Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (PC) adjustment to account for population structure and relatedness in high-dimensional penalized models. However, their use in binary trait GWAS rely on the invalid assumption that the residual variance does not depend on the estimated regression coefficients. Moreover, LMMs use a single spectral decomposition of the covariance matrix of the responses, which is no longer possible in generalized linear mixed models (GLMMs). We introduce a new method called pglmm, a penalized GLMM that allows to simultaneously select genetic markers and estimate their effects, accounting for between-individual correlations and binary nature of the trait. We develop a computationally efficient algorithm based on PQL estimation that allows to scale regularized mixed models on high-dimensional binary trait GWAS (~300,000 SNPs). We show through simulations that penalized LMM and logistic regression with PC adjustment fail to correctly select important predictors and/or that prediction accuracy decreases for a binary response when the dimensionality of the relatedness matrix is high compared to pglmm. Further, we demonstrate through the analysis of two polygenic binary traits in the UK Biobank data that our method can achieve higher predictive performance, while also selecting fewer predictors than a sparse regularized logistic lasso with PC adjustment. Our method is available as a Julia package PenalizedGLMM.jl.
翻译:基因组全协会研究目前广泛采用分解的常规回归方法,以解决限制发现潜在重要预测器的多重测试负担,线性混合模型(LMMs)已成为主要组成部分(PC)调整的有吸引力的替代方法,以考虑到人口结构和高维受罚模型的相关性。然而,在二元特征中,在二元特征中采用这些方法的无效假设是,剩余差异并不取决于估计回归系数。此外,LMMs使用一个单一的频谱组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合组合组合组合组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合式组合