Integrating genomics for chickpea improvement: achievements and opportunities

Abstract

Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

Fig. 1

Applications of genomic technologies for bridging the genotype–phenotype gap in chickpea. An overview of genetic resources together with genomic resources and technologies for bridging the genome-to-phenome gap to produce climate-resilient chickpea varieties. Sequencing/genotyping of chickpea germplasm resources, such as composite collection, mini-core and reference set, can be performed. Similarly, elite varieties, mapping populations including bi-parental (recombinant inbred lines, RILs; introgression lines, ILs; F2), multi-parental (multi-parent advanced generation intercrossing, MAGIC; nested association mapping, NAM) populations segregating for important agronomic traits, and mutant populations can also be used. With the availability of the reference genome, these genetic resources can be subjected to whole-genome re-sequencing (WGRS) or high- to low-density genotyping, based on the objective of the study, using the available genotyping platforms (e.g. genotyping by sequencing, GBS; array-based genotyping). Analysis at the transcriptome, proteome and metabolome levels can be performed to gain novel insights into the candidate genes and biological processes involved. A pangenome can be constructed to capture the entire set of genes from Cicer species. Analysis of this sequencing/genotyping data along with phenotyping data with high-throughput decision support technologies can provide solutions for genetic diversity analysis, genetic mapping and QTL analysis, identify candidate genes and superior haplotypes, and develop diagnostic marker, early generation selection, marker-assisted backcrossing (MABC) and genomic selection (GS). Integration of such resources should bridge the genotype–phenotype gap and accelerate the development of climate change ready varieties with higher yields, improved resistance against biotic and abiotic stresses and enhanced genetic gains in farmers’ fields, particularly in the dryland tropics.

Fig. 2

Sequence-based holistic breeding approach to accelerate genetic gains in chickpea breeding. Integration of functional omics, GWAS, parent selection, high-throughput sequencing and phenotyping, and genomic selection to improve and accelerate the development of superior chickpea varieties in breeding. Multi-omics data can enable positional cloning by providing information on genes/superior alleles in the target region. High-throughput sequencing and multi-location phenotyping will harness superior alleles with diverse genetic resources. Newly identified genes/superior alleles can feed directly into the breeding population for parental selection. Models can also be used to predict the genomic estimated breeding values (GEBVs) based on high-coverage sequencing and phenotyping data. The integration of sequencing-based approaches with breeding programmes will be reflected in high productivity gains under limited resources and time