A quantile integral linear model to quantify genetic effects on phenotypic variability

Jiacheng Miao, Yupei Lin, Yuchang Wu, Boyan Zheng, Lauren L. Schmitz, Jason Fletcher and Qiongshi Lu
Additional contact information
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;
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

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 39, e2212959119

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.

Keywords: vQTL; quantile regression; GxE; vPGS (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.pnas.org/content/119/39/e2212959119.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:119:y:2022:p:e2212959119

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().