Chapter 3Chapter 3 Materials and MethodsMaterials and...

Chapter 3Chapter 3Chapter 3Chapter 3 Materials and MethodsMaterials and MethodsMaterials and MethodsMaterials and Methods

Materials and Methods

A Genomic Study on the Sub-Structured Chaudhari Tribe of Southern Gujarat 62

MATERIALS AND METHODS

After identifying the objectives of the study, it is important to have a strategically

designed research plan to achieve coherent and methodological accomplishment of the

goals of the study. The present section outlines step by step the conception, planning

and underlying reasoning followed for methodological execution of the study to obtain

meaningful and reliable inferences.

3.1. Selection of the Study Population and Field Area

Prior to the pilot survey, information on various tribal populations of India from

different sources such as books, journals and the internet was collected. Since

population sub-structuring in Indian populations is a unique feature and not many

populations have been investigated to observe the patterns of micro-differentiation

among the subdivided populations (where each subdivision constitutes a Mendelian

unit), the search was initiated for identification of populations bearing this distinct

characteristic. After going through literature review it was found that Chaudhari tribe

from Gujarat (Bhatt, 1985) fulfilled this criterion. Moreover, keeping in view the dearth

of molecular genetics work on tribal populations of Gujarat, Chaudhari tribe from South

Gujarat appeared to be an apt choice. Chaudhari tribe constitutes 3.80% of the total

schedule tribe population of Gujarat (Census of India, 2001) and is mainly restricted to

the Surat district of Gujarat as per 2001 census data which led to selection of the Surat

district as study area. However, talukas for the collection of blood samples and

ethnographic information were finalized after the pilot survey.

3.2. Pilot Survey

A pilot study, also called feasibility study, is a small scale survey designed to test

logistics, gather preliminary information and to establish rapport with the people prior

to a larger, full fledged study, in order to improve the latter’s quality and efficiency. A

pilot survey can reveal loopholes in the design of the proposed research that failed to

meet the empirical field work conditions, thereby giving the opportunity to the

3



researcher to modify the methodology accordingly and to achieve successful fieldwork.

A pilot study may address a number of logistic issues. As a part of research strategy the

following information can be gathered prior to the main study.

1. Distribution of study populations

2. Adequacy of sampling frame

3. Climatic conditions of the area

4. Probability estimation of expenditure and duration of subsequent field work

5. Rapport establishment

6. The response rate and cooperation from the locals including study populations and

officials

7. The suitability and adequacy of the methodology to be followed.

A pilot study is normally small in comparison with the main study and therefore can

provide only limited information on the source and magnitude of troubles that one

might face during the main field work. But, it provides vital information on the

efficiency of the proposed procedure and results in improvements of the research design

prior to the main survey.

For the present work, pilot survey was undertaken from 5th

September to 22nd

September, 2008 for getting an overview of the population distribution, to check field

conditions and for assessing the feasibility of research and data collection in the area.

During the pilot survey sincere attempts were made to collect information on primary

health centers (PHCs) and hospitals, tribal community centers, educational institutes,

tribal welfare centers, missionaries and non government organizations (NGOs),

museums and science centers. Several government offices were also visited to appraise

them about the purpose of the visit and to seek help required during the data collection.

The various officers contacted were the Collector, District Development Officer, Chief

District Health Officer, Tribal Development Officer, Taluka Development Officers,

Principals of the various schools and colleges.

Valsad Raktdan Kendra, a Voluntary Blood Bank and Haematology Research Centre,

working in the health sector in the area was also approached for help. The blood bank

has all the facilities for the collection of blood, its storage and processing. The bank



also has the laboratory facilities for carrying out biochemical analysis of the blood.

Consent and permission for procuring help necessary for the collection of blood

samples in the subsequent field trips was taken from Dr. Y. Italia, Honorary Secretary

of the blood bank and also the Honorary Director of Sickle Cell Anemia Control

Program in Gujarat. Dr. Y. Italia, laboratory in-charge Mr. Bhavesh and other

laboratory members were cooperative and extended their full support for the proposed

research problem.

Few people were also interviewed during the pilot survey to inquire about the existence

of population sub-structuring among the Chaudhari tribe of Surat district. Some of the

knowledgeable people from the area were contacted and informed about the research

work. Prominent names among them were Dr. Bharat Desai, Girish Bhai Chaudhari,

Ashok Bhai Chaudhari, Manoj Bhai Chaudhari and Dr. Arvind Bhatt.

Dr. Arvind Bhatt is a retired professor in Anthropology from Gujarat Vidyapith,

Ahmedabad. Dr. Bhatt has also worked on the Chaudhari tribe of Surat and found the

existence of population subdivision as a result of adherence to the rule of strict

endogamy by the community members. Girish Bhai, Ashok Bhai and Manoj Bhai

Chaudhari belong to the same community and are involved in the social welfare of

tribal communities in Gujarat. They also told about the existence of division among the

Chaudhari tribe. Dr. Bharat Desai who is an eminent sociologist and a renowned social

worker also provided some valuable literature to support the presence of population

sub-structuring among the Chaudhari tribe of Surat district, Gujarat.

Secondary information on demographic aspects such as population distribution and

population size was also collected during the pilot survey. Census data of the Surat

district was collected to understand the distribution and preponderance of the Chaudhari

tribal population in the region. Ethnographic information on culture, food habits, and

social practices especially, mating system was collected from various libraries,

museums and local people.

From the pilot survey it was gathered that the Chaudhari population is widespread

throughout the Surat district especially in its Mandvi, Vyara, Umarwada and Mahuva

talukas. It was also established that the Chaudhari tribe has four major subdivisions

namely Pavagadhi Chaudhari, Mota Chaudhari, Valvi Chaudhari and Nana Chaudhari.



Since during the pilot survey it was found that the Chaudhari tribe is subdivided into

four major subgroups, the present study was designed to attempt and apportion genetic

diversity and differentiation among the sub-structured groups of Chaudhari. This can

also be seen as a model of population structure where the subgroups are nested inside

the total population of Chaudhari.

3.3. Collection of the Data

After the initial pilot survey and rapport establishment, the field area was revisited in

the subsequent years to collect both ethnographic details and blood samples from

people of the Chaudhari tribe.

3.3.1. Blood Sample Collection

3.3.1.1. Ethical Clearance

Ethical clearance was obtained from the Ethical Review Committee of the Department

of Anthropology, University of Delhi. The certificate of clearance by ethical committee

is attached as Appendix I.

