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Multikernel linear mixed model with adaptive lasso for complex phenotype prediction
Author(s) -
Wen Yalu,
Lu Qing
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8477
Subject(s) - lasso (programming language) , computer science , linear model , mixed model , curse of dimensionality , artificial intelligence , machine learning , genomic selection , generalized linear mixed model , feature selection , biology , gene , genetics , single nucleotide polymorphism , world wide web , genotype
Linear mixed models (LMMs) and their extensions have been widely used for high‐dimensional genomic data analyses. While LMMs hold great promise for risk prediction research, the high dimensionality of the data and different effect sizes of genomic regions bring great analytical and computational challenges. In this work, we present a multikernel linear mixed model with adaptive lasso (KLMM‐AL) to predict phenotypes using high‐dimensional genomic data. We develop two algorithms for estimating parameters from our model and also establish the asymptotic properties of LMM with adaptive lasso when only one dependent observation is available. The proposed KLMM‐AL can account for heterogeneous effect sizes from different genomic regions, capture both additive and nonadditive genetic effects, and adaptively and efficiently select predictive genomic regions and their corresponding effects. Through simulation studies, we demonstrate that KLMM‐AL outperforms most of existing methods. Moreover, KLMM‐AL achieves high sensitivity and specificity of selecting predictive genomic regions. KLMM‐AL is further illustrated by an application to the sequencing dataset obtained from the Alzheimer's disease neuroimaging initiative.