Integration of various molecular breeding approaches (MABC, MARS, and GS) in the product development process at ICRISAT. Accelerated rate of genetic gain across all mandate crops by leveraging expertise from various groups inside and outside ICRISAT.

1. Forward Breeding: tools and
technologies for accelerating rate
of genetic gain
Manish Roorkiwal
Senior Scientist, Forward Breeding
Research Program- Genetic Gains
5 Jan, 2021

2.  Establish and use of high throughput and cost-effective genotyping platforms
 Forward breeding-based breeding solutions to crop improvement
 Deploy molecular breeding approaches e.g. MAS/MARS/GS in collaboration with the themes on Crop
Improvement and Genomics and Trait Discovery
 Collaborate with the theme on Genomics and Trait Discovery to validate identified markers
 Convert candidate molecular markers to high-throughput enable marker system
 Lead/engage in developing analytical tools, platforms and databases for molecular breeding
 Meta-analysis using sequencing data for haplotypes-based selection
Forward Breeding
 Integration of various molecular breeding approaches (MABC, MARS, and GS)
in the product development process at ICRISAT.
 Accelerated rate of genetic gain across all mandate crops by leveraging
expertise from various groups inside and outside ICRISAT.
Key responsibilities:

3. Markervalidation
Convertmarkers
HTPG
Biomarkers
GAB
Identify
research needs
Precision and
high throughput
phenotyping
Use of markers
to increase
efficiency
Define baseline
Strategic
planning of
experiments
Improved lines
Modernize the
breeding
Enhanced rate
of genetic gain
Inputs OutputsForward Breeding
Forward Breeding Strategies
y
ir
R A
t



4. Forward Breeding
 Many lines having undesirable alleles are discarded
 Opportunity to the evaluation of fewer lines in later generations
Provides tools, technologies and platforms to deploy markers in breeding
programs for developing improved lines in cost- and time-effective manner

5. Gene Pyramiding
Single gene/QTL introgression
Single backcross DH Scheme
Multi-trait introgression
MES: Marker-evaluated selection
SLS-MAS: Single large scale MAS
Forward Breeding based solutions

6. building a global community of knowledge through workshops, hackathons and cross-training to
transform breeding http://cbsugobii05.tc.cornell.edu/wordpress/
GOBii: A Global Community
A stable, high quality, easy to deploy, genomic data management system,
with a web service layer incorporating BrAPI for integration
Developed and released key tools for breeders
To improve data availability, working to integrate GOBii with breeding
management systems and tools

7. GOBii Tools
GOBii Genomics Data Manager (GDM)
Scalable genomic data management system, and
a marker tools portal to access all GOBii-GDM
tools for data loading, QC, extract & breeding.
GOBii DArT Tools
The GOBii-QC (quality control)
module run by KDCompute is fully
integrated with the GOBii-GDM
system.
GOBii Genomic Selection (GS)
Genomic Selection (GS-Galaxy) Analysis
Pipeline is under active development by
GOBii team and CGIAR contributors using
open-source Galaxy platform.
GOBii JHI Tools
Marker-Assisted Back Crossing (MABC)
module is an open-source platform for
conducting marker-assisted backcross
visualization and selection analysis.
http://cbsugobii05.tc.cornell.edu:6084/x/GQDcAQ
MABC module:
http://flapjack.hutton.ac.uk/en/latest/mabc.html
F1 pedigree verification module:
http://flapjack.hutton.ac.uk/en/latest/pedver_f1s_known_
parents.html
http://galaxy-demo.excellenceinbreeding.org/

8. Collaboration
space with
Open-Source,
BrAPI enabled
analytics
Analytics
M5: Common vision
of system
deployment,
aligned training
Source: Elizabeth Jones

9. 8 CG
Centers
30+
NARS
28+
Countrie
s
4+
Private
partners
18+
Crops
100+
Traits
1000+
SNPS
500+ QC
SNPS
Lead
Institute
Service
Provider
Funding
Support
Coordination
Support
Partner
Institute(s)
High Through-Put Genotyping
(HTPG) project

