Keynote Presentation 06 by Rejeev K. Varshney at the International Conference on Pulses in Marrakesh, Morocco, 18-20 April 2016

1. Pulse genomics comes
of age!
Rajeev K. Varshney
Research Program Director - Genetic Gains

2. Contributors Investors
Rajeev Varshney
Mahendar Thudi, Rachit Saxena, Manish Roorkiwal,
K Himabindu, Anu Chitikineni, Vikas Singh, PT Lekha
Pallavi Sinha,, Gaurav Agrawal, Aamir Khan, Deepa,
Sandip Kale, Abhishek Rathore, Chris Ojiewo, NVPR
Gangarao, PM Gaur, HD Upadhayaya, Sameerkumar
Shiv Kumar Agrawal, Michael Baum
Aladdin Hamwieh, Fida Aloe
Ashutosh Serkar, Surendra Barpete
IIPR:
NP Singh, SK Chaturvedi
KR Soren, Aditya Garg
IARI:
Ch Bharadwaj, Shailesh Tripathi
Wang Jun, Xun Xu, Huanming Yang
Gengyun Zhang Chi Song, Wenbin Chen, Sheng Yu,
Guangyi Fan, Shancen Zha, Ying Wang, Xudong
Zhang, Weiming He,, Chunyan Xu, Bicheng Yang
KHM Siddique, Dave Edwards, Tim Colmer
Jacqui Batley, Pradeep Ruperao
Jiayin Pang
Bunyamin Taran
Amit Deokar
Doug Cook
R Varma Penmetsa
Ming Cheng Luo
Asnake Fikre
Musa Jarso
Million Eshete
Paul Kimurto
Richard Mulwa
Serah Songok
Md Yasin
Priynaka Joshi
S J Singh
M S Pithia
K N Yamini
G Anuradha
G Rajani
Muniswamy
D M Mannur
Tim Sutton
Jenny Davidson
Government of
India
Ministry of
Agriculture &
Farmers Welfare
DAC
ICAR
Ministry of
Science &
Technology
DST
DBT

3. Thanks to all colleagues and collaborators
✔ ✔
✔
✔
✔
✔

4. Second most important
protein rich food
legume
Cool season tropical
legume
Self-pollinated
Diploid, 2n=16
Genome size ~740 Mbp
Key pulse crops
Protein rich grain
legume
A rain-fed crop
Often cross-pollinated
Diploid, 2n=22
~835 Mbp
Chickpea (Cicer arietinum L.) Pigeonpea (Cajanus cajan L.)

5. http://www.livemint.com/Opinion/1BcMNygMDdIrgLQsegvNgK/The-
pulses-crisis-why-reinvent-the-wheel.html
Crop Area
(mha)
Production
(mt)
Yield
(t/ha)
Chickpea 13.54 13.10 0.97
Pigeonpea 6.21 4.74 0.76
Source: FAO 2015 (access on Jun 26,2015)
Feeding billions and bringing
prosperity in developing countries !!!

6. Breeding
Translational genomics in
agriculture (TGA)
Genomics
(incl. informatics)
Genetics -logy disciplines
PLoS Biol 2014, Crit Rev Plant Sci 2015

7. Adzuki bean
(PNAS 2015
Sci Rep 2015)
Chickpea
(Nature Biotechnology- 2013)
Pigeonpea
(Nature Biotechnology- 2012)
Mungbean
(Nature Commun.- 2014)
Pearl millet- 2016
Genomes sequenced…
Groundnut
Sesame
(Genome Biology- 2014)
(Nature Genetics- 2016)
(in revision)

8. • 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

9. Integrated physical, genetic and
genome map in chickpea
Funct Integ Genom 2014

10.  Illumina sequencing tech
used to generate 237.2 Gb
 72.7% (605.78 Mb) of the
total pigeonpea genome
assembled into scaffolds
 Genome analysis
predicted 48,680 genes
 High levels of synteny
observed between the
pigeonpea and soybean
 >50,000 SSR and SNP
markers identified
 Higher abundance of
drought tolerance genes
The pigeonpea genome

11. 1000+
Pulse
Genomes
Chickpea
Pigeonpea
Diverse lines
(90)
Elite varieties
(129)
Reference set
(300)
104 parental
lines of hybrids
Reference set
(300)
157 parental
lines/ RIL
- 554 lines in
chickpea
- 526 lines in
pigeonpea
1200 MAGIC lines of chickpea,
100 CWRs (18 Cajanus spp.)
+

12.  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
Marker genotyping platforms

