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A Fast and Accurate Algorithm to Test for Binary Phenotypes and Its Application to PheWAS.

Rounak Dey1, Ellen M Schmidt1, Goncalo R Abecasis1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.

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Summary
This summary is machine-generated.

A new computationally fast score-test method accurately analyzes phenome-wide association studies (PheWAS) with unbalanced case-control ratios. This approach enhances genetic variant discovery for clinical phenotypes using electronic health records.

Keywords:
GWASPheWASrare variantssaddlepoint approximationsingle-variant testunbalanced case-control

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Electronic health records (EHRs) enable genome-wide association analyses for thousands of clinical phenotypes.
  • Phenome-wide association studies (PheWAS) interpret genetic variant associations across diverse traits.
  • Existing PheWAS methods struggle with unbalanced case-control ratios, limiting scalability and accuracy.

Purpose of the Study:

  • To develop a computationally fast and accurate method for PheWAS.
  • To address challenges posed by extremely unbalanced case-control ratios in PheWAS.
  • To enable robust genetic variant association analysis in large-scale EHR data.

Main Methods:

  • A score-test-based method utilizing saddlepoint approximation for statistical inference.
  • Implementation of a computationally efficient algorithm, approximately 100 times faster than Firth's test.
  • Covariate adjustment and type I error rate control for unbalanced case-control data.

Main Results:

  • The proposed method demonstrates computational speed and accuracy.
  • It effectively controls type I error rates, even with highly unbalanced case-control ratios.
  • Successfully replicated known genetic association signals in PheWAS data from the Michigan Genomics Initiative.

Conclusions:

  • The developed method offers a scalable and accurate solution for PheWAS.
  • It facilitates the identification of genetic variants associated with clinical phenotypes using EHR data.
  • This approach improves the analysis of genetic associations, particularly for rare traits.