Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.
OBJECTIVE
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.
Nicholas S. Roetker, James A. Yonker, Vicky Chang, Carol L. Roan, Pamela Herd, Taissa S. Hauser, and Robert M. Hauser are with the Department of Sociology, University of Wisconsin-Madison. Pamela Herd is also with La Follete School of Public Affairs, University of Wisconsin-Madison. C. David Page is with the Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison. Craig S. Atwood is with the Geriatric Research, Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, and the Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health.
Nicholas S
NS
Roetker
C David
CD
Page
James A
JA
Yonker
Vicky
V
Chang
Carol L
CL
Roan
Pamela
P
Herd
Taissa S
TS
Hauser
Robert M
RM
Hauser
Craig S
CS
Atwood
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D015331:Cohort Studies; D003863:Depression / Q000453:epidemiology / Q000209:etiology* / Q000235:genetics*; D005260:Female; D005544:Forecasting / Q000379:methods; D059647:Gene-Environment Interaction; D057166:High-Throughput Screening Assays; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D060388:Support Vector Machine; D014922:Wisconsin / Q000453:epidemiology