Genome-wide association analysis and genomic selection for leaf-related traits of maize. - Yukang Zeng; Xiaoming Xu; Jiale Jiang; Shaohang Lin; Zehui Fan; Yao Meng; Atikaimu Maimaiti; Penghao Wu; Jiaojiao Ren (PLoS ONE)
Maize is an important food crop worldwide. The length, width, and area of leaves are crucial traits of plant architecture and further influencing plant density, photosynthesis, and crop yield. To dissect the genetic architecture of leaf length, leaf width, and leaf area, a multi-parents doubled haploid (DH) population was used for genome-wide association study (GWAS) and genomic selection (GS). The length, width, and area of the first leaf above the uppermost ear, the uppermost ear leaf, and the first leaf below the uppermost ear were evaluated in multi-environment trials. Using BLINK and FarmCPU for GWAS, 19 significant single nucleotide polymorphisms (SNPs) on chromosomes 1, 2, 5, 6, 8, 9, and 10 were associated with leaf length, 49 SNPs distributed over all 10 chromosomes were associated with leaf width, and 37 SNPs distributed on all 10 chromosomes except for chromosome 3 were associated with leaf area. The phenotypic variation explained (PVE) by each QTL ranged from 0.05% to 27.46%. Fourteen pleiotropic SNPs were detected by at least two leaf-related traits. A total of 57 candidate genes were identified for leaf-related traits, of which 44 were annotated with known functions. Candidate genes Zm00001d032866, Zm00001D022209, and Zm00001d001980 are involved in leaf senescence. Zm00001d026130, Zm00001d002429, Zm00001d023225, and Zm00001d046767 play important roles in leaf development. GS analysis showed that when 60% of the total genotypes was used as the training population and 3000 SNPs were used for prediction, moderate prediction accuracy was obtained for leaf length, leaf width, and leaf area. The prediction accuracy would be improved by using top significantly associated SNPs for GS. The current study provides a better understanding of the genetic basis of leaf length, leaf width, and leaf area, and valuable information for improving plant architecture by implementing GS.