2015/12/05 by Bianca Dumitrascu, Dumitrascu, Bianca, Gregory Darnell +7
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Biological sciences #FOS: Computer and information sciences #Genetic Associations and Epidemiology #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Genomics (q-bio.GN) #Methodology (stat.ME) #Quantitative Methods (q-bio.QM) #q-bio.GN #q-bio.QM #stat.ME
paper · pdf · doi:10.48550/arxiv.1512.01616
arxiv created 2015/12/05 · openalex publication_date 2015/12/05 · arxiv updated 2015/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying genetic variants that regulate quantitative traits, or QTLs, is the primary focus of the field of statistical genetics. Most current methods are limited to identifying mean effects, or associations between genotype and the mean value of a quantitative trait. It is possible, however, that a genetic 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 methodological approach to identifying covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model. We show that our Bayesian test for heteroskedasticity (BTH) outperforms classical tests for differences in variation across a large range of simulations drawn from scenarios common to the analysis of quantitative traits. We apply BTH to methylation QTL study data and expression QTL study data to identify variance QTLs. When compared with three tests for heteroskedasticity used in genomics, we illustrate the benefits of using our approach, including avoiding overfitting by incorporating uncertainty and flexibly identifying heteroskedastic effects.