Rajeev K Varshney FRS

Chickpea (Cicer arietinum L.) plays a vital role in food systems and sustainable agriculture, but its genetic improvement has been hindered by a narrow cultivated gene pool and limited integration of modern genomic tools. This restricted diversity limits the opportunities for breeding chickpea varieties more resilient to biotic and abiotic stresses. The germplasm maintained by the International Center for Agricultural Research in the Dry Areas (ICARDA) offers a valuable reservoir of haplotype diversity that can drive genetic gain when effectively harnessed through genomics-enabled breeding strategies. In this study, we developed a haplotype catalogue with 289 diverse ICARDA chickpea accessions by combining high-density genotyping with yield phenotypic data. Linkage disequilibrium haploblocks were defined based on recombination patterns observed in the genotyping data. Each haplotype, defined by SNP combinations within a block, was assigned a local genomic estimated breeding value (localGEBV) based on the sum of SNP effect estimates. We investigated haploblocks with high variance for haplotype effect across the genome, and targeted superior haplotypes with strong, positive effect on yield. Using AI-guided parent selection and simulation-based haplotype stacking, we investigated optimal parental combinations and crossing pathways to accumulate favourable haplotypes for yield improvement that can outperform traditionnal selection strategies while maintaining genetic diversity. This approach provides a practical support tool for ICARDA and Australian breeders to develop high performing chickpea lines. The discovery of superior haplotypes within ICARDAs' germplasm can also benefit other breeding programs by enriching their genetic base with valuable and novel diversity

Groundnut, an allotetraploid legume crop, serves as an important source of food, feed, and confectioneries worldwide. Translational genomics research has accelerated precision breeding efforts in groundnut using marker-assisted and genomic selection approaches. To support these efforts, three cost-effective and high-throughput genotyping panels have been developed to facilitate genetic studies and breeding applications. We developed high density genotyping array with 58K SNP markers which has been extensively used by global peanut research community. The first mid-density genotyping assay, comprises 5081 SNP markers, including 5000 highly informative SNPs with high polymorphism information content (PIC) derived from previously developed high-density ‘Axiom_Arachis’ array of 58,233 SNPs. An additional 81 SNPs associated with resilience and quality traits were incorporated for marker-assisted selection. This panel demonstrated robust performance, with PIC values ranging from 0.34 to 0.37 and explained 82.08% of genetic variance across 2573 genotypes from diverse sets of breeding populations. This panel holds promise for possible deployment in the identification of varietal seed mixture, genetic purity within gene bank germplasms and seed systems, foreground and background selection in backcross breeding programs, genomic selection, and sparse trait mapping studies in groundnut. The second panel, a mid-density array of 2500 SNP markers with an average density of 1 SNP per Mbp, is powerful tool for global groundnut breeding programmes. These SNPs were identified through whole-genome resequencing of 263 cultivated groundnut accessions representing diverse agronomic types and geographical regions. The panel includes 20-quality control (QC) and 72 associated markers for 8 key traits, namely leaf rust resistance, late leaf spot resistance, net blotch resistance, high oleic acid, seed weight, shelling percentage, fresh seed dormancy and blanchability. Designed through rigorous filtering processes, this panel is particularly suited for marker-assisted backcrossing, diversity analyses, and to study genetic purity for efficient germplasm management. Together, these genotyping panels provide versatile tools for advancing groundnut breeding programs, enabling precise and efficient genetic interventions for sustainable crop improvement.

In recent years, a truly impressive number of advances in genetics and genomics have greatly enhanced our understanding of structural and functional aspects of plant genomes. The complete genome sequences have become available for many crop species now. Due to advances in next generation sequencing technologies, it is possible to re-sequence mapping populations as well as germplasm collections in large numbers. In addition to routinely used linkage mapping and marker-assisted selection (MAS) approaches, several novel genetic and genomics tools/approaches such as genome-wide association studies (GWAS), marker-assisted recurrent selection (MARS), genomic selection (GS), functional genomics, etc. offer the possibilities to examine and utilize the structural as well as functional genetic variation in crop breeding. Holistic approach of enhancing the prediction of the phenotype from a genotype using genomics tools/approaches has been termed as 'genomics assisted breeding' (GAB) (Trends Plant Sci 10: 621 630). In fact, genomics assisted breeding has already shown its potential for crop improvement in several cereal species (Trends Biotech 24:490-499) and also in few legume species (Plant Breeding Rev 33:257-304). A critical assessment of the status and availability of genomic resources and genomics research in crop plant species and devising the strategies and approaches for effectively exploiting genomics research for crop improvement is the need of the hour (Nature Biotechnology 30:1172–1176). Genomics specialists having extensive experience in applying genomics in breeding from both public and private sectors share their experience and propose novel ideas in this workshop to use GAB in an efficient manner.