3.3.1.2. Sampling Procedure

The blood samples were collected from the individuals unrelated up to at least second

cousin level, belonging to the four Chaudhari groups. Three generation pedigree charts

were also prepared to ascertain un-relatedness in all the samples and to reduce the

chances of any kind of bias in sampling. Prior to the blood collection, the purpose and

procedure involved in the study were elaborately explained in groups as well as

individually to the participants. Written informed consents were obtained before the

collection and the blood was collected by a trained medical practitioner. The consent

form used for the study is attached as Appendix II. It was made as informative as

possible for the subject. For collection of blood samples sterilized disposable syringes

were used which were properly disposed after single use with the help of syringe

crusher. 9 ml vacutainers coated with anti-coagulant Ethylenediaminetetraacetic acid

(EDTA) were used to collect 5 ml blood. On each tube serial number, date, person’s

name, age and sex were neatly written to avoid the mixing of samples.



3.3.1.3. Field Visits for Data Collection

Once the rapport was established, the collection of data was initiated. Both primary and

secondary ethnographic data were collected during all the field works to meet the

objectives of the study. While collecting the blood samples all the necessary precautions

were taken. 5 ml intravenous blood samples were collected by a trained medical

practitioner from randomly chosen individuals, unrelated up to second cousin level,

from the study populations with prior informed written consent.

� First Phase of Field Work (10th

February to 10th

March, 2009)

During the first field work, 27 samples from Nana Chaudhari, 22 samples from Mota

Chaudhari divisions were collected with prior informed written consent. The collected

blood samples were sent to the Biochemical and Molecular Genetics Laboratory of

Department of Anthropology, University of Delhi for the purpose of DNA extraction.

Along with the samples secondary information on social aspects such as mating pattern,

cultural practices, food habits and other rituals was also collected.

� Second Phase of Field Work (21st June to 11

th July, 2009)

The second field trip was conducted from 21st June, 2009 to 11

th July, 2009. During this

period attempts were made to achieve the targeted sample size (50 each) for both Nana

and Mota Chaudhari. Apart from random blood sampling special emphasis was given

on collection of ethnographic account of the tribe. By the end of second field work 52

Nana Chaudhari and 50 Mota Chaudhari samples had been collected. This time the

work of DNA isolation was undertaken in the sickle cell unit of Valsad Raktdan

Kendra, Gujarat.

� Third Phase of Field Work (6th

April to 27th

April, 2010)

The third field work was undertaken in the month of April from 6th

to 27th

April, 2010.

During this field trip Pavagadh region of Panchmahal district was visited to inquire

about the migration of Pavagadhi Chaudhari. Going by the information obtained,



Mahuva taluka of Surat district was visited to collect blood samples from Pavagadhi

Chaudhari. By the end of the field work, 41 samples had been collected.

� Fourth Phase of Field Work (8th

January to 25th

January, 2011)

The fourth field work was undertaken between 8th

to 25th

January, 2011. In this field

work 50 blood samples were collected from Valvi Chaudhari along with their

ethnographic details.

3.3.1.4. Sample Size

A total of 193 Samples were collected for the present study. 50 blood samples from

Mota Chaudhari, 52 from Nana Chaudhari, 41 from Pavagadhi Chaudhari and 50 from

Valvi Chaudhari were collected randomly from 29 villages of 3 talukas namely

Mahuva, Mandvi and Umarwada of Surat district Gujarat and 4 villages of Vyara taluka

of Tapi district. Table 3.1 presents the distribution of samples according to the area of

sample collection.

3.3.2. Ethnographic Details

Deliberate sampling, which is also called purposive sampling, was used to gather

ethnographical facts from the population under study. Besides the basic information

such as name, gender, age, current residence, ethnicity of the subject, information on

Chaudhari subgroup affiliation was also collected. Census of India, State Gazetteers or

any other local enumeration project has never collected subdivision based data on the

Chaudhari population. These surveys have always addressed the Chaudhari population

as a single unit. Except few studies such as Shah (1964) and Bhatt (1985), no written

record of the existence of internal population subdivision and their adherence to group

endogamy is available. Along with group affiliation, information was also collected on

mating pattern and other social practices. The detailed schedule used for the study is

attached as Appendix III.



Table 3.1. Distribution of samples according to the area of sample collection

Population Village Taluka District

Pavagadhi Chaudhari

Dungari

Mahuva

Surat

Naladhara

Karchiliya

Mahuva

Nana Chaudhari

Muritha

Mandvi

Kharoli

Satvaov

Kamal Kua

Gantholi

Gordha

Andhatari

Damodia

Utewa

Simbariamba

Ghasiyameda

Naren

Ghotadav

Vyara Tapi Gordha

Kada

Mota Chaudhari

Bilwan Umarwada

Surat

Kimdungara

Mandvi

Luharwad

Isher

Balathi

Peeperwad

Regama

Kamalkua

Devgadh

Amba padi

Vadh

Ladkua

Gharbadar

Valvi Chaudhari Nanicher Vyara Tapi

3.4. Laboratory Analysis

The collected blood samples were subjected to molecular analysis. Three different sets of

markers namely, Autosomal, Mitochondrial and Y chromosomal were screened. The

autosomal markers were analyzed at Biochemical and Molecular Genetics Laboratory of



Department of Anthropology, Delhi University, whereas mtDNA and Y chromosomal

markers were analyzed at the Anthropological Survey of India, Southern Regional Centre

at Mysore, Karnataka. Techniques used to assess the variation in the above mentioned

genomic sites and the work flow involved are given in Figure 3.1.

Figure 3.1. General overview of laboratory work



3.4.1. Extraction of Genomic DNA

The first step in the molecular analysis of the collected blood samples was the

extraction of DNA. DNA was extracted using salting-out method (Miller et al., 1988).

The salting-out method is a rapid, safe, inexpensive method that gives high yields of

good quality DNA. The detailed procedure followed for the extraction of DNA has

been given in Appendix IV.

3.4.2. Quantification of DNA

Following extraction, DNA was quantified using NanoDrop instrument, ND-1000. It is

based on the same principle as spectrophotometer but requires much less quantity of

DNA sample to measure nucleic acid concentrations. NanoDrop can measure nucleic

acid concentration and purity of nucleic acid samples up to 3700 ng/ul using 2 µl of

sample. In the present study the DNA concentration of the samples was found within

the range of 250-350 ng/µl.

