Pearl millet is a staple food for more than 90 million farmers in arid and semi-arid regions of sub-Saharan Africa, India and South Asia. ICRISAT highlight the substantial enrichment for wax biosynthesis genes, which may contribute to heat and drought tolerance in this crop. ICRISAT resequenced and analyzed 994 pearl millet lines, enabling insights into population structure, genetic diversity and domestication. We use these resequencing data to establish marker trait associations for genomic selection, to define heterotic pools, and to predict hybrid performance.

1. Pearl millet: a crop of
prodigious opportunities
Rakesh K. Srivastava
Principal Scientist (Genomics & Trait Discovery)
ICRISAT, Hyderabad
05 Jan, 2020
r.k.srivastava@cgiar.org

2. Genetic resources
• Word association mapping panel (PMiGAP)
• Bi-parental mapping populations (23 pairs)
• Chromosome Segment Substitution Lines (CSSLs)
• TILLING population
• NAM population
• Alloplasmic-isonuclear population
Genomic resources
• World reference genome Tift23D2B1-P1-P5
• ~1,000 genomes re-sequenced
• DM genome (Pathotype 1, India) sequenced
• PMiGAP with >29.5 million WGRS-SNPs & 3.8 million InDels
• Panel of ~61K genic SNPs for GWAS on PMiGAP
• Virtual 60K SNP array
• RAD/GbS SNPs for GS
• Consensus map
• Transcriptome assemblies
• QC panel
• Improvement and new reference genomes
Available genetic & genomic resources

3. Mapped traits in pearl millet
Grain and forage quality traits
 Grain Fe and Zn content
 In-vitro organic matter digestibility
 Metabolizable energy
 Neutral detergent fiber (cellulose,
hemicellulose, lignin)
 Nitrogen on dry matter basis
 Gas volume
 Sugar content on dry matter basis
 Fresh and dry stover yield
Biotic constraints
 DM resistance (12 pathotype-
isolates)
 Blast resistance
 Rust resistance
Abiotic constraints
 Terminal drought tolerance (tiller number, panicle diameter,
total biomass dry weight, leaf dry weight, root dry weight,
shoot dry weight, stem dry weight, leaf area, specific leaf
weight, transpiration efficiency, transpiration rate, absolute
transpiration, leaf rolling, delayed leaf senescence, low VPD
transpiration rate, high VPD transpiration rate)
Yield and yield-related traits
 Flowering time
 Plant height
 Panicle length
 Seed weight
 Panicle harvest index
 Grain harvest index
 Grain number per panicle
 Harvest index
 Biomass
 Nitrogen use efficiency & related traits
 Grain yield under moisture stress and
irrigated conditions
Other traits
 Heterotic gene pools for hybrid
parental lines
 General and specific combining ability
for grain yield under drought stress
and irrigated conditions

