title: Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis.
abstract: Complex illnesses, like depression, are thought to arise from the interplay between psychosocial stressors and genetic predispositions. Approaches that take into account both personal and neighborhood factors and that consider gene regions as well as individual SNPs may be necessary to capture these interactions across race and ethnic groups. We used novel gene-region based analysis methods [Sequence Kernel Association Test (SKAT) and meta-analysis (MetaSKAT), gene-environment set association test (GESAT)], as well as traditional linear models to identify gene region and SNP × psychosocial factor interactions at the individual- and neighborhood-level, across multiple race/ethnicities. Multiple regions identified in SKAT analyses showed evidence of a significant gene-region association with averaged depressive symptom scores across race/ethnicity (MetaSKAT p values <0.001). One region × neighborhood-environment interaction was significantly associated with averaged depressive symptom score across race/ethnicity after multiple testing correction (chr 18:21454070-21494070, Fisher's combined p value = 0.001). The examination of gene regions jointly with environmental factors measured at multiple levels (individuals and their contexts) may shed light on the etiology of depressive illness across race/ethnicities.
authors.affiliation: Institute for Social Research, University of Michigan, #4614 SPH Tower, 1415 Washington Heights, Ann Arbor, MI, 48109, USA. ebakshis@umich.edu.|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA. ebakshis@umich.edu.
authors.forename: Erin B
authors.identifier: http://orcid.org/0000-0003-4731-8158
authors.initials: EB
authors.lastname: Ware
authors.affiliation: Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Smith
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Seunggeun
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Sharon L R
authors.initials: SL
authors.lastname: Kardia
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, USA.
authors.forename: Ana V
authors.initials: AV
authors.lastname: Diez-Roux
mesh_terms: D000368:Aged; D050197:Atherosclerosis / Q000209:etiology / Q000235:genetics* / Q000523:psychology*; D003627:Data Interpretation, Statistical; D003863:Depression / Q000209:etiology / Q000235:genetics* / Q000523:psychology*; D003866:Depressive Disorder / Q000209:etiology / Q000235:genetics; D005006:Ethnicity; D005260:Female; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies / Q000379:methods; D020022:Genetic Predisposition to Disease; D006801:Humans; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D012111:Residence Characteristics
keywords: Depressive symptoms; GESAT; Gene environment set association testing; Gene × environment; Gene-set testing