3.4.3. Polymerase Chain Reaction (PCR)

The targeted genomic regions were amplified using the PCR technique. PCR is an in

vitro method for the enzymatic synthesis of specific DNA sequence using

oligonucleotide primers that hybridize to opposite strands at the regions flanking the

target DNA sequence. It carries out the exponential amplification of target DNA

sequence through repeated cycles of DNA synthesis. Each newly synthesized molecule

of target DNA acts as a template for the synthesis of new target molecules in the next

cycle. The final number of copies of amplicons (amplified targeted DNA) generated is

2n

, where n = number of cycles. The detailed technique of Polymerase Chain Reaction

is given in Appendix V.

3.4.4. Agarose Gel Electrophoresis

Prior to carrying out any further analysis, it was confirmed that the amplification has

taken place and ensured that the amplicons generated were indeed the required ones.

This task was done by comparing the molecular size of the amplified DNA with a

standard DNA ladder using electrophoresis and visualizing the results under UV ray



transilluminator (Figure 3.2). Depending upon the size of the PCR product

concentration of the gel used was decided. In case of the Alu markers, the genotypes

were analyzed at this stage whereas for RFLPs, PCR products were first digested with

restriction enzymes, followed by gel electrophoresis.

Figure 3.2. Agarose gel showing amplified PCR products

3.4.5. Restriction Digestion

In case of Restriction Fragment Length Polymorphism (RFLP) markers, the amplified

DNA was subjected to restriction digestion. Restriction Digestion is the process in

which a class of enzyme known as Restriction Enzymes (REs) are used to cut DNA into

smaller restriction fragments. These enzymes identify specific sequences (4-6 base pair

long) in the DNA molecule known as restriction sequences (i.e. the site where the

enzyme actually cuts the DNA molecule). On the basis of the presence or absence of the

specific sequence, restriction sites vary and so the fragment lengths of the digested PCR

product are generated which are identified by variable banding patterns. Each enzyme

has its specific temperature of activity.

3.4.6. Genotyping by Agarose Gel Electrophoresis

The genotypes of the digested target regions were determined using Agarose gel

electrophoresis. Gel electrophoresis refers to the technique in which macromolecules-



either nucleic acids or proteins, are forced across a span of gel, acted upon by an

electrical current. The macromolecules are separated on the basis of size and electric

charge under the influence of an electric field. Their rate of migration through the

electric field depends on the strength of the field, size and shape of the molecules,

relative hydrophobicity of the samples, and on the ionic strength and temperature of the

buffer in which the molecules are moving. Agarose is a natural colloid extracted from

seaweed. It is very fragile and easily destroyed by handling. Agarose gels have very

large "pore" size and are used primarily to separate very large molecules up to 10,000

base pairs. The higher is the concentration of the gel, lower is the porosity, which is

suitable for movement of low molecular weight substances and vice versa. Depending

upon the digested product size, Agarose gels of various concentrations were formed by

suspending dry agarose in aqueous Tris Acetate EDTA (TAE) buffer (composition

given in Appendix VI), then boiling the mixture until a homogenous solution is formed.

Ethidium bromide used in the gel gives fluorescence under UV rays, permitting

visualization of the series of DNA bands spread across the gel according to their size

and viewed against a commercially available ladder which contains DNA fragments of

known size.

3.4.7. DNA Sequencing

There are various methods available for DNA sequencing like chemical degradation,

chain termination method, sequencing by ligation etc. Advances in automation have

opened gates for faster and more reliable automated DNA sequencing technologies.

Owing to its greater efficiency and speed, dye-terminator sequencing is now the

mainstay of automated sequencing. Dye-terminator sequencing is a slight modification

of the Sanger’s chain termination method. It utilizes labeling of the chain terminator

ddNTPs, which permits sequencing in a single reaction. In dye-terminator sequencing,

each of the four dideoxynucleotide chain terminators is labeled with fluorescent dyes,

each with different wavelengths of fluorescence and emission. The dye labeled DNA

fragments are then capillary electrophoresed and a detection system identifies the

labeled bases when they pass through a laser that activates the dye.



3.4.7.1. Cycle Sequencing

Cycle sequencing is similar to PCR except that only one primer is used in each cycle

sequencing reaction so that the amplification of product is linear not exponential; and

the addition of dideoxynucleotides which interrupt the extension of the DNA strands

when incorporated.

Following usual PCR and gel electrophoresis, 0.5µl of the generated PCR amplicons

were subjected to cycle sequencing reaction with one primer and fluorescent dye

labeled ddNTPs using ABI Prism® BigDyeTM

terminator v3.1 cycle sequencing ready

reaction kits (Applied Biosystems, USA) following the manufacturer’s guidelines.

3.4.7.2. Sequencing Cleanup

One of the most important factors in automated DNA sequencing is clean and pure

templates. For a sequencing reaction to be successful all excess primers, dNTPs, salts

and residual DNA must be removed from the sample. The detailed procedure for

processing the plates prior to sequencing is given in Appendix VII.

3.4.7.3. Sequencing Run

10 µl of Hi-Di formamide was added to each well of the sample plate. The samples were

heated to 96°C and immediately cooled to 4

°C to denature the DNA. Sample information

sheets which contain analysis protocol along with the sample details were prepared and

imported into the data collection software. Prepared samples were analyzed on ABI 3730

genetic analyzer (Applied Biosystems, USA) to generate DNA sequences.

3.4.7.4. Sequence Quality Check

After completion of the sequencing reaction, quality of the generated sequences was

checked by using Sequencing Analysis version 5.2 software (Applied Biosystems,

USA). The Applied Biosystems Sequencing Analysis Software version 5.2 is designed

to analyze, display, edit, save, and print sample files generated using ABI genetic

analyzers. The program has a basecaller algorithm that performs basecalling for pure

and mixed base calls. It provides quality values (QV) for every single base and sample

scores for the assessment of the average quality value of the bases in the clear range

A Genomic Study on the Sub

sequence for the sample. The QV is a per

QVs are calibrated on a scale corresponding to:

Where, Pe is the probability of error.

set to 20 to 50 and typical

samples which didn’t

amplification.

