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

Publication ,  Journal Article
Roetker, NS; Page, CD; Yonker, JA; Chang, V; Roan, CL; Herd, P; Hauser, TS; Hauser, RM; Atwood, CS
Published in: Am J Public Health
October 2013

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.

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Published In

Am J Public Health

DOI

EISSN

1541-0048

Publication Date

October 2013

Volume

103 Suppl 1

Issue

Suppl 1

Start / End Page

S136 / S144

Location

United States

Related Subject Headings

  • Wisconsin
  • Support Vector Machine
  • Public Health
  • Polymorphism, Single Nucleotide
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Humans
  • High-Throughput Screening Assays
  • Gene-Environment Interaction
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Roetker, N. S., Page, C. D., Yonker, J. A., Chang, V., Roan, C. L., Herd, P., … Atwood, C. S. (2013). Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms. Am J Public Health, 103 Suppl 1(Suppl 1), S136–S144. https://doi.org/10.2105/AJPH.2012.301141
Roetker, Nicholas S., C David Page, James A. Yonker, Vicky Chang, Carol L. Roan, Pamela Herd, Taissa S. Hauser, Robert M. Hauser, and Craig S. Atwood. “Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.Am J Public Health 103 Suppl 1, no. Suppl 1 (October 2013): S136–44. https://doi.org/10.2105/AJPH.2012.301141.
Roetker NS, Page CD, Yonker JA, Chang V, Roan CL, Herd P, et al. Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms. Am J Public Health. 2013 Oct;103 Suppl 1(Suppl 1):S136–44.
Roetker, Nicholas S., et al. “Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.Am J Public Health, vol. 103 Suppl 1, no. Suppl 1, Oct. 2013, pp. S136–44. Pubmed, doi:10.2105/AJPH.2012.301141.
Roetker NS, Page CD, Yonker JA, Chang V, Roan CL, Herd P, Hauser TS, Hauser RM, Atwood CS. Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms. Am J Public Health. 2013 Oct;103 Suppl 1(Suppl 1):S136–S144.

Published In

Am J Public Health

DOI

EISSN

1541-0048

Publication Date

October 2013

Volume

103 Suppl 1

Issue

Suppl 1

Start / End Page

S136 / S144

Location

United States

Related Subject Headings

  • Wisconsin
  • Support Vector Machine
  • Public Health
  • Polymorphism, Single Nucleotide
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Humans
  • High-Throughput Screening Assays
  • Gene-Environment Interaction