13. Precise and high-throughput
phenotyping

14. 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
Chickpea

15. Hybrid related traits
Obcordate leaf shape
Fertility restoration
Seed purity kits
CMS seed purity
Hybrid seed purity
Yield related traits
Flowering time
Days to maturity
Pods per plant
100 seed weight
Plant height
Seeds per pod
Seed yield per plant
Primary branches
Secondary branches
Quality trait
Protein content
Biotic stress
Fusarium wilt
Sterility mosaic disease
Abiotic stress
Drought
ca. 20 traits mappedPigeonpea

16. Mapping and molecular breeding for
drought tolerance in chickpea

17. 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

18. Root related traits
Root traits
Traits Seasons
Root length (cm) 3
Root length density (cm cm
-3
) 3
Root volume (cm
3
) 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

19. 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/m
2
) 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/m
2
) 2
Days to maturity 9 Transpiration efficiency
Seeds per pod 7
13
C 2
Seeds/plant 2 SPAD 2
Phenotyping under
rainfed and irrigated
environments

20. “QTL- hotspot” in two
mapping populations
TAA170
GA24
STMS11
ICCM0249
CaM0856
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
CAM1903
TA130
ICCM0249
TAA170
NC142
209
Theor Appl Genet 2014
13 out of 20 drought
tolerance traits
explaining 10-
58.20% phenotypic
variation

21. “QTL-hotspot” on CaLG04
ICC 4958 × ICC 1882 Consensus map ICC 283 × ICC 8261
Theor Appl Genet 2014
PVE 58.2%

22. 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”

23. Saturating “QTL-hotspot”
Mol Genet Genomics 2015
Varshney et al 2014 Jaganathan et al 2015
49 SNPs

24. 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

26. 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

27. High resolution mapping population
6,000 F2 lines in field @
Dharwad, India

28. Putative regions/genes
associated with 100SDW
“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

29. Re-sequencing Reference Set
(300 lines from 33 countries)
4.9 M SNPs,
596 K indels,
512 K CNVs

30. 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)

31. 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 (δ13
C) 1 6.02 × 10-7
20.7
249 MTAs for drought tolerance

32. 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”

33. 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

34. Partners
The 3000 Chickpea Genome
Sequencing Initiative

35. 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

36. Introgression of “QTL- hotspot” for root and other
drought tolerance related traits through MABC
Donors
Cultivars
JG 11 Chefe KAK 2

37. 12- 24% higher yield than the elite varieties
Enhanced grain yield under rainfed
environments in India

38. 0
50
100
150
200
250
300
Yield Gms/plot Biomss/plot
0
50
100
150
200
250
300
350
400
450
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

39. Enhanced grain yield under irrigated
conditions in Ethiopia
0
500
1000
1500
2000
2500
3000
3500
MABC11
MABC4
MABC16
MABC13
MABC14
MABC10
ICCV-939554
ICCV-4958
MABC9
MABC7
MABC6
MABC18
MABC19
MABC3
MABC2
MABC22
DALOTA
>40% yield above
standard check
Yield(kg/ha)

40. Trait mapping and mechanism in pigeonpea

41. QTL mapping for FW and SMD
resistance
Features PRIL_A PRIL_B PRIL_C
No. of SNPs identified 86,052 2,18,560 73,368
No. of polymorphic SNPs 4,025 2,417 3,843
Final filtered SNPs 1,537 1,789 1,297
Number of SNPs mapped 964 1101 484
Linkage map length (cM) 1120.56 921.20 798.24
PRILs Trait Pedigree Phenotyping
PRIL_A FW ICPB 2049 × ICPL 99050 Patancheru, Gulbarga and Tandur
PRIL_B FW ICPL 20096 × ICPL 332 Patancheru, Gulbarga and Tandur
PRIL_B SMD ICPL 20096 × ICPL 332 Patancheru and Tandur
PRIL_C SMD ICPL 20097 × ICPL 8863 Patancheru and Tandur
Fusarium wilt (FW)
Genotyping and construction of linkage map
Sterility mosaic disease
(SMD)

