Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data

• Published in: Frontiers in plant science Vol. 9; p. 190
• Main Authors: Li, Yongle, Ruperao, Pradeep, Batley, Jacqueline, Edwards, David, Khan, Tanveer, Colmer, Timothy D., Pang, Jiayin, Siddique, Kadambot H. M., Sutton, Tim
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.02.2018
• Subjects: auxin; chickpea; drought tolerance; genome-wide association mapping; genomic selection; Plant Science; whole-genome resequencing; genomic selection; drought tolerance; genome-wide association mapping; auxin; chickpea; whole-genome resequencing
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2018.00190

Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.