively to PC1 included fibre (0.617)

and ash (0.684), while moisture contents contributed maximum genetic variance to

PC2 as presented in the Table 4.4. The Figure 4.13 presents the first two PCs

plotted graphically to study the relationship between germplasm for these

components. The digits refer to the accessions numbers in Chapter 3. The separation

indicated one main group, whereas eight accessions (18682, 11348, 11351, 11359,

11355, 11352, 11353 and 18693) were scattered indicating genetic differences of

higher magnitude.

48

Fig. 4.9 Frequency distribution for 100 seed weight in wheat germplasm(left) and

seed size in Punjab (right)

Fig. 4.10 Frequency distribution for seed color (left) and seed shriveling (right) in

Punjab

23

51

55

10

0

10

20

30

40

50

60

< 2.99 3.00-3.78 3.79-4.57 > 4.57

2

44

0 0

5

10

15

20

25

30

35

40

45

50

Small Intermediate Large

11

17 18

0

2

4

6

8

10

12

14

16

18

20

White Creamy white Red

3

42

1

0

5

10

15

20

25

30

35

40

45

Plump Intermediate Shrivelled

49

Fig. 4.11 Frequency distribution for seed size (left) and seed color (right) in

Baluchistan

Fig. 4.12 Frequency distribution for seed shriveling in Baluchistan

14

77

2

0

10

20

30

40

50

60

70

80

90

Small Intermediate Large

14

32

47

0

5

10

15

20

25

30

35

40

45

50

White Creamy white Red

8

77

8

0

10

20

30

40

50

60

70

80

90

Plump Intermediate Shrivelled

50

Table 4.4 Principal components based on nutritional traits of wheat accessions


PC1 PC2

Eigen value 1.46 1.19

Variance 29.38 23.87

Cumulative variance 29.38 53.26

Traits Eigen value

Fibre (%) 0.61 -0.27

Oil (%) -0.40 -0.59

Moisture (%) 0.16 0.74

Ash (%) 0.68 0.12

Protein (%) -0.65 0.43

51

Fig. 4.13 Scattered diagram of first two PCs for nutritional traits in wheat accessions


(29.38%)

(23.8

7%

)

52

The PCA regarding nine mineral contents among the germplasm collected

from Punjab is presented in the Table 4.5. More than 70 % of the variation was

contributed by first four components with > 1 eigenvalues. The PC1 contributed one

third of the total variation, PC2 14.4%, PC3 12.4% and PC4 exhibited 12.0% of the

total variation. Mineral contents contributing more positively to PC1 included P

(0.889), Zn (0.733), Mn (0.817) and Fe (0.593), whereas K (0.816) contributed

maximum to PC2. The Na (0.624) contributed more positively to PC3 and Cu (0.635)

gave maximum variance to PC4.

The relationship among wheat germplasm for first three components was

plotted and presented in the Fig. 4.14 and Fig. 4.15. One main group was observed

whereas six accessions (18687, 11364, 18708, 18696, 11351 and 11355) were

different from rest of the germplasm on the basis of first two components. Three

groups were observed when plotted for the PC1 and PC3 (Fig. 4.15). These groups

were not closely related and three accessions (11362, 18687 and 18694) were

different from rest of the germplasm. Based on seed characteristics, as shown in the

Table 4.6, first two components, exhibited > 1.0 eigenvalues with more than half of

the variance amongst germplasm collected from Punjab. In the PC1 maximum genetic

variance (0.889) was contributed by size of seed, and, 100 seed weight (0.676) and

degree of seed shriveling (0.700) contributed positively to PC2. The graphic

representation on the basis of two factors indicated that most of the accessions were

closely related, and only seven accessions (11348, 11349, 11361, 11355, 18683,

18676 and 18679) were scattered far off from the central point (Fig. 4.16).

On the basis of combined data, seven components were possessed > 1.0

eigenvalues and contributed 67.1% of the total variation amongst 46 accessions

(Table 4.7). The PC1 contributed 20.9%, PC2 11.2%, PC3 8.9%, PC4 7.5%, PC5 6.5%,

PC6 6.3% and PC7 contributed 5.5% of the total variation. The characteristics with

the maximum genetic variance in PC1 included P (0.756), Fe (0.610) and Mn (0.673).

In the PC4, 100 seed weight (0.693) contributed more positively, whereas in PC5 B

(0.716) and in PC6 moisture (0.519) and K (0.516) showed maximum genetic

variance. First three components plotted and presented in the Fig. 4.17 and Fig. 4.18

indicated that accessions formed two groups based on PC1 and PC2 along with few

accessions (18708, 18696, 11348, 11352, 11349, 11355, 11353, 11351, 18679, 18683

and 18687) scattered far from the origin (Fig. 4.17).

53

Table 4.5 Principal components based on mineral contents of wheat accessions


PC1 PC2 PC3 PC4

Eigen value 2.86 1.29 1.12 1.08

Variance 31.78 14.40 12.48 12.06

Cumulative variance 31.78 46.19 58.67 70.73

Traits Eigen factors

Nitrogen (%) -0.56 0.38 0.24 0.44

Phosphorus (%) 0.88 0.18 0.02 -0.15

Potassium (%) -0.27 0.81 -0.20 -0.14

Boron (ppm) 0.02 0.34 0.51 -0.55

Zinc (ppm) 0.73 0.29 0.11 0.05

Copper (ppm) 0.29 0.09 0.58 0.63

Manganese (ppm) 0.81 0.14 -0.03 0-.00

Iron (ppm) 0.59 -0.28 -0.11 0.12

Sodium (%) -0.18 -0.35 0.62 -0.34

54

Fig. 4.14 Scattered diagram of first two PCs for mineral contents in wheat accessions


Fig. 4.15 Scattered diagram of first and third PC for mineral contents in wheat


(31.78%)

(14.4

0%

)

(14.40%)

(12.4

8%

)

55

Table 4.6 Principal components based on seed characteristics of wheat accessions


PC1 PC2




Traits Eigen factor

Seed length (mm) -0.88 0.01

Seed width (mm) 0.49 -0.14

100 seed weight (g) -0.06 0.67

Seed size 0.88 -0.09

Seed color -0.36 -0.33

Seed shriveling 0.11 0.70

56

Fig. 4.16 Scattered diagram of first two PCs for seed characteristics in wheat


(32.17%)

(18.1

7%

)

57

Table 4.7 Principal components based on combined traits of wheat accessions


PC1 PC2 PC3 PC4 PC5 PC6 PC7

Eigen value 4.18 2.24 1.79 1.51 1.31 1.26 1.10

Variance 20.90 11.21 8.97 7.55 6.59 6.33 5.53

Cumulative variance 20.90 32.11 41.08 48.64 55.24 61.57 67.10


Fibre (%) 0.27 -0.32 0.34 0.16 0.03 -0.20 0.18

Oil (%) -0.15 0.43 0.33 -0.41 -0.17 0.07 -0.12

Moisture (%) -0.14 -0.41 -0.03 0.09 0.30 0.51 0.32

Ash (%) 0.34 -0.22 0.38 0.37 0.25 0.09 -0.13

Protein (%) -0.74 0.21 -0.35 0.26 0.23 0.02 -0.02

Nitrogen (%) -0.72 0.26 -0.36 0.27 0.20 -0.01 -0.00

Phosphorus (%) 0.75 0.42 0.19 -0.03 0.15 0.09 0.01

Potassium (%) -0.40 0.39 0.10 -0.07 0.27 0.51 0.14

Boron (ppm) -0.02 -0.00 0.27 -0.19 0.71 -0.13 -0.26

Zinc (ppm) 0.57 0.57 0.08 0.11 0.16 -0.08 0.21

Copper (ppm) 0.18 0.38 -0.33 0.24 0.01 -0.46 -0.21

Manganese (ppm) 0.67 0.41 0.05 -0.09 0.02 0.14 0.06

Iron (ppm) 0.61 -0.13 -0.16 0.22 0.18 0.10 -0.36

Sodium (%) -0.10 -0.17 -0.22 -0.41 0.23 -0.45 0.25

Seed length (mm) -0.69 0.05 0.42 0.16 0.04 -0.13 -0.17

Seed width (mm) 0.34 -0.63 -0.19 0.12 0.01 0.10 -0.34

100 seed weight (g) 0.17 0.07 0.09 0.69 -0.01 -0.18 0.49

Seed size 0.48 -0.14 -0.63 -0.26 -0.00 0.15 0.23

Seed color -0.22 -0.14 0.36 0.06 -0.46 0.06 0.07

Seed shriveling 0.06 0.36 -0.27 0.28 -0.30 0.30 -0.30

58

Fig. 4.17 Scattered diagram of first two PCs for combined traits in wheat accessions


Fig. 4.18 Scattered diagram of first and third PC for combined traits in wheat


(20.90%)

(11.2

1%

)

(11.21%)

(8.9

7%

)

59

Based on PC1 and PC3, one group was observed and 8 accessions (11348, 11349,

11352, 11355, 11353, 18679, 18703 and 18687) were scattered showing varying

degrees of genetic differences.

4.3.1.2 Baluchistan

The germplasm collected from Baluchistan indicates that first three PCs

contributed 68.1% of the total variation amongst 93 accessions (Table 4.8). The PC1

contributed 25.3%, PC2 contributed 22.7% and PC3 showed 20.0% contributed to the

total variation. The nutritional component with maximum variance in PC1 was

moisture (0.696), in PC2 oil (0.784) and protein (0.576), whereas fibre (0.626) and ash

(0.754) contributed more towards PC3. Based on the PC1 and PC2, one cluster was

observed along with six accessions (11154, 11171, 11283, 11284, 11236 and 11233)

scattered in the graph as presented in the Fig. 4.19. Similarly one cluster closer to

origin and two accessions (11233 and 11236) scattered were observed on the basis of

PC1 and PC3 (Fig. 4.20).

The PCA for mineral contents indicated >1.0 eigenvalue for first four PCs.

The PC1 contributed 23.0%, PC215.9%, PC3 14.8% and PC4 contributed 11.9% of the

total variation, and collectively these four components showed 65.8% of the total

variance.PC1 was more positively contributed by N (0.678), P (0.622), Zn (0.726) and

Cu (0.593), Whereas K (0.809) exhibited higher contribution for PC3, and Mn (0.670)

towards PC 4 as shown in Table 4.9. The Fig. 4.21 depicts graphic relationship based

on PC1 and PC2 that indicated two groups along with three accessions (11154, 11280

and 11272) scattered. Similarly based on PC1 and PC3, two groups along with

scattered accessions (11171, 11195, 11198, 11259, 11315, 11200, 11272, 11280,

11211, 11534, 11304, 11528 and 11538) were observed (Fig. 4.22)

The PCA for seed characteristics indicated the first three components with > 1

eigenvalues and these contributed 66.4% of the total variation (Table 4.10). The PC1

contributed 30.2% variation, PC2 18.4% and PC3 explained however 17.8% of the

total variation amongst wheat germplasm. The traits, seed length, seed width and 100

seed weight contributed more positively to PC1, whereas 100 seed weight and seed

size contributed maximum to PC2. Two groups were observed on the basis of PC1 and

PC2, whereas the accessions (11170, 11171, 11237, 11283, 11307 and 11305) were

scattered and were away from the centre (Fig. 4.23). Similarly on the basis of PC1 and

PC3 two groups were observed as in Fig. 4.24 including the accessions (11305, 11280

60



PC1 PC2 PC3

Eigen value 1.27 1.13 1.00

Variance 25.39 22.72 20.00

Cumulative variance 25.39 48.11 68.12


Fibre (%) -0.54 0.25 0.62

Oil (%) 0.18 0.78 -0.11

Moisture (%) 0.69 -0.27 -0.08

Ash (%) 0.45 -0.22 0.75

Protein (%) 0.49 0.57 0.14

61

Fig. 4.19 Scattered diagram of first two PCs for nutritional traits in wheat accessions


Fig. 4.20 Scattered diagram of first and third PC for nutritional traits in wheat


(25.39%)

(22.72%)

(22.7

2%

)

(20.0

0%

)

62



PC1 PC2 PC3 PC4

Eigen value 2.07 1.43 1.33 1.07

Variance 23.07 15.95 14.87 11.97



Nitrogen (%) 0.67 0.33 -0.24 -0.36

Phosphorus (%) 0.62 -0.49 -0.02 -0.11

Potassium (%) 0.11 0.21 0.80 0.13

Boron (ppm) 0.31 -0.64 0.08 0.11

Zinc (ppm) 0.72 0.41 -0.21 -0.23

Copper (ppm) 0.59 -0.43 0.26 0.14

Manganese (ppm) 0.27 0.11 -0.40 0.67

Iron (ppm) 0.39 0.47 0.34 0.47

Sodium (%) 0.06 0.07 0.47 -0.39

63

Fig. 4.21 Scattered diagram of first two PCs for mineral contents in wheat accessions




(23.07%)

(15.95%)

(15.9

5%

) (1

4.8

7%

)

64

Table 4.10 Principal components based on seed characteristics of wheat

accession collected from Baluchistan

PC1 PC2 PC3

Eigen value 1.81 1.10 1.07

Variance 30.20 18.40 17.85



Seed length (mm) 0.80 0.01 0.02

Seed width (mm) 0.85 -0.11 -0.11

100 seed weight (g) 0.54 0.61 0.18

Seed size -0.35 0.76 -0.24

Seed color -0.06 0.21 0.88

Seed shriveling 0.12 0.29 -0.42

65



Fig. 4.24 Scattered diagram of first and third PC for seed characteristics in wheat


(30.20%)

(18.40%)

(18.4

0%

)

(17.8

5%

)

66

(11302, 11307, 11263, 11265, 11194, 11155, 11272 and 11236) were scattered

throughout the graph.

The PCA for all the sets of data as presented in the Table 4.11 indicated that

nine components with eigenvalue > 1 contributed 72.2% of the total variation. The

populations with higher variations contributing to PC1 possessed high values for

protein, N, and Zn , whereas moisture , P and B contributed maximum genetic

variance to PC2.In PC3 maximum genetic variance was contributed by seed length and

seed width, whereas K and Fe contributed more positively to PC4.

One group was observed for PC1 and PC2scattered diagram closer to the

central point, whereas the accessions (11284, 11283, 11259, 11164, 11171 and 11154)

were scattered (Fig. 4.25). Similarly the Fig. 4.26 revealed one cluster based on PC1

and PC3 close to the origin, whereas the accessions (11171, 11229, 11263, 11280,

11309, 11298 and 11305) were scattered and could be different from rest of the

germplasm.

4.4 Genetic diversity in wheat germplasm collected from Punjab and

Baluchistan provinces

For understanding of genetic diversity on the basis of geographic patterns for

two provinces from where wheat germplasm was collected, the data for all the 139

accessions were analyzed and presented. First two PCs with > 1 eigenvalue

contributed 23.7% and 23.4% of the variation, respectively. Moisture contents and ash

contribute more positively to PC1 while oil and protein imparted maximum to PC2

(Table 4.12). The scatter plots between PC1 and PC2 indicated one main group but all

mixed in general but exceptionally few of them scattered (Fig. 4.27).

For mineral contents, first four PCs with > 1 eigenvalue contributed 62.3% of

the total variations amongst 139 accessions (Table 4.13). The PC1 contributed 21.1%,

PC2 15.4%, PC3 13.8% and PC4 contributed 11.8% to the total variation. The N, P, Zn

and Cu contributed maximum genetic variance to PC1, whereas the PC2 was more

contributed by K, and Fe. The mineral, B contributed more to PC3, whereas Mn and

Na contributed more positively to PC4. The scattered plot drawn on y-axis and x-axis

based on first two components indicated intermingled of the germplasm collected

from both the provinces in the lower half, whereas the upper half was occupied by the

accessions collected from Baluchistan, but three from Punjab (Fig. 4.28). Similarly

based on PC1 and PC3 as in the Fig. 4.29, one group was observed closer to the centre.

67



PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9

Eigen value 3.26 2.04 1.74 1.57 1.30 1.24 1.21 1.04 1.02

Variance 16.30 10.22 8.70 7.85 6.52 6.22 6.05 5.24 5.14

Cumulative.

variance 16.30 26.53 35.24 43.09 49.62 55.84 61.90 67.14 72.29


Fibre (%) -0.04 -0.34 -0.18 -0.21 0.20 -0.11 0.35 0.32 -0.43

Oil (%) 0.34 -0.20 -0.06 0.24 -0.16 0.21 0.33 -0.49 -0.06

Moisture (%) 0.17 0.72 -0.05 -0.01 -0.18 -0.07 0.07 0.04 0.29

Ash (%) 0.02 -0.01 0.12 0.09 -0.55 -0.54 -0.08 0.21 0.10

Protein (%) 0.82 -0.15 0.31 -0.26 0.02 -0.09 -0.13 -0.02 0.16

Nitrogen (%) 0.82 -0.18 0.28 -0.27 0.04 -0.08 -0.16 -0.01 0.17

Phosphorus

(%) 0.43 0.62 0.08 0.07 0.23 -0.00 -0.11 0.21 -0.10

Potassium

(%) 0.10 -0.24 -0.29 0.50 0.29 -0.27 -0.10 -0.36 0.25

Boron (ppm) 0.19 0.63 -0.06 0.08 0.19 -0.11 0.19 -0.15 0.01

Zinc (ppm) 0.71 -0.21 0.27 0.00 0.01 0.20 -0.07 0.01 -0.12

Copper

(ppm) 0.39 0.36 -0.11 0.32 0.41 0.02 0.11 0.12 -0.15

Manganese

(ppm) 0.20 0.07 -0.21 -0.10 -0.13 0.75 0.01 0.22 0.15

Iron (ppm) 0.36 -0.24 -0.03 0.61 -0.17 0.19 0.10 0.05 0.09

Sodium (%) -0.07 -0.11 0.05 0.27 0.33 -0.01 -0.69 0.05 -0.26

Seed length

(mm) -0.40 0.13 0.63 0.09 0.17 0.01 0.22 -0.04 0.10

Seed width

(mm) -0.47 0.09 0.64 0.16 0.01 0.15 0.03 -0.10 0.04

100 seed

weight (g) -0.32 -0.27 0.32 0.22 0.27 0.15 0.00 0.36 0.43

Seed size -0.07 -0.16 -0.55 -0.12 0.25 -0.07 0.02 0.20 0.48

Seed color 0.37 -0.23 0.20 0.18 0.14 -0.27 0.52 0.26 -0.02

Seed

shriveling -0.08 -0.08 0.08 -0.57 0.42 -0.00 0.12 -0.34 0.14

68

Fig. 4.25 Scattered diagram of first two PCs for combined traits in wheat accessions




(16.30%)

(10.22%)

(10.2

2%

) (1

1.2

1%

)

69



PC1 PC2





Fibre (%) -0.48 -0.25

Oil (%) -0.21 0.71

Moisture (%) 0.73 -0.01

Ash (%) 0.50 -0.36

Protein (%) 0.33 0.67

70

Fig. 4.27 Scattered diagram of first two PCs for nutritional traits in wheat germplasm


(23.79%)

(23.4

6%

)

71



PC1 PC2 PC3 PC4

Eigen value 1.90 1.39 1.24 1.07

Variance 21.13 15.49 13.80 11.89



Nitrogen (%) 0.57 -0.02 -0.58 -0.00

Phosphorus (%) 0.58 -0.53 0.15 -0.07

Potassium (%) 0.22 0.71 0.40 -0.03

Boron (ppm) 0.29 -0.40 0.53 0.10

Zinc (ppm) 0.72 0.08 -0.45 0.07

Copper (ppm) 0.61 -0.09 0.45 -0.03

Manganese (ppm) -0.04 -0.01 -0.05 0.76

Iron (ppm) 0.43 0.64 0.09 -0.00

Sodium (%) 0.03 0.04 0.09 0.67

72

The accessions collected from Punjab province were grouped in the lower half, while

the accessions collected from Baluchistan province were scattered all around.

The Table 4.14 presents the PCA results for seed characteristics that indicated

first three components with > 1 eigenvalues with cumulative contributed of 62.6% to

the total variation. The PC1 contributed 26.8%, PC2 18.7% and PC3 contributed

17.0% to the total genetic variance. The seed characteristics contributing positively to

PC1 were seed length, whereas and seed width the PC2 was affected by seed size and

others (seed width and seed color) imparted maximum genetic variance to PC3.The

Fig. 4.30 indicated intermixing of that one group was formed, regarding seed traits.

