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FEATURE SELECTION FOR GENERALIZED VARYING COEFFICIENT MIXED-EFFECT MODELS WITH APPLICATION TO OBESITY GWAS.

ABSTRACT: Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be "fat genes." The method also discovers SNPs that significantly influence the age-dependent variability of BMI. The proposed procedure takes into account individual variations of genetic effects and can also be directly applied to longitudinal data with continuous, binary or count responses. We employ Monte Carlo simulation studies to assess the performance of the proposed method and further carry out causal inference for the selected SNPs.

SUBMITTER: Chu W

PROVIDER: S-EPMC7426018 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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FEATURE SELECTION FOR GENERALIZED VARYING COEFFICIENT MIXED-EFFECT MODELS WITH APPLICATION TO OBESITY GWAS.

Chu Wanghuan W Li Runze R Liu Jingyuan J Reimherr Matthew M

The annals of applied statistics 20200301 1

Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be "fat genes." The method also discovers SNPs that signifi ...[more]

PMID: 32802245

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