Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Bioinformatics. 2019 Jan 15;35(2):200-210. doi: 10.1093/bioinformatics/bty565.
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.
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.
Supplementary data are available at Bioinformatics online.
识别与定量性状（QT）相关的离散和连续变体是数量遗传学的主要关注点。大多数当前的方法仅限于识别均值效应，或基因型或协变量与定量性状的均值之间的关联。然而，变体可能会影响定量性状的方差，而不是（或除了）影响性状均值。在这里，我们开发了一种通用方法，使用贝叶斯异方差线性回归模型（BTH）来识别对定量性状具有方差效应的协变量。我们比较了 BTH 与现有方法，以检测从常见于定量性状分析的场景中得出的大量模拟中的方差效应。
我们发现 BTH 和双广义线性模型（dglm）在检测最近基因组研究中方差效应的经典测试中表现更好。我们表明，BTH 和 dglm 通过模拟和应用于鉴定甲基化方差 QT 和表达方差 QT 更不可能产生虚假发现。我们在心血管和药物遗传学研究中鉴定了四个性别方差效应。我们的工作是第一个提供方差识别方法学全面视图的工作。我们确定了先前使用的方法中的缺点，并提供了更保守和稳健的替代方法。我们将方差效应分析扩展到广泛的协变量，从而在研究性别和年龄特定定量性状效应方面提供了新的统计维度。
补充数据可在 Bioinformatics 在线获得。