Accessions from both the provinces on the basis of PC1 and PC2, and similar results

were observed on the basis of PC1 and PC3 (Fig. 4.31).

Based on combined data for all the traits, nine components were with > 1

eigenvalue with varying degrees of share towards cumulative effects (Table 4.15).

Among twenty traits, protein, N and Zn contributed maximum towards PC1, and

others were distributed among various components. The relationship between PC1 and

PC2 indicated that the accessions collected from Punjab were in the upper half

intermingled with accessions from Baluchistan, while the lower half was

predominantly occupied with the accessions collected from Baluchistan with few

exceptions (Fig. 4.32). Similarly plotting against PC1 and PC3 presented in the Fig.

4.33 revealed that accession from both the provinces formed two groups, whereas few

accessions belonging to Baluchistan region were scattered far from the centre.

4.5 Cluster Analysis

4.5.1 Based on Geographic Pattern for the germplasm collected from Punjab

For investigation of genetic diversity based on various data sets, i.e., nutritional

characteristics, mineral contents and seed characteristics, the germplasm was analyzed

on the basis of collecting sites separately, as well as combined to have comprehensive

information. The cluster diagram based on Ward’s methods with Euclidean

dissimilarities for 46 accessions collected from Punjab province based on nutritional

characteristics was constructed and presented in the Fig. 4.34. Four clusters were

observed at 50% dissimilarity that were further categorized into nine sub-clusters at

75% distance. The cluster I, II and IV consisted of two sub-clusters in each case,

whereas the cluster III was subdivided to three sub-clusters.

73

Fig. 4.28 Scattered diagram of first two PCs for mineral contents in wheat germplasm



germplasm collected from Punjab and Baluchistan (Combined)

(21.13%)

(15.49%)

(15.4

9%

) (1

3.8

0%

)

74

Table 4.14 Principal components based on seed characteristics of wheat accessions


PC1 PC2 PC3

Eigen value 1.61 1.12 1.02

Variance 26.86 18.71 45.58



Seed length (mm) 0.74 -0.18 0.02

Seed width (mm) 0.74 0.26 -0.17

100 seed weight (g) 0.49 0.36 0.59

Seed size -0.50 0.51 0.41

Seed color 0.01 -0.68 0.65

Seed shriveling 0.10 0.39 0.17

75



Fig. 4.31 Scattered diagram of first and third PC for seed characteristics in wheat


(26.86%)

(18.71%)

(18.7

1%

) (4

5.5

8%

)

76



PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9

Eigen value 2.82 1.75 1.71 1.44 1.24 1.22 1.17 1.10 1.05

Variance 14.13 8.75 8.55 7.23 6.22 6.10 5.88 5.54 5.29

Cumulative.

variance 14.13 22.88 31.44 38.67 44.89 51.00 56.88 62.42 67.72


Fibre (%) -0.10 0.02 -0.20 -0.17 -0.59 0.32 -0.26 0.09 0.11

Oil (%) 0.31 -0.02 -0.17 0.13 -0.11 -0.24 -0.46 -0.28 -0.25

Moisture (%) 0.08 0.06 0.63 0.00 -0.13 -0.15 0.34 0.11 0.04

Ash (%) -0.00 -0.03 0.06 0.33 -0.39 -0.00 0.58 -0.09 0.10

Protein (%) 0.82 0.37 -0.15 -0.06 0.10 -0.07 0.20 0.01 0.14

Nitrogen (%) 0.83 0.36 -0.19 -0.09 0.12 -0.05 0.20 0.01 0.14

Phosphorus

(%) 0.37 0.06 0.61 -0.03 -0.06 0.38 -0.15 0.06 0.09

Potassium

(%) 0.14 -0.60 -0.16 0.22 0.30 -0.34 0.03 0.21 0.14

Boron (ppm) 0.15 -0.02 0.65 0.01 0.03 -0.12 -0.11 0.12 -0.16

Zinc (ppm) 0.68 0.02 -0.15 0.12 -0.00 0.32 -0.09 -0.09 -0.01

Copper (ppm) 0.36 -0.28 0.35 0.10 0.15 0.15 -0.36 0.31 0.16

Manganese

(ppm) -0.00 -0.01 -0.03 -0.07 0.20 0.24 0.27 0.25 -0.80

Iron (ppm) 0.31 -0.45 -0.15 0.53 -0.04 0.06 0.01 -0.00 -0.03

Sodium (%) -0.05 -0.06 -0.02 0.08 0.53 0.43 0.05 -0.27 0.02

Seed length

(mm) -0.26 0.49 0.04 0.41 0.22 -0.33 -0.17 0.21 0.10

Seed width

(mm) -0.45 0.38 0.06 0.40 0.08 0.14 -0.05 -0.01 0.15

100 seed

weight (g) -0.26 0.18 -0.28 0.26 0.08 0.43 0.07 0.46 0.15

Seed size -0.07 -0.51 -0.13 -0.42 0.02 0.01 0.15 0.29 0.27

Seed color 0.30 0.06 -0.18 0.28 -0.35 -0.10 -0.07 0.47 -0.25

Seed

shriveling -0.03 0.33 -0.14 -0.47 0.15 -0.14 -0.11 0.33 0.01

77

Fig. 4.32 Scattered diagram of first two PCs for Combined traits in wheat germplasm




(14.13%)

(8.75%)

(8.7

5%

) (8

.55%

)

78

Fig. 4.34 Phenogram for 46 accessions of wheat germplasm collected from Punjab based on nutritional traits

0 2 4 6 8 10 12 14 16 18

Linkage Distance

18699186981870818702187011868918692186881870718705186761867518685186721868218696186941868718678186811867418669113491135911364113601135618683186771867918695186701136218703186901136311361113501135318680186731135218693113551135111348

79

It is evident from the Table 4.16 that two accessions (18699 and 18698) were

in the sub-cluster 1, four (18708, 18702, 18701 and 18689) in sub-cluster 2, eight

(18692, 18688, 18707, 18705, 18676, 18675, 18685 and 18672) in sub-cluster 3 and

nine (18682, 18696, 18694, 18687, 18678, 18681, 18674, 18699 and 11394) in sub-

cluster 4. Four accessions (11359, 11364, 11360 and 11356) were observed in the

sub-cluster 5, six (18683, 18677, 18679, 18695, 18670 and 11362) in sub-cluster 6,

whereas the sub-cluster 7 consisted of five accessions (18703, 18690, 11363, 11361

and 11350). The accession 11353 constituted the sub-cluster 8 and seven accessions

(18680, 18673, 11352, 18693, 11355, 11351 and 11348) were grouped in the sub-

cluster 9.

The performance of accessions in various sub clusters was observed in mean

values within sub cluster and presented in the Table 4.17. It indicates that the

accessions from the sub-cluster 1 could be selected for high protein content as this sub

cluster exhibited the highest mean value of 13.55±0.69, whereas the sub cluster 2 and

4 did not exhibit high mean value for any of nutritional traits. The sub-cluster 3

showed the lowest mean value for fibre (0.98±0.08%) and ash (1.26±0.10%), whereas

the sub-cluster 5 was observed to have minimum protein content (11.33±0.31%). The

maximum genetic distance (6.55) was recorded between the accessions 18699 vs

11353, whereas 18683 vs 18677 showed the lowest genetic distance.

The cluster diagram regarding mineral contents revealed four clusters at 50%

distance, whereas at 75% distance these four clusters were further divided into eleven

sub-clusters (Fig. 4.35). Five accessions were grouped in the sub-cluster 1 and 2, in

each case. The sub-cluster 3 consisted of three accessions, sub-cluster 4 of one

accession sub-cluster 5 of seven, sub-cluster 6 of one, sub-cluster 7 of five accessions,

sub-cluster 8 of six, sub-cluster 9 of two, sub-cluster 10 of five and sub-cluster 11

consisted of six accessions (Table 4.18) . The members of the sub-cluster 1 exhibited

higher mean values for B and Na (Table 4.19). The accessions of sub-cluster 2 were

better for N, sub-cluster 3 for Na, sub-cluster 4 for P, K, Zn and Mn, sub-cluster 6 for

Cu, sub-cluster 9 for N and Fe. The highest genetic distance (6.55) was observed

between the accessions 18699 and 11353 that were followed by 18698 and 18682.

The cluster diagram for seed traits is presented in the Fig. 4.36 that indicated

five major groups that were further subdivided at 75% dissimilarities and seven sub-

clusters were observed. Twelve accessions were in the sub-cluster 1, nine in

80

Table 4.16 Clusters based on linkage distance for nutritional characteristics of wheat

germplasm collected from Punjab accessions

Cluster Sub-cluster Frequency Accession(s)

I 1 2 18699, 18698

2 4 18708, 18702, 18701, 18689

II 3 8

18692, 18688, 18707, 18705 18676, 18675,

18685, 18672

4 9

18682, 18696, 18694, 18687, 18678, 18681,

18674, 18669, 11349

III 5 4 11359, 11364, 11360, 11356

6 6 18683, 18677, 18679, 18695, 18670, 11362

7 5 18703, 18690, 11363, 11361, 11350

IV 8 1 11353

9 7

18680, 18673, 11352, 18693, 11355, 11351,

11348

81

Table 4.17 Mean and standard deviation within clusters for nutritional traits in wheat accessions collected from Punjab

Sub-cluster

1

Sub-cluster

2

Sub-cluster

3

Sub-cluster

4

Sub-cluster

5

Sub-cluster

6

Sub-cluster

7

Sub-cluster

8

Sub-cluster

9

Frequency 2 4 8 9 4 6 5 1 7

Fibre (%) 1.41±0.66 1.01±0.13 0.98±0.08 1.24±0.12 1.48±0.12 1.65±0.17 1.74±0.18 1.54 1.78±0.16

Oil (%) 1.93±0.13 2.20±0.24 1.84±0.19 1.78±0.14 1.93±0.05 1.92±0.07 2.25±0.14 1.66 1.55±0.14

Moisture (%) 6.10±0.00 6.88±0.31 7.43±0.20 7.73±0.33 7.15±0.17 7.62±0.17 7.50±0.37 7.50 7.39±0.33

Ash (%) 1.30±0.13 1.33±0.14 1.26±0.10 1.42±0.25 1.70±0.69 1.41±0.10 1.37±0.21 5.52 1.60±0.38

Protein (%) 13.55±0.69 11.98±0.63 13.26±0.42 13.07±0.95 11.33±0.31 13.34±0.25 12.38±0.63 11.87 11.76±1.51

82

Fig. 4.35 Phenogram for 46 accessions of wheat germplasm collected from Punjab based on mineral contents

0 2 4 6 8 10 12 14 16

Linkage Distance

18687186831868518670113621870318702187071867618672113521136011349186961868918682186801868818701186981867418694187081136311355113561135318679186781867718669186811136118692186901869918695187051867518673113511136411350186931135911348

83

Table 4.18 Clusters based on linkage distance for mineral contents in accessions of

wheat germplasm collected from Punjab


I 1 5 18687, 18683, 18685, 18670, 11362

II 2 5 18703, 18702, 18707 18676, 18672

3 3 11352, 11360, 11349

III 4 1 18696

5 7

18689, 18682, 18680, 18688, 18701, 18698,

18674

6 1 18694

7 5 18708, 11363, 11355. 11356, 11353

IV 8 6 18679, 18678, 18677, 18669, 18681, 11361

9 2 18692, 18690

10 5 18699, 18695, 18705, 18675, 18673

11 6 11351, 11364, 11350, 18693, 11359, 11348

84

Table 4.19 Mean and standard deviation within clusters for mineral contents in wheat accessions collected from Punjab

Sub-cluster

1

Sub-cluster

2

Sub-cluster

3

Sub-cluster

4

Sub-cluster

5

Sub-cluster

6

Sub-cluster

7

Sub-cluster

8

Sub-cluster

9

Sub-cluster

10

Sub-cluster

11

Frequency 5 5 3 1 7 1 5 6 2 5 6

N (%) 2.32±0.04 2.35±0.15 1.90±0.24 2.60 2.22±0.12 2.41 2.06±0.16 2.25±0.12 2.35±0.13 2.34±0.08 2.04±0.07

P (%) 0.24±0.05 0.26±0.02 0.38±0.03 0.44 0.32±0.02 0.24 0.35±0.06 0.26±0.03 0.33±0.02 0.26±0.03 0.32±0.02

K (%) 0.41±0.04 0.44±0.06 0.41±0.06 0.62 0.50±0.08 0.42 0.33±0.02 0.50±0.05 0.54±0.00 0.45±0.04 0.39±0.09

B (ppm) 3.23±0.47 1.22±0.23 1.94±0.70 3.02 3.22±0.28 2.89 2.61±0.34 2.16±0.26 1.30±0.35 1.11±0.46 1.41±0.44

Zn (ppm) 24.66±3.14 29.68±2.52 34.33±3.88 38.40 30.63±4.46 22.00 37.12±6.65 25.57±2.87 32.30±3.25 27.64±3.24 28.27±4.06

Cu (ppm) 2.30±1.04 2.88±0.54 3.07±0.76 4.00 2.51±1.04 7.00 3.48±0.88 2.15±0.34 1.30±0.14 3.24±0.91 2.70±0.95

Mn (ppm) 22.00±9.99 23.56±2.59 33.80±7.03 41.60 28.00±6.23 24.80 32.38±5.69 24.73±4.84 34.30±6.65 23.60±6.68 32.90±3.88

Fe (ppm) 24.68±20.57 25.52±10.19 71.07±22.72 54.80 29.76±13.78 72.40 68.12±20.43 23.97±5.57 88.80±78.35 39.80±30.37 50.67±14.99

Na (%) 0.07±0.01 0.06±0.02 0.07±0.01 0.04 0.03±0.01 0.02 0.02±0.01 0.03±0.01 0.02±0.00 0.02±0.00 0.03±0.01

85

sub-cluster 2, eight in sub-cluster 3, five in sub-cluster 4, seven in sub-cluster 5,

three in sub-cluster six, and two accessions grouped in sub-cluster 7 (Table 4.20).

The sub-cluster 1 was characterized by high values for seed length and 100 seed

weight, whereas the sub-cluster 3 showed high value for seed width (Table 4.21).

Phonograms on the basis of combined data for quality traits, nutrients and seed

traits were constructed on the basis of provinces and the Fig. 4.37 depicts the cluster

diagram for the germplasm collected from the Punjab province. The Table 4.22

revealed seven clusters at 50% genetic dissimilarities and these were further

categorized into 25 sub-clusters at 75% Euclidean distance. Sub-cluster 1 consisted of

two accessions, sub-cluster 2, 3, 6, 10, 11, 15, 17 consisted two accessions in each

case, sub-cluster 4 of three accessions, sub-cluster 5, 9, 12, 13, 14, 16, 18, 19, 21, 22,

23, 24 and 25 of one accession in each case, sub-cluster 7 of five accessions, sub-

cluster 8 of four accessions, and sub-cluster 20 consisted of five accessions The

maximum genetic distance (6.55) was observed between 18699 and 11353 accessions

that was followed by the genetic distance (6.54) between 18698 and 18682.

4.5.2 Based on Geographic Pattern for the germplasm collected from

Baluchistan

The phenogram for quality traits of 93 wheat accessions collected from

Baluchistan presented in the Fig. 4.38 revealed five clusters and the members of

clusters are listed in the Table 4.23. Five clusters at 50% distance were further

categorized into eleven sub-clusters. The sub-cluster 1, 3 consisted two accessions in

each case, sub-cluster 2 fifteen, sub-cluster 4 of three, sub-cluster 5 of twelve, sub-

cluster 6 of sixteen, sub-cluster 7 of five, sub-cluster 8 and 10 consisted of six

accessions in each case, sub-cluster 9 of eight, and sub-cluster 11 of eighteen

accessions. Mean values along with standard deviation of each sub-cluster is

presented in Table 4.24. The Sub-cluster 1 was characterized by better ash contents

(6.74±0.16%), whereas the accessions of the sub-cluster 4 possessed high moisture

and members of sub-cluster 8 were better for protein content (16.63±0.30%). The

accessions grouping in the sub-cluster 10 were identified for high fibre and high oil

contents (2.07±0.13%).

The Fig. 4.39 presents clustering on the basis of mineral contents that

exhibited five clusters which were further subdivided into 10 sub-clusters at 75%

dissimilarities and the members of clusters are listed in the Table 4.25. The

86

Fig. 4.36 Phenogram for 46 accessions of wheat germplasm collected from Punjab based on seed characteristics

0 2 4 6 8 10 12 14 16

Linkage Distance

18708186981869618674187051870318690186931867218670187021866918707186881869518681113641870118699186921136018675113621135311352113631135611359113511867618687186891867811361186941868518677186731868018682113501867918683113551134911348

87

Table 4.20 Clusters based on linkage distance for seed characteristics in accessions

of wheat germplasm collected from Punjab


I 1 12 18708, 18698, 18696, 18674, 18705, 18703,

18690, 18693, 18672, 18670, 18702, 18669,

II 2 9 18707, 18688, 18695, 18681, 11364, 18701,

18699, 18692, 11360

3 8 18675, 11362, 11353, 11352,11363,11356,

11359, 11351

III 4 5 18676, 18687, 18689, 18678, 11361

5 7 18694, 18685, 18677, 18673, 18680, 18682,

11350

IV 6 3 18679, 18683, 11355

V 7 2 11349, 11348

88

Table 4.21 Mean and standard deviation within clusters for seed characteristics in wheat accessions collected from Punjab

Sub-cluster

1

Sub-cluster

2

Sub-cluster

3

Sub-cluster

4

Sub-cluster

5

Sub-cluster

6

Sub-cluster

7

Frequency 12 9 8 5 7 3 2

Seed length (mm) 6.10±0.24 5.53±0.10 5.58±0.29 5.89±0.25 6.04±0.15 5.83±0.52 3.99±0.88

Seed width (mm) 2.35±0.21 2.41±0.08 2.840.15± 2.44±0.14 2.62±0.11 2.57±0.13 2.82±0.07

100 seed weight (g) 4.23±0.37 3.76±0.37 4.16±0.38 2.65±0.21 3.49±0.14 3.83±0.26 3.52±1.07

89

Fig. 4.37 Phenogram for 46 accessions of wheat germplasm collected from Punjab based combined traits

0 2 4 6 8 10 12 14 16

Linkage Distance

18683186791868718676187051867518702187071867218694186771867318680186851867818674186701868218695186811866918696186921868818689113611869818708186991870111360186931870318690113631136211351113591135611364113501135311355113521134911348

90

Table 4.22 Clusters based on linkage distance combined traits in accessions of wheat

germplasm collected from Punjab


I 1 2 18683, 18679

2 2 18687, 18676

II 3 2 18705, 18675

4 3 18702, 18707, 18672

III 5 1 18694,

6 2 18677, 18673

7 5 18680, 18685, 18678, 18674, 18670

8 4 18682, 18695, 18681, 18669

IV 9 1 19696

10 2 18692, 18688

11 2 18689, 11361

12 1 18698

13 1 18708

14 1 18699

15 2 18701, 11360

V 16 1 18693

17 2 18703, 18690

18 1 11363

19 1 11362

20 5 11351, 11359, 11356, 11364, 11350

VI 21 1 11353

22 1 11355

23 1 11352

VII 24 1 11349

25 1 11348

91

Fig. 4.38 Phenogram for 93 accessions of wheat germplasm collected from Baluchistan based on nutritional traits

0 5 10 15 20 25

Linkage Distance

112361123311538115361129411244115341123911300112371127211333112431124211240112621122611304112811128411288112211152811328112831117111531112461122411527113441119811183111551129811334112781130511267112381131111325113081123511315112961131011295112481122011259113121130711302112141128011263112611130911229112111125511231112001119911299112931126511170111771133511202111841116711150111941121011174111781116411190111861116211160111931118811303111871115611154111951118511145

92

Table 4.23 Clusters based on linkage distance for nutritional characteristics in



I 1 2 11236, 11233

II 2 15 11538, 11536, 11294, 11244, 11534, 11239,

11300, 11237, 11272, 11333, 11243, 11242,

11240, 11262, 11226

3 2 11304, 11281

4 3 11284, 11288, 11221

5 12 11528, 11328, 11283, 11171, 11531, 11246,

11224, 11527, 11344, 11198, 11183, 11155

III 6 16 11298, 11334, 11278, 11305, 11267, 11238,

11311, 11325, 11308, 11235, 11315, 11296,

11310, 11295, 11248, 11220

7 5 11259,11312, 11307, 11302, 11214

IV 8 6 11280, 11263, 11261, 11309, 11229, 11211

V 9 8 11255, 11231, 11200, 11199, 11299, 11293,

11265, 11170

10 6 11177, 11335, 11202, 11184, 11167, 11150

11 18 11194, 11210, 11174, 11178, 11164, 11190,

11186, 11162, 11160, 11193, 11188, 11303,

11187, 11156, 11154, 11195, 11185, 11145

93

Table 4.24 Mean and standard deviation within clusters for nutritional traits in wheat accessions collected from Baluchistan

Sub-cluster

1

Sub-cluster

2

Sub-cluster

3

Sub-cluster

4

Sub-cluster

5

Sub-cluster

6

Sub-cluster

7

Sub-cluster

8

Sub-cluster

9

Sub-cluster

10

Sub-cluster

11

Frequency 2 15 2 3 12 16 5 6 8 6 18

Fibre (%) 1.28±0.36 1.10±0.17 1.12±0.25 1.12±0.17 1.50±. 0.33 1.15±0.21 0.96±0.23 1.15±0.25 1.54±0.22 1.87±0.01 1.30±0.23

Oil (%) 1.75±0.03 1.65±0.10 1.27±0.08 1.76±0.09 1.37±0.16 2.06±0.13 2.00±0.16 1.98±0.12 1.86±0.08 2.07±0.13 1.85±0.21

Moisture (%) 7.75±0.21 7.53±0.17 7.55±0.07 7.77±0.55 7.33±0.38 7.62±0.19 7.62±0.18 7.60±0.31 7.44±0.25 7.02±0.19 6.78±0.35

Ash (%) 6.74±0.16 1.43±0.22 2.20±0.36 3.46±0.65 1.36±0.36 1.460.38± 1.24±0.30 1.50±0.26 1.42±0.28 1.77±0.32 1.71±0.32

Protein (%) 12.93±0.72 11.97±0.92 15.33±0.40 11.35±0.41 10.92±1.15 12.71±0.75 9.35±1.41 16.63±0.30 13.26±0.95 12.33±1.16 11.75±0.94

94

Sub-cluster 1 had ten accessions, sub-cluster 2 eight accessions, sub-cluster 3 one

accession, sub-cluster 4 four accessions, sub-cluster 5 six accessions, sub-cluster 6

eleven accessions, sub-cluster 7 twelve accessions, sub-cluster 8 ten accessions, sub-

cluster 9 eleven accessions and sub-cluster 10 twenty accessions. The average

performance given in the Table 4.26 indicated that high values of P (0.34±0.04%) and

Cu (6.82±1.74%) were possessed by the accessions of sub-cluster 1, whereas sub-

cluster 2 showed high N and Zn. The accessions of the sub-cluster 3 were better for

Na (0.06), whereas the low sodium accessions could be selected from the sub-cluster

7. Higher values for P, Mn, and Fe were possessed by the sub-cluster 4, whereas K

was high in sub-cluster 5, and high B (2.88±0.45ppm) was in sub-cluster 7. The

maximum genetic distance (12.9) was recorded between the accessions 11261 vs

11244 that was followed by the genetic distance (12.6) between 11244 vs 11315. The

genetic distance between 11244 and 11200 was also found to be 12.6.