10. Circa. 2.0 Million USD
Genotyping Volume
Circa. 7.5 Million
Data points
AUG 2016 -
DEC 2019
With 4 million US$ investment,
HTPG saved significant resources
for CGIAR & NARS and enabled
generation of genotyping data at
1/3rd- 1/4th price.
By assuming a minimum cost of
US$1 per data point, CG and
NARS might have spent about
US$ 7.5 Million on the data
generation.
$0.26 per data
point
The project has saved about US$
5.0 Million, with total
investment of about US$ 3.25
Million by 2019
Groundnut,
662160
Pigeon Pea,
49104Chick Pea,
82320
Finger Millet,
145536
Pearl Millet,
193536
Sorghum,
348768
ICRISAT Crops - Data point
AUG 2016 - JUNE 2020
Barley, 46080 cassava,515040
Common Bean,
736512
Cowpea,166272
Maize,837792
Potato,306720Rice, 2316576
Wheat,1137120
Other CGIAR Crops - Data point
AUG 2016 - DEC 2019
High Throughput Genotyping Project (HTPG)

11.  1.8 Tb sequence data generated (5 X to 14 X)
 4.9 million SNPs, 596K Indels, 4.9K CNVs, 60.7K PAVs & 70K SVs
 Domestication analysis reported 122 CDR regions that underwent
selection
 Selection sweep analysis reported a significant reduction in diversity
from wild to landraces to breeding lines
 Candidate genes for yield, heat and flowering time identified

12. Global chickpea
genetic variations
based on
sequencing of
3,366 genome

13. Axiom®CicerSNP array: a high-throughput SNP
genotyping platform
Plant Biotechnol J. 2018

14. Axiom®CicerSNP array
based high-
density genetic map
(ICC 4958 × ICC 1882) (ICC 283 × ICC 8261)

15. Mid Density – Targeted GBS with AgriSeqTM
NGS Platform
 Targeted GBS—a flexible, powerful,
highly accurate genotyping system
 Sequencing based identification
of Novel SNPs in addition to
known SNPs

16.  ~4.9 million markers on diverse chickpea lines
 Based on different QC criteria, a set of 8654 highly
polymorphic markers tested
 5000 markers in 4349 amplicons
 Primer panel in manufacturing
Mid Density – Targeted GBS
panel for chickpea
Priority
Total
Markers
Dropped in
preDesignQC
No Design
Available
Remaining Design (not included in the
5000 markers final panel)
Included in Final
Panel
Priority 1 422 53 126 0 243
Priority 2 8232 149 381 2945 4757
Total 8654 202 507 2945 5000
 Validation of mid-density panel by Dec 2020
 Genotyping of chickpea breeding material for possible deployment in
routine chickpea breeding
 ICRISAT chickpea breeding specific panel

17. Sample genotype table: AgriSum Toolkit
17
Sample Matrix
Table Output
Top/Bottom
Output
Sample 1;IonCode_0601 Sample 2;IonCode_0602 Sample 3;IonCode_0603 Sample 4;IonCode_0604 Sample 5;IonCode_0605
Target-1 C/G G/G C/C C/G C/G
Target-2 C/G G/G C/C ./. C/G
Target-3 A/G G/G A/A A/G A/G
Target-5 G/A A/A G/G G/A G/A
Target-6 G/A A/A G/G G/A G/A
Target-9 G/A A/A G/G G/A G/A
Target-11 G/A A/A G/G G/A G/A
Target-14 C/G G/G C/C ./. C/G
Target-17 C/T T/T C/C C/T C/T
Sample 1;IonCode_0601 Sample 2;IonCode_0602 Sample 3;IonCode_0603 Sample 4;IonCode_0604 Sample 5;IonCode_0605
Target-1 AB BB AA AB AB
Target-2 AB BB AA ./. AB
Target-3 AB BB AA AB AB
Target-5 AB AA BB AB AB
Target-6 AB AA BB AB AB
Target-9 AB AA BB AB AB
Target-11 AB AA BB AB AB
Target-14 AB BB AA ./. AB
Target-17 AB AA BB AB AB
Target-20 AB BB AA AB AB

