Abstract
Key messageIntegration 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.
Highlights
Chickpea (Cicer arietinum L.) is one of the most economically important food legume crops
In Ethiopia, a multi-location evaluation of the marker-assisted backcrossing (MABC) lines facilitated the release of a high-yielding chickpea variety, locally named as ‘Geletu’—means thanks God—with pedigree [(JG 11 × ICC 4958) × 3*JG 11] – 29], which was named after the eminent pulse scientist late Dr Geletu Bejiga from EIAR, Ethiopia
Optimum genomic resources are available for chickpea, which can be used in breeding programmes
Summary
Chickpea (Cicer arietinum L.) is one of the most economically important food legume crops. The GBS approach has been used to detect genome-wide SNPs in chickpea to understand allelic diversity and population structure, and develop high-density linkage maps, QTL analysis, GWAS and GS. The GBS approach has been widely used for linkage mapping and QTL detection of ascochyta blight resistance (Deokar et al 2019a), heat tolerance (Paul et al 2018), seed iron and zinc concentrations (Upadhyaya et al 2016) and seed quality (Verma et al 2015) among others, using RIL populations in chickpea (Table 1) This technology has been used to identify and validate SNPs from cultivated and wild Cicer accessions to study allelic diversity, population structure and linkage disequilibrium patterns in these gene pools (Bajaj et al 2015; Kujur et al 2015; Pavan et al 2017). GBS was used for high-resolution association analysis and GWAS integrated with QTL mapping and transcript profiling in germplasm
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