The Fig. 4.40 indicated clustering on the basis of seed traits that revealed four

clusters at 50% dissimilarities and these were further grouped into six sub-clusters at

75% distance. The sub-cluster 1 was composed of two accessions, whereas sub-

cluster 2 consisted of 13 accessions (Table 4.27). Six accessions were in the sub-

cluster 3 and thirty eight were in sub-cluster 4. The sub-cluster 5 consisted of 20

accessions, and 14 accessions were grouped in the sub-cluster 6. The highest mean

values for all the seed traits were possessed by the members of the sub-cluster 1

(Table 4.28)

Based on combined data the cluster diagram is presented in the Fig. 4.41 four

clusters were observed for the germplasm collected from Baluchistan province. These

clusters were further subdivided in to 15 sub-clusters at 75% distance. The Table 4.29

listed the member of sub clusters that indicated three accessions in the sub-cluster 1,

six in sub-cluster 2 and 10 accessions in the sub-cluster 3. Five accessions were

grouped in sub-cluster 4, seven in sub-cluster 5, and one in sub-cluster 6. Sub-cluster

7 was having eight accessions, whereas two accessions were in sub-cluster 8. The

sub-cluster 9 consisted of four accessions, whereas 15 accessions were in sub-cluster

10. Eight accessions were in sub- cluster 11, twelve in sub-cluster 12, two in sub-

cluster 13, and four in sub-cluster 14, whereas the sub-cluster 15 composed of six

accessions.

95

Fig. 4.39 Phenogram for 93 accessions of wheat germplasm collected from Baluchistan based on mineral contents

0 5 10 15 20 25 30 35

Linkage Distance

113091128111263112611130811299112651129611294112001128011211113351130411534111701122911156112441131511311112981127211238112331123511185111941115411295112841128311259115311130711333113281128811312112461130011293112781125511248113441126211310112371119911267111931152811334113251153811527113051124311242111601115511303112261130211210112021119811195111901118611177111711122411183111641118411536111671122111220111871116211239112361117411240111501123111214111881117811145

96

Table 4.25 Clusters based on linkage distance for mineral contents in wheat



I 1 10 11309, 11281, 11263, 11261, 11308, 11299,

11265, 11296, 11294, 11200

2 8 11280, 11211, 11335, 11304, 11534, 11170,

11229, 11156

II 3 1 11244

III 4 4 11315, 11311, 11298, 11272

5 6 11238, 11233, 11235, 11185, 11194, 11154

IV 6 11 11295, 11284, 11283, 11259, 11531, 11307,

11333, 11328, 11288, 11312, 11246

7 12 11300, 11293, 11278, 11255, 11248, 11344,

11262, 11310, 11237, 11199, 11267, 11193

8 10 11528, 11334, 11325, 11538, 11527, 11305,

11243, 11242, 11160, 11155

V 9 11 11303, 11226, 11302, 11210, 11202, 11198,

11195, 11190, 11186, 11177, 11171

10 20 11224, 11183, 11164, 11184, 11536, 11167,

11221, 11220, 11187, 11162, 11239, 11236,

11174, 11240, 11150, 11231, 11214, 11188,

11178, 11145

97

Table 4.26 Mean and standard deviation within clusters for mineral contents in wheat accessions collected from Baluchistan

Sub-cluster

1

Sub-cluster

2

Sub-cluster

3

Sub-cluster

4

Sub-cluster

5

Sub-cluster

6

Sub-cluster

7

Sub-cluster

8

Sub-cluster

9

Sub-cluster

10

Frequency 10 8 1 4 6 11 12 10 11 20

Nitrogen (%) 2.52±0.31 2.64±0.33 2.12 2.24±0.17 2.22±0.17 1.84±0.24 2.27±0.15 2.10±0.16 1.91±0.25 2.03±0.15

Phosphorus (%) 0.34±0.04 0.32±0.07 0.14 0.34±0.07 0.20±0.07 0.32±0.07 0.33±0.06 0.30±0.07 0.21±0.06 0.18±0.05

Potassium (%) 0.70±0.09 0.51±0.10 0.58 0.68±0.11 0.72±0.05 0.59±0.10 0.60±0.07 0.43±0.10 0.70± 0.60±0.11

Boron (ppm) 2.50±0.66 1.54±0.64 2.49 2.76±0.29 1.25±0.44 2.60±0.49 2.88±0.45 2.26±1.14 1.75±1.05 1.78±0.76

Zinc (ppm) 35.72±7.94 45.28±6.69 26.80 36.90±12.07 35.40±5.06 20.64±4.27 33.33±5.37 32.18±4.71 29.05±7.17 25.65±5.75

Copper (ppm) 6.82±1.74 2.07±0.62 2.30 5.33±1.90 2.330.68± 3.51±1.07 3.84±1.52 2.79±1.13 3.05±1.2 2.45±0.81

Manganese (ppm) 24.11±6.58 28.11±9.36 29.40 32.60±4.17 27.85±5.47 23.09±7.49 27.55±6.10 26.07±4.10 24.82±6.32 24.41±5.81

Iron (ppm) 68.40±21.44 57.94±34.68 33.20 290.53±8.27 235.13±53.14 47.58±20.66 74.23±44.8

0 63.46±38.84 53.25±30.07 51.16±19.90

Sodium (%) 0.04±0.01 0.03±0.02 0.06 0.05±0.03 0.03±0.01 0.03±0.01 0.02±0.01 0.05±0.02 0.07±0.02 0.02±0.01

98

Fig. 4.40 Phenogram for 93 accessions of wheat germplasm collected from Baluchistan based on seed characteristics

0 10 20 30 40 50

Linkage Distance

111711116411302112981129511177113051128011309112311153111328112811122411156112391123611195111941127211155115381130011226112141128811255113341130811210111931125911186111701126211325112381123711248112211120011167112421116011284112431152811220112351122911183113101126711244112611120211335113121115411307112651128311315112991153611211113031129311240112781124611178115341117411233111991116211188111501130411263113331119011185113111118411198115271118711296112941134411145

99

Table 4.27 Clusters based on linkage distance for seed characteristics in accessions of

wheat germplasm collected from Baluchistan


I

1 2 11171, 11164

2 13 11302, 11298, 11295, 11177, 11305, 11280,

11309, 11231, 11531, 11328,

11281,11224,11156

II 3 6 11239, 11236, 11195, 11194, 11272, 11155

III 4 38 11538, 11300, 11226, 11214, 11288, 11255,

11334, 11308, 11210, 11193, 11259, 11186,

11170, 11262, 11325, 11238, 11237, 11248,

11221, 11200, 11167, 11242, 11160, 11284,

11243, 11528, 11220, 11235, 11229, 11183,

11310, 11267, 11244 , 11261, 11202, 11335,

11312, 11154 IV 5 20 11307, 11265, 11283, 11315, 11299, 11536,

11211, 11303, 11293, 11240, 11278,11246,

11178, 11534, 11174, 11233, 11199, 11162,

11188, 11150,

6 14 11304, 11263, 11333, 11190, 11185, 11311,

11184, 11198, 11527, 11187 11296, 11294,

11344, 11145

100

Table 4.28 Mean and standard deviation within clusters for seed characteristics in wheat accessions collected from Baluchistan

Sub-cluster 1 Sub-cluster 2 Sub-cluster 3 Sub-cluster 4 Sub-cluster 5 Sub-cluster 6

Frequency 2 13 6 38 20 14

Seed length (mm) 7.35±0.11 5.02±0.23 5.67±0.25 5.97±0.35 5.76±0.29 5.48±0.13

Seed width (mm) 2.95±0.08 2.06±0.27 2.62±0.26 2.62±0.22 2.58±0.33 2.24±0.21

100 seed weight (g) 4.86±0.25 3.34±0.57 4.38±0.28 4.04±0.40 2.90±0.42 3.48±0.23

101

Fig. 4.41 Phenogram for 93 accessions of wheat germplasm collected from Baluchistan based on combined traits

0 5 10 15 20 25 30 35 40

Linkage Distance

113151129811272112991126511296112941130811200112671131011262112371127811248112551130011293111931153411211113041122911199112811126311261113091128011170111561124411307113021130511295112311132811531112241117111164112881128411283112591124611243112421153811221115361122011214113331131211190115271134411528111831133411325113031122611210112021119811160111771133511184111671118811178111741118711162112401118611150 112361123311239111951119411155113111123811235111541118511145

102

Table 4.29 Clusters based on linkage distance for combined traits in accessions of

wheat germplasm collected from Baluchistan


I 1 3 11315, 11298, 11272

2 6 11299, 11265, 11296, 11294, 11308, 11200

3 10 11267, 11310, 11262, 11237, 11278, 11248,

11255, 11300, 11293, 11193

4 5 11534, 11211, 11304, 11229, 11199

5 7 11281, 11263, 11261, 11309, 11280, 11170,

11156

II 6 1 11244

7 8 11307, 11302, 11305, 11295, 11231, 11328,

11531, 11224

8 2 11171, 11164

III 9 4 11288, 11284, 11283, 11259

10 15 11246, 11243, 11242, 11538, 11221, 11536,

11220, 11214, 11333, 11312,11190, 11527,

11344, 11528, 11183 11 8 11334, 11325, 11303, 11226, 11210, 11202,

11198, 11160

12 12 11177, 11335, 11184, 11167, 11188, 11178,

11174, 11187, 11162, 11240, 11186, 11150

IV 13 2 11236, 11233

14 4 11239, 11195, 11194, 11155

15 6 11311, 11238, 11235, 11154, 11185, 11145

103

4.6 Correlation Analysis among Various Traits Based on Geographic Pattern

The Table 4.30 presented the results of correlation among various traits for the

germplasm collected from Punjab province. The ash contents were positively

correlation with P, whereas protein was positively associated with N, K and seed

length. Phosphorus (P) exhibited positive correlation with Zn, Mn and Fe, whereas it

was negatively associated with seed length. The K was negatively correlated with

seed width and Zn showed positive correlation with Mn and 100 seed weight. Mn was

positively associated with Fe and negatively with seed length. On the basis of

germplasm collected from Baluchistan province, positive correlation was observed

between oil and Zn (Table 4.31). Oil contents also observed positively correlated with

Fe, whereas moisture contents exhibited positive correlation with P and B. Protein

showed strong positive correlation with N along with P and Zn, whereas protein was

negatively association with seed width. Nitrogen was positively correlated with P and

Zn, and negatively with seed width. Phosphorus showed positive association with B,

Zn and Cu, whereas K exhibited positive association with Fe, and Boron was

positively associated with Cu, Zn with Fe. Seed Length was positively correlated with

seed width and 100 seed weight. Oil contents were positively correlated with Zn,

whereas moisture contents showed positive association with P and B when the

combined data were analyzed (Table 4.32). Protein exhibited positive association with

N and Zn, and negative correlation with seed width. Nitrogen (N) exhibited positive

association with Zn and negative association with seed width. Phosphorus exhibited

positive correlation with B, Zn, Cu and Mn. The K showed negative correlation with

Mn, and positive correlation with Fe. Boron was positively correlated with Cu,

whereas Zn showed positive association with Mn and Fe. Seed length was observed

to be positively associated with seed width, and seed width showed positive

correlation with 100 seed weight.

4.7 High Molecular Glutenin Subunits (HMW-GS) for the germplasm collected

from Punjab province

In addition to the germplasm collected from Punjab and Baluchistan, the High

Molecular Glutenin Subunits were conducted for 69 commercial varieties and details

are given in the materials and methods Three types of allelic variants (Null, 1 and 2*)

were found at Glu-A1, the Glu-B1 locus was highly polymorphic and in total 26 allelic

variants were detected at Glu-B1 and 19 subunit/subunit pairs were recorded i.e., 16,

(14*+9), (9, 17+18), 17+18, 7**+8, 7**, 7**+8*, 7, 7+8, 7(7**), (6+7), 7*+9, 7*+8,

104

Table 4.30 Coefficient of correlation for combined traits in wheat accessions collected from Punjab

Fibre Oil Moisture Ash Protein N P K B Zn Cu Mn Fe Na Seed

length

Seed

width

Oil (%) -0.04

Moisture (%) 0.02 -0.17

Ash (%) 0.15 -0.17 0.09

Protein (%) -0.25 0.04 0.09 -0.22

Nitrogen (%) -0.28 0.05 0.05 -0.24 0.98**

Phosphorus (%) 0.05 0.04 -0.19 0.34* -0.45** 0.44**

Potassium (%) -0.20 0.23 0.14 -0.17 0.34* 0.30* -0.07

Boron (ppm) 0.10 0.00 0.05 0.15 0.02 -0.01 0.11 0.12

Zinc (ppm) 0.04 0.08 -0.23 0.13 -0.29* -0.21 0.64** -0.02 0.04

Copper (ppm) -0.05 -0.05 -0.26 -0.09 0.07 0.10 0.18 -0.16 0.04 0.24

Manganese (ppm) 0.06 0.19 -0.10 0.10 -0.34* -0.34* 0.75** -0.10 -0.03 0.51** 0.19

Iron (ppm) 0.13 -0.18 0.02 0.23 -0.28 -0.28 0.40** -0.28 -0.03 0.28 0.12 0.31*

Sodium (%) -0.12 0.00 0.02 -0.09 0.05 0.05 -0.11 -0.15 0.09 -0.10 -0.03 -0.12 -0.14*

Seed length (mm) -0.13 0.19 0.08 -0.08 0.37* 0.36* -0.40** 0.22 0.12 -0.34* -0.10 -0.39** -0.37** 0.02

Seed width (mm) 0.18 -0.22 0.14 0.25 -0.23 -0.28 -0.07 -0.32* -0.02 -0.22 -0.05 0.05 0.44** 0.00 -0.25

100 seed weight (g) 0.21 -0.21 0.04 0.18 -0.03 -0.01 0.10 -0.06 -0.19 0.30* 0.13 0.10 0.04 -0.07 0.00 0.02

105

Table 4.31 Coefficient of correlation for combined traits in wheat accessions collected from Baluchistan

Fibre Oil Moisture Ash Protein N P K B Zn Cu Mn Fe Na Seed

length

Seed

width

Oil (%) 0.03

Moisture (%) -0.17 0.00

Ash (%) -0.01 -0.04 0.13

Protein (%) -0.06 0.16 0.07 0.07

Nitrogen (%) -0.07 0.16 0.03 0.06 0.98**

Phosphorus (%) -0.15 -0.09 0.43** -0.08 0.23* 0.22*

Potassium (%) -0.07 0.13 -0.11 0.00 0.00 0.00 -0.07

Boron (ppm) -0.13 0.02 0.40** -0.04 0.02 0.01 0.30** -0.01

Zinc (ppm) 0.01 0.28** -0.07 -0.04 0.60** 0.61** 0.21* -0.01 0.00

Copper (ppm) 0.02 0.12 0.15 -0.10 0.15 0.14 0.43** 0.17 0.27** 0.15

Manganese (ppm) -0.05 0.06 0.09 -0.13 0.05 0.06 0.05 -0.16 0.01 0.16 0.10

Iron (ppm) -0.03 0.26* -0.01 0.04 0.16 0.14 0.03 0.29** -0.03 0.26* 0.08 0.18

Sodium (%) -0.02 -0.10 -0.18 -0.01 -0.05 -0.03 0.04 0.13 -0.03 0.06 0.04 -0.09 0.04

Seed length (mm) -0.09 -0.14 -0.01 -0.02 -0.18 -0.20 -0.10 -0.15 0.02 -0.15 -0.02 -0.18 -0.11 -0.01

Seed width (mm) -0.12 -0.07 -0.03 0.02 -0.23* -0.26* -0.09 -0.12 -0.07 -0.12 -0.11 -0.14 -0.13 0.04 0.53**

100 seed weight (g) 0.00 -0.11 -0.14 -0.03 -0.15 -0.12 -0.16 0.03 -0.18 -0.11 -0.13 -0.01 0.06 0.15 0.25* 0.32**

106

Table 4.32 Coefficient of correlation for combined traits in wheat accessions collected from Punjab and Baluchistan (Combined)

Fibre Oil Moisture Ash Protein N P K B Zn Cu Mn Fe Na Seed length Seed width

Oil(%) 0.03

Moisture(%) -0.09 -0.05

Ash(%) 0.02 -0.09 0.11

Protein(%) -0.09 0.14 0.07 0.01

Nitrogen(%) -0.1 0.15 0.03 0 0.98**

Phosphorus(%) -0.07 -0.04 0.27 -0.02** 0.14 0.13

Potassium(%) -0.17 0.05 -0.06 0.03 -0.01 -0.01 -0.15

Boron(ppm) -0.04 0.01 0.28** 0.01 0.02 0.01 0.25* 0.01

Zinc(ppm) 0 0.22* -0.11 0 0.46** 0.47** 0.26** 0.04 0

Copper (ppm) -0.03 0.06 0.04 -0.08 0.12 0.11 0.34** 0.19 0.21* 0.18

Manganese(ppm) 0.02 0.12 0.02 -0.08 -0.03 -0.02 0.25* -0.2* 0 0.22* 0.09

Iron(ppm) -0.04 0.14 -0.02 0.09 0.08 0.06 0.02 0.34** -0.03 0.27** 0.13 0.13

Sodium(%) -0.05 -0.06 -0.1 -0.04 -0.03 -0.01 0.01 0.04 0.01 0.02 0.02 -0.1 0

Seed length(mm) -0.1 -0.03 0.02 -0.04 -0.04 -0.07 -0.16 -0.06 0.05 -0.19 -0.04 -0.25* -0.14 0

Seed width(mm) -0.03 -0.09 0.02 0.06 -0.22* -0.26** -0.07 -0.18 -0.06 -0.15 -0.12 -0.07 -0.08 0.03 0.32**

100 seed weight(g) 0.09 -0.12 -0.08 0.01 -0.11 -0.09 -0.08 -0.05 -0.18 -0.03 -0.08 0.04 0.02 0.08 0.17 0.26**

107

(8, 13+16, 13+16,9, 7+9, 6+9 and (7*, 7**+8). The Glu-D1 locus consisted of four

(12, 2+12, 5, 5+10) allelic sub-units or subunit pairs.

