A Quantile Integral Linear Model to Quantify Genetic Effects on Phenotypic Variability

• Activity Number: 248 - Recent Advances in Genetic Association and Gene-Environment Interaction Studies
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jiacheng Miao* and Yupei Lin and Yuchang Wu and Boyan Zheng and Lauren Schmitz and Jason Fletcher and Qiongshi Lu
• Companies: University of Wisconsin–Madison and Baylor College of Medicine and University of Wisconsin–Madison and University of Wisconsin–Madison and Robert M. La Follette School of Public Affairs, University of Wisconsin–Madison and University of Wisconsin–Madison and University of Wisconsin–Madison
• Keywords: vQTL; quantile regression; gene-environment interaction; vPGS

Abstract

Detecting genetic variants associated with the variance of complex traits (vQTL) can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank, QUAIL identified 11 novel vQTL for BMI. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Further, variance polygenic scores based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.