Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment. Issue 1 (16th November 2021) - Li, Yongle; Ruperao, Pradeep; Batley, Jacqueline; Edwards, David; Martin, William; Hobson, Kristy; Sutton, Tim
Abstract: Achieving yield potential in chickpea ( Cicer arietinum L.) is limited by many constraints that include biotic and abiotic stresses. Combining next‐generation sequencing technology with advanced statistical modeling has the potential to increase genetic gain efficiently. Whole genome resequencing data was obtained from 315 advanced chickpea breeding lines from the Australian chickpea breeding program resulting in more than 298, 000 single nucleotide polymorphisms (SNPs) discovered. Analysis of population structure revealed a distinct group of breeding lines with many alleles that are absent from recently released Australian cultivars. Genome‐wide association studies (GWAS) using these Australian breeding lines identified 20 SNPs significantly associated with grain yield in multiple field environments. A reduced level of nucleotide diversity and extended linkage disequilibrium suggested that some regions in these chickpea genomes may have been through selective breeding for yield or other traits. A large introgression segment that introduced from C. echinospermum for phytophthora root rot resistance was identified on chromosome 6, yet it also has unintended consequences of reducing yield due to linkage drag. We further investigated the effect of genotype by environment interaction on genomic prediction of yield. We found that the training set had better prediction accuracy when phenotyped under conditions relevant to the targeted environments. We also investigated the effect of SNP functional annotation on prediction accuracy using different subsets of SNPs based on their genomic locations: regulatory regions, exome, and alternative splice sites. Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs. Core Ideas: We recommend updating the training set with phenotypes from relevant environments for genomic selection. Subsetting SNP based on its functional annotations did not affect prediction accuracy for yield. An introgression segment for disease resistance has unintended consequences of reducing yield.