4.7.1 Patterns of Allelic Distribution

The results regarding patterns of allelic distribution for the germplasm

collected from Punjab province are presented in the Table 4.33. The allelic variants at

Glu-1 loci indicated that among 51 accessions. 41% comprised the subunit pair

17+18, thirteen percent consisted of 7 alone. Rest of the allelic variants at Glu-B1

contributed less than 4%. At Glu-D1 locus, the 2+12 subunit pair was found in 80.39

% of the accessions whereas 5+10 was observed in 15.68 % only. Rest of the two

subunits, 12 and 5, contributed 3.92 % of the total variation found at Glu-D1 locus.

The Figure 4.42 represents gel photograph of SDS-PAGE indicating HMW-GS in

wheat accessions and varieties along with checks.

Twenty six clusters were found (Figure 4.43) and the members of these

clusters are presented in Table 4.34. Nineteen accessions did not group with any other

accession but stayed single in each case. The cluster 4 comprised of six accessions

and the cluster 5 consisted of 13 accessions. Two accessions were grouped together

in the cluster 7 and five accessions were observed in cluster 10. Cluster 12 was

comprised of two accessions, the cluster 16 and 17 composed of two accessions in

each case.

Among the germplasm collected from Baluchistan province, four allelic

variants (Null, 1, 2* and 2′) were observed at the Glu-A1 locus. The Glu-B1 locus was

highly polymorphic and out of 43 allelic variants, 30 subunit pairs or subunits were

found at this locus including as 7*+8, 7*+8(8**), 7+8, 7, 7+9, 7(7*)+9, 8*, 7+8*,

7+8**, 7**, 7**+9, 7**+8, 7(7**)+9, 13, 7**+8**, 7(7**), 17+18, 8**(17+18),

14+15, (6, 14+15), (7, 14+15), 20, 9, 7*+9, 7(7*)+8, 13+16, (8*, 7+9), 8*(7*+9), (6,

17+18) and 17 (Table 4.35). The Glu-D1 locus comprised of nine allelic subunits or

subunit pairs, i.e., 2+12, 3+12, 2+12*, 10, 12*, 12, 5+10, 5+12*and 5+12.

Among 122 accessions, 18% of the population possessed the subunit pair 7+8,

whereas 11% of the population exhibited the subunit pair 7+9. The subunit 7 was

present among 10% of the germplasm and the rest of all the allelic variants at Glu-B1

locus were in less than 8% accessions. At Glu-D1 locus, 57% germplasm possessed

the subunit pair 2+12 and 27% comprised of subunit pair 5+10, whereas other allelic

108

Table 4.33 Allelic frequency of three high molecular glutenin in wheat accessions


HMW-GS Frequency Percentage

Glu-A1

1 9 17.64

2* 17 33.33

N 24 47.05

Glu-B1

16 1 1.96

14*+9 1 1.96

9(17+18) 1 1.96

17+18 21 41.17

7**+8 2 3.92

7** 1 1.96

7**+8* 1 1.96

7 7 13.72

7+8 2 3.92

7(7**) 1 1.96

6+7 2 3.92

7*+9 2 3.92

7*+8 2 3.92

8(13+16) 1 1.96

13+16 1 1.96

9 1 1.96

7+9 1 1.96

6+9 1 1.96

7*(7**)+8 1 1.96

Glu-D1

12 1 1.96

2+12 41 80.39

5 1 1.96

5+10 8 15.68

109

Fig. 4.42 Gel photograph of SDS-PAGE indicating HMW-GS in wheat accessions

and varieties along with checks.

1

10

2 2*

17 18

7**

12

2

12

17 7

8 13+16 18

9

1

5

1867

5

1867

6

1867

7

1867

8

1867

9

1868

0

1868

1

1868

2

1868

3

1868

4

Chin

ese

spri

ng

Bah

awal

pu

r 20

00

Bah

kar

200

2

C-5

91

Fak

hr-

e-S

arhad

Meh

ran

89

Pav

on

110

Fig. 4.43 Dendogram for wheat accessions collected from Punjab based on high molecular glutenin subunits

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Linkage Distance

187081870018675187051869218674113611870118696186991869811353113521869518704186831869718680113591870618693186881867611351187071135518670113501135618703187021869418689186851868418682186811867918678186721136311362186901868718677186731866911349113481136011364

111

Table 4.34 Allelic summary in quality score in wheat accesssions collected from Punjab

Cluster Accession (s) Allelic combinations Quality scores

Freq. Glu-A1 Glu-B1 Glu-D1 Glu-A1 Glu-B1 Glu-D1 Total

1 11364 1 2* 16 12 3 ? ? 3+?

2 11360 1 2* 14*+9 2+12 3 ? 2 5+?

3 11348 1 N 9(17+18) 2+12 1 ? 2 3+?

4 11349, 18669, 18673, 18677, 18687, 18690 6 N 17+18 2+12 1 3 2 6

5 11362,11363,18672,18678,18679,18681,

18682,18684,18685,18689,18694,18702, 18703 13 2* 17+18 2+12 3 3 2 8

6 11356 1 1 17+18 2+12 3 3 2 8

7 11350, 18670 2 N 7**+8 2+12 1 ? 2 3+?

8 11355 1 N 7** 2+12 1 ? 2 3+?

9 18707 1 N 7**+8* 2+12 1 ? 2 3+?

10 11351, 18676, 18688, 18693, 18706 5 N 7 2+12 1 1 2 4

11 11359 1 N 2+12 1 ? 2 3+?

12 18680, 18697 2 N 7+8 2+12 1 3 2 6

13 18683 1 2* 7(7**) 2+12 3 ? 2 5+?

14 18704 1 2* 6+7 2+12 3 ? 2 5+?

15 18695 1 N 7 2+12 1 1 ? 2+?

16 11352, 11353 2 N 7*+9 2+12 1 2 2 5

17 18698. 18699 2 N 7*+8 2+12 1 3 2 6

18 18696 1 N 8(13+16) 5 1 ? ? 1+?

19 18701 1 1 13+16 5+10 3 3 4 10

20 11361 1 1 17+18 5+10 3 3 4 10

21 18674 1 1 9 5+10 3 ? 4 7+?

22 18692 1 1 7+9 5+10 3 2 4 9

23 18705 1 1 6+9 5+10 3 ? 4 7+?

24 18675 1 1 6+7 5+10 3 ? 4 7+?

25 18700 1 1 7 5+10 3 ? 4 7+?

26 18780 1 1 7*(7**)+8 5+10 3 ? 4 7+?

112

variants were rare and unique to some accessions. The Fig. 4.44 presents the cluster

analysis that indicated 66 clusters were observed which are listed in the Table 4.36.

Fifty one accessions were alone and did not group with any other accession. The

cluster 5 consisted of 14 accessions. The cluster 8 consisted of 12 accessions, whereas

11accessions were grouped in the cluster 9. Cluster 12, 15, 16, 53 and 61 comprised

of three accessions in each case, two accessions in cluster 13, 32, 51 and 58, 60 in

each case, whereas five accessions were in the cluster 18. Four accessions grouped

in the cluster 41. Based on the quality score assigned to HMW-GS,17 accessions had

high score of 10, whereas 20 accessions were observed with quality score on 9. These

accessions have been identified as high quality wheat that can be used in wheat

breeding for quality. In addition to the germplasm collected from two provinces, 69

commercial varieties were also analyzed for SDS-PAGE to assess genetic variability

on the basis of high molecular weight glutenin subunits. Among commercial varieties,

three allelic variants (Null, 1 and 2*) were observed at the Glu-A1 locus of the

germplasm. The Glu-B1 locus was found to be highly polymorphic as also observed

in the germplasm. Out of fourteen allelic variants detected, ten subunit pairs or

subunits were found at this locus as including 7+9, 7*+9, 7**+9, 17+18, 13+16, 7+8,

7*+8, 7+8(8*), 14 and 7* (13+16). The Glu-D1 locus comprised of two allelic

subunit pairs, i.e., 5+10 and 2+12. The frequency distributions of allelic variants at

Glu-1 in commercial wheat varieties are presented in Table 4.37. Out of 69 varieties,

36% possessed the subunit pair 17+18, whereas 30% varieties comprised of subunit

pair 7+9 at Glu-B1 locus.

On the basis of HMW-GS, five groups were observed which were further

subdivided into 22 clusters as presented in the Fig. 4.45 and the members of clusters

are listed in the Table 4.38. The cluster 3, 6, 7, 9, 12, 13, 14, 15, 16, 17, 18, 19 and 22

consisted of single genotype in each case. The cluster 1 consisted of six genotypes,

the cluster 2, 10 and 21 consisted of three varieties in each case. The cluster 4

consisted of two varieties in each case, the cluster 5 of eleven varieties and cluster 8

comprised of eight genotypes. The cluster 11 comprised of seven genotypes and 14

varieties were grouped in the cluster 20. The Table 4.38 presents the quality score for

varieties indicating that 42.02 % (29 out of 69) of the varieties possessed high quality

score, i.e., >9. The maximum genetic distance (3.32) was measured between Morocco

and eleven commercial varieties (Kohinoor 83, Parwaz 94, Pasban 90, Seriab 92,

113

Table 4.35 Allelic frequency of high molecular glutenin subunits in wheat accessions



Glu-A1 1 27 22.13 2* 20 16.39 N 75 61.47 2′ 1 0.82

Glu-B1 7*+8 1 0.82 7*+8(8**) 1 0.82 7+8 23 18.85 7 13 10.65 7+9 14 11.47 7(7*)+9 1 0.82 8* 1 0.82 7+8* 3 2.46 7+8** 4 3.27 7** 4 3.27 7**+9 5 4.09 7**+8 4 3.27 7(7**)+9 7 5.73 13 2 1.63 7**+8** 3 2.46 7(7**) 1 0.82 17+18 9 7.73 8**(17+18) 2 1.63 14+15 5 4.09 6, 14+15 1 0.82 7, 14+15 1 0.82 20 1 0.82 9 5 4.09 7*+9 2 1.63 7(7*)+8 1 0.82 13+16 3 2.46 8*, 7+9 1 0.82 8*,7*+9 2 1.63 6, 17+18 2 1.63 17 1 0.82

Glu-D1 2+12 70 57.37 3+12 1 0.82 2+12 6 4.91 10 3 2.46 12* 1 0.82 12 3 2.46 5+10 33 27.04 5+12* 1 0.82 5+12 1 0.82

114

Fig. 4.44 Dendogram for wheat accessions collected from Baluchistan based on high molecular glutenin subunits

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Linkage Distance

1153811303115271130711300113281120211200115301119811288111761117411214113131126211210113321121111194115341319211267112651319011298113111123211233113081119911195112311118711185111841118811167113101116411183111571129911186111781115511162113251133511528112241127211261113151115611531111931116111312113021129711296112951129413191113441133811263112461124311221112231130611158113341132911281112371130411293112781125911255112441124211240112391123611235112261132311317113051128511284112831127611248112291122811227111771128011238115361132711309112861127111220112181121211209111901118911171111701116011154111501133311145

115

Table 4.36 Allelic summary and quality score in wheat accessions collected from Baluchistan

Cluster Accession (s) Allelic Combination Quality Score

Freq. Glu-A1 Glu-B1 Glu-D1 Glu-A1 Glu-B1 Glu-D1 Total

1 11145 1 2* 7*+8 2+12 3 3 2 8

2 11333 1 1 7*+8(8**) 2+12 3 ? 2 5+?

3 11150 1 2* 7+8 3+12 3 3 2 8

4 11154 1 N 7+8 2+12 1 3 2 6

5 11160,11170,11171,11189,11190,11209,11212,11218,11220,

11271,11286,11309,11327, 11536 14 N 7+8 2+12 1 3 2 6

6 11238 1 2* 7 2+12 3 1 2 6

7 11280 1 2* 7+8 2+12 3 3 2 8

8 11177,11227,11228,11229,11248,11276,11283,11284,11285,

11305,11317,11323 12 N 7+9 2+12 1 2 2 5

9 11226.11235,11236,11239,11240,11242,11244,11255,11259,

11278,11293 11 N 7 2+12 1 1 2 4

10 11304 1 N 7(7*)+9 2+12 1 ? 2 3+?

11 11237 1 N 8* 2+12 1 ? 2 3+?

12 11281, 11329, 11334 3 N 7+8* 2+12 1 3 2 6

13 11158, 11306 2 N 7+8** 2+12 1 ? 2 3+?

14 11223 1 2* 7+8** 2+12 3 ? 2 5+?

15 11221, 11243, 11246 3 N 7** 2+12 1 ? 2 3+?

16 11263, 11338, 11344 3 N 7**+9 2+12 1 ? 2 3+?

17 13191 1 N 7**+8 2+12 1 ? 2 3+?

18 11294,11295,11296, 11297, 11302 5 2* 7(7**)+9 2+12 3 ? 2 5+?

19 11312 1 2* 7+9 2+12 3 2 2 7

20 11161 1 N ? ? 1 ? ? ?

21 11193 1 N 13 2+12 1 ? 2 3+?

22 11531 1 1 13 12 3 ? ? 3+?

23 11156 1 2* 7**+8** 2+12 3 ? 2 5+?

24 11315 1 2* 7**+8** 2+12* 3 ? ? 3+?

25 11261 1 2* 7(7**) 2+12* 3 ? ? 3+?

26 11272 1 N 7** 2+12* 1 ? ? 1+?

27 11224 1 2* 17+18 2+12 3 3 2 8

28 11528 1 N 17+18 2+12 1 3 2 6

29 11335 1 N 8**,17+18 2+12 1 ? 2 3+?

30 11325 1 2* 17+18 2+12* 3 3 ? 6+?

31 11162 1 2′ 7+8 10 ? 3 ? 3+?

32 11155, 11178 2 N 14+15 2+12* 1 2 ? 3+?

33 11186 1 N 14+15 2+12 1 2 2 5

34 11299 1 N 6, 14+15 12* 1 ? ? 1+?

35 11157 1 2* 9 ,14+15 12 3 ? ? 3+?

36 11183 1 2* 14+15 12 3 2 ? 5+?

116

37 11164 1 N 7, 14+15 12 1 ? ? 1+?

38 11310 1 N 14+15 5+10 1 2 4 7

39 11167 1 1 20 5+10 3 1 4 8

40 11188 1 1 ? 5+10 3 ? 4 7+?

41 11184, 11185, 11187, 11231 4 1 9 5+10 3 ? 4 7+?

42 11195 1 1 7**+9 5+10 3 ? 4 7+?

43 11199 1 1 7**+9 5+10 3 ? 4 7+?

44 11308 1 1 7 (7**)+9 5+10 3 ? 4 7+?

45 11233 1 N 7*+9 5+10 1 2 4 7

46 11232 1 1 7 10 3 1 ? 4+?

47 11311 1 1 7+8** 5+10 3 ? 4 7+?

47 11311 1 1 7+8** 5+10 3 ? 4 7+?

48 11298 1 1 7(7*)+8 5+10 3 3 4 10

49 13190 1 1 7+8 5+10 3 3 4 10

50 11265 1 N 7**+8 5+10 1 ? 4 5+?

51 11267, 13192 2 1 78 5+10 3 ? 4 7+?

52 11534 1 N 7**+8** 5+10 1 ? 4 5+?

53 11194, 11211, 11332 3 1 13+16 5+10 3 3 4 10

54 11210 1 N 8*,7+9 5+10 1 ? 4 5+?

55 11262 1 N 7+9 5+10 1 2 4 7

56 11313 1 N 7(7**)+9 5+10 1 ? 4 5+?

57 11214 1 2* 9 5+10 3 ? 4 7+?

58 11174, 11176 2 1 8*(7*+9) ? 3 ? ? 3+?

59 11288 1 1 7*+9 10 3 2 ? 5+?

60 11198, 11530 2 1 17+18 5+10 3 3 4 10

61 11200, 11202,11328 3 N 17+18 5+10 1 3 4 8

62 11300 1 N 8**(17+18) 5+10 1 ? 4 5+?

63 11307 1 2* 17+18 5+10 3 3 4 10

64 11527 1 1 6,17+18 5+10 3 ? 4 7+?

65 11303 1 N 6,17+18 5+12* 1 ? ? 1+?

66 11538 1 1 17 5+12 3 ? ? 3+?

117

Table 4.37 Allelic frequency of three high molecular weight glutenin in commercial

wheat varieties


Glu-A1

1 17 24.64

2* 38 55.07

N 14 20.28

Glu-B1

7+9 21 30.43

7*+9 13 18.84

7.00E+09 1 1.45

17+18 25 36.23

13+16 1 1.45

7+8 3 4.35

7*+8 1 1.45

7+8(8*) 1 1.45

14 1 1.45

7 1 1.45

7*(13+16) 1 1.45

Glu-D1

5+10 34 49.28

2+12 35 50.72

118

Sarsabz, Soughat 90, Tandojam 83, SH 2002, Pak 81, Abadgar 93 and Shafaq 2006).

On the other hand Bakhtawar 92, Zarghoon, Nowshehra 96, Kohsar 95, Fakhr-e-

Sarhad and Mehran 89 were similar on the basis of HMW-GS. Subunit 6 was not

found in varieties and Punjab accessions. Frequency of 7* was higher in commercial

varieties (21.7%) than accessions from Baluchistan (7.3 %) and Punjab (9.8%)

whereas 7**was found in only 1.4% of the commercial wheat varieties. 8* was not

found in Punjab accessions.

Table 4.39 shows that at Glu-A1 locus, the frequency of subunit 2* in

commercial varieties (55.0%) is much higher than Baluchistan accessions (16.3%). 2'

was found in only one accession (11162) of Baluchistan. Subunit 6 was not found in

varieties and Punjab accessions. Frequency of 7* was higher in commercial varieties

(21.7%) than accessions from Baluchistan (7.3 %) and Punjab (9.8%) whereas

7**was found in only 1.4% of the commercial wheat varieties. 8* was not found in

Punjab accessions. The subunit 8** was only found in 6.5% of the accessions from

Baluchistan (11335, 11311, 11534 and 11300). Subunit 9 was found in 50.7%

commercial varieties as compared to 31.1% and 13.7% in Baluchistan and Punjab

accessions respectively. Subunit 13 was observed to be rare as it was found only in

4.1% of the total accessions. Band No.4 was found in one commercial variety and 8

accessions from Baluchistan.14* was observed only in one accession of Punjab. Eight

accessions from Baluchistan contained subunit.15 that was not found in commercial

varieties, accessions from Punjab and checks. 18** was observed only in two

accessions belonging to Baluchistan, and 20 was present in one of the Baluchistan

accession. 22.1 was found in one commercial variety only. At Glu-D1 locus, the

subunit 2 was found in 49.2% commercial varieties as compared to 63.9%, 80.3% and

66.6% Baluchistan accessions, Punjab accessions and checks respectively. Almost the

same percentage was found for subunit 12. Subunit 5 was more frequent (49.2%) in

commercial varieties as compared to Baluchistan wheat accessions (28.6%), Punjab

wheat accessions (17.6%) and checks (33.3%). Almost the same percentages were

observed in case of 10. The most frequent band in commercial varieties was 2*

(55.0%), in Baluchistan accessions 2 (63.9%), in Punjab accessions 2 (80.3%) and 12

(80.3%) and in checks also 2 (66.6%) and 12 (66.6%).

