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

Jiacheng Miao1, Yupei Lin2, Yuchang Wu1

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI 53706.

Proceedings of the National Academy of Sciences of the United States of America
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method, QUAIL, to detect genetic effects on trait variability, identifying novel genetic variants for body mass index (BMI). This approach enhances understanding of gene-environment interactions and improves prediction of BMI variability.

Keywords:
GxEquantile regressionvPGSvQTL

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Area of Science:

  • Genetics
  • Quantitative Trait Analysis
  • Bioinformatics

Background:

  • Understanding genetic influences on complex traits is crucial for population health.
  • Genetic variants can affect not only the average phenotype but also its variability.
  • Existing methods for detecting genetic effects on trait variability (vQTLs) have limitations.

Purpose of the Study:

  • To introduce a novel statistical model, the quantile integral linear model (QUAIL), for identifying variance quantitative trait loci (vQTLs).
  • To assess the computational efficiency, statistical power, and robustness of QUAIL.
  • To apply QUAIL to a large dataset to discover novel vQTLs and evaluate variance polygenic scores (vPGSs).

Main Methods:

  • Development of the quantile integral linear model (QUAIL) for vQTL mapping.
  • Extensive simulations to evaluate QUAIL's performance under various conditions.
  • Application of QUAIL to UK Biobank data (n=375,791) for body mass index (BMI) analysis.
  • Calculation and evaluation of variance polygenic scores (vPGSs) using QUAIL estimates.

Main Results:

  • QUAIL demonstrated computational efficiency and statistical power in vQTL mapping.
  • QUAIL is robust to non-Gaussian phenotypes and confounding factors affecting variability.
  • QUAIL identified 11 novel vQTLs for BMI in the UK Biobank cohort.
  • Top vQTL findings were enriched for interactions with physical activity and sedentary behavior.
  • vPGSs derived from QUAIL showed superior predictive performance for BMI variability.

Conclusions:

  • QUAIL provides a unified and powerful framework for quantifying genetic effects on phenotypic variability at single-variant and vPGS levels.
  • The method addresses critical limitations of existing vQTL approaches.
  • QUAIL has broad applicability for future gene-environment interaction studies and understanding complex trait variation.