3.4.7.5. Sequence Alignment, Editing and Recording of V

The generated sequences were aligned to

use of SeqScape version

of high quality, characterized by sharp peaks and little to no background

matched against the reference seq

(Figure 3.3). The electropherogram

peak colour. The sequences having a different base and peak

sequence at a given position were considered to

mtDNA, variants were reported in terms of the

Reference Sequence (rCRS

for diverse Y chromosomal sites by

sequence obtained from NCBI,

Figure 3.3. Electrophe

A Genomic Study on the Sub-Structured Chaudhari Tribe of Southern Gujarat

e sample. The QV is a per-base estimate of the basecaller accuracy. The

QVs are calibrated on a scale corresponding to:

QV= –10 log10 (Pe)

is the probability of error. For this study high-quality pure bases QVs were

set to 20 to 50 and typical high-quality mixed bases QVs were set to 10 to 50

samples which didn’t follow the above conditions were re-sequenced after fresh PCR

Alignment, Editing and Recording of Variants

The generated sequences were aligned to their respective reference sequences with the

ersion 2.5 software (Applied Biosystems, USA). The DNA sequence

of high quality, characterized by sharp peaks and little to no background

matched against the reference sequence by carefully observing in

electropherogram is a plot of results where each base

. The sequences having a different base and peak

sequence at a given position were considered to have a variant at that position

variants were reported in terms of the mutations of revised Cambridge

(rCRS) (Andrews et al., 1999). Likewise, variants were identified

for diverse Y chromosomal sites by comparing the generated sequence with

obtained from NCBI, GenBank data base.

Electropherogram showing distinct peak colours for each base


74

base estimate of the basecaller accuracy. The

quality pure bases QVs were

quality mixed bases QVs were set to 10 to 50. The

sequenced after fresh PCR

their respective reference sequences with the

The DNA sequence

of high quality, characterized by sharp peaks and little to no background noise, were

carefully observing in electropherogram

each base shows a distinct

. The sequences having a different base and peak than the reference

variant at that position. For

mutations of revised Cambridge

variants were identified

d sequence with a reference

for each base



3.5. Genomic Regions Analyzed

3.5.1. Autosomal Markers

14 autosomal markers, which included seven Alu Insertion-Deletion (InDel) markers

and seven Restriction Fragment Length Polymorphisms (RFLPs), were screened. The

details pertaining to the technique of PCR such as PCR components, concentrations and

temperature conditions for autosomal markers are given in Appendix VIII and IX.

3.5.1.1. Alu Insertion-Deletion (InDel) Markers

All the Alu InDels considered in the present study were human specific and biallelic

codominant loci. Except for CD4, all other Alu loci were insertion polymorphisms. The

protocols for amplification of the Alu InDel markers were taken up from Majumder et

al. (1999).

3.5.1.1.1. Predicted Variant 92 (PV92)

PV92 Alu insertion belongs to Alu Ya5 subfamily, part of the youngest subfamily Alu Y

of Alu insertions. It is present on chromosome 16.

3.5.1.1.2. Coagulation Factor XIII B (FXIIIB)

This Alu insertion polymorphism is located in intron 10 of FXIIIB gene on chromosome

1 at position 1q31-q32.1. This gene encodes coagulation factor XIII B subunit. Factor

XIII deficiency can result in a lifelong bleeding tendency, defective wound healing, and

habitual abortion.

3.5.1.1.3. D1

D1 belongs to Sb2 Alu family. This Alu insertion polymorphism is located on

chromosome 3 at 3q26.32.

3.5.1.1.4. Apolipoprotein (APO)

This site is adjacent to the APOA1, APOC3 and APOA4 gene cluster and is located at

11q23.3. It is present 4.3 kb upstream of the APOA1 gene.



3.5.1.1.5. Angiotensin I Converting Enzyme (ACE)

InDel ACE is located in intron 16 of the gene Angiotensin I Converting Enzyme (ACE)

positioned on chromosome 17q23 (Mattei et al., 1989). The gene is also known as

Dipeptidyl carboxypeptidase I (DCPI). The gene plays an important role in the

regulation of blood pressure via the renin-angiotensin-aldosterone system (RAAS).

3.5.1.1.6. Cluster of Differentiation 4 (CD4)

The deletion allele ends 1.665 kb upstream of the CD4 start codon. A cluster of

Differentiation (CD4) gene encodes the T4/leu3 antigen, expressed on the surface of

helper T lymphocytes. The gene is located on chromosome 12 and is composed of 10

exons and spans at least 33 kb (Maddon et al., 1987). CD4 also serves as a receptor for

human immunodeficiency virus (HIV) (Lyerly et al., 1987).

3.5.1.1.7. Plasminogen Activator Tissue (PLAT)

This Alu polymorphism belonging to PV subfamily is present in intron 8 of the

Plasminogen Activator Tissue (PLAT) gene. The gene is also known as Tissue type

Plasminogen Activator (TPA). It is located on chromosome 8 at 8p11.2, spans 36,594

bp and comprises of 14 exons (Degen et al., 1986). In the adult brain, t-PA is highly

expressed in the hippocampus, amygdala, cerebellum, and hypothalamus; regions

regulating biological functions such as learning and memory, emotions, motor

coordination, endocrine function among others. It also plays a role in neuronal

degeneration (Alzheimer's disease) and seizure (Tsirka et al., 1995).

3.5.1.2. Restriction Fragment Length Polymorphisms (RFLPs)

Seven unlinked RFLPs were considered in the present investigation. Their expected

band sizes and protocols for amplification were taken from Eccles Institute of Human

Genetics website (http://www.genetics.utah.edu/~swatkins/pub/RSP_links.html) as

described in Jorde et al. (1995) and Watkins et al. (2001).

3.5.1.2.1. Estrogen Receptor (ESR)

The RFLP under study, a C→T single nucleotide polymorphism (SNP) altering a PvuII

restriction site, is located in intron 1 of the gene ESR1, 0.4 kb upstream of exon 2 on



chromosome 6. The gene encodes the estrogen receptor (ER) which is a member of a

superfamily of transcription factors. Several RFLPs such as XbaI (exon 2), BstUI (exon

1) and PvuII (intron 1) have been used to report an association between the gene and

breast cancer (Andersen et al., 1994).

3.5.1.2.2. N-acetyltransferase 2 (NAT)

Arylamine N-acetyltransferase 2 or N-acetyltransferase 2 gene encodes a drug-

metabolizing enzyme. The enzyme is found in liver (Jenne, 1965) and intestinal

epithelium (Hickman et al., 1998) and functions to both activate and deactivate

arylamine and hydrazine drugs and carcinogens. The NAT2 locus is located on

chromosome 8p23.1-p21.3 (Hickman et al., 1994). The locus is highly polymorphic and

more than 20 alleles have been reported (Hein et al., 2000). Polymorphisms in this gene

are also associated with higher incidences of cancer and drug toxicity. The

polymorphism under study is a C→T SNP altering KpnI restriction site.

