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Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel.

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 (?=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 (?=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.

SUBMITTER: Yang N

PROVIDER: S-EPMC4161304 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel.

Yang Ning N Lu Yanli Y Yang Xiaohong X Huang Juan J Zhou Yang Y Ali Farhan F Wen Weiwei W Liu Jie J Li Jiansheng J Yan Jianbing J

PLoS genetics 20140911 9

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 51 ...[more]

PMID: 25211220

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Project description:BACKGROUNDRoot systems play important roles in crop growth and stress responses. Although genetic mechanism of root traits in maize (Zea mays L.) has been investigated in different mapping populations, root traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root traits and, more importantly, their connection to other agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root traits and other agronomic traits in a recombinant inbred line (RIL) population (Zong3?×?Yu87-1).RESULTSEight seedling root traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0-23.2% of the phenotypic variation in root traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root traits examined here and six other traits reported previously in the same RIL population, including grain yield-related traits, plant height-related traits, and traits in relation to stress responses. Maize chromosomal bins 1.02-1.03, 1.07, 2.06-2.07, 5.05, 7.02-7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more traits in addition to seedling root traits.CONCLUSIONSOur identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root traits with several other traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other agronomic traits simultaneously.

Dataset Information

Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel.

Publications

Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel.

Similar Datasets

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