4. Product concept Estimated
area
(m ha)
% area/
effort
Target and spillover agroecologies Maturity
(days)
Resistance/tolerance required Other criteria Product development
goals
(1) Early-duration pearl
millet (OPV/hybrids) for
adaptation to Sahelian zone
of West Africa
8 25 Target: Niger, Mali, Burkina Faso,
Senegal and Nigeria
Spillover: Parts of Sudan, Chad,
Cameroon and India
70-80 Biotic stresses: Downy mildew and
head miner
Abiotic stresses: Drought,
flowering period heat stress, low P
tolerance
Must have traits: Grain yield:1.5-
2.0 t/ha; plant height: 170-200 cm;
panicle length: 30-50 cm; panicle
width: 8-10 cm; test grain weight:
10-15 g; high grain Fe and Zn
content
10% increase in grain
yield and stover yield
over local and improved
check
(2) Medium gero pearl millet
(OPV/hybrids) for
adaptation to better
endowed environments of
West Africa
7.5 20 Target: South of Niger, Mali Nigeria,
Burkina Faso, Ghana and Senegal
Spillover: Parts of Sudan, Chad and
Cameroon
85-100 Biotic stresses: Downy mildew and
Striga
Abiotic stress: Drought
Must have traits: Grain yield: 2.0-
2.5 t/ha; plant height:170 - >200
cm; panicle length: 60-75 cm;
panicle width: 7-10 cm; test grain
weight: 10-15 g
10% increase in grain
yield and stover yield
over local and improved
check
(3) Dual-purpose maiwa
pearl millet (OPV/hybrids)
for adaptation to better
endowed environments of
West Africa
3-4 10 Target: Nigeria, Mali, Senegal and
Burkina Faso
Spillover: Parts of Sudan, Chad and
Cameroon
110-120 Biotic stresses: Downy mildew and
Striga
Abiotic stresses: Drought
tolerance; flowering period heat
stress
Must have traits: Grain yield: 2.0-
2.5 t/ha; plant height: >200 cm,
panicle length: 70->85 cm; panicle
width: 8-12 cm; high grain Fe and
Zn content
10% increase in grain
yield and stover yield
over local check with >40
ppm Fe
(4) Early- to medium-
maturity high-yielding
varieties and hybrids for
Eastern and Southern Africa
3.0 10 Target: Sudan, Tanzania and Uganda
Spillover: Kenya, Zimbabwe, Namibia,
Eritrea, Malawi, Somalia and
Mozambique
65-90 Biotic stresses: Striga, downy
mildew, covered and kernel smut,
stem borer
Abiotic Stresses: Drought
Must have traits: High yield: 1.5-
2.0 t/ha (varieties) and 2.0-2.5 t/ha
(hybrids), high grain Fe and Zn
10% grain yield increase
compared to the
commercial check
(5) Parent lines of medium-
to late-maturing, dual-
purpose hybrids for
adaptation to better
endowed environments of
South Asia
6.0 25 Target: India: East Rajasthan, Central
and South Gujarat, Haryana, Uttar
Pradesh, Maharashtra and Peninsular
India
Spillover: Tanzania, Kenya and Uganda
(ESA)
75-90 Biotic stresses: Downy mildew and
blast
Abiotic stresses: Flowering period
heat stress tolerance (summer
season)
Must have traits: Parents with high
productivity and good GCA for
grain yield, hybrids with grain yield
of 3-4 t/ha, high grain Fe and Zn
content, better fodder quality
Hybrid parents to
develop hybrids with
10% increase in grain
yield over representative
checks
(6) Parent lines of early-
maturing, dual-purpose
hybrids for adaptation to
drought prone environments
in South Asia
1.5 5 Target: India: Western Rajasthan and
drier parts of Gujarat and Haryana
(200-400 mm/annum)
Spillover: Sudan (ESA), Northern Niger
and Senegal (WCA)
65-75 Biotic stresses: Downy mildew and
blast
Abiotic stress: Drought
Must have traits: Parents with high
productivity and good GCA for
grain yield, hybrids with grain yield
of 2.0-2.5 t/ha
Hybrid parents to
develop hybrids with
10% increase in grain
yield over representative
(7) Cultivars and hybrid
parents exclusively for
forage and high biomass in
South Asia
1.0 5 Target: India: Gujarat, Punjab,
Rajasthan, Uttar Pradesh, Madhya
Pradesh, Peninsular India (summer and
rainy season)
Spillover: Central Asian countries and
Brazil
Single cut
(50-80);
Multicut (50-
110)
Biotic stresses: Downy mildew,
blast and rust
Must have traits: Green biomass of
40-55 t/ha, dry biomass of 15-20
t/ha, non-hairy, leaf: stem ratio of
3-5, IVDMD of 50-55% with protein
of 10-12%
5% increase in biomass
yield over best check

5. • Mapped combining ability loci using chromosome
segment substitution lines (CSSLs). Major loci for
general combining ability (GCA) and specific
combining ability (SCA) were mapped for yield and
yield-related traits (Kumari et al. 2019. PLOS ONE)
• Mapped major effect QTLs for downy mildew
resistance (DMR) for the three new pathotype-
isolates. The largest amount of observed
phenotypic variation (R2 of 76.6%) was contributed
by the QTL on LG4 for the Sg519 isolate
(Durgaraju et al., 2019. European J. Plant Path.)
Trait mapping
A
B
C

6. Heterotic Gene Pools defined
 343 lines (160 B- and 182 R- lines along with
world reference germplasm Tift 23D2B1-P1-P5 as
control) used in the study
 B10R5, B3R5, B3R6, B4UD, B5R11, B2R4, and
B9R9 represent putative heterotic gene pools in
pearl millet.

