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

Angela H Chen,Liudmila Sergeevna Mainzer,Alexander E Lipka,Eric Jakobsson,William Metcalf,Weihao Ge

doi:10.1038/s41437-018-0162-2

Abstract

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.

Highlights

FastEpistasis, which tests the epistatic effect of one pair of loci at a time, tended to yield higher false positive (FP) rates than the other two stepwise approaches, while SPAEML tended to have low FP rates at the maximum sample sizes in both data sets (Supplementary Figure 2)
We found SPAEML to be much more capable of distinguishing between additive and epistatic signals for traits simulated in the human data set, despite that the same number of markers, similar number of individuals, and the same simulated genetic architectures were evaluated in the maize and human data sets
To ensure that the most appropriate biological conclusions are made by breeding, medical, and quantitative genetics research communities, it is imperative that statistical models which approximate the genetic architecture of traits are accurate

Summary

Introduction

The ability to identify genomic regions containing gene(s) associated with quantitative phenotypes has great potential for elucidating the genetic architecture of traits (e.g., number of genes, their effect sizes, additive vs non-additive sources), as well as identifying targets for marker-assisted selection in plants and animals and therapy in humans. One analysis that seeks to identify such regions is the genome-. Statistically significant marker-trait associations suggest that functional variants for the trait under study are located in the surrounding genomic region. The ability of GWAS to identify specific genomic regions associated with traits critical for human health and agronomic performance has been demonstrated, and continued refinement of the statistical approaches in GWAS could make this analysis even more relevant for quantitative genetics research and its applications

Methods

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Discussion

Conclusion