18. Chrom Position Ref Variant Allele Call Filter Frequency Quality Filter Type Allele Source Allele Name
Ca1 389832 T C Heterozygous - 53.2 330.717 - SNP Novel tvc.novel.1
Ca1 389920 C G Heterozygous - 48.5 236.899 - SNP Hotspot Target-1
Ca1 391254 C G Heterozygous - 46.5 166.596 - SNP Hotspot Target-2
Ca1 393860 TGGTC - Heterozygous - 52.8 316.761 - DEL Novel tvc.novel.2
Ca1 393871 G A Heterozygous - 52.8 319.98 - SNP Novel tvc.novel.3
Ca1 393894 A G Heterozygous - 54 346.287 - SNP Hotspot Target-3
Ca1 395378 G C Heterozygous - 48.2 232.03 - SNP Novel tvc.novel.4
Ca1 395496 G A Heterozygous - 48.6 238.193 - SNP Hotspot Target-5
Ca1 396587 G A Heterozygous - 49.2 247.027 - SNP Hotspot Target-6
Ca1 401320 G A Heterozygous - 49.2 250.298 - SNP Hotspot Target-9
Ca1 405686 G A Heterozygous - 49.7 257.24 - SNP Hotspot Target-11
Ca1 407777 G A Heterozygous - 34.8 52.0189 - SNP Novel tvc.novel.5
Ca1 407795 C T Heterozygous - 34.8 51.8514 - SNP Novel tvc.novel.6
Ca1 407849 C G Heterozygous - 33.5 41.8833 - SNP Hotspot Target-14
Ca1 407887 C T Heterozygous - 34.5 49.9275 - SNP Novel tvc.novel.7
Ca1 407897 G A Heterozygous - 37.1 73.0879 - SNP Novel tvc.novel.8
Ca1 407901 T C Heterozygous - 39.6 101.75 - SNP Novel tvc.novel.9
Ca1 407928 CTC AT Heterozygous - 42.3 136.08 - COMPLEX Novel tvc.novel.10
Novel SNP Detection
18
Novel Markers - 2899

19. Chickpea QC (CaQC) SNP panel
 Available re-sequencing data on 66 chickpea parental lines
from chickpea breeding (1.9 million markers) were used
 Based on different criteria and analysis, a set of 48 markers
selected & used for genotyping
 Marker data was analyzed on 94 different cross combination
 Set of 14 markers for testing on larger set of lines for
validation
 2-12 polymorphic markers polymorphic for all the crosses
except 2 cross combinations
 The panel is also being tested on parental lines from NARS
partners
An affordable and effective genotyping platform for hybridity
testing and seed quality control & assurance
SNP panel ready for deployment
during crop season 2020-21

20.  Selected 14 SNPs deployed in ICRISAT chickpea breeding
program
snpCA00171; snpCA00177; snpCA00178; snpCA00181;
snpCA00184; snpCA00188; snpCA00192; snpCA00193;
snpCA00197; snpCA00203; snpCA00206; snpCA00207;
snpCA00209; snpCA00216
 32 plates for genotyping (2020-2021 chickpea crop season)
Deployment of CaQC SNP panel
 Sequencing of chickpea parental lines with Chickpea
breeding team for identification of more markers
 Upload of data on all parental lines to GOBii and provide
access to chickpea team

21. Optimization of genomic prediction based
selection strategy in chickpea
Frontiers in Plant Science 2016;
Scientific Reports 2018
Frontiers in Plant Science 2020
 Restructuring training population
 Genotyping of new training population with
new mid-density genotyping platform
 Optimization and establishment of GS models

22.  Total individuals in training set: 315 (162 Desi, 153 Kabuli)
 5000 F5 plants from IARI and ICRISAT genotyped using LD DArT
 Comparison of visual selection vs selection based on GEBV: two set of
~200 lines (based on GEBVs and visual selection) were evaluated in the
field conditions for yield and yield related traits during crop season
2019-2020
 Lines selected based on GEBVs performed better in terms of yield and
100 seed weight as compared to lines selected based on visual selection
Deployment of genomic prediction based
selection strategy in chickpea
All Predict All Desi Predict Desi Kabuli Predict Kabuli Desi Predict Kabuli Kabuli Predict Desi
Seed Yield 0.48 (0.015)a 0.26 (0.029) 0.25 (0.020) 0.08b 0.04c
Seed Weight 0.92 (0.002) 0.76 (0.012) 0.74 (0.014) 0.20 0.58
Biomass 0.50 (0.013) 0.39 (0.019) 0.26 (0.026) 0.11 0.16
Plant Height 0.65 (0.011) 0.75 (0.010) 0.42 (0.038) -0.13 0.16
Days to Flower 0.68 (0.007) 0.63 (0.016) 0.56 (0.031) -0.34 0.07
Days to Maturity 0.70 (0.003) 0.53 (0.021) 0.53 (0.038) -0.16 0.09
Frontiers in Plant Science 2020

