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Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in

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We developed pglmm, a penalized generalized linear mixed model, to improve genetic marker selection and prediction accuracy in binary trait genome-wide association studies (GWAS). This method accounts for relatedness and binary traits more effectively than existing approaches.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Sparse regularized regression is crucial for genome-wide association studies (GWAS) to manage multiple testing.
  • Linear mixed models (LMMs) adjust for population structure but have limitations with binary traits and generalized linear models.
  • Existing methods struggle with the complex covariance structures inherent in generalized linear mixed models (GLMMs).

Purpose of the Study:

  • Introduce pglmm, a penalized GLMM for simultaneous genetic marker selection and effect estimation.
  • Address the challenges of population structure and binary traits in high-dimensional GWAS.
  • Develop a computationally efficient algorithm for penalized regularized mixed models.

Main Methods:

  • Developed a penalized quasi-likelihood estimation algorithm for efficient computation.
  • Applied the pglmm method to high-dimensional binary trait GWAS.
  • Validated through simulations and real-world data analysis.

Main Results:

  • Simulations show pglmm outperforms penalized LMM and logistic regression with PC adjustment in marker selection and prediction accuracy.
  • Analysis of UK Biobank data demonstrates pglmm's superior predictive performance for polygenic binary traits.
  • pglmm selects fewer predictors compared to sparse regularized logistic lasso with PC adjustment.

Conclusions:

  • pglmm offers a robust and computationally efficient solution for high-dimensional binary trait GWAS.
  • The method effectively accounts for individual correlations and trait characteristics.
  • pglmm enhances the discovery of important genetic predictors and improves predictive accuracy.