Enhancing genetic gain in chickpea breeding in marginal environments in Africa and South Asia Rajeev K. Varshney on behalf of Team – TLI, TLII & TLIII projects

Chickpea Second most important food legume globally Genome size ~740 Mb

Grown on about 13.54 m ha across 59 countries with 13.10 m tons of total production

Marginal environments in Sub-Saharan Africa and Asia

Production constraints

-1500-1000

-5000

50010001500200025003000

Mill

ion

(US

$)

Tota

l pro

duct

ion

Dro

ught

Salin

ity

Wilt

Asco

chyt

a bl

ight

Pod

bore

r

Abiotic stresses

Terminal drought Salinity

Wilt Pod borer Blight

Biotic stresses

Botrytis grey mould

Impact on agriculture

Breeding

Translational genomics in agriculture (TGA)

Genomics (incl. informatics)

Genetics -logy disciplines PLoS Biol 2014, Crit Rev Plant Sci 2015

PLoS Biology 2014

Some approaches of TGA

Resources developed in recent years…

Illumina sequencing used to generate 153.01 Gb

73.8% of the genome is captured in scaffolds

Genome analysis predicted 28,269 genes

High levels of synteny observed between chickpea and Medicago

> 81,845 SSRs and 4.4 million variants (SNPs and INDELs)

The chickpea genome

Capillary electrophoresis: > 3000 SSRs GoldenGate assays : 1536 SNPs KASP Assays : >2000 SNPs

DArT/ DArTseq : 15,360 features

Genotyping by sequencing (GBS)

Arachis Axiom Array (Affy): 58,000 SNPs Exome capture sequencing arrays

Marker genotyping platforms

Precise and high-throughput phenotyping

Mapping population No. of markers

Map length (cM)

Reference

ICC 4958 × PI 489777 521 2602.00 Nayak et al. 2010 ICC 4958 × PI 489777 1,291 845.56 Thudi et al. 2011 ICC 4958 × PI 489777 300 766.56 Gujaria et al. 2011 ICCV 2 × JG 62 144 442.28 Anuradha et al. 2011 ICC 4958 × PI 489777 1,328 788.60 Hiremath et al. 2012 C 214 × WR 315 57 347.90 Sabbavarapu et al. 2013 C 214 × ILC 3279 58 386.30 Sabbavarapu et al. 2013 ICC 4958 × ICC 1882 241 621.51 Varshney et al. 2014 ICC 283 × ICC 8261 168 533.06 Varshney et al. 2014 Consensus 352 771.39 Varshney et al. 2014 ICCV 2 × JG 62 135 310 Vadez et al. 2012 ICC 4958 × ICC 1882 1007 727.29 Jaganathan et al. 2014 ICCV 2 × JG 11 56 329.60 Pushpavalli et al. 2015

More than 10 genetic maps…

Drought tolerance Root traits- root length density, root length, root surface area Yield, harvest index, 100-seed weight, number pods per plant, biomass, specific leaf area, delta carbon ratio, days to flowering, days to maturity

Heat tolerance Pods per plant, heat tolerance index, yield, biomass, harvest index, days to flowering, days to maturity

Salinity tolerance Pod number, seed number, seed yield, Shoot dry weight, harvest index 100 seed weight

Ascochyta blight Seedling resistance and adult plant resistance Helicoverpa Leaf damage rating (flowering), Unit larval weight, Helicoverpa larvae/10 plants, Days to first flowering

Botrytis grey mould

Heat tolerance

ca. 50 traits mapped

Pod borer

Ascochyta blight

Salinity tolerance

Drought tolerance

Fusarium wilt

Fusarium wilt, Botrytis grey mould, Protein content

“QTL-hotspot” / drought tolerance story…

Intra-specific mapping populations for drought tolerance in chickpea

300

1. ICC 4958 × ICC 1882 - 268 RILs

2. ICC 283 × ICC 8261 - 289 RILs Parental lines

Accession Root depth (cm)

Root dry wt (g)

ICC 4958 116.6 1.06 ICC 1882 83.9 0.71 ICC 283 91.6 0.73 ICC 8261 123.3 1.25

and many other drought tolerance traits!