7. pgpb6112
pgpb9106
Xipes0042
pgpb11431 pgpb7814
pgpb10695
pgpb9498
pgpb9152 pgpb7328
Xipes0017
pgpb6756 pgpb12900
pgpb13184
pgpb10531 pgpb7001
pgpb9927 pgpb13376
pgpb9130
pgpb12275 pgpb7069
pgpb9393
pgpb5822
pgpb11882
pgpb12077
pgpb12612
pgpb12664 pgpb11990
pgpb12385
pgpb7349 pgpb7938
pgpb11716
pgpb11894 pgpb6149
pgpb6981 pgpb10653
pgpb11467
pgpb7094 pgpb6723
pgpb9205
Xipes0226
Xicmp3032
Xicmp3017
Xipes0146
Xipes0197
Xipes0079
Xipes0203
Xpsmp2273
Xipes0098
LG1
pgpb120940.0
pgpb10685 pgpb102177.8
sts32223.5
Xipes018136.7
Xipes000745.9
Xpsmp208851.3
Xpsmp207257.8
pgpb10293 pgpb10563
pgpb9172 pgpb741369.7
pgpb9822 pgpb10840
pgpb960675.0
Xipes016288.6
Xipes0163102.8
Xipes0117120.0
Xipes0160128.3
pgpb8902137.2
pgpb8978138.6
pgpb8139 pgpb7284
pgpb6665 pgpb7736140.0
Xipes0236143.0
Xpsmp2059146.5
Xpsmp2206147.0
Xpsmp2231 Xpsmp2013149.2
pgpb9747156.1
Xipes0210163.4
pgpb7861 pgpb7028173.1
pgpb10959193.4
Xipes0118208.0
Xipes0221263.7
LG2
Xipes01610.0
Xipes009536.0
pgpb663454.7
pgpb1164761.5
Xipes016672.3
pgpb11874 pgpb10791
pgpb1313580.7
pgpb1007681.8
Xipes021386.7
pgpb7379 pgpb9688
pgpb1123589.6
pgpb7983 pgpb10464
pgpb12994 pgpb827293.5
Xpsmp2227107.5
Xctm10109.0
Xpsmp2214112.9
Xipes0142118.1
pgpb10327 pgpb7799
pgpb10182130.7
pgpb10926132.6
pgpb5938 pgpb7699136.8
pgpb10033161.5
pgpb12395178.2
pgpb6174196.1
pgpb6901203.6
pgpb8757212.3
LG3
Xpsmp20850.0
Xipes02255.5
Xipes012911.1
Xpsmp207621.4
Xipes017429.9
pgpb13161 pgpb11422
pgpb12125 pgpb12964
pgpb11170 pgpb6413
39.2
pgpb1132542.0
pgpb1292950.3
pgpb990354.6
pgpb9967 pgpb876468.1
pgpb739070.8
pgpb11527 pgpb1107079.3
pgpb12505 pgpb9424
pgpb1149683.8
pgpb1145985.4
pgpb938990.6
pgpb9189 pgpb7539
pgpb9351 pgpb796693.4
pgpb1235496.3
pgpb10500 pgpb10228
pgpb6267100.2
pgpb12538106.8
pgpb6707112.8
pgpb7394122.6
pgpb5883 pgpb6988
pgpb12366125.0
pgpb9788126.4
pgpb6590 pgpb6827
pgpb12278129.8
pgpb9328135.9
pgpb6967142.6
pgpb11249 pgpb8864143.6
pgpb7925 pgpb9653
pgpb10230144.6
pgpb9450 pgpb6246146.6
sts305154.0
Xipes0219165.1
LG4
Xpsmp22290.0
Xpsmp227627.6
Xpsmp227729.4
pgpb934343.4
pgpb7186 pgpb602860.2
pgpb9755 pgpb846369.6
pgpb6766 pgpb1300270.6
Xpsmp207887.0
Xipes021790.2
Xipes017592.8
Xipes021495.2
Xipes0152104.4
pgpb11029 pgpb5908123.0
pgpb8456 pgpb6774
pgpb10577130.5
pgpb10505133.4
pgpb12521 pgpb9241135.6
Xipes0230143.8
Xipes0093148.7
pgpb9647158.0
pgpb8532 pgpb8909167.9
Xicmp3027191.7
LG5
F MLG3
Fe Zn
50%FT
50%FT
Fe-Zn QTL location for (ICMB 841-P3 × 863B-
P2)-based RILs
Zn- R2 50.1, LOD 14.9
Fe- R2 19.4, LOD 4.7
Co-localized QTLs
Kumar et al., 2016. Frontiers in Plant Sciences

8. Genomic positions of
significantly
associated SSR
markers with grain iron
and zinc content in the
consensus map of
Rajaram et al. (2013).
Color code: Orange
for iron; Blue for zinc;
and Green for both
iron and zinc.
Front. Plant Sci. 2017. 8:412.
Association mapping of
Fe Zn content

