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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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repfdr: a tool for replicability analysis for genome-wide association studies.

Ruth Heller1, Shay Yaacoby1, Daniel Yekutieli1

  • 1Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv 6997801, Israel.

Bioinformatics (Oxford, England)
|July 12, 2014
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Summary

This study introduces the repfdr R package for empirical Bayes replicability analysis in genome-wide association studies (GWAS). It offers a powerful method for identifying consistently associated genetic variants across multiple studies.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Identifying single nucleotide polymorphisms (SNPs) associated with phenotypes across multiple studies is crucial in genome-wide association studies (GWAS).
  • Replication of findings enhances scientific confidence and understanding of genetic underpinnings of traits.
  • Existing methods may lack the power or flexibility to adequately assess replicability.

Purpose of the Study:

  • To introduce the repfdr R package, a novel tool for assessing the replicability of genetic findings.
  • To provide a flexible implementation of the empirical Bayes approach for meta-analysis and replicability analysis.
  • To demonstrate the utility of the repfdr package for the GWAS research community.

Main Methods:

  • The study utilizes an empirical Bayes approach to analyze results from multiple studies examining the same set of null hypotheses.
  • The repfdr R package is developed to facilitate this analysis, offering a user-friendly interface.
  • The package is designed for efficient computation and robust statistical inference.

Main Results:

  • The repfdr package provides a flexible and powerful implementation of the empirical Bayes method for replicability analysis.
  • The empirical Bayes approach has been demonstrated to be a reliable and near-optimal method for detecting replicated associations.
  • The package is discussed in the context of its usefulness for the GWAS community.

Conclusions:

  • The repfdr R package offers a valuable resource for researchers conducting genome-wide association studies.
  • It enables robust identification of consistently replicated genetic associations, enhancing the reliability of GWAS findings.
  • The package supports meta-analysis and replicability assessments, contributing to a deeper understanding of genetic architectures.