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A Scalable Bayesian Method for Integrating Functional Information in Genome-wide Association Studies.

Jingjing Yang1, Lars G Fritsche2, Xiang Zhou1

  • 1Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.

American Journal of Human Genetics
|August 29, 2017
PubMed
Summary

This study introduces a new Bayesian model for genome-wide association studies (GWASs) that integrates functional data to pinpoint causal variants. The method enhances the discovery of genetic associations, particularly in complex diseases like age-related macular degeneration.

Keywords:
AMDBVSRBayesian variable selection regressionEMGWASMCMCMarkov chain Monte Carloage-related macular degenerationexpectation-maximizationfunctional informationgenome-wide association study

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Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWASs) identify numerous genetic loci associated with complex traits.
  • Most identified loci are in noncoding regions with unknown biological functions.
  • Integrating functional information into GWASs is crucial for understanding biological mechanisms and prioritizing variants.

Purpose of the Study:

  • To develop a flexible Bayesian variable selection model for integrative analysis of GWAS data.
  • To improve the prioritization of functional variants by incorporating genomic annotations.
  • To account for linkage disequilibrium (LD) and allow for multiple causal variants per locus.

Main Methods:

  • Developed a Bayesian variable selection model incorporating functional annotations.
  • Employed efficient computational techniques leveraging block-wise LD structures.
  • Modeled effect-size distributions and causality probabilities for annotated variants.
  • Jointly modeled genome-wide variants for accurate LD handling.

Main Results:

  • The method demonstrated improved computational speed and posterior sampling convergence.
  • Simulations showed accurate quantification of functional enrichment and superior power in prioritizing true associations, especially with linked variants.
  • Application to age-related macular degeneration GWAS revealed significant enrichment for non-synonymous variants and regulatory regions.
  • Identified five novel candidate loci for age-related macular degeneration.

Conclusions:

  • The developed method efficiently integrates functional information into GWASs.
  • It aids in identifying functional associated variants and elucidating underlying biological mechanisms.
  • This approach enhances the discovery of genetic associations and biological insights from complex trait studies.