9. pgpb91660.0
pgpb12666 pgpb6468
pgpb73282.6
pgpb675614.0
pgpb10565 pgpb1336217.2
Xipes01729.3
pgpb1290034.3
pgpb1053149.7
pgpb913054.3
pgpb695560.4
pgpb9393 pgpb12275
pgpb706978.6
pgpb6874 pgpb9135
pgpb968485.1
Xipes09897.0
Xipes079103.6
Xipes091110.4
Xipes146114.4
pgpb7504 pgpb11518120.4
pgpb10307 pgpb5896
pgpb6818120.8
pgpb8836 pgpb7553125.5
pgpb9433 pgpb10653145.9
pgpb12525 pgpb11693167.1
pgpb10397177.5
pgpb10394 pgpb12204183.6
pgpb5859201.7
pgpb5822217.8
LG1
Xipes0270.0
pgpb10526 pgpb12897
pgpb9302 pgpb13198
pgpb13146 pgpb9338
pgpb6665
7.0
pgpb73308.6
pgpb72849.9
pgpb1111214.0
pgpb787915.0
pgpb5966 pgpb1042416.3
pgpb1171716.9
pgpb9970 pgpb12444
pgpb1063722.1
Xipes06028.6
pgpb8259 pgpb1155245.1
pgpb683249.6
pgpb1145254.7
pgpb960656.7
pgpb817760.9
pgpb10685 pgpb1138861.4
pgpb10684 pgpb1063862.3
pgpb13083113.8
LG2
Xipes1800.0
pgpb109267.8
pgpb1019811.1
Xipes18815.6
pgpb906923.4
pgpb1231026.5
pgpb779930.1
pgpb737934.0
pgpb6812 pgpb849040.9
pgpb6754 pgpb845743.7
pgpb1313545.6
pgpb1123547.6
Xipes16648.3
pgpb12497 pgpb1174851.8
pgpb1195754.4
pgpb724162.3
pgpb1042565.2
pgpb1225972.6
pgpb1054684.0
LG3
0.0
23.6
29.8
38.8
42.2
44.0
49.9
56.9
65.3
66.7
72.1
93.9
97.6
101.5
125.3
139.1
140.7
144.5
150.7
151.5
153.7
155.2
157.4
178.1
LG4
LG1 M
Fe Zn
Co-localization
Fe-Zn QTL positions for (ICMS 8511 × AIMP
92901-08)-based RILs
Fe- R2 9.0, LOD 25.4
Zn- R2 30.0, LOD 23.9
Kumar et al., Genes, 2018

10. CIRCOS of highly expressed genes in grain of different genotypes
(AIMP, ICMS and MRC) thick ness of ribbon showing the
expression levels
Candidate genes for Fe and Zn metabolism
Sci Rep. 2020. 10, 16562

11. SubstrateEnzymes
Environment
Lipase
Lipoxygenases
Polyphenol oxidase (PPO)
Peroxidase (POD)
Lipids
Glycosylflavones
Mining candidate genes for flour rancidity

12. LG2 Terminal drought tolerance (Kholová et al. 2010)
• From a total of 52,028 ddRAD-SNPs that were generated, a
total of 6,821 SNPs were used for mapping
• A panel of 10 SNPs is being used in forward breeding
• All the A1 zone material from ICRISAT and NARS are being
genotyped
Early drought stress tolerance (Debieu et al. 2018)
• 11 SNPs under validated on breeding lines from WCA
• May be applicable to Indian breeding programs
Grain Fe and Zn content (Kumar et al. 2016)
• Validated LG3 high grain Fe-Zn QTL interval mined for SNPs
• 4-SNP panel is being used in breeding programs
Currently available markers

13. Blast
• Bi-parental mapping populations (7 populations, F3/F4)
• QTL-Seq using F3:4 mapping populations
• TILLING approach in ICMR 11019 genetic background
• GWAS using PMiGAP data from major hot-spot locations (5 environments)
Fe-Zn
• Positive selection, high Fe (~110 ppm) Zn (~70 ppm) (Kumar et al., 2018.
Front Pl Sci)
• DArT-seq genotyping of a F10 mapping population
• Phenotyping in three environments
• Candidate genes for grain Fe and Zn discovered (Mahendrakar et al.,
2020. Sci Rep)
Fertility restoration
• In-silico approach
• Alloplasmic-isonuclear mapping population
GWAS for Nitrogen Use Efficiency (NUE)
• Association mapping, three season data on 400 lines
• Initial MTA analysis being improved
Markers available by this year

14. • Blast resistance (2022)
• DM resistance (2022)
• NUE (2022)
• WUE (2022)
• Striga
• Head-miner
• Seedling-stage heat tolerance
• Lodging tolerance
• Forage quality
• Flour rancidity
• Low GI
Markers available by next year and beyond