23. Based on initial results from pilot experiments AICRP Chickpea
initiated efforts to deploy GS in routine breeding program
Included additional parental lines from national chickpea
breeding program to extend the training population
Training population genotyped using newly developed Mid-
density SNP arrays
Training population evaluated at NARS locations for yield and
quality traits
Based on initial analysis ICRISAT suggested new sets of crosses to
AICRP centres
These crosses are being made in the ongoing crop season
Deployment of Genomic selection in Indian
national chickpea breeding program

24. BGM 10216 a drought tolerant MABC line in field
 BGM 10216 First MABC line released in India for
commercial cultivation in central zone
 Pusa Chickpea 10216 (BGM 10216) is developed after
introgression QTL-hotspot in “Pusa 372” genetic
background at IARI in coll. with ICRISAT
 16% yield advantage over recurrent parent across all
the centers tested under AICRP
 It’s grain protein content is 22.6%
Support to NARS: Pusa Chickpea
10216 (BGM 10216), drought
tolerant variety - 2019

25. Support to NARS: Pusa Chickpea Manav (BGM
20211) enhanced fusarium wilt resistance -
2020
 Pusa Chickpea Manav developed by introgression
of “QTL region” for wilt resistance from WR 315
to recurrent parent Pusa 391
 28 % yield advantage over recurrent parent in
National WRIL Trials under AICRP under wilt
stress conditions
 Its average 100-seed weight is 19.5 g.
 It’s grain protein content is 18.92%.

26. Trial Entries
Recurrent
Parent
AVT1 (2019-20)
Mean yield (Kg/ha)
Yield increase (%)
over recurrent
parent
DTIL
AVT 2
BGM 10218 RSG 888 1342 21.23
NBeG 506 JG 11 1826 10.13
BG 4005 Pusa 362 1604 18.64
AVT 1
BGM 10220 JAKI 9218
IPC (L4-25) DCP 92-3
IPC (L22-33-2) DCP 92-3
IPC (L4-14) DCP 92-3
BGM 10221 JG 16 (IARI)
WRIL
AVT 2 BG 20213 Pusa 391 2280 29.69
AVT 1
KCD-2019-5 JG 11
KCD-2019-7 JG 11
More products in pipeline…
In collaboration with ICAR-IARI, ICAR-IIPR, UAS-Raichur and AICRP-Chickpea

