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A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits.

Weiqiang Wang1, Zeny Feng1, Shelley B Bull2

  • 1Department of Mathematics and Statistics, University of Guelph Guelph, ON, Canada.

Frontiers in Genetics
|November 5, 2014
PubMed
Summary

This study introduces a novel two-step method to detect genetic pleiotropy, where one gene affects multiple traits, in longitudinal studies. The method efficiently identifies genetic variants associated with multiple traits measured over time.

Keywords:
genetic associationlongitudinal datamixed effects modelmultiple traitspleiotropic effectsingle nucleotide polymorphisms (SNPs)

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Biostatistics

Background:

  • Genetic pleiotropy, where a single gene influences multiple traits, is a key factor in genetic correlations.
  • Genome-wide association studies (GWAS) aim to identify genetic variants associated with multiple traits to detect pleiotropy.
  • Longitudinal study designs are crucial for complex disease research, involving repeated trait measurements over time.

Purpose of the Study:

  • To develop and validate a novel statistical method for simultaneously testing genetic association with multiple longitudinal traits.
  • To address the challenge of detecting pleiotropic effects in studies with repeated measures.
  • To provide a flexible approach applicable to various data types (quantitative, binary, count).

Main Methods:

  • A two-step statistical approach is proposed.
  • Step 1: Mixed-effects models are used to analyze individual longitudinal traits, estimating subject-specific genetic random effects and controlling for confounding covariates.
  • Step 2: A simultaneous association test is performed on the estimated random effects across multiple traits.

Main Results:

  • The proposed two-step method efficiently detects pleiotropic effects on multiple longitudinal traits.
  • The method demonstrates flexibility in handling diverse data types.
  • Validation through simulation studies and application to Framingham Heart Study data (GAW16).

Conclusions:

  • The developed method offers a robust framework for identifying pleiotropy in longitudinal genetic studies.
  • It effectively separates genetic contributions from other confounding factors.
  • This approach enhances the understanding of genetic architecture underlying complex traits measured over time.