119

Fig. 4.45 Dendogram for commercial wheat varieties based on high molecular glutenin subunits

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Linkage Distance

Chakwal 97Manthar 3

TakbeerBathoor

Faisalbad 08Meraj 08

SaussiPirsabak 2005

Auqab 2000Iqbal 2000

AS 2002Tatara

WL 711Suleman 96Mexipak 65

Kirin 95Inqilab 91

Sehar 2006Morocco

Bahawalpur 2000Sind 81

Chakwal 86Punjab 96

Kohistan 97MH 97

RaskoohV 87094

Punjnad 1Bahawalpur 97

Shahkar 95Shaheen 94

Kaghan 93Bahkhar 2002Faisalabad 83

Blue silverPasban 90

Shafaq 2006Abadgar 93

SH 2002Tandojam 83

Soughat 90Sarsabz

Seriab 92Parwaz 94

Faisalabad 85Darawar 97

Pirsabak 2004Khyber 87

Saleem 2000Margalla 99

Wafaq 01GA 2002

ZariashataMoomal 2002

ZardanaRohtas 90

Lu 26Pak 81

Kohinoor 83Watan 94

Fareed 06Lasani 08

Chakwal 50Mehran 89

Fakhr-e-SarhadKohsar 95

Nowshehra 96Zarghoon

Bakhtawar 92

120

Table 4.38 Allelic summary and quality score in commercial wheat variety of Pakistan

Cluster Variety(s) Allelic Combination Quality Score

Frequency Glu-A1 Glu-B1 Glu-D1 Glu-A1 Glu-B1 Glu-D1 Total 1 Bakhtawar 92, Zarghoon, Nowshera 96, Kohsar 95, Fakhr-e-sarhad,

Mehran 89 6 1 7+9 5+10 3 2 4 9

2 Chakwal 50, Lasani 08, Fareed 06 3 N 7+9 5+10 1 2 4 7 3 Watan 94 1 2* 7+9 5+10 3 2 4 9 4 Kohinoor 83, Pak 2 1 7*+9 5+10 3 2 4 9 5 Lu 26, Rohtas 90, Zardana, Moomal 2002, Zariashata, GA 2002,

Wafaq 01, Margalla 99, Saleem 2000, Khyber 87, Pirsabak 2004 11 2* 7*+9 5+10 3 2 4 9

6 Darawar 97 1 2* 7**+9 5+10 3 ? 4 7+? 7 Faisalabad 85 1 N 17+18 5+10 1 3 4 8 8 Parwaz 94, Seriab 92, Sarsabz, Soughat 90, Tandojam 83, SH 2002,

Abadgar 93, Shafaq 2006 8 1 17+18 5+10 3 3 4 10

9 Pasban 90 1 1 13+16 5+10 3 3 4 10 10 Blue silver, Faisalabad 83, Bakhhar 2002, 3 N 7+9 2+12 1 2 2 5 11 Kaghan 93, Shaheen 94, Shahkar 95, Bahawalpur 97, Punjnad 1,

V87094, Raskooh 7 2* 7+9 2+12 3 2 2 7

12 MH 97 1 2* 7 2+12 3 1 2 6 13 Kohistan 97 1 2* 7+9 2+12 3 2 2 7 14 Punjab 97 1 N 7+8 2+12 1 3 2 6 15 Chakwal 86 1 N 7*+8 2+12 1 3 2 6 16 Sind 81 1 N 7+8(8*) 2+12 1 ? 2 3+? 17 Bahawalpur 2000 1 N 7+8 2+12 1 3 2 6 18 Morocco 1 2* 7+8, 14 2+12 3 ? 2 5+? 19 Sehar 2006 1 2* 7+8 2+12 3 3 2 8 20 Inqilab 91, Kirin 95, Mexipak 65, Suleman 96, WL 711, Tatara, As

2002, Iqbal 2000, Auqab 2000, Pirsabak 2005, Saussi, Meraj 08,

Faisalabad 08, Bathoor 14 2* 17+18 2+12 3 3 2 8

21 Takbeer, Manthar 3 2 N 17+18 2+12 1 3 2 6 22 Chakwal 97

1 N 7*

(13+16) 12 1 ? ? 1+?

121

Table 4.39 Allelic frequency of high molecular glutenin subunits in wheat accessions and commercial varieties

HMW-

GS

Frequency

(Varieties)

%age

(V)

Frequency

(Baluchistan)

%age

b

Frequency

(Punjab)

%age

(P)

Frequency

(Check)

%age

(C)

Combined

frequency

Combined

%age

Null 14 20.28 75 61.47 24 47.05 1 33.33 114 47.10

1 17 24.64 27 22.13 9 17.65 1 33.33 54 22.31

2 34 49.28 78 63.93 41 80.39 2 66.67 155 64.05

2* 38 55.07 20 16.39 17 33.33 1 33.33 76 31.40

2` 0 0.00 1 0.82 0 0.00 0 0.00 1 0.41

3 0 0.00 1 0.82 0 0.00 0 0.00 1 0.41

5 34 49.28 35 28.69 9 17.65 1 33.33 79 32.64

6 0 0.00 3 2.46 3 5.88 0 0.00 6 2.48

7 27 39.13 65 53.28 13 25.49 1 33.33 106 43.80

7* 15 21.74 9 7.38 5 9.80 0 0.00 29 11.98

7** 1 1.45 23 18.85 6 11.76 0 0.00 30 12.40

8 5 7.25 26 21.31 8 15.69 1 33.33 40 16.53

8* 1 1.45 7 5.74 1 1.96 0 0.00 9 3.72

8** 0 0.00 8 6.56 0 0.00 0 0.00 8 3.31

9 35 50.72 38 31.15 7 13.73 0 0.00 80 33.06

10 35 50.72 36 29.51 8 15.69 1 33.33 80 33.06

12 34 49.28 75 61.48 41 80.39 2 66.67 152 62.81

12* 0 0.00 8 6.56 1 1.96 0 0.00 9 3.72

13 2 2.90 5 4.10 2 3.92 1 33.33 10 4.13

14 1 1.45 8 6.56 0 0.00 0 0.00 9 3.72

14* 0 0.00 0 0.00 1 1.96 0 0.00 1 0.41

15 0 0.00 8 6.56 0 0.00 0 0.00 8 3.31

16 2 2.90 3 2.46 3 5.88 1 33.33 9 3.72

17 25 36.23 14 11.48 22 43.14 1 33.33 62 25.62

18 25 36.23 13 10.66 22 43.14 1 33.33 61 25.21

18** 0 0.00 2 1.64 0 0.00 0 0.00 2 0.83

20 0 0.00 1 0.82 0 0.00 0 0.00 1 0.41

22.1 1 1.45 0 0.00 0 0.00 0 0.00 1 0.41

122

4.8 Screening of Rust

One hundred and ninety two accessions/commercial varieties were screened

against stem rust and stripe rust. The screening material included 87 accessions from

Baluchistan, 37 collected from Punjab and 68 commercial varieties.

For stripe rust, nine accessions collected from Punjab, 21 from Baluchistan

and 34 varieties were resistant (Table 4.40). Out of 78 moderately resistant wheat

accessions/commercial varieties, 23 belonged to Punjab, 25 from Baluchistan and 30

were commercial varieties. One hundred and fifty three genotypes were resistant to

stem rust (Table 4.41). Among resistant genotypes, 23 accessions belonged to

Punjab, 72 were from Baluchistan, whereas 58 were commercial varieties. Twenty

three moderately resistant genotypes included 10 accessions from Punjab, six from

Baluchistan and seven varieties..

4.8.1 Effect of Rust on Combined Traits

The summary statistics of 192 genotypes accessions collected from Punjab and

Baluchistan was carried out to find out the effect of rust on nutritional traits, mineral

contents and seed characteristics of wheat germplasm. The Table 4.42 represents the

performance of wheat germplasm for various characteristics as categorized for stem

rust as well as stripe rust. This set of data were analyzed due to evaluation of these

accessions for rust and other characteristics including nutritional traits, mineral

contents and seed characteristics, whereas varieties were not analyzed for other

characteristics. As yellow rust is concerned, among 123 accessions, 29 were resistant

and gave higher values for fibre, moisture, ash and seed characteristics. Higher values

were observed for protein contents, N, P, K, Zn and Fe in by the accessions with

moderately susceptible accessions that indicated the importance of these accessions,

although otherwise these were not better for seed traits. Similarly, the results for stem

rust indicated contrarily to the effect by yellow rust, that seed characteristics were

better for the susceptible accessions, whereas fibre, protein contents, and oil contents

were higher in moderately resistant accessions, whereas resistant accessions were

categorized with higher moisture contents, higher P and Fe.

123

Table 4.40 Screening of wheat germplasm for stripe rust

Resistant

Punjab

18670,18674,18677,18680,18682,18693,18695,18705,18708

Baluchistan

11164,11167,11171,11186,11221,11224,11226,11236,11239,11240,11242,11243,112

48,11281,11300,11307,11325,11334,11335,11528,11530,

Commercial varieties

Anmo91,Bahawalpur-97,Bathoor,Chakwal -50,Darawar-97,Durum-97,Faisalabad

85,Faisalabad-08,Fakhr-e-Sarhad,Fareed-06,GA-2002,Iqbal 2000,Kohinoor

83,Kohistan 97,Kohsar 95,Lasani-08,Manthar-3,Mehran-89,Moomal 2002,Parwaz

94,Pirsabak 2004,Punjnad-1,Saleem 2000,Saussi,Sehar 2006,Shaheen 94,Shahkar

95,Soorab-96,Takbeer,V-87094,Wafaq-01,Watan 94,Zarghoon,Zarlashata

Moderately resistant

Punjab

11356,11359,11363,11364,18669,18672,18673,18679,18681,18683,18685,18687,186

88,18689,18690,

18694,18696,18698,18699,18701,18702,18703,18707

Baluchistan

11178,11183,11188,11194,11210,11214,11229,11235,11237,11238,11244,11246,112

59,11272,11278,11288,11293,11295,11296,11299,11302,11303,11308,11311,11328


AS-2002,Auqab-2000,Bahawalpur-2000,Bahkhar-2002,Chakwal 86,Chakwal-

97,Faisalabad 83,Khyber 87,Kirin 95,LU-26,Margalla-99,Mexipak 65,MH-

97,Nowshehra 96,Pasban 90,Pirsabak 2005,Punjab 96,Raskooh,Rohtas 90,Sariab-

92,Sarsabz,SH-2002,Shafaq 2006,Sind-81,Soughat 90,Suleman 96,Tadojam 83,WL

711,Zardana

Moderately susceptible

Punjab

11360,18675,18676,18678,18692

Baluchistan

11145,11150,11154,11155,11156,11160,11162,11170,11174,11177,11184,11185,111

87,

11190,11193,11195,11198,11199,11200,11202,11211,11231,11233,11255,11261,112

65,

11267,11280,11294,11298,11304,11305,11309,11310,11312,11315,11344,11531,115

34,

11536,11538


Abadgar 93,Inqilab 91,Kaghan 93,Pak 81,Morocco

124

Table 4.41 Screening of wheat germplasm for stem rust

Resistant

Punjab

11356,11359,11360,11363,11364,18669,18674,18675,18676,18678,18680,18682,186

83,18685,18689,18692,18694,18696,18698,18701,18702,18703,18705

Baluchistan

11154,11155,11156,11164,11171,11174,11177,11185,11188,11190,11193,11194,111

95,11198,11199,11202,11210,11211,11214,11221,11224,11226,11229,11231,11233,

11235,11237,11238,11239,11240,11242,11243,11246,11248,11255,11259,11261,112

65,11267,11272,11278,11280,11281,11288,11293,11294,11295,11296,11298,11299,

11300,11302,11303,11304,11305,11307,11308,11309,11311,11312,11315,11325,113

28,11334,11335,11344,11528,11530,11531,11534,11536,11538


Anmo91,AS-2002,Auqab-2000,Bahawalpur-2000,Bahawalpur-97,Bahkhar-

2002,Chakwal -50,

Chakwal 86,Chakwal-97,Darawar-97,Durum-97,Faisalabad 83,Faisalabad

85,Faisalabad-08,Fakhr-e-Sarhad,Fareed-06,GA-2002,Inqilab 91,Kaghan 93,Kirin

95,Kohinoor 83,Kohsar 95,Lasani-08,LU-26,Manthar-3,Margalla-99,Mexipak

65,MH-97,Moomal 2002,Nowshehra 96,Pak 81,Parwaz 94,Pasban 90,Pirsabak

2004,Pirsabak 2005,Punjab 96,Punjnad-1,Raskooh,Rohtas 90,Saleem

2000,Sarsabz,Sehar 2006,SH-2002,Shafaq 2006,Shaheen 94,Sind-81,Soorab-

96,Soughat90,Suleman96,Tadojam 83,Takbeer,V-87094,Watan 94,WL

711,Zardana,Zarghoon,Zarlashata

Moderately resistant

Punjab

18670,18677,18681,18688,18690,18693,18695,18699,18707,18708

Baluchistan

11145,11150,11167,11170,11236,11310


Abadgar 93,Bathoor,Iqbal 2000,Khyber 87,Kohistan 97,Mehran-89,Saussi

Susceptible

Punjab

18672,18673,18679,18687

Baluchistan

11160,11162,11178,11183,11184,11186,11187,11200,11244


Sariab-92,Shahkar 95,Wafaq-01,Morocco

125

Table 4.42 Effect of yellow rust and stem rust on combined traits

Yellow rust Stem rust

Resistant Moderately

resistant Moderately

susceptible Resistant

Moderately

resistant Susceptible

Fibre (%) 1.35±0.32 1.27±0.31 1.31±0.34 1.27±0.31 1.416±0.390 1.38±0.34

Oil (%) 1.75±0.28 1.89±0.25 1.85±0.26 1.84±0.28 1.931±0.166 1.69±0.22

Moisture

(%) 7.44±0.31 7.34±0.43 7.26±0.49 7.38±0.41 7.256±0.491 7.09±0.49

Ash (%) 1.70±1.05 1.48±0.48 1.63±0.84 1.57±0.70 1.730±1.381 1.54±0.23

Protein (%) 12.05±1.31 12.42±1.56 12.80±1.66 12.43±1.70 12.791±0.837 12.41±1.11

N (%) 2.12±0.23 2.18±0.28 2.25±0.31 2.18±0.31 2.261±0.156 2.17±0.20

P (%) 0.26±0.08 0.28±0.07 0.28±0.09 0.28±0.08 0.261±0.070 0.22±0.07

K (%) 0.52±0.12 0.56±0.14 0.59±0.13 0.57±0.14 0.511±0.108 0.56±0.13

B (ppm) 2.12±1.00 2.41±0.77 1.84±0.85 2.22±0.90 1.891±0.801 1.76±0.79

Zn (ppm) 28.77±6.43 30.10±8.13 33.34±8.36 31.43±8.28 32.194±5.625 26.37±7.54

Cu (ppm) 3.01±1.36 3.26±1.76 3.21±1.66 3.23±1.71 2.938±1.215 3.13±1.50

Mn (ppm) 25.34±4.68 31.60±36.01 25.45±6.13 26.15±6.92 27.550±5.747 40.26±69.01

Fe (ppm) 44.40±26.02 66.29±73.51 85.12±70.08 75.60±71.49 41.294±35.552 47.57±24.46

Na (%) 0.03±0.02 0.04±0.02 0.04±0.02 5.78±0.50 5.741±0.280 5.82±0.39

126

5 DISCUSSION

The extent of genetic diversity is important for efficient and effective

maintenance, evaluation and utilization of germplasm. The magnitude of genetic

diversity has linear relationship with crop improvement (Hamblinet al., 2011), hence

breeding programmes rely mainly on the magnitude of genetic diversity (Shanmugan

and Sheerangaswamy, 1982; Smith et al., 1991). The present study focused research

on the indigenous wheat genetic resources collected from two provinces (Punjab and

Baluchistan) of Pakistan. The data were recorded for five nutritional traits, nine

mineral contents, six seed characteristics, and screening against rust. For predicting

bread quality, High Molecular Glutenin Subunits (HMW-GS) were analyzed through

slab type gel electrophoresis using sodium dodecyl sulphate polyacrylamide gel

electrophoresis (SDS-PAGE) that has been a known biochemical technique for

investigation of markers in relation to quality in wheat (Ram et al., 2011). After

attaining self-sufficiency in wheat, the Pakistani breeders have recently focused their

research for the development of high quality wheat and the information on HMW-GS

can accelerate the scope of success (Reynold et al., 2011). The plant material in the

present study evaluated for nutritional traits, mineral contents, seed characteristics and

seed proteins for High Molecular Glutenin Subunits(HMG-GS) are being discussed

as under:

5.1 Nutritional Traits

Low variance for nutritional traits restricted the scope of selection for quality

improvement among the indigenous wheat genetic resources, hence acquisition of

diversified germplasm is desired (Jarviset al., 2011). The fibre contents in the

indigenous genetic resources varied from 0.64 % to 1.87 % with the mean value of

1.32 %, whereas Ikhtiar and Alam (2007) detected that the fibre content ranged from

1.72 % to 1.85 % in the Pakistani wheat germplasm. The material analyzed in the

present study was quite diverse, hence broader range for various characteristics were

observed, although diversity for these types of characteristics has been reported low

(Brandolini et al., 2011). Relatively higher magnitude of range was observed in the

present material, especially in the germplasm collected from Baluchistan that

indicated the prevalence of landraces and the material analyzed in the dissertation has

not been reported before for these traits. Witcombe (1975) examined wheat plants

from Pakistan, and based on the qualitative traits, concluded that wheat was more

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diverse in Pakistan that supported the accepted theory that Pakistan is primarily centre

of diversity for wheat. Similarly higher variation even for the nutritional traits was

observed in the present study, although these traits are known with narrow genetic

base. The moisture content of wheat accessions belonging to Punjab was 7.38% and

that of Baluchistan was 7.35%, which was lower than the moisture content studied by

Safdar et al. (2009) in wheat germplasm collected from Punjab. The low moisture

content is suitable for storage and would be less prone to microbial attack. Moisture

content is affected by genotypes as well as agronomic and climatic conditions along

with the stage of harvesting and seed processing scenario (Mahmood et al., 2004;

Ahmad et al., 2001). Determination of moisture contents of wheat is very important

in terms of its productivity (Khan and Kulachi, 2002). Although the moisture contents

vary from time of harvest to the conditions of processing and storage of seed, hence

the present material was standardized prior to analyses as these accessions have been

preserved ex-situ in the gene bank.

The germplasm collected from Punjab ranged from 1.17% to 5.51% for ash

contents with the mean value of 1.51%, whereas Safdar et al. (2009) determined

1.52% to 1.70% ash in the varieties developed in the Punjab province, that could be

due to narrowing down because of selection pressure, whereas indigenous wheat

germplasm exhibited considerable magnitude of variation for ash contents. The ash

contents are directly proportional to the quantity of flour bran so to yield of flour.

High ash content means poor wheat quality with higher percentage of small or

shriveled kernels, hence the accessions with low ash contents are likely be identified

for future utilization in wheat breeding programme (Vitaet al., 2007). High ratio of

ash content is because of the presence of higher amount of micro – and macro

elements (Forssel and Wieser, 1995). Wheat grain ash is greatly influenced by

location and climatic conditions of the crop year as compared to genotype (Morris et

al., 2009). Protein content is a key quality factor that determines the suitability of

wheat for a particular type of product (Shah et al., 2008). Protein quantity and quality

both are considered important in estimating the potential of flour for its end use

quality (Farooq et al., 2001). Safdar et al. (2009) reported narrow range of protein

content in Punjab wheat varieties (12.34% - 12.78%) as compared to the protein

content in the present material that had a wide range from 9.26% to 14.87% in the

accessions collected from Punjab. Any variation in protein content is the outcome of

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genetic makeup (Randhawa, 2001), fertilization (Waraich et al., 2010) and

environmental factors (Kent and Evers, 1994), e.g., protein contents are largely

affected by environmental factors as compared to genotypes (Zhang et al., 2010). As

the experimental conditions were uniform for all the samples included in the present

study, hence the variation is largely attributed by genetic factor and low variations

was expected due to environmental conditions.

5.2 Mineral Contents

Higher iron concentration is an important factor in wheat related to chlorosis

resistance because it improves the early establishment of seedling (Shen et al., 2002).