3.5.1.2.3. PSCR

PSCR or D21S13E locus has been localized to 21q11.1-q21. RFLPs such as TaqI

(Stinissen et al., 1990), EcoRI (Stinissen et al., 1990; Pulst et al., 1990a) and HaeIII

(Pulst et al., 1990b) have been identified in the PSCR locus. The site under

consideration in the present study is the TaqI polymorphism.

3.5.1.2.4. 5-Hydroxytryptamine Receptor 2A (T2)

The RFLP under study, a synonymous C→T SNP altering an MspI restriction site, is

located at nucleotide position 102 in the T2 gene (Warren et al., 1993). T2 or HTR2A

gene encodes one of the seven surface subtype receptors, 5-hydroxytryptamine 2A,

which mediates the functioning of the hormone and neurotransmitter Serotonin or 5-

hydroxytryptamine (Frazer et al., 1990). The neurotransmitter serotonin has been

implicated in a wide range of psychiatric conditions (Lucki, 1998). The HTR2A gene

has been assigned to chromosomal region 13q14-q21 (Sparkes et al., 1991).



3.5.1.2.5. Lipo Protein Lipase (LPL)

Lipoprotein Lipase (LPL) gene encodes lipoprotein lipase, which plays a major role in

lipoprotein metabolism by hydrolyzing core triglycerides of circulating chylomicrons

and very low density lipoprotein (VLDL). Human LPL locus is present on chromosome

8, localized to 8p22 (Sparkes et al., 1987), and comprises of 10 exons. Eighty-eight

variable sites have been identified across the 9.7 kb region by the 3′ end of the LPL

gene in three different populations: African-American, European, and European-

American (Nickerson et al., 1998; Templeton et al., 2000). The most extensively

studied polymorphism sites are HindIII in the 3′ flanking region (Heinzmann et al.,

1987) and PvuII in intron 6 (Oka et al., 1989). The PvuII site is altered by a C→T SNP

and has been considered in the present study.

3.5.1.2.6. Alcohol Dehydrogenase (ADH2)

The Class I Alcohol Dehydrogenase gene ADH1B, previously known as ADH2 is

located in chromosomal region 4q21-q23. The protein encoded by this gene is a

member of the alcohol dehydrogenase family. Members of this enzyme family

metabolize a wide variety of substrates, including ethanol, retinol, other aliphatic

alcohols, hydroxysteroids, and lipid peroxidation products. A C→T SNP in intron 3 of

the gene results in a variable RsaI restriction site that has been taken up in the present

investigation.

3.5.1.2.7. Aminolevulinate, delta-, Dehydratase (ALAD)

ALAD locus located in 9q34 region, encodes the ALAD enzyme that catalyzes the second

step in the porphyrin and heme biosynthetic pathway. Its activity is inhibited by lead and

a defect in the ALAD structural gene can cause increased sensitivity to lead poisoning and

acute hepatic porphyria.A T→C polymorphism altering RsaI restriction site is located 3.4

Kb upstream of the polyadenylation signal and is the RFLP under study.

3.5.2. Y Chromosomal Markers

Y-chromosome is a powerful tool to study genealogies. It is inherited paternally (Jobling

and Smith, 1995), lacks recombination, and carries a wide range of polymorphisms



(Underhill et al., 2000). It is a large linear molecule (approximately 60 Mb), and

preserves a unique record of mutational events (Hammer, 1994). Y-chromosome

haplotype’s combination has been used as a tool to study human migrations (Hammer et

al., 1998). It contains a simple record of the past that helps elaborate the evolutionary

relationships of modern Y-chromosome (Jobling and Smith, 1995). Two types of

markers are studied in the Y-chromosome, microsatellites and biallelic polymorphic

sites. Microsatellite markers demonstrate high levels of heterozygosity due to the high

mutation rate that allows for the inference of phylogenies among populations (Shriver et

al., 1997; White et al., 1999). Binary markers on the other hand, have a lower mutation

rate, which allows reconstruction of the ancestral state and can preserve the population

specific haplotype information that spans the human population history (Underhill et al.,

1997). Binary markers represent unique event polymorphisms (UEPs) in human

evolution; these events could be single nucleotide polymorphisms (SNPs) or insertion

/deletions at specific sites on the Y-chromosome. They allow identification of deep splits

in the Y-chromosome genealogy. The Y-chromosomal classification and nomenclature

system is maintained and updated periodically (Y Chromosome Consortium, 2002;

Karafet et al., 2008).

In the present study 54 Y UEPs namely, M9, M89, M201, P91, M427, P96, P254, M69,

M52, M82, M36, M97, M39, APT, M145, P143, M216, M356, P92, M45, M207,

M242, P36.2, M346, M173, SRY10831.2, M174, M56, M157, M87, PK5, P98, M124,

M343, M11, M27, P123, M170, M304, M172, M12, M205, M241, M99, M280, M321,

P84, M410, M147, P60, P79, P261, M214, M175 were examined. Their details such as

primer sequence, PCR conditions, position on the Y chromosome and the cycle

sequencing protocol has been given in Appendices X and XI. The phylogeny of these

markers is a perfect tree whose hierarchical structure corresponds to the historical

accumulation of mutations. The hierarchical trees having standardized nomenclature

systems of haplogroups which in turn have haplotype groups carrying specific motifs of

UEPs is given in Figure 3.4. Currently there are 20 haplogroups (A-T) divided into a

number of subhaplogroups (Karafet et al., 2008).

A Genomic Study on the Sub

Figure 3.4. The global Y Chromosome phylogenetic tree illustrating topology of major

Y chromosome haplogroup

A Genomic Study on the Sub-Structured Chaudhari Tribe of Southern Gujarat

The global Y Chromosome phylogenetic tree illustrating topology of major

Y chromosome haplogroups (Source: Karafet et al., 2008)


80

The global Y Chromosome phylogenetic tree illustrating topology of major



3.5.3. Mitochondrial DNA Regions

Mitochondrial DNA (mtDNA) is maternally inherited and lacks recombination which

makes it an ideal tool to study populations through maternal line. The haploid, circular

mitochondrial genome consists of 16,569 base pairs containing 37 densely packed

intronless genes and a short regulatory region, the D-loop. mtDNA accumulates

mutations faster than the nuclear genome. There are no major repetitive elements,

insertions or deletions. The control region of the mitochondrial genome is highly variable,

and it is used to determine the genetic structure and origin of populations (Parsons et al.,

1997). The high rate of base substitution in the control region of the mitochondrial

genome, and the fact that the effective population size of this region is one-fourth of the

nuclear genome (leading to increased genetic drift) allows maternal genealogy to be

constructed with high specificity (Richards et al., 1996; Macaulay et al., 1999). Similar to

Y Chromosome, the ease of reconstructing the phylogeny is the main advantage of

mtDNA. Figure 3.5 presents the simplified mtDNA phylogenetic tree, displaying the

major mtDNA haplogroups identified on the basis of mtDNA variation on both coding

and control regions. The root of the tree is indicated by a star representing the most recent

common matrilineal ancestor of all humans. The L haplogroups are the most deep-rooting

lineages and are African specific indicating the African origin of modern humans.

