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Bayesian GWAS with Structured and Non-Local Priors.

Adam Kaplan1, Eric F Lock1, Mark Fiecas1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.

Bioinformatics (Oxford, England)
|October 26, 2019
PubMed
Summary
This summary is machine-generated.

We introduce a novel Bayesian approach for Genome-Wide Association Studies (GWAS), called SNLPs GWAS, using structured and non-local priors to enhance association detection power and improve statistical modeling for complex diseases.

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Bayesian frameworks offer flexibility for Genome-Wide Association Studies (GWAS).
  • Existing GWAS methods can be improved with more informative prior models.
  • Incorporating marker characteristics can enhance the power of association studies.

Purpose of the Study:

  • To introduce a novel Bayesian approach for GWAS, Structured and Non-Local Priors (SNLPs) GWAS.
  • To develop a model that utilizes gene-parent membership and other marker characteristics to influence association probability.
  • To implement a non-local alternative model for differential minor allele rates with no common support between null and alternative hypotheses.

Main Methods:

  • Employed a non-parametric model for gene clustering integrated with a regression model for marker-level covariates.
  • Utilized a non-local alternative model for differential minor allele rates, ensuring symmetric convergence rates for null and alternative hypotheses.
  • Applied the SNLPs GWAS method to single nucleotide polymorphisms (SNPs) data from Alzheimer's disease patients and cognitively normal individuals.

Main Results:

  • The SNLPs GWAS method demonstrated robustness and flexibility across various data scenarios and signal-to-noise ratios.
  • The non-local alternative model exhibited symmetric convergence rates, unlike traditional local models that favor the alternative hypothesis.
  • Incorporating marker characteristics through the non-parametric and regression models improved statistical power in association detection.

Conclusions:

  • The SNLPs GWAS approach offers an improved Bayesian method for Genome-Wide Association Studies.
  • The structured and non-local prior models enhance the ability to detect genetic associations by leveraging marker characteristics.
  • The developed method provides a powerful and flexible tool for genetic research, applicable to complex diseases like Alzheimer's.