42. QTL mapping for FW resistance (PRIL_A)

43. QTL mapping for SMD resistance (PRIL_C)

44. QTL-seq for FW
and SMD
resistance
Plant Biotechnology Journal 2015

45. Association of nsSNPs to the
candidate genes responsive to FW
and SMD diseases
Linkage
group
Genes nsSNPs
position
(bp)
Seq-BSA
approach
nsSNPs substitution approach
ICPL20096
(R*toFW&SMD)
R-bulka
(R*toFW&SMD)
S-bulkb
(S*toFW&SMD)
FW SMD
ICPL99050(R*)
ICPL20097(R*)
ICP8863(R*)
ICPB2049(HS†)
ICPL99050(R*)
ICPL20097(R*)
ICPB2049(R*)
ICP8863(HS†)
FW associated nsSNPs
CcLG02 C.cajan_07078 27,426,866 T T G T T T G T T G T
CcLG02 C.cajan_07124 27,861,114 G G A G G G A G G A G
CcLG11 C.cajan_02962 32,606,065 T T C T T T C T T C T
CcLG11 C.cajan_03203 35,228,097 C C A C C C A C C A C
SMD associated nsSNPs
CcLG02 C.cajan_07067 27,324,239 T T G T T G T T T T G
CcLG02 C.cajan_07067 27,324,261 T T G T T G T T T T G
CcLG08 C.cajan_15535 2,014,125 G G G C C G C C C C G
CcLG11 C.cajan_01839 19,958,148 A A C A A C A A A A C
Plant Biotechnology Journal 2015

46. Markers for purity testing of hybrids
and their parental lines
Markersfor
testingpurity
Markers for testing purity

47. QTL-seq for identification of genomic
regions for obcordate leaf type
ICP 5529 × ICPL 11605
(Obcordate) (Normal)
Pooled 20 F2 plants in extremes
Genome wide
∆ SNP Index
CcLG08
Days to flowering: (CcLG08) 19.22 to 20.80 Mb 56 SNPs (0 to 1)
7 genes; 2 Exonic
1nsSNP and
1sSNP

48. Epigenomics for understanding hybrid
vigor in pigeonpea

49. Global Epigenome map of hybrids and
parental lines
ICPA 2043
ICPH 2671
ICPR 2671
CG
CHG
CHH
C CG CHG CHH
19.14 60.87 46.13 7.13
Global methylation level
The epigenome is a
multitude of
chemical compounds
that can tell the
genome what to do.
Cytosine DNA methylation is a
heritable epigenetic mark present in
many eukaryotic organisms. Most of
plant DNA methylation is restricted
to symmetrical CG sequences, but
plants also have significant levels of
cytosine methylation in the
symmetric context CHG (where H is
A, C or T) and even in asymmetric
sequences.

50. Canonical DNA methylation profiles of
genes

51. Increased DNA methylation in
centromeric regions

52. Increased DNA methylation in
transposable elements (TE) regions

53. Differentially methylated region (DMR)

54. What’s next for pulses?

55. Predict
Phenotypes
Inbreeding
Multi-location, Multi-year
testing
Seed Increase
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
cheaper to genotype =
larger populations for
same $$
make selections in
‘off target’ years
maintain favorable
rare alleles
Select years
earlier on single
plant basis
The Breeding Cycle

56. Selection intensity (i)
Large F2 populations
Large screening nurseries
Large number of crosses
y
ir
R A
t



57. CG centers have target of 40 Million data
points in the next five years
Needs a flexible, scalable, cost savings
solution
Genotyping cost @ US$ 2 per sample
Paradigm changing high-
throughput solutions

58. Low genetic gains in classical breeding
50- 100 crosses
5,000- 20, 000 Plant architecture
SinglePlantSelections
10, 000
4, 000
1, 000
500
50
entries to regional
trial
recommended variety
(disease, days to flowering,
plant stand etc
F7 lines
Selection intensity (i) : Low
Selection efficiency (r) : Low
Genetic variance (σ) : Low
Years per cycle (Y) : High
100- 200 F2s per cross

59. US$2 per sample based Forward Breeding
F2 F3 F4 F5 F6-8
• 200,000
• Screening for
disease, plant
habit, quality,
yield etc.
• ~5,000
homozygous
lines based on
allelic
contributions
• ~1000 single
plant selection
based on other
morphological
traits
• ~50 entires
selected based
on replicated
multi-location/
station trials
• ~5-10 superior
breeding lines
recommended
as variety for
targeted traits
• MAS for
homozygocity
test
• DNA
fingerprinting
200F1Crosses(1000F2each)
Disease Plant habit Quality YieldPositive alleles
Selection intensity (i) : High
Selection efficiency (r) : High
Genetic variance (σ) : High
Years per cycle (Y) : Low

60. In summary…
http://www.pulsecanada.com
 Chickpea and piegonpea have become
genomic resources rich crops now
 Large number of traits mapped using
GBS, QTL-Seq, GWAS approaches
 Molecular breeding is becoming routine
in pulses. Need to target mega-varieties
for introgression of needed traits
 Forward breeding and digitization of
breeding to enhance genetic gain
 Analytical and decision support tools
need to be implemented in breeding
 National and international support
essential

61. 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 Past Chair:
Roberto Tuberosa, Uni Bologna, Italy
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