An efficient and cost-effective approach for genic microsatellite marker-based large-scale trait association mapping: identification of candidate genes for seed weight in chickpea

Abstract

The large-scale validation and high-throughput genotyping of numerous informative genic microsatellite markers are required for association mapping to identify candidate genes for complex quantitative traits in chickpea. However, the screening and genotyping of such informative markers in individual genotypes/whole association panels for trait association mapping involves massive costs in terms of resources, time and labour due to low genetic polymorphism in chickpea. We have developed an alternative time-saving and cost-effective pool-based trait association mapping approach by combining pooled DNA analysis (with 616 genic microsatellite markers) and individual genotype (large structured association panel) genotyping. Using this approach we have identified seven seed weight-associated transcription factor gene-derived microsatellite markers (with minor allele frequency >15 %) in desi and kabuli chickpea. Strong marker allele effects of these five transcription factors with increasing seed weight in the contrasting desi and kabuli genotypes were evident. Bi-parental linkage mapping using 241 of the informative gene-based microsatellite markers resulted in the identification and mapping of nine such markers linked with three major quantitative trait loci (explaining a total phenotypic variance of 23.5–34.7 %) on chromosomes 1 (CaqSW1.1: 73.5–74.5 cM and CaqSW1.2: 79.3–81.3 cM) and 2 (CaqSW2.1: 65.7–67.5 cM) controlling 100-seed weight in chickpea. The integration of pool-based trait association mapping with differential expression profiling, traditional bi-parental linkage mapping and high-resolution microsatellite-single nucleotide polymorphism marker-based haplotyping/linkage disequilibrium mapping delineated four transcription factor genes (DUF3594, bZIP, DUF1635 and SBP) controlling seed weight in desi and kabuli chickpea. The strategies implemented in our study can be used in large-scale trait association mapping for the rapid identification of candidate genes and in the development of functional markers for traits of agricultural importance in crop species including chickpea.