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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Additive varying-coefficient model for nonlinear gene-environment interactions.

Cen Wu1, Ping-Shou Zhong2, Yuehua Cui2

  • 1Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.

Statistical Applications in Genetics and Molecular Biology
|February 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to analyze how gene-environment interactions influence complex diseases. It helps identify genetic variants and their varying effects moderated by environmental factors.

Keywords:
B-splineSCAD penaltygene-set analysislocal quadratic approximationvariable selection

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-environment (G×E) interactions are crucial for understanding complex diseases.
  • Assessing genetic sensitivity across varying environmental conditions is key.
  • Gene set-based association analysis is gaining prominence over single-variant approaches.

Purpose of the Study:

  • To propose an additive varying-coefficient model for jointly analyzing variants within a genetic system.
  • To investigate how gene sets are moderated by environmental factors in disease phenotypes.
  • To perform variable selection for identifying G×E interactions, constant genetic effects, and null effects.

Main Methods:

  • Developed an additive varying-coefficient model to jointly model variants in a genetic system.
  • Employed a variable selection approach to identify variants with varying, constant, or zero coefficients.
  • Utilized a two-stage iterative estimation algorithm with the smoothly clipped absolute deviation (SCAD) penalty function.

Main Results:

  • Established consistency in variable selection and effect separation for the two-stage estimators.
  • Demonstrated optimal convergence rates for estimates of varying effects.
  • Showcased the oracle property for estimates of non-zero constant coefficients.

Conclusions:

  • The proposed method effectively identifies gene-environment interactions and individual genetic effects within gene sets.
  • The variable selection approach allows for nuanced understanding of genetic contributions to complex diseases.
  • Simulation studies and real data analysis confirm the utility and robustness of the developed procedure.