EcoTILLING-Based Association Mapping Efficiently Delineates Functionally Relevant Natural Allelic Variants of...

The large-scale mining and high-throughput genotyping of novel gene-based allelic variants in natural mapping population are essential for association mapping to identify functionally relevant molecular tags governing useful agronomic traits in chickpea. The present study employs an alternative time-saving, non-laborious and economical pool-based EcoTILLING approach coupled with agarose gel detection assay to discover 1133 novel SNP allelic variants from diverse coding and regulatory sequence components of 1133 transcription factor (TF) genes by genotyping in 192 diverse desi and kabuli chickpea accessions constituting a seed weight association panel. Integrating these SNP genotyping data with seed weight field phenotypic information of 192 structured association panel identified eight SNP alleles in the eight TF genes regulating seed weight of chickpea. The associated individual and combination of all SNPs explained 10-15 and 31% phenotypic variation for seed weight, respectively. The EcoTILLING-based large-scale allele mining and genotyping strategy implemented for association mapping is found much effective for a diploid genome crop species like chickpea with narrow genetic base and low genetic polymorphism. This optimized approach thus can be deployed for various genomics-assisted breeding applications with optimal expense of resources in domesticated chickpea. The seed weight-associated natural allelic variants and candidate TF genes delineated have potential to accelerate marker-assisted genetic improvement of chickpea.