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Title: Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population

item BIAN, YANG - North Carolina State University
item YANG, QIN - North Carolina State University
item Balint-Kurti, Peter
item WISSER, RANDALL - University Of Delaware
item Holland, Jim - Jim

Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/19/2014
Publication Date: 12/5/2014
Citation: Bian, Y., Yang, Q., Balint Kurti, P.J., Wisser, R., Holland, J.B. 2014. Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population. Biomed Central (BMC) Genomics. 15:1068.

Interpretive Summary: In this work we revisited a previous study in which we had mapped loci (ie positions in the genome) associated with resistance to southern leaf blight (SLB) disease of corn. We had determined the disease resistance levels of a mapping population of almost 5000 lines and had used a detailed genetic map of this population (1106 markers on the linkage map and 1.6 million genomic variants for association analysis) to identify loci associated with the trait. Here we used the same phenotypic information combined with updated genotypic information (7000 linkage markers, 28.5 million association analysis markers) to perform a reanalysis. We show that this reanalysis resulted in more accurate identification of associated loci, report candidate genes and discuss the reasons for the differences in the results of the two analyses.

Technical Abstract: A previous study reported a comprehensive quantitative trait locus (QTL) and genome wide association study (GWAS) of southern leaf blight (SLB) resistance in the maize Nested Association Mapping (NAM) panel. Since that time, the genomic resources available for such analyses have improved substantially. An updated NAM genetic linkage map has a nearly six-fold greater marker density than the previous map and the combined SNPs and read depth variants (RDVs) from maize HapMaps 1 and 2 provided 28.5 M genomic variants for association analysis, 17 fold more than HapMap 1. In addition, phenotypic values of the NAM RILs were re-estimated to account for environment-specific flowering time covariates and a small proportion of lines were dropped due to genotypic data quality problems. We evaluated the effects of changing linkage map density, GWAS marker density, population sample size, and phenotype estimates on QTL and GWAS analysis results. Of the four parameters varied, map density caused the largest changes in QTL and GWAS results. The updated QTL model had better cross-validation prediction accuracy than the previous model. Whereas joint linkage QTL positions were relatively stable to input changes, the residual values derived from those QTL models (used as inputs to GWAS) were more sensitive, resulting in substantial differences between GWAS results. The updated NAM GWAS identified several candidate genes consistent with previous QTL fine-mapping results. The highly polygenic nature of resistance to SLB complicates the identification of causal genes. Joint linkage QTL are relatively stable to perturbations of data inputs, but their resolution is generally on the order of tens or more Mbp. GWAS associations have higher resolution, but lower power due to stringent thresholds designed to minimize false positive associations, resulting in variability of detection across studies. The updated higher density linkage map improves QTL estimation and, along with a much denser SNP HapMap, greatly increases the likelihood of detecting SNPs in linkage with causal variants. We recommend use of the updated genetic resources and results but emphasize the limited repeatability of small-effect associations.