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Statistical tests for detecting variance effects in quantitative trait studies

Author(s): Dumitrascu, Bianca; Darnell, Gregory; Ayroles, Julien; Engelhardt, Barbara E

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Abstract: Motivation Identifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits. Results We find that BTH and a double generalized linear model (dglm) outperform classical tests used for detecting variance effects in recent genomic studies. We show BTH and dglm are less likely to generate spurious discoveries through simulations and application to identifying methylation variance QTs and expression variance QTs. We identify four variance effects of sex in the Cardiovascular and Pharmacogenetics study. Our work is the first to offer a comprehensive view of variance identifying methodology. We identify shortcomings in previously used methodology and provide a more conservative and robust alternative. We extend variance effect analysis to a wide array of covariates that enables a new statistical dimension in the study of sex and age specific quantitative trait effects. Availability and implementation Supplementary information Supplementary data are available at Bioinformatics online.
Publication Date: 2019
Citation: Dumitrascu, Bianca, Gregory Darnell, Julien Ayroles, and Barbara E. Engelhardt. "Statistical tests for detecting variance effects in quantitative trait studies ." Bioinformatics 35, no. 2 (2019): pp. 200-210. doi:10.1093/bioinformatics/bty565
DOI: 10.1093/bioinformatics/bty565
ISSN: 1367-4803
Pages: 200 - 210
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Bioinformatics
Version: Final published version. This is an open access article.

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