27. AX-1236443280.0
AX-1236439940.1
AX-12361765212.7
AX-12364483313.4
AX-12364417015.0
AX-12364511346.8
AX-123615540113.0
AX-123615611113.1
AX-123643054121.9
AX-123615557122.0
CaLG01
AX-1236392650.0
AX-1236478340.1
AX-12362263914.3
AX-12362272914.4
AX-123621900153.4
AX-123621911167.6
AX-123639136178.7
AX-123621873178.8
AX-123659615194.4
AX-123621627194.5
CaLG03
AX-1236649210.0
AX-1236309010.1
AX-12363014686.4
AX-12364203887.5
AX-123623881216.2
AX-123615349217.6
AX-123624139223.1
AX-123648479230.3
AX-123624245230.4
CaLG04
AX-1236333160.0
AX-1236541170.1
AX-1236333310.8
AX-1236334501.3
AX-12364258543.6
AX-12366333443.7
AX-12363439587.9
AX-12363451988.0
AX-12363436989.5
AX-12366310489.6
AX-123634069103.5
AX-123634068103.8
CaLG06
qYPP1.1
qPLHT1.1
qYPP1.2
AX-1236443280.0
AX-1236439940.1
AX-12361765212.7
AX-12364483313.4
AX-12364417015.0
AX-12364511346.8
AX-123615540113.0
AX-123615611113.1
AX-123643054121.9
AX-123615557122.0
CaLG01
AX-1236392650.0
AX-1236478340.1
AX-12362263914.3
AX-12362272914.4
AX-123621900153.4
AX-123621911167.6
AX-123639136178.7
AX-123621873178.8
AX-123659615194.4
AX-123621627194.5
CaLG03
AX-1236649210.0
AX-1236309010.1
AX-12363014686.4
AX-12364203887.5
AX-123623881216.2
AX-123615349217.6
AX-123624139223.1
AX-123648479230.3
AX-123624245230.4
CaLG04
AX-1236333160.0
AX-1236541170.1
AX-1236333310.8
AX-1236334501.3
AX-12364258543.6
AX-12366333443.7
AX-12363439587.9
AX-12363451988.0
AX-12363436989.5
AX-12366310489.6
AX-123634069103.5
AX-123634068103.8
CaLG06
qPB3.1
q100SW3.1
q100SW3.2
AX-1236443280.0
AX-1236439940.1
AX-12361765212.7
AX-12364483313.4
AX-12364417015.0
AX-12364511346.8
AX-123615540113.0
AX-123615611113.1
AX-123643054121.9
AX-123615557122.0
CaLG01
AX-1236392650.0
AX-1236478340.1
AX-12362263914.3
AX-12362272914.4
AX-123621900153.4
AX-123621911167.6
AX-123639136178.7
AX-123621873178.8
AX-123659615194.4
AX-123621627194.5
CaLG03
AX-1236649210.0
AX-1236309010.1
AX-12363014686.4
AX-12364203887.5
AX-123623881216.2
AX-123615349217.6
AX-123624139223.1
AX-123648479230.3
AX-123624245230.4
CaLG04
AX-1236333160.0
AX-1236541170.1
AX-1236333310.8
AX-1236334501.3
AX-12364258543.6
AX-12366333443.7
AX-12363439587.9
AX-12363451988.0
AX-12363436989.5
AX-12366310489.6
AX-123634069103.5
AX-123634068103.8
CaLG06
qPLHT4.1
q100SW4.1
qPLHT4.2
qYPP4.1
AX-1236443280.0
AX-1236439940.1
AX-12361765212.7
AX-12364483313.4
AX-12364417015.0
AX-12364511346.8
AX-123615540113.0
AX-123615611113.1
AX-123643054121.9
AX-123615557122.0
CaLG01
AX-1236392650.0
AX-1236478340.1
AX-12362263914.3
AX-12362272914.4
AX-123621900153.4
AX-123621911167.6
AX-123639136178.7
AX-123621873178.8
AX-123659615194.4
AX-123621627194.5
CaLG03
AX-1236649210.0
AX-1236309010.1
AX-12363014686.4
AX-12364203887.5
AX-123623881216.2
AX-123615349217.6
AX-123624139223.1
AX-123648479230.3
AX-123624245230.4
CaLG04
AX-1236333160.0
AX-1236541170.1
AX-1236333310.8
AX-1236334501.3
AX-12364258543.6
AX-12366333443.7
AX-12363439587.9
AX-12363451988.0
AX-12363436989.5
AX-12366310489.6
AX-123634069103.5
AX-123634068103.8
CaLG06
qPPP6.1
qPLHT6.1
q100SW6.1
q100SW6.2
Theor Appl Genet (2014); Plant Biotechnol J. 2018
Int J Mol Sci (2020) Submitted…
High resolution trait mapping
Drought
Salinity Seed Size
Plant Genome (2020)
Helicoverpa
component traits

28. Wide phenotypic variability observed for the
11 nutritional traits
Trait (PVE %) Candidate gene analysis results for
significant MTAs
Beta carotene (15-20) CA_4 (Ca_03822)
Iron (10-17) CA_6 (Ca_08678)
Phytic acid (12-17) CA_1 (Ca_02905) ; CA_4 (Ca_03574)
Vitamin B1 (25-31) CA_1 (Ca_26128) ; CA_4 (Ca_12127)
CA_4 (Ca_03836) ; CA_5 (Ca_13399)
CA_6 (Ca_09604)
Zinc (11-15) CA_3 (Ca_12279)
Candidate gene analysis
GWAS for nutrition traits in chickpea
 Chickpea reference set analyzed for 11 nutritional traits
 237K markers from WGRS

29. Data management
 All the chickpea genotyping data in stored in GOBii database and public repositories
(NCBI; CEGSB open access)
 All the chickpea datasets
uploaded in ICRISAT dataverse

30. Challenges and way forward…
 Lack of funding support and recognition in different institutional
initiatives including CRP-GLDC, AVISA and CtEH
 No-clarity in activity alignment with GTD and Crop Improvement themes
with defined role and responsibilities and due recognition
 Need to support ICRISAT and NARS breeding programs through team of
specialists in genomics and molecular breeding, and information technology
to design breeding process
 Primary focus would be cost effective genotyping, pedigree verification
system, genome-wide marker based prediction and haplotype based
breeding through identification of novel superior haplotypes for target
traits

31. Strong collaboration – chickpea research

32. Thank you!