Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.

MOTIVATION: 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 (PCs) 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). RESULTS: 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 penalized quasi-likelihood estimation that allows to scale regularized mixed models on high-dimensional binary trait GWAS. We show through simulations that when the dimensionality of the relatedness matrix is high, penalized LMM and logistic regression with PC adjustment fail to select important predictors, and have inferior prediction accuracy compared to pglmm. Further, we demonstrate through the analysis of two polygenic binary traits in a subset of 6731 related individuals from the UK Biobank data with 320K SNPs that our method can achieve higher predictive performance, while also selecting fewer predictors than a sparse regularized logistic lasso with PC adjustment. AVAILABILITY AND IMPLEMENTATION: Our Julia package PenalizedGLMM.jl is publicly available on github: https://github.com/julstpierre/PenalizedGLMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.

In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or non-normally distributed traits. Therefore, there is a need to develop unified and flexible methods to study association between a set of (possibly rare) genetic variants and non-normal multivariate phenotypes. Copulas are multivariate distribution functions with uniform margins on the [0, 1] interval and they provide suitable models to deal with non-normality of errors in multivariate association studies. We propose a novel unified and flexible copula-based multivariate association test (CBMAT) for discovering association between a genetic region and a bivariate continuous, binary or mixed phenotype. We also derive a data-driven analytic p-value procedure of the proposed region-based score-type test. Through simulation studies, we demonstrate that CBMAT has well controlled type I error rates and higher power to detect associations compared with other existing methods, for discrete and non-normally distributed traits. At last, we apply CBMAT to detect the association between two genes located on chromosome 11 and several lipid levels measured on 1477 subjects from the ASLPAC study.

Considering strategies for SNP selection in genetic and polygenic risk scores.

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.