Genome Wide Association Studies Using a New Nonparametric Model Reveal the Genetic Architecture of 17 Agronomic Traits in an Enlarged Maize Association Panel

TLDR

Association mapping uses high‑density SNP markers in maize to dissect the genetic architecture of complex quantitative traits. The study expanded the association panel to 513 inbred lines, imputed missing genotypes with an IBD‑based projection plus KNN algorithm, and performed GWAS on 17 agronomic traits using both a mixed linear model and a new Anderson‑Darling test. Using the MLM, ten loci for five traits were identified, whereas the Anderson‑Darling test uncovered 107 loci for plant height and up to 34 loci for other traits, revealing many known and novel loci and demonstrating that the IBD‑KNN imputation is efficient and that the A‑D test complements GWAS, especially for traits with abnormal distributions, thereby providing a rich resource of candidate SNPs and genes for maize breeding.

Abstract

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold −log10 (P) >5.74 (α = 1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold −log10 (P) >7.05 (α = 0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.