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A generalized linear mixed model association tool for biobank-scale data.

Longda Jiang1,2, Zhili Zheng1, Hailing Fang2,3

  • 1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.

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|November 5, 2021
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Summary
This summary is machine-generated.

A new tool, fastGWA-GLMM, significantly speeds up genome-wide association studies for binary traits. This advance enables the discovery of rare genetic variants linked to complex diseases in large populations.

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Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genetics

Background:

  • Generalized linear mixed models (GLMMs) offer superior statistical power for binary traits compared to linear mixed models (LMMs) in genome-wide association (GWA) studies.
  • However, the computational demands of GLMM-based GWA methods have limited their application in large-scale genetic studies.

Purpose of the Study:

  • To develop a computationally efficient GLMM-based GWA tool, named fastGWA-GLMM.
  • To enable the analysis of large biobank datasets for the discovery of genetic variants associated with binary traits.

Main Methods:

  • Developed fastGWA-GLMM utilizing sparse matrix-based algorithms for enhanced computational speed.
  • Validated the tool's performance through simulations, assessing the calibration of test statistics for common and rare variants.
  • Applied fastGWA-GLMM to the UK Biobank (UKB) dataset, comprising over 450,000 individuals and millions of genetic variants.

Main Results:

  • fastGWA-GLMM demonstrated substantial speed improvements (severalfold to orders of magnitude) over existing state-of-the-art tools.
  • The tool's test statistics were shown to be well-calibrated under the null hypothesis, even with extreme case-control ratios.
  • Analysis of UKB data identified 259 rare variants associated with 75 distinct binary traits.

Conclusions:

  • fastGWA-GLMM provides a scalable and efficient solution for GLMM-based GWA studies on large cohorts.
  • The tool facilitates the discovery of rare variants influencing binary complex traits using imputed genotype data.
  • This advancement opens new avenues for genetic research in large-scale population studies.