QTL Mapping on a Background of Variance Heterogeneity

• Published in: G3 : genes - genomes - genetics Vol. 8; no. 12; pp. 3767 - 3782
• Main Authors: Corty, Robert W, Valdar, William
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.12.2018; Genetics Society of America
• Subjects: background variance heterogeneity; Cao’s tests; Chromosome Mapping; Generalized linear models; Genetic Variation; heteroscedastic; heteroskedastic; Investigations; Models, Genetic; Quantitative Trait, Heritable; reweighting; variable transformation; vQTL; heteroskedastic; Cao’s tests; heteroscedastic; vQTL; background variance heterogeneity; variable transformation; reweighting
• ISSN: 2160-1836; 2160-1836
• DOI: 10.1534/g3.118.200790

Abstract Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such “background variance heterogeneity” (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene’s test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term “mean-variance QTL mapping”, to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.