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Genome-wide association study for multiple phenotype analysis.

Xuan Deng1, Biqi Wang1, Virginia Fisher1

  • 1Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118 USA.

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|September 29, 2018
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Summary
This summary is machine-generated.

Multivariate genetic analyses improve detection of disease-related variants compared to single-trait methods. Incorporating family structure enhances power, identifying more significant genetic signals for complex diseases.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) frequently gather data on multiple phenotypes for complex diseases.
  • Multivariate joint analyses offer superior power for detecting genetic variants over marginal analyses of individual phenotypes.
  • Joint analyses can also identify genetic loci exhibiting pleiotropic effects, influencing multiple traits.

Purpose of the Study:

  • To extend the unified score-based association test to effectively incorporate family structure.
  • To apply and compare different multivariate analysis approaches for multiple traits using real-world samples (GAW20).
  • To evaluate the performance of a pedigree-based unified score association test through simulations.

Main Methods:

  • Development and application of a pedigree-based unified score association test.
  • Comparative analysis of different multivariate association methods on GAW20 real samples.
  • Simulation studies to assess Type I error rates and statistical power.

Main Results:

  • Simulation studies confirmed appropriate control of the Type I error rate for the pedigree-based unified score association test.
  • Marginal analysis of triglyceride levels identified one subgenome-wide significant variant on chromosome 6.
  • Joint analyses revealed several suggestive genome-wide significant signals, with the pedigree-based method yielding the most significant results.

Conclusions:

  • The pedigree-based unified score association test is a robust method for analyzing multiple phenotypes in the presence of family structure.
  • Multivariate joint analyses, particularly those accounting for relatedness, are more powerful for detecting genetic associations than marginal analyses.
  • This approach enhances the discovery of genetic variants influencing complex diseases and potential pleiotropic effects.