An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers.

Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML's ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.

The Use of Targeted Marker Subsets to Account for Population Structure and Relatedness in Genome-Wide Association Studies of Maize (Zea mays L.).

A typical plant genome-wide association study (GWAS) uses a mixed linear model (MLM) that includes a trait as the response variable, a marker as an explanatory variable, and fixed and random effect covariates accounting for population structure and relatedness. Although effective in controlling for false positive signals, this model typically fails to detect signals that are correlated with population structure or are located in high linkage disequilibrium (LD) genomic regions. This result likely arises from each tested marker being used to estimate population structure and relatedness. Previous work has demonstrated that it is possible to increase the power of the MLM by estimating relatedness (i.e., kinship) with markers that are not located on the chromosome where the tested marker resides. To quantify the amount of additional significant signals one can expect using this so-called K_chr model, we reanalyzed Mendelian, polygenic, and complex traits in two maize (Zea mays L.) diversity panels that have been previously assessed using the traditional MLM. We demonstrated that the K_chr model could find more significant associations, especially in high LD regions. This finding is underscored by our identification of novel genomic signals proximal to the tocochromanol biosynthetic pathway gene ZmVTE1 that are associated with a ratio of tocotrienols. We conclude that the K_chr model can detect more intricate sources of allelic variation underlying agronomically important traits, and should therefore become more widely used for GWAS. To facilitate the implementation of the K_chr model, we provide code written in the R programming language.