Simulation study evaluating the ability of two statistical approaches to identify variance quantitative trait loci Arabidopsis and maize

Abstract

AbstractGenomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance quantitative trait loci (vQTL) in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used to detect vQTL are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that they are deployed to variance genome-wide association studies as effectively as possible, it is critical to study the factors that influence their ability to identify vQTL. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by variance quantitative trait nucleotides (vQTNs) and then quantified true and false positive detection rates of the BFT and DGLM. We observed that the DGLM yielded similar or higher true positive vQTN detection rates than the BFT, regardless of the effect size or minor allele frequency (MAF) of the vQTNs. Low true positive detection rates were noted for QTNs with low MAFs (~0.10), especially when tested on subsets of n = 500 individuals. We recommend that larger data sets than those used in our study (i.e., n > 2,532) be considered to overcome these low observed true positive detection rates. Such an undertaking should maximize the potential of the BFT and DGLM to highlight which vQTLs should be considered for further study.