title: Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance.
abstract: To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM) to model multiple, putative genetic and environmental factors involved in energy imbalance ('obesity') using subjects from a colon polyp case-control study. We found that modeling constructs for the leptin receptor (LEPR) gene (defined by SNPs rs1137100, rs1137101, rs1805096, rs6588147) and the fat mass-and-obesity-associated (FTO) gene (defined by SNPs rs9939609, rs1421085, rs8044769) together with demographic (age, race, gender), physical activity, diet and sleep variables increased the strength of the association (β(std)=-0.13 ± 0.06; p=0.03) between the FTO and obesity constructs compared to that observed in a reduced model with only the FTO and LEPR constructs and demographic variables (β(std)=-0.05 ± 0.03; p=0.08). Several indirect paths, including an association between the LEPR and physical activity constructs (β(std)=-0.15 ± 0.04; p=0.01), were found. Interestingly, removing FTO revealed a marginal association between the LEPR and obesity constructs (β(std)=0.24 ± 0.14; p=0.09), which was not present when FTO was in the model. These results illustrate the importance of modeling multiple relevant genes and other factors in the same model, which is a major strength of this approach. Moreover, our latent gene construct approach exploits the correlation structure between SNPs while capturing overall effects of variation in that gene, which will enable better utilization of candidate gene and genome-wide SNP array data.
authors: Nora L
authors: NL
authors: Nock
authors: Li
authors: L
authors: Li
authors: Robert C
authors: RC
authors: Elston
references: PLoS One. 2007 Dec 26;2(12):e1361
references: 18159244
references: Psychiatry Res. 1989 May;28(2):193-213
references: 2748771
references: Mamm Genome. 1999 Oct;10(10):983-6
references: 10501967
references: Psychol Bull. 1990 Mar;107(2):238-46
references: 2320703
references: J Clin Endocrinol Metab. 2008 Sep;93(9):3640-3
references: 18583465
references: Cancer Epidemiol Biomarkers Prev. 2007 Dec;16(12):2533-47
references: 18086756
references: Int J Obes (Lond). 2009 Jan;33(1):42-5
references: 18838977
references: BMC Proc. 2007;1 Suppl 1:S118
references: 18466459
references: PLoS One. 2008 Aug 13;3(8):e2958
references: 18698412
references: Med Sci Sports Exerc. 2001 Nov;33(11):1959-67
references: 11689750
references: Cancer Epidemiol Biomarkers Prev. 1999 Oct;8(10):941-6
references: 10548325
references: Int J Obes Relat Metab Disord. 2001 May;25(5):714-20
references: 11360155
references: Am J Epidemiol. 2005 Jul 15;162(2):101-14
references: 15972940