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

Abstract

BackgroundA 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. Comparisons of original and updated QTL and GWAS results confound the effects of linkage map density, GWAS marker density, population sample size, and phenotype estimates. Therefore, we evaluated the effects of changing each of these parameters individually and in combination to determine their relative impact on marker-trait associations in original and updated analyses.ResultsOf 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1068) contains supplementary material, which is available to authorized users.

Maize Homologs of CCoAOMT and HCT, Two Key Enzymes in Lignin Biosynthesis, Form Complexes with the NLR Rp1 Protein to Modulate the Defense Response

“…An Arabidopsis CCoAOMT mutant line displayed increased disease symptoms (SenthilKumar et al, 2010). CCoAOMT was also identified as a candidate gene in resistance to maize southern leaf blight from the NAM-GWAS analysis (Kump et al, 2011;Bian et al, 2014), suggesting a possible association between disease resistance and HR in this case.…”

Quantitative Resistance: More Than Just Perception of a Pathogen

“…Interestingly, Bian et al (2014) found an association between flowering time and southern leaf blight resistance among lines within the NAM population. Controlling for this association did not dramatically affect the number or identity of loci found associated with resistance, but it raises the question of pleiotropy and potential phenotypic trade-offs among traits in the measurement of quantitative disease resistance.…”

Genetic Architecture of Domestication-Related Traits in Maize

“…imputed recombinants as described above); b i is a vector of the family-specific additive effects associated with locus i relative to B73; k is the number of significant loci in the final model selected via a stepwise selection and optimization process (Bian et al 2014); and e is the residual vector. Segregation for the masculinized ear tip traits was restricted to the B73 3 CML69 RIL population.…”

“…where Y is a vector of BLUE values for each inbred line for a given phenotype; A is an incidence matrix relating RILs to their corresponding population p; m is a vector of population main effects; X i is an incidence matrix indicating that RIL's genotype score at locus i, and the elements of X i are estimated dosages of the non-B73 parental allele at SNP i (coded as "0" for lines homozygous for the B73 reference allele, "2" for homozygotes with the alternate parental allele, "1" for heterozygotes, and a noninteger between 0 and 2 for the imputed recombinants as described above); b i is a vector of the family-specific additive effects associated with locus i relative to B73; k is the number of significant loci in the final model selected via a stepwise selection and optimization process (Bian et al 2014); and e is the residual vector. Segregation for the masculinized ear tip traits was restricted to the B73 3 CML69 RIL population.…”

“…Each family has 200 RILs, resulting in a total population size of 4892 (McMullen et al 2009;Bian et al 2014). Cob length values for NAM RILs were taken from Hung et al (2011).…”