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.
Highlights
Maize (Zea mays L.) is one of the most important food, feed and industrial crops globally
We examined the genetic architecture of maize oil biosynthesis in 368 diverse maize inbred lines with over 1.06 million SNPs obtained from RNA sequencing and DNA array using the Genome-wide association studies (GWAS) strategy [5]
We developed a twostep data imputation method to meet the challenge of large proportion missing genotypes
Summary
Maize (Zea mays L.) is one of the most important food, feed and industrial crops globally. Identifying the underlying natural allelic variations for the phenotypic diversity will have immense practical implications in maize molecular breeding for improving nutritional quality, yield potential, and stress tolerance. Despite the great potential that GWAS has to pinpoint genetic polymorphisms underlying agriculturally important traits, false discoveries are a major concern and can be partially attributed to spurious associations caused by population structure and unequal relatedness among individuals in a given panel [6]. Zhao et al [8] performed GWAS using a NAIVE model in each subpopulation and MLM with inferred population structure as a fixed effect in the whole mapping panel of rice, and their results suggested that MLM may lead to false negatives by overcompensating for population structure and relatedness. To improve the MLM, some strategies to best utilize marker data have been
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