Haplogroup L3 gave rise to macrohaplogroups M, N and R (the latter itself a subclade of

N), which encompass all variations observed outside Africa. Haplogroup symbols

followed by a star represent all other descendant lineages (besides the ones shown) of a

particular clade, for which no unique alphabetical letters have been reserved.

The mtDNA control region is divided into 2 regions: Hyper Variable Region-I and II

(HVR-I and HVR-II). The numbering of the sequence data was done according to

revised Cambridge Reference Sequence (rCRS) for human mtDNA by Andrew et al.

(1999). In the present study HVR I corresponding to mtDNA sequence 15904 to 16540

and HVR II corresponding to nucleotide positions 70 to 300 were screened using 23

forward (F), reverse (R) and 24 F, R primer sets (Rieder et al., 1998). In addition to

HVR I and II regions, nucleotide position 10400, diagnostic for “M” haplogroup was

also screened using 15F and R primer sets (Rieder et al., 1998). Samples having T

mutation in place of C at 10400 were classified under haplogroup M while others were



classified under haplogroup N. The detailed protocol followed for the amplification of

mtDNA regions is given in Appendix XII. Its cycle sequencing protocol has been given

along with the cycle sequencing protocol for Y SNPs in Appendix XI.

Figure 3.5. The global mtDNA phylogeny illustrating topology of major mtDNA

haplogroups (Source: van Oven and Kayser, 2008)

3.6. Statistical Analysis

In the field of population genetics the data generated following varied number of

methodologies (Classical or Molecular markers) itself do not make any sense unless

submitted to appropriate statistical analysis. Statistical methods include a battery of

analytical measures required for describing, comparing, interpreting and finally

concluding the data with some generalization about the larger data set. Various

parameters used to assess population variability, structure, affinities or disparities and to

examine what evolutionary forces most significantly contributed in maintaining the

amount of genetic variation in the present investigation have been described below.

3.6.1. Allele Frequency

An allele is defined as one of the two or more alternative forms of a gene or DNA

sequence at specific chromosomal location. Allele frequency is the frequency of an



allele within a population. Allele frequencies were calculated from the genotype data by

direct gene counting at each locus separately for each population.

3.6.2. Hardy-Weinberg Equilibrium and Bonferroni’s Correction

The allele frequencies were subjected to Chi-square goodness-of-fit test to determine if

they were in Hardy-Weinberg equilibrium within the population.

Bonferroni’s correction was applied to correct for multiple comparisons. It is the

simplest correction of individual p-values for multiple hypotheses testing in order to

maintain an overall significance level α. It was estimated as:

pcorrected = ��

Where n is the number of Chi square tests performed.

3.6.3. Heterozygosity

Heterozygosity is a measure of the diversity of a polymorphic locus Nei (1973). In other

words it can be defined as the proportion of heterozygotes per locus in a randomly

mating population. The unbiased estimate of heterozygosity for a single locus was

computed using the following formula:

� � 2��1 � � ��

��/�2� � 1�

Where, n is the number of individuals sampled and xi is the population frequency of the

ith

allele at a locus. Average heterozygosity (H) is the average of this quantity over all

loci.

3.6.4. Haplotype Diversity or Gene Diversity

Haplotype diversity is equivalent to the expected heterozygosity for diploid data. It is

defined as the probability that two randomly chosen haplotypes are different in the

sample. It is also denoted as Gene diversity. It was computed using the same formula as

employed for the calculation of heterozygosity (given above). For haploid markers such

as mtDNA or Y chromosomal haplogroups, this measure was calculated by replacing



2n with n. The method for estimating sampling variance was also same as that for

heterozygosity.

3.6.5. Estimates of Gene Diversity (GST and NST)

One of the methods for apportionment of gene diversity in the total population into its

components is Nei’s (1973) method.

Genetic differentiation is defined as the accumulation of differences in allele

frequencies between completely or partially isolated populations. This differentiation

between populations could be due to operation of diverse evolutionary forces.

Coefficient of gene differentiation denoted as GST, is a very good measure of gene

differentiation. It may be defined as the ratio of the inter-population gene diversity

(DST) to the total gene diversity (HT) among the populations i.e.

GST = DST / HT

Where, DST = HT - HS

HS represents the average expected heterozygosity of subpopulations assuming random

mating within each subpopulation and is calculated as following:

HS=2n (1-∑ ��)/(2n-1)

And HT denotes the expected heterozygosity of the total population assuming random

mating within. It is calculated as follows:

�� 1‐ � �� /�2� �

NST is an analogue to GST at the nucleotide level (Lynch and Crease, 1990). It is

calculated as the ratio of the average genetic distance between genes from different

populations relative to that among genes in the population at large.

!�� ∑ "#$ %#$#$ &'

Where, πij are defined as distances between the haplotypes i and j, cij denotes the

covariance between them. νT is the total diversity in population and is defined as:



(� � ∑ )�*+�+*�*

Where, pi and pj are the frequency of the ith

and jth

haplotypes in population.

Extreme NsT estimates of 0 and 1 indicate zero and complete population subdivision,

respectively.

3.6.6. Wright’s F– Statistics

Hartl and Clark (1989) have illustrated the Wright’s (1921) method to quantify the

inbreeding effect of population sub-structure. Wright (1921) had formulated fixation

index which is defined as the reduction in heterozygosity expected with random mating

at any one level of a population hierarchy relative to another, more inclusive level of the

hierarchy. At the first level, FIS is estimated which is defined as the average difference

between observed and Hardy–Weinberg expected heterozygosity within each

subpopulation due to non-random mating. It can also be interpreted as the correlation

between the states of two alleles in a genotype sampled at random from any

subpopulation.

FIS = �� ,---/ HS

Where, HS is the average expected heterozygosity of subpopulations assuming random

mating within each subpopulation and HI is the average observed heterozygosity within

each subpopulation.

