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Latent variable modeling paradigms for genotype‐trait association studies
Author(s) -
Liu Yan,
Foulkes Andrea S.
Publication year - 2011
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201000218
Subject(s) - latent variable , genetic association , disease , single nucleotide polymorphism , latent variable model , trait , population , structural equation modeling , biology , computational biology , genetics , bioinformatics , genotype , computer science , medicine , gene , machine learning , pathology , programming language , environmental health
Characterizing associations among multiple single‐nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, this presents a significant analytic challenge due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling approaches offer a natural framework for analysis of data arising from these population‐based genetic association investigations of complex diseases as they are well‐suited to uncover simultaneous effects of multiple markers. In this manuscript we describe application and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and, finally, an application to data arising from a study of anti‐retroviral‐associated dyslipidemia in HIV‐infected individuals is provided for illustration.