The Fe and Zn indicated high variance in the material evaluated, whereas Cakmak et

al. (2000) found low variation in the concentration of Fe and Zn in modern wheat

varieties. The high range for Fe and Zn in the material evaluated under present study

may be due to large number of accessions that gave high amount of variation. Cooper

et al. (2011) investigated grain metal (Al, Cd, Cu, Ni, Pb, and Zn) concentrations in

organic and conventional crop production. They reported higher yields in the

conventional crop management, and reported variation in metal concentrations for

two crop production regimes, hence specific crop management practices affect crop

uptake of metals. Pakistani wheat germplasm has not been evaluated much for

nutrients at such a high magnitude where 139 accessions were evaluated for mineral

elements, except few reports on a limited number of varieties (Shar et al., 2002). High

range along with high variation of Zn, Fe and Mn indicates that these traits could be

selected for breeding purposes from the present germplasm. For N, P, K and Na low

genetic variability seemed to limit the selection scope for these characteristics in this

wheat germplasm (Ghafoor, 1999)

El-Metwally et al. (2010) reported that concentrations of Fe, Zn and Mn can

be increased from 114 ppm to 176.5 ppm, 31.2 ppm to 50.0 ppm and 18.00 ppm to

27.0 ppm, respectively by the application of fertilizer. Interestingly some of the

accessions (11233, 11235, 11238, 11272, 11298, 11311 and 11315) collected from

Baluchistan possessed higher Fe contents, the accession “11280” was better for Zn,

whereas the accessions, 11154, 11156, 11177, 11210, 11214, 11220, 11235, 11237,

11238, 11239, 11242, 11244, 11248, 11262, 11263, 11267, 11272, 11278, 11280,

11284, 11298, 11300, 11303, 11305, 11308, 11311, 11312, 11333, 11335, 11344,

11528, 11531, 11534, 11536 and 11538 possessed higher concentrations for Mn.

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Similarly the accessions (11348, 11349, 11350, 11351, 11355, 11356, 11359, 11360,

11361, 11362, 11363, 11364, 18669, 18670, 18688, 18689, 18690, 18692, 18693,

18695, 18696, 18698, 18699 and 18708) collected from Punjab exhibited higher

concentration for Mn. In Chinese spring, modern hexaploid wheat, several

chromosomes exerted a pronounced effect on the concentration of Zn and Fe. In

Chinese spring substitution lines, chromosome ‘5B’, ‘6A’ and ‘6B’ resulted in large

increases in concentrations of Zn and Fe (Distalfeld et al., 2004). If ‘NAM’ genes are

expressed, they enhance the concentration of Fe and Zn, as they control the degree of

remobilization from the leaves (Waters et al., 2009).

Although genetic makeup is very important in determining mineral contents

but due to qualitative nature of inheritance, these are being influenced by a number of

environmental factors including soil, climate, cultural practices (Dikeman et al., 1982)

and fertilization (Svecnjak et al., 2008; Cakmak et al., 2009; Gunes et al., 2009;

Kovacevic et al., 2009; Pahlavan-Rad and Pessarakli, 2009; Shi et al., 2010).

Conventional and organic farming greatly affect mineral contents of wheat grain

(Bourn and Prescott, 2002; Uyanoz et al., 2006). Lower contents of Mn and Cu were

observed in organically grown than inorganically grown wheat (Ryan et al., 2004;

Punia and Khetarpaul, 2007). Several other factors also have a greatly influence the

concentration of minerals e.g. crop management (Ryan et al., 2008), drought (Hu et

al., 2006), crop rotation (Turmel et al., 2009), growth stage (Akman and Kara,

2003), application of organic material (Uyanoz et al., 2006), location and growing

year (Sabo and Ugarcic-Hardi, 2002), sowing date (Patel et al., 1999), soil pH

(Fageria and Baligar, 1999), number of irrigations (Waraich et al., 2010), position of

spike (Sipos et al., 2006) and rhizotrophic microorganisms (Zaidi and Khan, 2005).

Mineral concentrations are also influenced by seed size (Peterson et al., 1986), grain

position (Sipos et al., 2006) within the ear (Simmons and Moss, 1978; Calderini and

Oritz-Monasterio, 2003a). Grains at distal position have tendency to be smaller with a

less mineral contents (Mc Donald et al., 2008). Genotype by environment

interactions also affect significantly the final grain micronutrient contents (Bänzinger

and Long, 2000; Oiken et al., 2003a; Oiken et al., 2003b; Oiken et al., 2004;

Morguonuov et al., 2007).

Concentration of Fe is largely genetic trait and affected by the genotype,

whereas Zn concentration is more dependent on location effects (Morgounov et al.,

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2007). Gómez – Becerra et al. (2010) detected the associations between Zn and Fe,

whereas Cu and Mn showed no correlation with rest of the mineral contents (P, K, Fe

and Zn). During the 20th

century, improved crop management and plant breeding have

resulted in successful increase of grain yield in hexaploid wheat throughout the world

(Calderini and Slafer, 1998; Zhuang, 2003; Zhou et al., 2007) but resulted in

malnutrition (Chen, 2004). Similarly, in Pakistan the better wheat varieties ensured

food self-sufficiency during the last decade and the wheat breeders have recently

focused their research aims to develop high quality wheat that could also serve as

supplement with minerals (Okafor et al., 2012). The production of staple crops with

nutrient-dense substances will cause the eradication of malnutrition and energy

shortage. Now a days there exists a unique opportunity for investment in this field

(Welch and Graham, 2002). For the reduction of malnutrition in humans, sustainable

and low cost strategy is to breed such wheat varieties which are having high grain

nutrient contents. This is the new and balanced paradigm for production of crops

(Welch and Graham, 2000). Gómez-Becerra et al. (2010) observed that wild emmer

wheat (Triticum turgidum spp. Dicoccoides) showed a substantial diversity and high

concentration of Fe, Zn and protein than modern wheat varieties (Cakmak et al.,

1999; Nevo et al.,2002; Graham et al., 2007; Peleg et al., 2008b). So they suggested

that in order to improve Zn and Fe concentration in modern wheat, wild emmer wheat

represents promising genetic resource. Moreover, emmer wheat did not exhibit any

correlation between grain mineral contents and yield (Chatzav et al., 2010), whereas

contrary to these results, Balint et al. (2001) reported that grains of ancient wheat

species did not exhibit higher mineral nutrient contents than recently cultivated

varieties except for iron.

Evaluation of indigenous wheat germplasm in the present study for nutritional

traits and mineral contents will provide a baseline for the future utilization of

identified genotypes for one or the other trait. This type of information was lacking on

indigenous wheat genetic resources, hence this will enrich the database already

available for wheat germplasm. A better understanding of the genotypic-

environmental interaction for nutritional and mineral contents is needed so that the

efforts made in breeding become more efficient (Feil et al., 2005).

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5.3 Seed Characteristics

Better seed size has been one of the primitive selection criteria, especially in

grain crops including wheat and it mainly depends upon the consumers’ preference

(Pfeiffer and McClafferty, 2008). Seed size and vigour also affect the germination and

ultimately the crop stand to ensure good produce (Farahani et al., 2011). Germination

from larger seeds enjoy good initial food supply (Agboola, 1996), although when the

seedlings are established, the dependence on cotyledon food storage minimize that is

true under favorable planting regimes (Ebofin, 2003). But under stress conditions,

larger seeds may have benefits in germination as compared to smaller seeds, hence

larger seeds may be beneficial in establishing plants under dry soil conditions (Mian

et al., 1994). Therefore, better seed size should be one of the criteria for seedling

establishment in low soil moisture condition due to larger root system and better food

supply (Leishman et al., 2000). The results of the present study regarding seed size

showed that the two accessions (11164 and 11171) collected from Baluchistan had

better 100 seed weight of 4.68g and 5.04g, respectively. Large seeds have positive

influence on biomass production and ultimately the economic yield as compared to

smaller seeds. Moreover, plants derived from large seed have greater vigor

(Stougaard and Xue, 2004), greater plant growth (Bredemeier et al., 2001; Singh and

Singh, 2003) and can acquire a large share of plant growth factors relative to plants

derived from small seed (Stougaard and Xue, 2004). Larger seeds increase the

competitiveness of wheat against wild oat (weed reducing wheat yield) as compared

to smaller seeds. Wheat plants derived from large seeds reduce wild oat biomass

25%, seed production 25% and panicle number 15% as compared to small seeds (Xue

and Stougaard, 2002). Therefore during evaluation process, selection of larger seeds

appears to be an efficient and inexpensive method of improving yield (Baalbaki and

Copeland, 1997) and storability (De et al., 2003).

Polyphenol oxidase (PPO) is an enzyme that causes browning of food

products of wheat, hence plant breeders intend to select germplasm with low

polyphenol oxidase activities (Peña, 2002). Large seeds contain higher amount of

polyphenol oxidase assay activity as compared to small seeds (Demeke et al., 2001).

Therefore, for this trait smaller seeds are to be preferred, but the art of breeding is to

break this undesirable combination to have larger seeds with low PPO in wheat.

Among the germplasm evaluated, 14 accessions collected from Baluchistan and two

from Punjab possessed smaller seeds. According to Ozturk et al. (2009), the changes

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in grain size can result in ‘concentration’ or ‘dilution’ effects on grain protein and

micronutrients, hence Piccinni et al. (2001) observed no association correlation

between seed size and final yield, and between seed size and disease index. In Asia,

yellowness of flour is not desirable for the production of flour noodles and breads

(Liu et al., 2003) and genetic variability exists for grain color among hard winter

hexaploid wheat genotypes (Matus-Cadiz et al., 2003). In the present germplasm, 11

accessions belonging to Punjab and 14 accessions of Baluchistan exhibited white

color that is a preferred characteristic of wheat in Pakistan (Rehman et al., (2007,

http://sincronia.cucsh.udg.mx/panhwarw06.htm), hence these accessions can be

utilized in the breeding programme. The non-crossover nature of genotypes-

environment interactions for grain color presents that white wheat that is chosen in

one environment to be superior will remain superior in any other environment. Gegas

et al. (2010) concluded that grain size of wheat is not dependent upon grain shape,

hence these characteristics are controlled by different genes. Their results also

indicated that variation in seed shape has significantly reduced in modern cultivars as

compared to ancestral accessions of wheat mainly due to recent attention towards

commercial varieties.

5.4 Coefficient of Correlation

Correlation is a handy technique that gives information regarding the degree

of relationship among various variables and one can decide the selection criterion in a

complex biological system (Ghafoor, 1999). In the present study, Zn showed positive

correlation with Fe and these results are in line with findings of Monasterio and

Graham et al. (2000). Due to linear relationship between these two important mineral

elements the simple selection for either of the mineral will improve the other. The

study by Monasterio and Graham (2000), showed that the introduction of ‘rht’ genes

resulted in the production of semi-dwarf wheat that showed increase yield in bread

wheat. But it also caused the reduction in the concentration of Zn and Fe in some

genotypes of bread wheat. Therefore, wheat breeding scientists should focus to

maintain high levels of Zn and Fe in high yielding material and the material for this

have been identified and is available to researchers for R & D. Fortification of iron

negatively affects the quality of the final product as it produces such changes in

sensory properties and physical traits which are not desirable (Akhtar et al., 2011).

Therefore selection of iron compound that do not adversely affect the quality is still a

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challenge (Kiskini et al., 2010). Iron fortification is a need of the day and almost all

the wheat consuming nations are working for iron fortification, but it is suggested that

the accessions with high Fe and Zn contents could be utilized in wheat improvement

program that will enhance iron contents along with high yield at an economic rate.

Linear association of protein contents with Zn can be exploited, and these are

being supported by the findings of Peleg et al. (2008) and Zhao et al. (2009) who also

detected strong positive correlation between protein contents and Zn. Therefore

breeding for high Zn levels will ultimately cause the production of high protein levels

if proper parents are selected (Cakmak et al., 2000). Although Gómez – Becerra et al.

(2010) reported positive association of P with K, but in the present findings this was

not the case. Similarly in contrast to our results, they observed negative correlation

between Cu and Fe, Cu and Zn, and Mn and Fe. Positive correlation between Zn and

P was reported by Oury et al. (2006) and Shi et al. (2008). However Morgonuov et al.

(2007) found negative correlation between Zn and P. Many scientists found high

correlations of several minerals with protein (Peterson et al., 1983; Kutman et al.,

2009). The evidences suggest that increasing protein content would greatly contribute

in biofortification with micronutrients (Kutman et al., 2009).

Production of high yielding wheat varieties have reduced the concentrations of

N, P (Slafer et al., 1990; Calderini et al., 1995; Feil, 1997; Ortiz – Monasterio et al.,

1997), Fe and Zn (Garvin et al., 2006) and other mineral nutrients (Löffler et al.,

1983; Gauer et al., 1992) in grain. Contrary to these Murphy et al. (2008) concluded

that the only minerals which were not negatively associated with yield were Fe and

Zn. Although some of the important traits are negatively associated among one

another and particularly yield, it is the skill of the breeder to combine all the important

traits scattered throughout the genome, in a single genotype. Otherwise, the

undesirable linkages are to be broken through selective breeding methods including

modern techniques of modern biotechnology (Gillham et al., 1995). Linear

association (P< 0.001) was observed between grain weight and water content by

Chanda and Singh (1998), whereas in the present study 100 seed weight showed

positive association with seed width and simple selection may increase the population

mean for these two important traits without losing genetic diversity. Wheat varieties

grown inorganically have significantly higher 1,000 grain weight than organically

grown wheat varieties (Nitika Punia and Khetarpaul, 2008). Grain weight is found to

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be affected by grain protein (Calderini and Ortiz-Manasterio, 2003b). Pahlavan-Rad

and Pessarakli (2009) reported increase in 1000 grain weight by the application of 80

Kg Zn and foliar Fe, anyhow seed weight is a quantitative trait in wheat and is

affected by environmental changes.

5.5 Genetic Diversity based on Multivariate Analyses

The scientists (Camassi et al., 1985; Falcinelli et al., 1988) reported

multivariate analysis a valid system regarding germplasm evaluation and gene bank

management. According to Dasgupta and Das (1984), multivariate analysis can be the

best tool for selection of parents in hybridization programme. Principal Component

Analysis (PCA) provides information on partitioning of genetic diversity among the

germplasm collections through data reduction that helps better germplasm

management (ĎImperio et al., 2011). The PCA of combined traits showed that nine

components contributed 67.7% of the total variation amongst all the accessions

collected from both the provinces. The PCA gives variable independence and

balanced weighing of characteristics leading to a significant contribution of various

traits based on concerned variation in a given population, hence can be used as a

method for pattern finding, so it complements the cluster analysis (Kantety et al.,

1995; Rincon et al., 1996; Johns et al., 1997; Lanza et al., 1997; Russell et al., 1997;

Schut et al., 1997; Barret and Kidwell, 1998; Dubreuil and Scharcosset, 1998;

Thompson et al., 1998; Lombard et al., 2000).

The accessions listed are recommended to be used directly or considered by

breeders for the development of variety. Some of the traits with linear relationships

are likely to exhibiting economically important traits for various populations based on

PCA and identified exploited through simple selection from distinct clusters or

utilization in breeding programme. For the evaluation and management of plant

genetic resources, determination of genetic diversity is very important (Kresovich and

McFerson, 1992) and subdivision of variance into components helps in the

conservation and utilization of genetic resources so it makes it possible in crop

improvement programmes to plan the appropriate use of gene pool for particular plant

attributes (Pecetti et al., 1996). The germplasm used in this study was classified

for giving rise to some elite lines for particular traits and the accessions for high fibre

(23), oil (19), moisture (21), ash (5), protein (12), N (11), P (18), K (9), B (20), Zn

(18), Cu (17), Mn (19), Fe (19), Na (13), seed length (5), seed width (17) and 100

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seed weight (11) have been selected, seventy accessions with low sodium were found

and it is suggested that these must be exploited in breeding programs. Moreover, the

accessions which were identified to be the best for particular traits could be used to

develop one accession having multiple economic traits.

In biological experiments, especially related to germplasm, cluster analysis

has been employed tremendously mainly for taxonomic and evolutionary studies

(Felsenstein, 1984). Paderewski et al., (2011) illustrated the importance of combined

additive main effects and multiplicative interaction (AMMI) analysis and cluster

analysis in Triticum aestivum L. and twenty one genotypes were divided into three

groups on the basis of homogeneousness. They suggested the combined AMMI and

cluster analysis for describing diverse patterns of yield response in wheat. Inter and

intra-accession genetic distance play a vital role for plant breeders regarding parental

lines selection for hybridization (van Becelaere et al., 2005; Singh et al., 2011),

elimination of duplicates in the germplasm collection (Sato et al., 2011), and

development of core collection to streamline utilization of genetic resources

(Couviouret al., 2011). Mir et al. (2011) investigated genetic diversity in wheat

cultivars based on developmental phases for last ten decades, and observed a

progressive decline among the cultivars released during the latest decades. The shift

in the diversity trend is expected when selection criterion is focused on some

particular objectives on plant breeding.

5.6 High Molecular Weight Glutenin Subunits (HMW-GS)

Seed proteins have gained much attention of the scientists for the resolution of

evolutionary and taxonomic problems of many plants (Khan, 1992; Das and

Mukarjee, 1995). Cultivars of a specific crop species can be distinguished by the help

of seed proteins (Jha and Ohri, 1996). But a few scientists reported that cultivars

could not be identified with SDS-PAGE (de Vries, 1996). The seed proteins with

special attention to HMW-GS have been thoroughly investigated by a number of

researchers that is being utilized in wheat breeding programmes for bread making

quality (Graybosch et al., 2011). The technique is simple and easier to determine

genetic diversity as compared to field evaluations and other molecular markers.

Moreover, the findings could be reproduced which are not dependent on the

environment (Nakamura, 2001).

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For the identification of wheat varieties suitable for better bread making,

HMW-GS has been reported by Prabhasankar (2002), some scientists, however,

reported limitations of SDS-PAGE (D'Ovidio et al., 1995). For example, anomalous

migration of certain HMW-GS on the gel might depict wrong molecular weight thus

misleading information for quality (Shewry et al., 1992). The effect of environmental

stress HMW-GS has been detected by Morgunov et al. (1990), but these studies

evaluated few varieties that may not be valid for concrete conclusion. The DNA

sequencing for genes of HMW-GS has been developed that provides a new insight

into protein subunits (Gianibelli et al., 2001), hence based on modern DNA

technology, misleading on the basis of HMW-GS can be minimized (Zhang et al.,

2011). Although bread-making quality is not only governed by HMW-GS, but their

proportions to low molecular glutenin subunits (LMW-GS) has the equal importance

regarding balance between elasticity and viscosity of dough essential for bread

making performance (Jood et al., 2000).

To predict the genetic diversity on the basis of HMW-GS, the SDS-PAGE was

conducted in all the germplasm collected from two provinces along with commercial

varieties for reference and future use. At Glu-A1 locus, four allelic variants (Null, 1,

2* and 2') were observed and maximum variation was at Glu-B1 comprising 10

subunits/subunit pairs in commercial varieties, 30 in the germplasm collected from

Baluchistan, and 19 subunits in the accessions belonging to Punjab. The Glu-D1 locus

comprised of two allelic pairs (5+10, 2+12) in commercial varieties, nine (2+12,

3+12, 2+12*, 10, 12*, 12, 5+10, 5+12* and 5+12) in the accessions collected from

Baluchistan, and four (12, 2+12, 5 and 5+10) in the germplasm collected from Punjab

province. It is quite evident that the germplasm collected from Baluchistan exhibited

the highest diversity for most of the loci, and this part of Pakistan has been reported as

the centre of diversity for most of the crops including wheat and barley (Tahir et al.,

1996). Afshan and Naqvi (2011) reported HMW-GS in 52 Pakistani wheat genotypes

and observed 3, 6 and 4 alleles encoding Glu-A1, Glu-B1 and Glu-D1 loci,

respectively. They reported average heterozygosity for the three Glu1 loci in Punjab

(60.36%), Sindh (48.88%), Baluchistan (33.33%), Azad Jammu Kashmir (30.36%)

and Khyber Pakhtunkhwa (55.98%). On the basis of findings of our results and the

results reported by Sultana et al., (2007), it has been observed after the onset of green

revolution, Pakistani wheat varieties were bread for higher yield potential, and few

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efforts were made to broaden wheat genetic resources. Recently the breeders have

focused their objectives to improve wheat quality that is suggested to incorporate in

modern wheat varieties without losing genetic diversity.