Chickpea (Cicer arietinum L.) is an important pulse crop grown in more than 50 countries across the globe especially in South Asia and Sub-Saharan Africa. During the last decade, genomic revolution empowered the chickpea community with large scale genomic resources for understanding the genetics of the trait and trait improvement using modern breeding approaches. Deploying the available genomic resources, we dissected important abiotic and biotic stresses that hinder chickpea production. The “QTL-hotspot” on CaLG04 explaining more than 58% phenotypic variation was introgressed into different elite backgrounds in India and Africa. Two high yielding and drought tolerant varieties, Geletu (in the background of ICCV 10) and BGM 10216 (in the background of Pusa 362) were released for commercial cultivation in Ethiopia and India respectively. In addition, MABC-WR-SA-1 a high yielding and Fusarium wilt resistant variety developed using marker assisted backcrossing approach was also release for comer cultivation in India. Following deciphering of the draft genome sequence of CDC Frontier variety, we re-sequenced >3000 chickpea germplasm accessions (that include global composite collection and 195 wild species accessions from primary, secondary and tertiary gene pools) at an average ~12X coverage. Large scale resequencing provided greater insights to genome-wide variations, the haplotype diversity, mutation burden, deleterious alleles, bottlenecks and selections sweeps during domestication. Extensive multi-location phenotyping data and the genome wide SNPs enabled us to identify genome-wide associations for agronomically important and > 40 nutritional quality traits. Furthermore, we are using these datasets for genomic prediction for developing climate resilient chickpea varieties.

Chickpea (Cicer arietinum) is the second most important food legume globally, which plays a key role in ensuring the nutritional food security. Average chickpea productivity has been restricted to ~ 1 t h-1 due to several biotic and abiotic stresses. Prolonged use of conventional breeding approaches have started to fall short of meeting the yield and nutrition demands. To address the issues related to complex traits such as yield which is controlled by multiple QTLs, genomic selection (GS) approach can be very useful in crop breeding to capture several genes with minor additive effects. GS offers breeders to select lines prior to field phenotyping using genotyping data, resulting in reduced cost and shortening of selection cycles. Initial results on GS in chickpea using 320 elite breeding lines suggested high prediction accuracies for diverse yield and yield related traits. Inclusion of G x E effects in GS models has shown significant improvement of prediction accuracies in breeding programs. In order to assess the potential of GS in chickpea breeding program 6000 F5 lines form 12 different crosses from ICRISAT and IARI breeding programs were selected and genotyped using DArTseq platform. After merging the markers from the training and prediction sets, which were run on different DArT marker platforms (DArTseq and LD DArT), about a thousand markers were used to run the prediction models. The cross validation prediction accuracy were run with a 10-fold consolidation scheme. Each cross validation was repeated 10 times with new random folds, and the mean of the prediction accuracies was calculated. To compare the potential of GS models, two set of ~200 lines each were identified based on visual selection by breeder and based on genomic prediction based GEBVs. Both of these set were evaluated in the field conditions during 2018-19. Selection efficiency of GS over visual phenotypic selection was found significantly better. Genomic prediction based line selection over visual selection saves time and cost involved in large scale screening of populations.

Significant advances in recent years have enhanced our understanding of structural and functional aspects of plant genomes. Complete genome sequence has become available for some plant species, while draft genome sequences are being assembled for others. Advances in next-generation sequencing and high-throughput genotyping technologies have made it possible to not only genotype at high-density, but to even re-sequence the mapping populations or germplasm collections. Routine linkage mapping approaches are being transformed to genome-wide association studies (GWAS) for the identification of important marker-trait associations. Marker-assisted selection (MAS) has become an integral part of crop breeding programmes in developed countries/private sector, and in developing countries the use of MAS in breeding programmes has been applied to develop superior varieties/hybrids with better agronomic performance and enhanced resistance/tolerance to biotic/abiotic stresses. New breeding approaches such as marker-assisted back crossing (MABC), marker-assisted recurrent selection (MARS) and genomic selection (GS) are also being used for improving complex traits such as drought tolerance. This workshop is planned to invite genomics specialists with extensive experience in genomics-assisted breeding from both public and private sectors to share their experience and propose novel ideas to translate existing genomics knowledge toward the development of new varieties/hybrids. This workshop is expected to catalyze public sector breeding programmes in Asia and Africa to undertake/accelerate translational genomics that will ensure food security in developing countries.

Targeting Induced Local Lesions in Genomes (TILLING) is considered a powerful reverse genetics approach for functional genomics studies. However, because of availability of low-cost and high-throughput sequencing technology, it has become possible to sequence TILLING lines and identify SNPs associated with genes responsible for traits. One TILLING population has been developed in the “Tifrunner” genotype of groundnut, an economically important oilseed crop grown in tropical and warm temperate regions of the world. The TILLING population has shown phenotypic variation for several traits including resistance to leaf spots and the features of prominent main stem. A total of 25 lines comprising of 16 susceptible and 9 resistant lines for leaf spots, and 11 lines with presence and 14 lines with absence of the prominent main stem from the TILLING population were sequenced on Illumina HiSeq 2500 and a total of 745.8 Gb sequencing data has been generated. These sequence data are being analyzed to identify structural variations including SNPs and INDELs across the lines with Tifrunner. In parallel, two mapping populations from these TILLING lines namely T47-7 (resistant to leaf spots) x T33-3 (susceptible to leaf spots) and T90-1 (presence of stem) x T71-2 (absence of stem) are being developed. It is planned to phenotype the segregating progenies and also sequence the extreme bulksA of segregating progenies for these traits. We anticipate identification of candidate genes and SNPs for these important traits by deploying the BSA-Seq approach in groundnut in due course.