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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.

Jianjun Zhang1, Qiuying Sha2, Han Hao1

  • 1Department of Mathematics, University of North Texas, Denton, Texas, USA.

Human Heredity
|May 18, 2020
PubMed
Summary

New methods for analyzing gene-environment interactions (G×Es) across multiple traits improve disease gene discovery. These approaches enhance power for detecting G×Es in both rare and common variants using sequencing association studies.

Keywords:
Fisher’s combination testGene-environment interactionsPrincipal component analysisStandardization analysis

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Area of Science:

  • Genetics
  • Biostatistics
  • Complex Disease Etiology

Background:

  • Complex diseases arise from gene-environment interactions (G×Es).
  • Studying G×Es for multiple traits offers insights into disease origins and enhances gene detection.
  • Current methods for multi-trait G×E analysis are limited.

Purpose of the Study:

  • To develop novel statistical approaches for testing gene-environment interactions (G×Es) across multiple traits in sequencing association studies.
  • To enhance the power and robustness of G×E detection for complex diseases.

Main Methods:

  • Trait transformation using principal component or standardization analysis.
  • Novel tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and variable weight TOW-GE (VW-TOW-GE).
  • Fisher's combination test to aggregate p-values for multi-trait G×E analysis.

Main Results:

  • Proposed methods demonstrate well-controlled type I error rates in simulations.
  • TOW-GE shows higher power for rare variants; VW-TOW-GE is more powerful for both rare and common variants compared to ISKAT.
  • Methods are robust to effect directions and effective in real data application (COPDGene Study).

Conclusions:

  • The developed methods are effective tools for identifying multi-trait G×Es.
  • Applicable to both common and rare variants, suitable for genome-wide association studies and next-generation sequencing data.
  • Facilitates a deeper understanding of the genetic and environmental underpinnings of complex diseases.