Tahir et al. (1996) carried out study on landraces of Pakistan to study the

genetic variability based on polymorphism of HMW-GS and identified four allelic

variants (Null, 1, 2* and 2.1*) at Glu-A1 locus, and thirteen subunits or subunit pairs

(17+18, 13+16, 7*+9, 7*+8, 7*, 7+8, 7 and 7+9, 14, 18, 7s+8, 20 7f+8) at Glu-B1

locus. Eight allelic forms (5+10, 2+12, 2+12*, 2+12s, 2+12', 2**+2', 2***+12' and

3+12') were recognized at Glu-D1 locus. Niwa et al. (2008) identified three allelic

subunits (Null, 1 and 2*) at Glu-A1, six allelic subunits or subunit pairs (7, 7+8, 7+9,

20, 14+15 and 17+18) at Glu-B1 locus, and four allelic subunit or subunit pairs (2+12,

5+10, 10, 2.1+10) at Glu-D1 locus in Pakistani wheat. Moreover, the most frequently

occurring genotypes possessed 2* 17+18 and 2+12 which were in agreement with the

findings of Anwar et al. (2003). Niwa et al. (2008) detected broad diversity of HMW-

GS in Pakistani wheat accessions that contrasts with the results reported by Anwar et

al. (2003), Tahir et al., (1995), that probably could be credited to the nature of

material. Distribution of the HMW-GS revealed that germplasm with subunit 1 or 2*

encoded by Glu-A1 locus possess better bread-making quality attributes because of

the linear relationship of these fragments with higher extensibility and better dough

strength (Alvarez et al., 2009). The predominant null allele at this locus has been

reported by several workers including by Pena et al., (1995) and Payne and Lawrence

(1983). Recently Li et al., (2009) reported Chinese wheat germplasm with higher

frequency of null alleles, whereas European spelt wheat genotypes were minimal (An

et al., 2005). The frequency of various allelic combinations is mainly affected by the

selection pressures based on breeding strategies and the traits of consumers’

preference (Ghafoor et al., 2001). Masood et al., (2004) and Sultana et al. (2007)

reported the higher proportion of 1Bx17+1By18 and 1Bx7+1By9 in land races and

local adapted cultivars of Pakistan. The varieties characterized by subunit

1Dx5+1Dy10 possessing superior and desirable alleles imparting greater visco-

elasticity and dough characteristics are good for bread making (Redaelli et al 1997).

Various researched have reported this subunit associated with good bread-making

quality in commercial wheat cultivars grown in Canada (Bushuk, 1998), Germany

(Wieser and Zimmermann, 2000), UK (Payne et al., 1987), Norway (Uhlen, 1990),

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Syria (MirAli et al., 1999) United States (Dong et al., 1991), New Zealand (Luo et al.,

2001) as well as in synthetic hexaploid wheats (Pena et al., 1995).

Sultana et al. (2007) reported genetic variability of Pakistani wheat based on

HMW-GS, i.e., three allelic variants (Null, 1 and 2*) at Glu-A1 locus, nine (7+8, 7*+8,

7(7*)+8, 7+9, 7*+9, 14, 13+16, 17+18 and 20) at Glu-B1 locus and three allelic

variants (5+10, 2+12 and 2**+12') at Glu-D1 locus that were in accordance with the

descriptions of Payne and Lawrence (1983) and Marchylo et al. (1992). Ahmad

(2004) observed no Null allele at Glu-A1 in commercial varieties and indicated that its

replacement by 2* was for better quality of wheat flour; which in turn resulted in

genetic erosion. Tahir et al. (1996) identified 2**+12' occurring frequently in the

accessions collected from Baluchistan. Tahir and Lafiandra (1994) detected 2***+12'

as novel allele in accessions belonging to Punjab and Baluchistan, whereas in the

present study no such allelic variant was recognized. Terasawa et al. (2009) detected

three allelic variants (Null, 1 and 2*) at Glu-A1 locus of Pakistani wheat samples. At

Glu-A1 locus four allelic subunits or subunit pairs (7+8, 13+16, 17+18 and 20) and, at

Glu-D1 locus three allelic variants (2+12, 4+12 and 5+10) were observed. The

subunits having slightly faster mobility than subunit 7 was named as 7* and the

differences in electrophoretic mobility were due to small differences in their primary

structure (Gianibelli et al., 2002).

Several novel HMW-GS including 7*, 7**, 8* and 8** (Liu et al., 2007)

detected in the present study have also been detected in Chinese wheat landraces that

might indicate intermixing of wheat genetic resources or the evidence that Pakistan is

in the closed vicinity of two centres of diversity. The novel alleles might have formed

due to mutation which occurs rarely. Twenty nine commercial varieties possessed

high quality scores of 9 and 10 among the germplasm evaluated in the present study.

The low quality scores reported for some of the Pakistani commercial wheat varieties

are in accordance with the expectations because most of the breeding programs of

wheat in the country have focused on improvement in yield (Zhong-hu et al., 1992).

Ahmad (2004) identified six accessions (Pak 17336, Pak 17647, Pak 16082, Pak

17620, Pak 16200 and Pak 17627) with bread quality score 10 and one (Pak 17216)

with quality score 9 so these could be used as source material for improving wheat

quality.

139

The present results indicate that the development of commercial varieties has

caused reduction in the genetic diversity of wheat regarding HMW-GS that is contrary

to the findings by Hirano et al. (2008). The differences in such a trait is mainly due to

the breeding strategies and the material under investigation, as the number of

accessions used by Hirano et al., (2008) were 47 that were not sufficient to draw a

valid conclusion. It is possible to use HMW-GS as a molecular marker of bread-

making quality (Gálová et al., 1998). The allelic variants 2*, 17+18 and 5+10 exhibit

linear association with bread making quality than subunits 7+8, Null and 2+12

(Payne, 1987). Li et al. (2009) reported that 13+16 has best effect on quality of wheat

that indicated positive effects of this subunit on four quality parameters, viz.,

development time, stability time, strengths and sedimentation volume were larger as

compared to those of 5+10, which is worldwide known for its high quality score. In

the present study, less genetic diversity in commercial varieties was observed

regarding HMW-GS that is mainly due to selection pressure for economic traits

including yield and quality. This is in line with the earlier studies of Atanasova et al.,

(2009), Todorov et al. (2006), and Morgunov et al. ( 1993). Since most of the wheat

breeding programs used 5+10, whereas on the basis of present knowledge, it is

advisable to use subunits 17+18, 13+16 and 14+15 additionally, which show positive

effect on the grain quality indices of wheat. As a result genetic diversity along with

the end-use quality would be increased (Liu et al., 2007; Deng, 2005). One of the

recombinant inbred lines (T-74), selected by Garg et al. (2006), possessed subunit

pair 2+12 at Glu-D1 locus, yet it had superior quality for bread-making which

indicated that amount of grain protein, above a particular minimum value, has greater

influence on bread making quality than glutenin subunit pair 5+10. The differences in

the nutritive value of wheat may be due to cultivar (Pasha, 2006); climatic conditions,

cropping year, fertilization (Puumalainen et al., 2002), process of harvest, storage

conditions (Pasha, 2006), crop rotation (Turmel et al., 2009) and grain section (Ando

et al., 2002). Wheat belonging to Punjab is grown over wide agro-climatic range and

exhibits differences in quality and yield (Chaudhry et al., 1995).

Glutenin proteins are important in the wheat flour processing for the

production of pasta products, bread or chapatti (Shewry et al., 1989). As significant

correlation exists between bread quality and certain high molecular glutenin subunits,

the variation at these loci is necessary for wheat breeders for the development of

140

varieties having better quality of bread. The Institute of Agri-Biotechnology and

Genetic Resources (IABGR), Islamabad maintains record on wheat germplasm and it

is possible to detect the location of diverse alleles that will enrich data base on wheat

genetic resources for future utilization. If we find location-specific alleles, it would be

of great importance to conserve and capture the alleles which show adaptation to local

environment. Moreover, it would help to make decisions on breeding strategies and

priorities for future collection areas (Hirano et al., 2008). Pakistan has been known for

its quality wheat and many researchers have reported quite a large numbers of wheat

germplasm having good quality score as well as bread making quality (Tahir et al.,

1995). From the present research it has been concluded that quality of wheat

accessions collected from both the provinces is good and it is comparable to

international standards of China, Canada, USA and Australia. Careful agronomic

practices such as seed treatment, use of balanced fertilizers, weed control and

improved harvest technology can further boost its quality (Liu et al., 2011).

Improvement of bread-making quality, wild wheat lines may be a source of

variation for HMW-GS (Nevo and Payne 1987), whereas Cross and Guo (1993)

concluded that hexaploid gene base must be used as the first step to produce varieties

because less genomic disruption occurs when same ploidy level is used. Due to

significant variation for HMW-GS in the present hexaploid wheat germplasm, the

identified accessions/genotypes could be better utilized in wheat quality improvement

programme as suggested by Cross and Guo (1993). The information generated

through the present study reflects that collection of wheat germplasm possessed a

wide range of HMW-GS encoding different Glu genes responsible for bread making

quality. The identified genotypes with better HMW-GS combinations could be utilized

in wheat quality improvement programme. The characterization of the remaining

indigenous wheat germplasm for these traits including molecular/genetic markers is

suggested that will strengthen the existing data base.

5.7 Screening of Rust

In addition to evaluation for nutritional characteristics, mineral contents, seed

traits and HMW-GS, the germplasm screened against stem rust in green house

condition indicated that some lines were moderately resistance. For better

understanding of stem rust status of the germplasm these are suggested to be

evaluated under field conditions for their adult plant reaction. The variety “Inqilab

141

91” was resistant against stem rust, while moderately susceptible against stripe rust

that indicated the genetic breakdown of this variety and Afzal et al. (2009) has

already suggested that the reliance upon Inqilab 91 should be minimized because it

showed greater susceptibility during 2007 than the previous years. Kisana et al.

(2003) reported that Yr27 genes present in Inqilab-91 had broken down its resistance

resulting in severe economic losses. Many researchers have evaluated Pakistani wheat

germplasm for rust resistance (Mirza et al., 2000; Shah et al., 2003). Afzal et al.

(2009) conducted an experiment to investigate resistance potential of wheat

germplasm against yellow rust under rain fed climate of Pakistan and determined that

out of 188 cultivars/lines, 150 had RRI (Relative resistance index) value ≥7≤9 and

were in desirable range. On the other hand, 28 cultivars were among the acceptable

range having RRI value ≥5≤7, whereas ten 10 cultivars were under undesirable range

having RRI value <5. Based on the present study, it was concluded that most of the

lines have potential to be used as a resistant germplasm source against stripe rust.

Loladze (2006) reported stripe rust resistance in wheat relatives and landraces and

found that 74 accessions (45% of the germplasm tested) had infection type ranging

from 1 to 4 and were considered to be resistant in adult stages of plant development.

Ali et al. (2009) worked on 37 winter wheat lines introduced from Oklahoma

State University and observed that none of the lines was immune, whereas most of the

lines were in the category of partial resistance. However, certain lines were marked as

susceptible to the prevalent races of yellow rust at North of Pakistan. They suggested

that as the F5-64 showed highly better level of partial resistance so may be used in

breeding program for the transfer of this trait. The intensity of stripe rust is affected

by temperature, humidity and rainfall (Te-Best et al., 2008). Hence screening process

should be under controlled environment and the resistant lines have potential to be

exploited to develop stem rust resistance cultivars. For this purpose, process may be

continued with more lines and experiment should be carried out at field conditions to

verify the adult plant response particularly in case of moderately resistant wheat

accessions/commercial varieties. Anyhow, the process of developing wheat cultivars

with stronger and more durable resistance needs to be accelerated by the use of

molecular markers (Yan et al., 2003). In Pakistan, for combating rust epidemics, the

best approach is to cultivate resistant cultivars in fields by producing quality seed at

142

large scale level. This is the only effective and viable approach as poor farmers cannot

afford chemical control (Singh et al., 2008).

The effect of stem rust on other characteristics revealed that fibre contents

were increased in the moderately resistant and susceptible accessions, whereas P, K,

B, Cu and Fe contents were decreased. Decrease in seed length, seed width and

hundred seed weight resulted in increased susceptibility to stripe rust that was

contrary to Afzal et al. (2008), who observed that 1000 kernel weight was

significantly reduced due to stripe rust. Similarly Dyck and Lukow (1988) found that

quality of wheat, kernel weight and grain yield was reduced as a result of leaf rust and

reduction in protein content of wheat was also observed by them. Therefore, rust

infected lines showed higher mixing strength of dough as compared to the lines

resistant to rust. Herrman et al. (1996) found that the control of leaf rust resulted in

improved quality of wheat. The quality parameters studied by them included size

measurements, uniformity, kernel weight, plump kernel percentage and protein

contents, and all the variables increased as a result of fungicide application. High

protein contents in moderately susceptible accessions to stripe rust in the present

material was observed that was in agreement with the results reported by Peturson et

al., (1945). It is concluded that wheat improvement programme should be designed

involving a multidisciplinary team including plant breeders, molecular, geneticists,

pathologist and economist for sustainable cultivar development for food security and

poverty elimination.

Beside ex-situ collections, genetic diversity may be maintained by in-situ on-

farm conservation, where the traditional farmers, along with the scientists and

technicians, may act as components of productive research (Ceccarelli and Grando,

2007). The evaluation of variation in wheat protein is very important regarding

studies of genetic diversity (Igrejas et al., 1999), breeding commercial varieties with

improved quality for bread-making and for aiding in optimization of variation in

germplasm collections (Caballero et al., 2004a). Many scientists reported high genetic

diversity in Pakistani wheat (Ali et al., 2008; Ahmed et al., 2010), and the

maintenance of this variation is very crucial for crop improvement. The data,

especially on nutritive and mineral contents presented in the present study could help

scientists for wheat improvement, and if HMW-GS are considered side by side, the

breeding pace will be enhanced even for better bread making quality. Moreover,

143

breeders could select the elite accessions identified here for the development of a

wheat cultivar rich in nutritional and mineral contents so that ‘hidden hunger’ could

be eradicated (Fabrice et al., 2011). The identification of elite accessions could be

used by the scientists for multipurpose application or the data could be used directly

for further improvement of the crop. The information provided could serve its purpose

for genetic and breeding improvement of wheat in local climatic conditions. Based on

the available knowledge, the consolidated interdisciplinary approaches are critical

target breeding, especially quality wheat that is a staple food for more than one third

world population. Mayer et al., (2011) considered micronutrient malnutrition affects

more than half of the world population, particularly in developing countries and

supported concerted international and national fortification and supplementation

efforts to curb the scourge of micronutrient malnutrition are showing a positive

impact. Research and breeding programmes are underway to enrich the major food

staples in developing countries with the most important micronutrients, including

iron, provitamin A, zinc and folate.

5.8 Conclusions

The important minerals regarding malnutrition ‘Hidden hunger’, i.e., zinc and

copper along with manganese exhibited high mean and variance in the present

germplasm. Therefore the accessions with optimum levels of vital trace

elements are suggested to be selected for breeding high quality wheat from

this material.

Concentrations of various mineral contents were in the order: Fe > Zn > Mn

>Cu >N >B >K >P >Na which is in line with the previous literature that

enhances the acceptance of our results.

Certain promising accessions were identified for high nutritional value,

mineral and seed traits along with low ash component. The accession “11255”

was better for fibre, ash, phosphorus, copper, seed length and seed width;

11309 better for oil, protein, nitrogen, zinc, copper and iron; 11315 for oil,

Phosphorus, copper, iron and sodium; the accession ”18696” was better for

moisture, protein, nitrogen, phosphorus and manganese; 11272 for moisture,

phosphorus, zinc, copper and iron. All of these accessions were identified and

the seed has been preserved in the gene bank and the seed is available for

144

research and development for wheat that will ultimately help in poverty

alleviation.

In the germplasm, two accessions collected from Baluchistan (11164 and

11171) produced bold seeds so these accessions could be used for improving

seed size in wheat.

Principal component analysis revealed that nine traits, out of twenty,

contributed 67.7 % of the total variation, hence data reduction in these

experiments proved practical implication for utilization by the breeders. The

characteristics which imparted maximum variance to PC1 included protein,

nitrogen and zinc. Moisture, phosphorus and boron contributed more

positively to PC3, iron to PC4, sodium to PC5, and ash contributed more

positively to PC7.

Cluster analysis based on SDS-PAGE was found to be more reliable than the

combined traits (Nutritional traits, mineral contents and seed characteristics).

All the accessions included in the present study had already been evaluated for

morphological characters; hence for combine study on morphological traits in

relation with present investigations will make wheat genetic resources

utilization more efficient and effective.

Coefficient of correlation revealed significant positive correlation between

seed length and seed width as well as between seed width and 100 seed

weight. Moreover zinc exhibited positive correlation with protein so for

efficient utilization of the germplasm, these traits could serve as a selection

criteria, where important trait combinations which were negatively associated

are suggested to exploit through modern techniques of plant breeding

including mutation breeding.

The SDS-PAGE analyses indicated that development of commercial varieties

has resulted in the reduction of genetic diversity of total genetic diversity due

to consistent selection pressure for quality that ultimately minimized diversity

for high molecular glutenin subunits. Breeding of wheat for better quality is

suggested to use new combinations of the subunits 13+16, 14+15 and 17+18

in addition to already considered as selection criterion.

145

5.9 Recommendations

The mineral contents desirable for nutritionally important wheat reported in

the present study could solve the burning issue of hidden hunger among the

poor masses, especially in the developing countries. High variation for various

important traits observed in the indigenous wheat genetic resources are likely

to strengthen database on wheat that is expected to be utilized for future wheat

cultivars. The identified for one or more traits are suggested to be tested under

wide range of agro-ecological zones and the best ones may be selected for

general cultivation on large scale.

The characteristics which exhibited low genetic diversity in the indigenous

wheat genetic resources are suggested to acquire from abroad to broaden the

selection horizon for otherwise important traits. Assembling of important

combinations of traits in one genotype is only possible when data are available

for diverse clusters of traits, and in the present study the data sets for various

traits could assist breeders in more systematic way.

Collection of germplasm from farmers’ field and marginal areas, especially in

remote areas are likely to present landraces with high economic values

including tolerance to biotic and abiotic stresses. Collection of as many

accessions as possible is suggested from geographically and ecologically

distinct areas to strengthen germplasm collection.

Low genetic diversity in commercial varieties for high molecular glutenin

subunits indicated the selection pressure for developing high yielding wheat

cultivars so there is a need to concentrate new allelic combinations to broaden

the genetic base of wheat cultivars.

To exploit economically important desirable traits, the results reported in this

study are suggested to be utilized for development of high yielding wheat

cultivars with good quality both for nutritive value and bread making quality.

Remaining germplasm is needed to be evaluated for the traits discussed in the

present study along with other DNA genetic markers including, SSLP, RAPD,

SSR for better understanding of genetic diversity at molecular level in the

indigenous wheat genetic resources.

