2025/01/19 by Julien St‐Pierre, St-Pierre, Julien, Sahir Bhatnagar +9
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Psychology · #Applications (stat.AP) #Applied mathematics #Association (psychology) #Biology #Computer science #Demography #Econometrics #FOS: Computer and information sciences #Gene #Generalized linear mixed model #Genetic and phenotypic traits in livestock #Genetic association #Genetics #Genotype #Mathematics #Medicine #Methodology (stat.ME) #Mixed model #Psychology #Single-nucleotide polymorphism #Sociology #Statistics
paper · pdf · doi:10.48550/arxiv.2501.11083
openalex publication_date 2025/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work is motivated by analyses of longitudinal data collected from participants in the Quebec Longitudinal Study of Child Development (QLSCD) and the Quebec Newborn Twin Study (QNTS) to identify important genetic predictors for emotional and behavioral difficulties in childhood and adolescence. We propose a lasso penalized mixed model for continuous and binary longitudinal traits that allows the inclusion of multiple random effects to account for random individual effects not attributable to the genetic similarity between individuals. Through simulation studies, we show that replacing the estimated genetic relatedness matrix (GRM) by a sparse matrix introduces bias in the variance components estimates, but that the obtained computational gain is major while the impact on the performance of the penalized model to retrieve important predictors is negligible. We compare the performance of the proposed penalized mixed model to a standard lasso and to a univariate mixed model association test and show that the proposed model always identifies causal predictors with greater precision. Finally, we show an application of the proposed methodology to predict three externalizing behavorial scores in the combined QLSCD and QNTS longitudinal cohorts.