15. Optimization of genomic prediction models
Varshney et al. 2017
• Phenotyped 64 pearl millet hybrids (23 × 20) in five environments for 15 traits
• 302,110 high-quality SNP marker data from 580 B- and R- lines, were used to
predict hybrid performance
• 170 promising hybrid combinations found, 11 combinations are existing, 159
combinations have never been used
Liang et al. 2018
• Evaluated two potential genotyping strategies RAD-seq and tGBS
• 320 hybrids and 37 inbreds at field trials in four locations
• Prediction accuracy was equivalent for RAD-seq/tGBS- SNPs
• tGBS generated greater number of high MAF SNPs per million reads
Optimization of SNPs (under preparation)
• Prediction accuracy evaluation of different classes of SNPs for 20 traits, 4
season data, 250 PMiGAP inbreds, 250 hybrids
• SNP types studied: exonic, intronic, upstream, downstream
• A total of 276,267 SNPs used for this study
Varshney et al. 2017. Nature Biotechnology
Liang et al. 2018. G3
Under preparation

16. Optimization of genomic prediction models
AICRP-PM/ICRISAT-Asia Centre (on-going)
• 370 inbreds (elite hybrid parental lines), 75 hybrids, 3 season
data from A1, A and B zones
• Genotying of the training population with mid-density panel
(~4K SNPs)
• Development of genomic prediction model
ICRISAT- WCA (on-going)
• 250 inbreds (elite parental lines- OPVs, hybrids), 150 hybrids, 4
season data from WCA countries
• Genotying of the training population with mid-density panel
• Development of genomic prediction model

17. • Used SNP data from the ~1,000 genomes project from
PMiGAP and B-/R- lines used (Varshney et al., 2017)
• A set of common SNPs between PMiGAP and B- and R- lines
were identified (~20,000)
• Using ICRISAT’s and Corteva’s proprietary pipeline a QC panel
of 48 and 54 (28) SNPs have been developed
• The 48 and 54 SNP set from ICRISAT and Corteva respectively,
used for assay development with Intertek
• Validation of the SNPs completed on breeding lines from
ICRISAT India & Africa, NARS Corteva Agriscience: 15+4 plates
• A total of 30 SNPs finalized post validation on 15 plates
• Breeding samples from ICRISAT (Asia/WCA), AICP-PM
30 SNPs (~16+4+16+12 plates)
Development of SNP-based QC Panel

18. Mid-density marker panel
• A marker panel of ~4,000 SNPs
• Being developed from the ~1,000 genomes
project
• ICRISAT-Corteva Agriscience
• Exploring separate pipeline for the African
breeding programs
• Good support form the ICRISAT breeders and
AICRP-PM for integration in their respective
breeding programs
• To also be used in MABC program especially for
the A1 zone
Mid-density array

19. Development of additional reference genomes
 ICRISAT In association with Corteva
Agriscience
 Two additional genomes 843B & CMR
06777
 Improvement of genome assembly of
Tift23 D2B1P1-P5
 Comparative genomics with iso-seq data
 HiC for one of the lines
 Platinum standard genomes

20. • HHB 67 Improved (grown in >10% area) second
cycle improvement completed with stacking of
downy mildew resistance (DMR) and high grain
iron and zinc density QTLs (AICRP-PM 2019).
• Double DMR QTL introgression lines in GHB 538
hybrid with promising results in the Indian
national trials (AICRP-PM 2019).
Molecular Breeding Products

21. Diabetes- a global threat
Source: WHO

22. • Two low glycemic index (GI) hybrids with high
slowly digestible starch (SDS) and resistant starch
(RS) fractions performed very well in the multi-
location trials during 2019.
• These hybrids recorded high grain yield superiority
and blast and downy mildew resistance over the
checks (Project report, Innovate UK Project No:
102726).
Molecular Breeding Products: Developing low GI millets

23. • Working closely for development optimization and
implementation of whole-genome prediction models,
QC panel and mid-density array
• Working with SAUs for release of DM improved version
of hybrid
• Working with AICRP-PM for evaluation and release of
HHB 67 improved version
• Support the National and Seed Companies for breeder
seed of the parental line of HHB 67 Improved
• Developed a mega proposal on the A1 zone with
AICRP-PM well complementing the breeding programs
• Similar proposal with Corteva-EiB
Engagement with the AICRP-PM/SAUs

24. • Discovery work is not a high priority for many funders
• Affected by the big-crop syndrome
• No allocation of money in proportion to the product
profiles
• Depend on special project funding
• Skewed distribution of money from projects such as
ICAR-ICRISAT, CtEH
• Duplication of genomics and trait discovery work
• Opportunities to work with ICRISAT and regional
breeders
• Complementing the breeding programs of National
Systems such as the A1 zone- upstream science
support
Challenges & Opportunities

25. Thank you!