146

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7 APPENDICES

Appendix I: Nutritional traits and mineral contents for selected wheat germplasm

S. No. Accession Fibre Oil Moisture Ash Protein N P K B Zn Cu Mn Fe Na

Baluchistan

1 11145 0.92 2.04 6.6 1.45 11.69 2.05 0.12 0.82 2.20 34.5 2.2 22.5 67.8 0.02

2 11150 1.87 1.95 6.7 1.20 13.12 2.30 0.20 0.54 0.96 30.0 1.5 25.4 54.6 0.04

3 11154 1.24 2.34 6.0 1.20 12.29 2.15 0.10 0.66 0.68 37.6 3.2 32.4 300.0 0.02

4 11155 1.53 1.42 6.7 1.20 12.26 2.15 0.32 0.45 0.90 37.0 3.8 26.0 162.4 0.06

5 11156 1.20 1.88 6.5 1.20 13.03 2.86 0.22 0.62 1.80 41.0 2.5 28.5 33.6 0.06

6 11160 0.72 1.43 6.2 1.52 13.92 2.44 0.25 0.48 0.68 33.8 4.0 24.7 33.6 0.08

7 11162 1.29 1.84 7.0 1.56 10.98 1.92 0.25 0.62 1.44 20.8 3.6 15.8 72.2 0.02

8 11164 1.53 1.75 7.1 1.47 11.49 2.01 0.13 0.52 1.10 16.2 2.5 20.5 62.8 0.04

9 11167 1.84 2.04 7.0 1.97 11.81 2.07 0.23 0.49 1.35 29.5 3.8 21.7 32.6 0.02

10 11170 1.53 1.98 7.1 1.47 13.50 2.37 0.26 0.54 1.48 40.5 2.9 26.4 40.5 0.02

11 11171 1.29 1.20 6.9 1.59 8.46 1.48 0.16 0.88 0.48 14.6 4.8 23.4 32.6 0.04

12 11174 1.44 1.93 7.1 1.58 10.40 1.82 0.15 0.54 2.06 31.5 3.7 19.5 69.0 0.02

13 11177 1.87 2.32 7.0 1.69 10.47 1.83 0.11 0.62 0.65 33.5 4.5 28.5 36.4 0.04

14 11178 1.44 1.70 7.0 1.45 12.00 2.10 0.17 0.82 2.45 35.0 2.5 25.4 43.8 0.02

15 11183 1.44 1.24 6.9 1.33 11.04 1.93 0.15 0.56 2.27 18.4 2.05 15.8 56.8 0.02

16 11184 1.87 2.01 7.0 1.71 11.85 2.07 0.22 0.5 1.49 17.0 3.9 25.0 32.6 0.02

17 11185 1.29 2.02 6.7 1.87 11.59 2.03 0.15 0.78 1.03 28.2 1.45 23.5 173.8 0.02

18 11186 1.51 1.74 7.0 1.94 11.20 1.96 0.19 0.58 0.75 26.6 2.45 24.9 42.4 0.06

19 11187 1.53 1.65 6.3 1.68 11.27 1.97 0.23 0.62 1.44 27.2 2.12 21.4 81.8 0.02

20 11188 1.45 1.75 6.9 2.06 12.74 2.23 0.17 0.76 2.06 25.4 2.78 20.1 25.6 0.02

21 11190 1.29 1.74 6.9 2.18 10.85 1.90 0.25 0.68 1.33 32.6 3.2 26.0 33.6 0.06

22 11193 1.29 1.70 6.9 2.18 12.93 2.26 0.31 0.64 3.16 35.0 2.0 15.8 103.4 0.02

23 11194 1.44 1.99 7.1 2.05 11.91 2.09 0.26 0.76 0.96 35.6 3.0 24.6 300.0 0.04

24 11195 0.92 1.95 7.0 1.93 10.69 1.87 0.23 0.74 1.45 23.0 1.0 17.6 122.4 0.08

185

25 11198 1.53 1.47 7.2 2.03 12.13 2.12 0.17 0.80 0.78 38.5 1.4 15.4 89.0 0.08

26 11199 1.44 1.78 7.4 1.75 15.13 2.65 0.32 0.74 3.10 46.0 1.5 21.0 140.6 0.02

27 11200 1.53 1.77 7.3 1.86 13.86 2.43 0.41 0.76 1.85 44.0 7.5 24.4 87.0 0.06

28 11202 1.87 1.98 7.1 2.10 13.54 2.37 0.17 0.8 2.09 38.5 3.0 23.4 73.2 0.08

29 11210 1.39 2.03 7.2 1.96 10.82 1.89 0.25 0.76 3.12 32.2 3.0 39.0 43.2 0.08

30 11211 0.93 1.93 7.3 1.66 16.29 2.85 0.27 0.38 0.75 49.5 2.5 24.4 49.0 0.02

31 11214 0.64 1.79 7.6 1.47 10.15 1.78 0.20 0.54 3.23 21.8 1.4 36.8 43.2 0.02

32 11220 0.72 1.92 8.0 1.57 12.29 2.15 0.17 0.62 0.68 23.0 1.5 36.8 49.4 0.02

33 11221 1.09 1.66 7.4 2.70 11.59 2.03 0.30 0.60 0.96 22.0 1.5 26.8 31.0. 0.02

34 11224 1.84 1.54 7.4 1.18 09.76 1.71 0.18 0.66 1.44 23.0 1.5 22.4 27.8 0.02

35 11226 1.29 1.65 7.6 1.59 11.11 1.95 0.25 0.54 2.79 22.6 2.6 22.2 23.8 0.08

36 11229 0.94 2.03 7.6 1.23 16.92 2.97 0.33 0.65 2.50 53.0 1.7 9.2 48.0 0.06

37 11231 1.87 1.94 7.9 1.32 12.00 2.10 0.11 0.74 3.40 22.0 2.2 22.8 56.0 0.02

38 11233 1.02 1.76 7.9 6.62 13.44 2.35 0.28 0.68 1.32 34.0 2.4 22.4 197.2 0.04

39 11235 1.20 1.99 7.5 1.23 12.61 2.21 0.17 0.68 1.75 33.5 1.8 36.2 224.2 0.02

40 11236 1.53 1.72 7.6 6.85 12.42 2.18 0.14 0.48 2.68 29.6 2.6 21.4 57.6 0.02

41 11237 0.94 1.77 7.4 1.12 13.73 2.40 0.21 0.52 3.52 27.5 2.6 28.0 114.8 0.02

42 11238 1.29 1.98 7.6 1.45 14.08 2.47 0.22 0.76 1.75 43.5 2.0 28.0 215.6 0.02

43 11239 0.94 1.73 7.5 1.94 12.29 2.15 0.13 0.54 1.70 33.0 2.9 29.2 49.0 0.02

44 11240 1.20 1.56 7.4 1.39 11.91 2.09 0.13 0.54 1.64 30.0 2.0 27.0 92.6 0.04

45 11242 1.20 1.61 7.6 1.16 12.35 2.16 0.36 0.54 2.37 28.8 2.3 27.8 64.6 0.06

46 11243 1.29 1.55 7.5 1.06 10.88 1.91 0.38 0.58 1.58 28.2 2.6 23.0 37.8 0.04

47 11244 0.94 1.54 7.4 1.60 12.10 2.12 0.14 0.58 2.49 26.8 2.3 29.4 33.2 0.06

48 11246 1.53 1.54 7.6 0.80 09.67 1.69 0.29 0.54 2.06 21.6 2.0 19.2 32.6 0.04

49 11248 0.94 2.12 7.5 1.23 12.29 2.15 0.36 0.58 2.12 35.5 7.0 32.6 41.8 0.02

50 11255 1.83 1.94 7.7 1.00 13.06 2.29 0.44 0.60 2.24 33.4 5.0 21.6 29.0 0.02

51 11259 0.94 1.88 7.9 0.90 07.12 1.25 0.43 0.74 3.28 24.2 4.8 7.8 94.0 0.04

52 11261 1.20 1.86 8.2 1.18 16.83 2.95 0.34 0.62 1.68 22.6 3.6 7.8 44.4 0.02

53 11262 1.29 1.63 7.6 1.49 11.91 2.09 0.26 0.62 2.91 28.8 4.0 37.4 58.0 0.02

54 11263 1.53 1.84 7.5 1.85 16.83 2.95 0.32 0.64 2.36 29.0 8.5 33 65.8 0.04

186

55 11265 1.53 1.85 7.4 1.22 13.18 2.31 0.39 0.60 2.37 36.0 9.0 25.2 70.4 0.04

56 11267 1.20 1.90 7.8 1.29 13.15 2.30 0.43 0.70 3.22 37.0 3.0 30.4 54.8 0.02

57 11272 1.29 1.55 7.9 1.49 12.90 2.26 0.41 0.62 2.50 50.0 8.0 34.6 298.5 0.04

58 11278 1.29 2.27 7.5 1.73 13.09 2.29 0.34 0.50 2.55 36.0 5.0 31.6 47.0 0.02

59 11280 1.29 1.98 7.5 1.54 16.69 2.92 0.29 0.48 1.48 54.0 2.3 39.6 68.6 0.02

60 11281 0.94 1.32 7.6 1.94 15.04 2.64 0.31 0.78 3.58 25.5 6.0 26.8 68.6 0.04

61 11283 0.94 1.19 7.9 1.94 10.69 1.87 0.38 0.56 2.79 20.0 5.0 26.8 52.2 0.02

62 11284 1.29 1.77 8.4 3.84 10.88 1.91 0.39 0.56 3.10 16.5 4.5 27.6 58.0 0.02

63 11288 0.94 1.83 7.5 3.83 11.59 2.03 0.25 0.50 2.85 19.0 3.0 11.8 24.6 0.04

64 11293 1.29 1.78 7.4 1.23 12.55 2.20 0.36 0.50 2.69 33.0 4.0 26.6 36.4 0.02

65 11294 0.94 1.55 7.6 1.50 12.67 2.22 0.40 0.80 1.68 33.5 5.5 22.8 71.8 0.06

66 11295 0.94 1.94 7.5 1.60 12.51 2.19 0.40 0.80 2.50 13.5 3.5 24.2 59.8 0.02

67 11296 0.94 2.08 7.4 1.90 13.86 2.43 0.31 0.82 2.38 40.5 6.0 22.6 82.8 0.04

68 11298 1.29 2.27 7.9 1.35 12.67 2.21 0.34 0.58 2.90 43.0 4.5 34.0 279.6 0.02

69 11299 1.29 1.83 7.3 1.45 12.77 2.24 0.30 0.68 3.27 38.0 8.0 24.2 44.0 0.02

70 11300 0.94 1.78 7.2 1.34 12.99 2.28 0.34 0.60 2.48 32.6 3.4 27.8 35.8 0.02

71 11302 0.94 1.99 7.4 1.45 08.81 1.54 0.30 0.76 2.65 29.0 3.0 22.6 33.4 0.06

72 11303 1.27 1.67 6.5 1.45 11.78 2.06 0.28 0.56 3.21 28.5 4.5 30.0 55.8 0.08

73 11304 1.29 1.21 7.5 2.45 15.61 2.74 0.43 0.48 1.48 45.0 1.2 26.2 54.8 0.04

74 11305 1.29 1.95 7.8 2.45 12.51 2.19 0.38 0.50 3.10 35.5 4.5 32.0 62.8 0.06

75 11307 1.29 2.18 7.6 1.45 10.12 1.77 0.24 0.50 2.68 27.2 4.2 21.0 30.0 0.02

76 11308 1.29 1.99 7.5 0.77 12.26 2.15 0.32 0.60 2.78 42.5 8.5 29.8 40.6 0.04

77 11309 0.94 2.17 7.5 1.52 16.22 2.84 0.29 0.72 3.01 45.6 5.6 24.5 108.6 0.02

78 11310 0.93 1.97 7.7 1.23 12.42 2.18 0.30 0.60 3.28 25.6 5.0 24.4 162.2 0.02

79 11311 1.29 1.93 7.8 1.26 11.59 2.03 0.24 0.82 2.55 22.6 3.6 35.4 295.0 0.06

80 11312 0.94 2.11 7.6 0.91 10.56 1.85 0.27 0.56 1.46 21.4 2.0 29.4 32.0 0.04

81 11315 0.94 2.25 7.4 1.24 13.92 2.44 0.38 0.70 3.10 32.0 5.2 26.4 289.0 0.08

82 11325 1.27 2.14 7.4 1.28 11.55 2.02 0.20 0.36 3.50 33.8 2.6 22.4 63.2 0.04

83 11328 0.94 1.21 7.3 1.12 11.33 1.98 0.28 0.64 2.65 19.4 2.4 26.2 56.8 0.04

84 11333 1.29 1.79 7.5 1.54 10.31 1.81 0.31 0.56 2.71 17.2 3.4 30.4 55.4 0.04

187

85 11334 1.53 2.17 7.6 1.67 12.55 2.20 0.20 0.40 3.50 40.5 2.0 19.2 51.6 0.06

86 11335 1.87 2.11 7.3 1.90 13.18 2.31 0.36 0.54 0.64 34.4 1.2 35.6 138.4 0.02

87 11344 1.87 1.35 7.2 1.01 12.07 2.11 0.31 0.56 3.25 29.6 3.6 33.4 67.0 0.04

88 11527 1.83 1.55 7.5 1.39 11.62 2.04 0.30 0.34 2.40 25.4 3.5 24.8 75.8 0.06

89 11528 1.28 1.18 7.4 1.45 11.40 2.00 0.31 0.32 3.50 30.4 1.6 31.4 63.4 0.02

90 11531 1.87 1.52 7.9 1.20 10.59 1.85 0.27 0.48 2.50 27.0 3.8 29.6 28.0 0.02

91 11534 0.94 1.70 7.4 1.45 12.10 2.12 0.36 0.40 2.20 44.8 2.2 35.0 30.6 0.02

92 11536 0.94 1.71 7.6 1.20 11.31 1.98 0.23 0.45 0.95 23.0 2.8 31.8 17.0 0.02

93 11538 0.94 1.54 7.8 1.45 11.01 1.93 0.28 0.30 1.03 28.4 1.0 29.4 19.4 0.04

Punjab

1 11348 1.53 1.38 7.7 1.35 11.87 2.08 0.36 0.38 1.56 33.8 3.6 36.8 70.6 0.02

2 11349 1.29 1.84 7.5 1.17 11.49 2.01 0.36 0.36 1.77 31.8 3.4 36.6 62.6 0.08

3 11350 1.53 2.13 7.6 1.70 11.78 2.06 0.32 0.38 1.46 24.0 2.6 29.4 47.8 0.02

4 11351 1.87 1.72 7.8 2.08 12.23 2.14 0.30 0.30 0.73 24.2 1.8 28.8 61.6 0.04

5 11352 1.87 1.50 7.0 1.98 09.26 1.62 0.37 0.48 2.71 38.8 2.2 25.8 96.8 0.06

6 11353 1.53 1.66 7.5 5.51 11.87 2.08 0.39 0.34 2.81 33.8 2.2 26.0 74.0 0.02

7 11355 1.87 1.72 7.4 1.91 10.60 1.86 0.37 0.30 2.81 35.8 3.2 39.0 82.4 0.04

8 11356 1.53 1.95 7.3 1.42 11.11 1.95 0.36 0.32 2.61 29.0 3.8 37.6 63.4 0.02

9 11359 1.29 1.85 7.3 2.72 11.27 1.97 0.32 0.45 2.08 27.0 2.6 38.2 51.8 0.04

10 11360 1.53 1.97 7.0 1.45 11.78 2.06 0.41 0.38 1.35 32.4 3.6 39.0 53.8 0.06

11 11361 1.54 2.34 7.2 1.20 11.62 2.04 0.29 0.48 1.87 25.4 1.8 29.8 20.8 0.02

12 11362 1.53 2.02 7.4 1.20 13.44 2.35 0.32 0.38 3.50 28.5 4.0 34.0 59.8 0.08

13 11363 1.87 2.07 7.2 1.20 12.69 2.27 0.25 0.36 2.81 40.6 3.6 28.5 85.8 0.02

14 11364 1.53 1.94 7.0 1.20 11.17 1.96 0.31 0.30 1.25 28.4 1.6 33.0 45.4 0.02

15 18669 1.29 1.61 7.8 1.45 12.74 2.23 0.24 0.40 2.28 29.2 2.5 30.6 32.0 0.02

16 18670 1.53 1.92 7.6 1.45 13.16 2.31 0.25 0.35 3.15 25.4 2.2 30.8 21.6 0.06

17 18672 0.94 1.63 7.5 1.20 13.41 2.35 0.25 0.40 1.04 25.8 3.6 24.8 34.6 0.08

18 18673 1.87 1.52 7.0 1.20 13.41 2.35 0.23 0.38 1.37 25.0 2.4 18.2 72.4 0.02

19 18674 1.29 1.79 7.7 1.20 12.83 2.25 0.33 0.35 3.25 29.6 2.8 22.5 50.5 0.04

188

20 18675 0.94 1.55 7.4 1.20 12.77 2.24 0.26 0.44 0.85 26.6 2.4 21.6 72.4 0.02

21 18676 0.94 1.92 7.2 1.39 13.13 2.30 0.25 0.42 1.45 29.4 2.2 23.8 15.6 0.08

22 18677 1.87 1.82 7.6 1.45 13.56 2.37 0.22 0.52 1.85 27.2 2.6 18.8 22.0 0.02

23 18678 1.29 1.65 7.4 1.20 13.49 2.36 0.25 0.54 2.46 22 2.2 20.4 30.0 0.04

24 18679 1.53 1.95 7.9 1.45 12.92 2.26 0.25 0.52 2.15 22.4 2.0 23.2 19.2 0.04

25 18680 1.53 1.39 7.2 1.45 13.49 2.36 0.31 0.50 3.25 24.2 1.6 22.0 18.0 0.02

26 18681 1.29 1.83 7.8 1.45 12.68 2.22 0.28 0.52 2.37 27.2 1.8 25.6 19.8 0.02

27 18682 0.94 1.54 8.5 1.45 12.38 2.17 0.29 0.54 3.22 28.6 2.6 25.4 35.2 0.02

28 18683 1.87 1.88 7.5 1.45 13.44 2.35 0.23 0.44 3.59 23.6 1.2 16.6 22.4 0.06

29 18685 0.94 1.71 7.5 1.20 12.86 2.25 0.23 0.44 2.45 25.8 1.8 18.2 10.8 0.06

30 18687 1.20 1.92 7.7 1.39 13.41 2.35 0.18 0.44 3.45 20.0 2.3 10.4 8.8 0.08

31 18688 0.94 2.07 7.6 1.45 13.13 2.30 0.32 0.54 2.87 37.6 1.2 37.6 15.8 0.04

32 18689 0.94 2.49 7.3 1.20 11.40 2.00 0.34 0.58 3.05 28.0 1.8 35.4 35.0 0.02

33 18690 1.87 2.34 7.4 1.39 12.86 2.25 0.34 0.54 1.05 34.6 1.4 39.0 33.4 0.02

34 18692 0.94 2.04 7.7 1.20 13.9 2.44 0.31 0.54 1.54 30.0 1.2 29.6 144.2 0.02

35 18693 1.87 1.59 7.6 1.20 11.45 2.01 0.31 0.52 1.36 32.2 4.0 31.2 26.8 0.04

36 18694 1.29 1.92 7.4 1.45 13.73 2.41 0.24 0.42 2.89 22.0 7.0 24.8 72.4 0.02

37 18695 1.53 1.88 7.7 1.45 13.50 2.36 0.26 0.50 1.78 32.0 4.2 29.2 22.8 0.02

38 18696 1.20 1.86 7.8 1.98 14.87 2.60 0.44 0.62 3.02 38.4 4.0 41.6 54.8 0.04

39 18698 1.87 2.02 6.1 1.39 13.06 2.29 0.33 0.48 3.78 31.8 3.8 28.6 14.6 0.02

40 18699 0.94 1.84 6.1 1.20 14.03 2.46 0.3 0.46 0.91 30.0 4.2 32.0 23.2 0.02

41 18701 1.20 2.13 6.6 1.45 12.29 2.15 0.34 0.54 3.15 34.6 3.8 24.5 39.2 0.04

42 18702 0.94 2.22 6.7 1.45 12.71 2.23 0.29 0.46 1.45 30.0 3.2 26.0 37.0 0.06

43 18703 1.87 2.36 8.1 1.33 12.97 2.27 0.25 0.54 1.20 30.4 2.6 19.2 15.4 0.04

44 18705 0.94 1.87 7.4 1.22 13.07 2.29 0.24 0.46 0.65 24.6 3.0 17.0 8.2 0.02

45 18707 1.18 1.92 7.1 1.20 13.83 2.60 0.26 0.40 0.95 32.8 2.8 24.0 25.0 0.06

1 18708 0.93 1.92 6.9 1.20 11.50 2.15 0.39 0.35 2.02 46.4 4.6 30.8 35.0 0.02

189

Appendix II: Seed chracteristics for selected wheat germplasm

Accession Seed

length

Seed

width

100 seed

weight

Seed size Seed color Seed

shriveling

Baluchistan

1 11145 5.52 2.18 3.26 Intermediate Creamy white Intermediate



4 11155 5.71 2.23 4.47 Intermediate Creamy white Plump

5 11156 4.94 1.81 4.33 Small Red Intermediate

6 11160 5.59 2.84 3.98 Intermediate White Intermediate

7 11162 5.42 2.45 2.87 Intermediate Red Intermediate

8 11164 7.42 2.89 4.68 Large Red Intermediate


10 11170 5.17 2.74 5.36 Small Creamy white Intermediate

11 11171 7.27 3.00 5.04 Large Red Intermediate













24 11195 5.56 2.81 4.44 Intermediate Red Plump






30 11211 5.72 2.48 2.48 Intermediate Red Shriveled



190











43 11239 5.92 2.88 4.20 Intermediate White Plump












55 11265 6.11 1.76 2.20 Intermediate Creamy white Shriveled




59 11280 5.04 1.75 2.96 Small Creamy white Intermediate


61 11283 6.05 2.97 3.24 Intermediate White Shriveled





66 11295 5.16 2.23 2.88 Small Red Shriveled


68 11298 5.17 2.32 2.56 Small Red Shriveled

191



71 11302 5.11 2.16 3.80 Small Red Plump




75 11307 5.74 1.97 2.44 Intermediate Red Plump






81 11315 5.37 2.76 2.44 Intermediate Creamy white Shriveled











92 11536 5.90 2.56 2.80 Intermediate White Shriveled


Punjab


2 11349 4.61 2.87 2.76 Small White Intermediate









192


















28 18683 5.85 2.60 3.72 Intermediate White Plump



















Fauzia Yasmeen - Higher Education...

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