ICC 4958 ICC 1882 ICC 283 ICC 8261

Root related traits Root traits Traits Seasons Root length (cm) 3 Root length density (cm cm-3) 3 Root volume (cm3) 3 Root dry weight (g) 3 Rooting depth (cm) 3 Root surface area 3 R-T ratio(%) 3 Shoot dry weight (g) 3 Stem dry weight (g) 3 Leaf dry weight (g) 3 Projected area 2 Average diameter 2

Experiment of chickpea root growth in ROS

Root length screening

Agronomic traits Traits

Seasons Traits Seasons

Morphological traits Yield related traits Plant height (cm) 14 Pods/plant 2 Plant width (cm) 7 100 SDW (g) 10 Plant stand 7 Yield (g/m2) 3 Apical primary branch

7 Yield (Kg/ha) 10

Apical secondary branch

7 Yield per plant 7

Basal primay branch

7 Production 7

Basal secondary branch

7 Biomass 6

Teritiary branches 7 Biomass/plant 2 Phenological traits Harvest index 6 Days to flowering 13 TDM weight (g/m2) 2

Days to maturity 9 Transpiration efficiency Seeds per pod 7 13C 2 Seeds/plant 2 SPAD 2

Phenotyping under rainfed and irrigated environments

“QTL- hotspot” in two mapping populations

TAA170

GA24

STMS11

ICC

M0249

CaM

0856

LG 4: ICC 4958 × ICC 1882

RLD_06

RLD_08

RDW_06

RDW_08

RT DEPTH_06

RT DEPTH_08

SDW_06

SDW_08

RT VOL_06

RT VOL_08

RSA_06

RSA_08

RL_06

RL_08

STEM DW_06

LDW_06

R-T RATIO_06

LG 4: ICC 283 × ICC 8261

CAM

1903

TA130

ICC

M0249

TAA170

NC

142

209

Theor Appl Genet 2014

13 out of 20 drought tolerance traits explaining 10- 58.20% phenotypic variation

“QTL-hotspot” on CaLG04

ICC 4958 × ICC 1882 Consensus map ICC 283 × ICC 8261

Theor Appl Genet 2014

PVE 58.2%

DNA Quantification

Restriction digestion

Ligation

Pooling and clean up

Polymerase chain reaction

Cleaning of PCR product

QC Check 1] QUBIT fluorometer BR/HS 2] Agilent Bio-Analyzer HS chip

GBS – 96 - Plex protocol

Good quality libraries are sequenced through Hi-seq-2500

Genotyping-by-sequencing (GBS)

SNP Calling

ICC4958 x ICC 1882 - 701.1 M reads, 59 Gb data (208 RILs) - 828 SNPs mapped - 49 SNPs integrated to “QTL-hotspot”

Saturating “QTL-hotspot”

Mol Genet Genomics 2015

Varshney et al 2014 Jaganathan et al 2015

49 SNPs

Skim sequencing and Bin mapping Sequencing: parents @ 8X coverage and 222 RILs @ 1X

No. of SNPs called: 53,169 (SGSautoSNP)

Bin construction: sliding window approach (Huang et al 2009)

No. of bins obtained: 1,610

No. of bins on chromosome 4: 281

No. of bins in “QTL-hotspot”: 38 (1,421 SNPs)

Bin map of RIL 142

Refining the “QTL-hotspot”