The next level in the hierarchy is FST which represents the average expected

heterozygosity for subpopulations compared with expected heterozygosity for the total

population.

FST = �� / HT

Where, HT is the expected heterozygosity of the total population assuming random mating

within subpopulations and no divergence of allele frequencies among subpopulations.

The final level in the hierarchy is FIT, the comparison of the average observed

heterozygosity for subpopulations with the heterozygosity expected for the total

population. This gives the departure from Hardy–Weinberg expected genotype frequencies



due to the combination of non-random mating within sub populations and divergence of

allele frequencies among subpopulations.

FIT = �� ,/ HT

3.6.7. Genetic Distance

Genetic distance is the extent of gene differences between populations or species that is

measured by some numerical quantity. Thus, the number of nucleotide substitutions per

nucleotide site or the number of gene substitutions per locus is the measure of genetic

distance. Various measures of genetic distances have been proposed by many scholars.

Nei (1972) proposed a genetic distance measure to estimate the number of genes or

codon substitution per locus between two populations. Using this method pairwise

genetic distance among the populations is estimated as,

D= -ln I

Where,

. � ∑ ��/�0∑ �� ∑ /��

And xi and yi are the frequencies of ith

allele at a locus in the two populations X and Y

respectively. Standard errors of the genetic distances were computed using Nei and

Roychoudhury’s 1974 method.

3.6.8. Pairwise FST and Slatkin’s Linearization

As mentioned earlier, Wright’s FST is a statistic for measuring genetic differentiation of

populations. This statistic can also be applied to quantify the genetic distance for a pair

of populations. Latter (1972) formulated a better estimate of FST for the case of two

populations and multiple alleles at many loci. The measure denoted by 12 and

equivalent to FST is calculated as following:

12 � ∑ ∑ �3#4�5#4 �67

84�79

2 :1 � ∑ ∑ 3#4;4# 5#4�47 <



Where mk is the number of alleles at the kth

locus and r is the number of loci examined.

xik and yik are the frequencies of k allele at ith

locus in population X and Y.

To incorporate the population divergence attribute to the pairwise FST, slight

transformation to linearize the distance with population divergence time was suggested

by Slatkin (1995). Slatkin considered a simple demographic model, where two haploid

populations of size N have diverged generations ago from a population of identical size.

These two populations have remained isolated ever since, without exchanging any

migrants. Under such conditions, FST can be expressed in terms of the coalescence

times, which are the mean coalescence time of two genes drawn from two different

populations, and which is the mean coalescence time of two genes drawn from the same

population.

3.6.9. Neighbor Joining Tree

The neighbor-joining (Saitou and Nei, 1987) is a method for reconstructing

phylogenetic trees from evolutionary distance data. The principle of this method is to

find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total

branch length at each stage of clustering of OTUs starting with a star like tree. The raw

data is provided as a distance matrix and the initial tree is a star tree. Then a modified

distance matrix is constructed in which the separation between each pair of nodes is

adjusted on the basis of their average divergence from all other nodes. The tree is

constructed by linking the least-distant pair of nodes in this modified matrix. When two

nodes are linked, their common ancestral node is added to the tree and the terminal

nodes with their respective branches are removed from the tree. This pruning process

converts the newly added common ancestor into a terminal node on a tree of reduced

size. At each stage in the process two terminal nodes are replaced by one new node. The

process is complete when two nodes remain, separated by a single branch.

3.6.10. Y Chromosome Haplogroup Assignment

A haplogroup is a group of related lineages defined by single nucleotide polymorphisms

which have accumulated along different lineages. The Y Chromosome Consortium

(YCC) has assigned hierarchical alphanumeric labels, which can be presented



graphically in the form of a phylogenetic or haplogroup tree (Y Chromosome

Consortium, 2002). In the present study, all the male samples were assigned specific

haplogroups following revised Y chromosomal tree nomenclature system (Karafet et

al., 2008).

3.6.11. Exact Test of Population Differentiation

In order to test the hypothesis of a random distribution of k different haplotypes or

genotypes among r populations, Exact test of population differentiation, as described in

Raymond and Rousset (1995) was carried out. This test is analogous to Fisher’s exact

test on a 2x2 contingency table extended to a r x k contingency table. All potential states

of the contingency table are explored with a Markov chain. A Markov chain is used to

explore the space of all possible tables instead of enumerating all possible contingency

tables. This Markov chain consists of a random walk in the space of all contingency

tables. It is done is such a way that the probability to visit a particular table corresponds

to its actual probability under the null hypothesis of linkage equilibrium. During this

random walk between the states of the Markov chain, the probability of observing a

table less or equally likely than the observed sample configuration, under the null

hypothesis of panmixia is estimated.

3.6.12. mtDNA Haplogroup Assignment

Based on the variations observed in mitochondrial DNA, putative haplogroups were

assigned to the samples using HaploGrep software (Kloss-Brandstaetter et al., 2011).

HaploGrep is a web application based on Phylotree, a periodically updated phylogenetic

tree of global human mitochondrial DNA variation. The tree is based on both coding

and control region mutations and provides haplogroup nomenclature for designation of

haplogroups (van Oven and Kayser, 2008).

3.6.13. Number of Polymorphic Sites

The number of polymorphic sites, also denoted as the number of segregating sites is

defined as any of the n nucleotide sites that maintain two or more nucleotides within the

population.



3.6.14. Nucleotide Diversity

Nucleotide diversity, also known as the average pairwise differences in a sample of

DNA sequences, is defined as the probability that two randomly chosen homologous

sites are different.

It is the average number of nucleotide differences per site between two sequences (Nei,

1987). ) � �

� � 1 � �� *

�* )�*

Where, n equals the number of sampled sequences, xi and xj are the frequencies of ith

and jth

sequences and πij is the proportion of nucleotide differences between them.

3.6.15. Tajima’s Test of Selective Neutrality

Tajima’s D is one of the common measures of selective neutrality. The statistic is

applied for distinguishing population expansion from constant population size. The null

hypothesis of the test is that the sample of DNA sequences was taken from a population

with constant effective population size and selective neutrality of all mutations. Natural

selection operating on DNA sequences as well as changes in effective population size

through time lead to the rejection of this null hypothesis. The test uses the nucleotide

diversity and the number of segregating sites observed in a sample of DNA sequences

to make two estimates of the mutation parameter θ, both of which are expected to be

approximately equal under the standard coalescent model where all mutations are

selectively neutral and the population maintains a constant size through time (Tajima,

1989). The test statistic D is estimated as:

D = =>�=?

