Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

Journal Article (Journal Article)

OBJECTIVES: We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. METHODS: We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. RESULTS: After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. CONCLUSIONS: We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Full Text

Duke Authors

Cited Authors

  • Roetker, NS; Page, CD; Yonker, JA; Chang, V; Roan, CL; Herd, P; Hauser, TS; Hauser, RM; Atwood, CS

Published Date

  • October 2013

Published In

Volume / Issue

  • 103 Suppl 1 / Suppl 1

Start / End Page

  • S136 - S144

PubMed ID

  • 23927508

Pubmed Central ID

  • PMC3786749

Electronic International Standard Serial Number (EISSN)

  • 1541-0048

Digital Object Identifier (DOI)

  • 10.2105/AJPH.2012.301141


  • eng

Conference Location

  • United States