Identified 26 candidate genes

Kale et al 2015

Varshney et al 2014

Jaganathan et al 2015

Scientific Reports 2015

26 candidate genes

High resolution mapping population

6,000 F2 lines in field @ Dharwad, India

KASPar marker development

KASPar marker profiles

CKAM2177 CKAM2178 CKAM2180

CKAM2210 CKAM2182 CKAM2181

Refined regions from bin mapping study

SNPs located in the refined regions

KASPar markers converted for screening fine mapping population

1,911 F2 plants analyzed

42 recombinant lines selected

284 F3 plants analyzed

59 homozygous recombinant lines

Selection of recombinants

Putative regions/genes associated with trait

“QTL-hotspot_a” “QTL-hotspot_b” 100SDW (g)

~113.03 Kb

No of KASPar markers used: 18

Phenotypic data: 100SDW on 59 homozygous F3 lines

~113.03 Kb region of “QTL-hotspot_a” delimited

Re-sequencing Reference Set (300 lines from 33 countries)

4.9 M SNPs, 596 K indels, 512 K CNVs

Selection sweep, reduction of diversity

A significant reduction in diversity was observed from wild genotypes (3.80 × 10−3) to landraces (0.86 × 10−3) and breeding lines (0.84 × 10−3)

Candidate domestication regions

A total of 122 regions (93 on eight pseudomolecules and 29 on unanchored scaffolds) that underwent selection

Trait Number of MTAs P-value PVE (%)

Root length density (RLD, cm cm-3) 3 5.73 × 10-6 - 2.1 × 10-8 6.5 - 16.6

Root dry weight (RDW, g plant-1) 11 6.81 × 10-6 - 9.18 × 10-10 5.58 - 10.49 Root surface area (RSA, cm2

plant-1) 6 9.17 × 10-6 - 1.65 × 10-7 5.9 - 10.12

Root volume (RV, cm3 plant-1) 13 7.28 × 10-6 - 1.43 × 10-7 5.77 - 10.41 Days to 50% flowering (DF) 24 8.1 × 10-6 - 7.8 × 10-9 9.09- 20.36 Days to maturity (DM) 48 9.06 × 10-6 - 4.82 × 10-8 8.96 -21.29 100 seed weight (100SDW, g) 98 1.07 × 10-6 - 2.89 × 10-22 10.34 - 14.4 Yield (YLD, Kg/ha) 22 9.42 × 10-6 - 2.77 × 10-7 7.16 - 18.6 Biomass (BM, g) 8 1.6 × 10-6 - 6.35 × 10-8 6.29 -12.02 Harvest index (HI, %) 15 8.87 × 10-6 - 1.46 × 10-8 5.97-14.84 Delta Carbon ratio (δ13C) 1 6.02 × 10-7 20.7

249 MTAs for drought tolerance

100 seed weight 166 MTAs total 98 unique (30 in >1 season) 43 SNPs (Ca4)- significant

Ca4_15950928 explained 28.1- 43.8% PVE and 1.5 Mb away from “QTL-hotspot_b”

Yield 22 (D), 16 (H) MTAs 6 SNPs with function (D)

Ca_12546 in ara1 QTL responsible for yield reduction in Australia & Canada and has 35 haplotypes in the reference set

Root length density

Ca_09763

Ca1 Ca2 Ca3 Ca4 Ca5 Ca6 Ca7 Ca8 Ca_09763 gene associated with correlated traits like

RSA and RV encoded for protein Aspartic protease Root proteases are shown to play a key role in nitrogen

acquisition and drought tolerance

Pa

rt

ne

rs

The 3000 Chickpea Genome Sequencing Initiative

Phenotyping of 3000 chickpeas 6 locations in India

Non-replicated augmented design

Target traits o Days to 50% flowering o Days to maturity o 100 seed weight o Yield of lines

Data on selected 5 lines o Plant height o Primary branches o No of pods/plant o Yield/plant

Developing superior lines for drought tolerance…

Introgression of “QTL- hotspot” for root and other drought tolerance related traits through MABC

