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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Pathway-based association study of multiple candidate genes and multiple traits using structural equation models.

Hela Romdhani1, Heungsun Hwang, Gilles Paradis

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.

Genetic Epidemiology
|January 6, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using structural equation models (SEM) for analyzing genetic associations with multiple traits. It improves power by jointly testing genetic variants and correlated traits, outperforming traditional methods.

Keywords:
clinical pathwaygeneralized structured component analysisgenetic pathwaypermutation testprior biological knowledge

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Area of Science:

  • Genetics
  • Biostatistics
  • Quantitative Trait Analysis

Background:

  • Traditional univariate association tests for genetic variants and quantitative traits suffer from reduced power due to multiple testing corrections.
  • Analyzing multiple genetic variants across multiple genes and correlated traits simultaneously is increasingly important in association studies.
  • Existing methods often fail to leverage prior knowledge of clinical and genetic pathways effectively.

Purpose of the Study:

  • To propose a novel approach for modeling complex relationships between multiple genetic variants in candidate genes and correlated quantitative traits.
  • To develop a single, powerful association test that integrates information from multiple genes and traits.
  • To enhance the detection of genetic associations by accounting for pathway knowledge.

Main Methods:

  • Utilized structural equation models (SEM), specifically generalized structured component analysis (GSCA).
  • Developed a single association test for multiple genetic variants within a gene and a set of correlated traits.
  • Incorporated prior knowledge of clinical and genetic pathways into the SEM framework.
  • Validated the method's performance through simulation studies.

Main Results:

  • The proposed GSCA-based SEM approach demonstrated improved power in detecting genetic associations compared to classical univariate methods.
  • Simulations confirmed the effectiveness of the joint testing strategy across multiple variants and traits.
  • Application to the Quebec Child and Adolescent Health and Social Survey data identified genetic associations with cardiovascular disease-related traits.

Conclusions:

  • Structural equation models provide a powerful framework for joint analysis of genetic variants and multiple correlated traits.
  • The developed GSCA-based association test offers a statistically robust method for genetic association studies.
  • This approach enhances the ability to uncover complex genetic underpinnings of quantitative traits, particularly in disease-related research.