@&A7�=>�=?�

Where, θπ is equivalent to the mean number of pairwise differences between sequences

(π) and θS is based on the number of nucleotide variant sites. Negative and statistically

significant score is indicative of larger values for θS relative to θπ signifying the potential

effects of population expansion and balancing selection. Positive or statistically non-

significant negative scores may indicate the effects of shrinking of the effective

population size or population bottlenecks as well as strong directional selection.



Differences in the shape of genealogies are the basis of Tajima’s D test. The coalescent

process with neutral alleles and constant effective population size results in

approximately the same total length along interior and exterior branches in a genealogy.

Longer external branches in a genealogy can also be caused by population structure if

the DNA sequences compared are sampled from different demes. Shrinking effective

population size or population bottlenecks as well as strong directional selection lead to

increasing probabilities of coalescence towards the present time and shorter external

branches. Substantial changes to the genealogical branching pattern lead to differences

in π and S that cause Tajima’s D to differ from zero.

3.6.16. Fu’s FS

Another test for measuring the selective neutrality and changes in the population size

over time is Fu’s Fs (1997). Like Tajima’s D, the test is also based on the infinite-site

model without recombination but utilizes data from the haplotype distribution. This test

statistic is based on the equation:

FS = B� C �DE��DF

Where, S′ is the probability of observing a random neutral sample and defined as

GH � IJ�K L KMNO|Q � Q"�

Where, K is equal to the number of alleles similar or smaller than the observed value

given θπ and Fs is the logit of S′. Statistically significant negative scores indicate an

excess of alleles, a signature of population expansion. This test is considered less

conservative than Tajima’s D and is more sensitive to large population expansions

expressed as large negative numbers whereas positive numbers indicate populations

impacted by genetic drift.

3.6.17. Mean Number of Mismatch, Mismatch Distribution and Raggedness Statistic

Mean number of mismatch is defined as the average number of nucleotide sites that

differ between unique pairs of DNA sequences (Tajima, 1983). Mismatch distribution,

also known as distribution of pairwise differences, is defined as the frequency

distribution of the number of nucleotide sites that differ between all unique pairs of



DNA sequences in a sample from a single species. The mismatch distribution is

constructed by counting the number of differences between each pair of subjects and

then using histograms or scatter plots to display the frequencies of sites that differ. The

mismatch distribution has distinct patterns depending on the demographic history of the

population (Slatkin and Hudson, 1991; Rogers and Harpending, 1992). A smooth,

unimodal distribution is indicative of population expansion whereas a ragged,

multimodal distribution indicates constant population size over a long time period. To

distinguish between these two types of distributions another measure, raggedness

statistic (r) is used (Harpending, 1994). Raggedness statistics is sum of the squared

difference between neighboring peaks, and is estimated by the equation below:

R � �� E��STE

�UE

Where, d is the greatest number of differences between alleles, xi is the relative

frequency of i pairwise differences.

3.6.18. Median Joining Tree

An important method for visualization of haplotype data is the construction of a

phylogenetic network of haplotypes, which allows inspection of their population and

allele frequency distributions. For haplotypes without recombination or recurrent

mutations, the analysis produces a perfect tree. In this study, median-joining networks

of mtDNA based on both HVR I and II, were constructed with the NETWORK

software (fluxus-engineering.com, Bandelt et al., 1999).

3.6.19. Principal Component Analysis (PCA) and Multidimensional Scaling (MDS)

PCA and MDS are methods for displaying complex data sets in fewer dimensions in

order to extract and visualize the most important trends. The first principal component

(PC) is an eigenvector fitted to the correlation or covariance matrix of the data (e.g.

allele frequencies of populations) that explains most of the observed variation. The

following PCs are always perpendicular to the preceding component. The eigenvalues

of the PCs express how much of the variation they account for. Another method for

visualizing complex data, classical MDS, takes the data as a matrix of dissimilarities,



such as genetic distances between individuals or populations, and produces an output of

distances in the desired number of dimensions so that the deviations from the original

distances are minimized.

3.6.20. Population Structure and Gene Flow

To determine the population structure, a regression analysis of heterozygosity on

genetic distance was carried out with the method described by Harpending and Ward

(1982). The model estimates the relative roles of genetic drift versus gene flow in

causing population differentiation. The model assumes that the islands exchange genes

among themselves and each receives in addition, a small constant input of genes from a

continent, the same proportion to each island. In this model, genetic heterozygosity is

negatively correlated with genetic distances from the gene frequency centroid (the

overall mean gene frequencies of the population system). If the linearity between

genetic distance of an island from the gene frequency centroid and the relative

homozygosity of the islands holds then the exchange with populations outside the

region is same for each island. If the gene flow from outside the region varies in

amount from island to island, this linear relationship no longer holds. Those populations

that have undergone systematic migrations will show greater heterozygosity than

predicted by the regression line, while those groups that are more isolated will exhibit

lower than predicted heterozygosity.

3.6.21. Analysis of Molecular Variance

Analysis of molecular Variance (Excoffier et al., 1992) is a method of estimating

population differentiation directly from molecular data and testing hypothesis about such

differentiation. Variety of molecular data such as restriction fragment length

polymorphism data, direct sequence data, haplotype or haplogroup frequency data can be

analyzed using this method. The analysis is based on analyses of variance of gene

frequencies, but it also takes into account the number of mutations between molecular

haplotypes (which first needs to be evaluated). Populations are first grouped into different

clusters in order to define a particular genetic structure that will be tested. A hierarchical

analysis of variance partitions the total variance into covariance components due to intra-

individual differences, inter-individual differences, and/or inter-population differences.



3.7. List of Softwares used

1. DISPAN (Ota, 1993)

2. POPGENE version 1.31 (Yeh et al., 1997)

3. ARLEQUIN version 3.5 (Excoffier et al., 2005)

4. DnaSP version 5.0 (Rozas and Rozas, 1999)

5. MEGA version 4.0 (Tamura et al., 2007)

6. HaploGrep (Kloss-Brandstaetter et al., 2011)

7. NETWORK version 4.1.0. Available at www.fluxus-engineering.com

8. SPSS version 16.0

3.8. Limitations of the Study

As with any investigation one is bound to encounter some difficulties that cannot be

completely overcome in the short course of the research being conducted. Some of the

problems faced during the study were:

1. One of the problems faced during the field work was that of transportation. Both

private and public transport services were very infrequent, therefore, the time period

for working in the field was restricted.

2. Scattered settlement of the villages visited was another factor that posed a serious

problem during the fieldwork.

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