Donors

Cultivars

JG 11 Chefe KAK 2

Phenotyping of MABC (BC3F3) products in ROS

12- 24% higher yield than the elite varieties

Enhanced grain yield under rainfed environments in India

0

50

100

150

200

250

300Yield Gms/plot Biomss/plot

050

100150200250300350400450

Yield gms/plot Biomass gms/plot

13 superior MABC lines: -17-47% higher seed yield - 12-43% higher biomass

Enhanced grain yield under rainfed environments in Kenya

Enhanced grain yield under irrigated conditions in Ethiopia

0

500

1000

1500

2000

2500

3000

3500

MAB

C 11

MAB

C 4

MAB

C 16

MAB

C 13

MAB

C 14

MAB

C 10

ICCV

-939

554

ICCV

- 495

8

MAB

C 9

MAB

C 7

MAB

C 6

MAB

C 18

MAB

C 19

MAB

C 3

MAB

C 2

MAB

C 22

DAL

OTA

>40% yield above standard check

Yiel

d (k

g/ha

)

Enhancing genetic gains…

Crossing

Field evaluation Line Selection

yirR A

tσ

=genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

Genomic resources available

MABC for major effect QTL and GS for complex traits

Forward breeding to enhance selection intensity with reduced genotyping cost (US$ 1 per sample)

Modernization of breeding- use of decision support tools, BMS implementation

Empowered NARS with technologies and skills

In summary…

Collaborators Funders

Pooran Gaur, Hari Upadhayaya, Mahendar Thudi, Manish Roorkiwal, NVPR Gangarao, Chris Ojiewo, Emmanuel Monyo, Abhishek Rathore, Deepa Jaganathan, L Krishnamurthy, K Himabindu, Anu Chitikineni, Vikas Singh, Gaurav Agrawal, Aamir Khan, Sandip Kale, Dadakhalandar Doddamani, Hari Sharma, Mamta Sharma Dave Edwards Jacqui Batley Pradeep Ruparao Bunyamin Taran Ravi Chhibbar Doug Cook R Varma Penmetsa, Benjamin Rosen Jongmin Baek, MingCheng Luo Karam Singh, KHM Siddique James K. Hane Scott Jackson Yupeng Li Aiko Iwata Teresa Millan Eva Madrid, Juan Gil Josefa Rubio Wang Jun, Xun Xu, Liu Xin, Gengyun Zhang, Chi Song, Wenbin Chen, Sheng Yu, Guangyi Fan, Shancen Zha, Ying Wang, Xudong Zhang, Weiming He,, Chunyan Xu, Bicheng Yang, Huanming Yang

Hwanseok Rhie Jihun Kim, Changhoon Kim, Obarley Yu Shaun An, Bellbull Kim

Swapan Dutta NP Singh SK Chaturvedi KC Bansal, Andrew G Sharpe, Janet A Condie Krishna K Gali, Larissa D Ramsay Steven Cannon Guenter Kahl Peter Winter Eric von Wettberg Asnake Fikre Million Eshete Musa Jarso Paul Kimurto Richard Mulwa Serah Songok Jarolslav Dolezel Hana Simkova Marie Kubalakova Michael Baum Shiv Agrawal Ashutosh Sarker Aladdin Hamwieh Akash Goyal

InterDrought-V Hyderabad International Convention Center (HICC) Hyderabad, India

21-25 February, 2017

Setting the biophysical context

Maximising dryland crop production

Plant productivity under drought

Effective capture of water Transpiration efficiency Vegetative Growth Reproductive development, yield, yield quality

Breeding for water-limited environments

Agronomic management for water-limited environments

Conference Topics:

InterDrought Chair: Francois Tardieu, INRA, France InterDrought Vice-Chair: J S Sandhu, ICAR, India Conference Organization Chair: Rajeev Varshney, ICRISAT, India

Contact: r.k.varshney@cgiar.org, id5.icrisat@gmail.com

Website: www.ceg.icrisat.org/idV