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QTL Mapping on a Background of Variance Heterogeneity

View ORCID ProfileRobert W. Corty, View ORCID ProfileWilliam Valdar
doi: https://doi.org/10.1101/276980
Robert W. Corty
*Department of Genetics, University of North Carolina, Chapel Hill, NC
†Bioinformatics and Computational Biology Curriculum, University of North Carolina, Chapel Hill, NC
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William Valdar
*Department of Genetics, University of North Carolina, Chapel Hill, NC
‡Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
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  • For correspondence: william.valdar@unc.edu
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 18, 2018.
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QTL Mapping on a Background of Variance Heterogeneity
Robert W. Corty, William Valdar
bioRxiv 276980; doi: https://doi.org/10.1101/276980
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QTL Mapping on a Background of Variance Heterogeneity
Robert W. Corty, William Valdar
bioRxiv 276980; doi: https://doi.org/10.1101/276980

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