Author
Listed:
- Jiacheng Miao
(a Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706;)
- Yupei Lin
(b Baylor College of Medicine, Houston, TX 77030;)
- Yuchang Wu
(a Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706;)
- Boyan Zheng
(c Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706;)
- Lauren L. Schmitz
(d Robert M. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI 53706;; e Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706;)
- Jason M. Fletcher
(c Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706;; d Robert M. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI 53706;; e Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706;)
- Qiongshi Lu
(a Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706;; e Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706;; f Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706)
Abstract
Detecting genetic variants associated with the variance of complex traits can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a new method to estimate genetic effects on trait variability that address critical limitations in existing approaches. Applied to UK Biobank, our method identified 11 variance quantitative trait loci (vQTLs) for body mass index (BMI) that have not been previously reported. Variance polygenic scores based on our method’s effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. It is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and variance polygenic score levels and may have broad applications in future gene–environment interaction studies.
Suggested Citation
Jiacheng Miao & Yupei Lin & Yuchang Wu & Boyan Zheng & Lauren L. Schmitz & Jason M. Fletcher & Qiongshi Lu, 2022.
"A quantile integral linear model to quantify genetic effects on phenotypic variability,"
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(39), pages 2212959119-, September.
Handle:
RePEc:nas:journl:v:119:y:2022:p:e2212959119
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