Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study
- Nicholas S Roetker1,
- James A Yonker1,
- Chee Lee1,
- Vicky Chang1,
- Jacob J Basson3,
- Carol L Roan1,
- Taissa S Hauser1,
- Robert M Hauser1,
- Craig S Atwood2,3,4
- 1Department of Sociology, University of Wisconsin, Madison, Wisconsin, USA
- 2Geriatric Research, Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI
- 3Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- 4School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Australia
- Correspondence to Dr Craig S Atwood;
- Received 26 January 2012
- Accepted 29 May 2012
- Published 2 July 2012
Objectives Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene–gene (G × G) interactions of 78 single nucleotide polymorphisms (SNPs) in genes associated with depression and age-related diseases would identify significant interactions with increased predictive power for depression.
Design A retrospective cohort study.
Setting A survey of participants in the Wisconsin Longitudinal Study.
Participants A total of 4811 persons (2464 women and 2347 men) who provided saliva for genotyping; the group comes from a randomly selected sample of Wisconsin high school graduates from the class of 1957 as well as a randomly selected sibling, almost all of whom are non-Hispanic white.
Primary outcome measure Depression as determine by the Composite International Diagnostic Interview–Short-Form.
Results Using a classification tree approach (recursive partitioning (RP)), the authors identified a number of candidate G × G interactions associated with depression. The primary SNP splits revealed by RP (ANKK1 rs1800497 (also known as DRD2 Taq1A) in men and DRD2 rs224592 in women) were found to be significant as single factors by logistic regression (LR) after controlling for multiple testing (p=0.001 for both). Without considering interaction effects, only one of the five subsequent RP splits reached nominal significance in LR (FTO rs1421085 in women, p=0.008). However, after controlling for G × G interactions by running LR on RP-specific subsets, every split became significant and grew larger in magnitude (OR (before) → (after): men: GNRH1 novel SNP: (1.43 → 1.57); women: APOC3 rs2854116: (1.28 → 1.55), ACVR2B rs3749386: (1.11 → 2.17), FTO rs1421085: (1.32 → 1.65), IL6 rs1800795: (1.12 → 1.85)).
Conclusions The results suggest that examining G × G interactions improves the identification of genetic associations predictive of depression. 4 of the SNPs identified in these interactions were located in two pathways well known to impact depression: neurotransmitter (ANKK1 and DRD2) and neuroendocrine (GNRH1 and ACVR2B) signalling. This study demonstrates the utility of RP analysis as an efficient and powerful exploratory analysis technique for uncovering genetic and molecular pathway interactions associated with disease aetiology.
To cite: Roetker NS, Yonker JA, Lee C, et al. Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study. BMJ Open 2012;2:e000944. doi:10.1136/bmjopen-2012-000944
Contributors CSA, RMH and TSH conceptualised the study. RMH, TSH, CLR, NSR, CL and CSA collected saliva samples and performed genotyping analyses. NSR, JAY, CL, VC and JJB performed statistical analyses on the Wisconsin Longitudinal Study data set. CSA and RMH directed the statistical analyses. NSR and CSA drafted the manuscript. All authors critically reviewed the manuscript and approved the final version.
Funding The study was supported by the National Institute on Aging (AG09775, AG21079 and AG33285).
Competing interests None.
Patient consent Obtained.
Ethics approval Ethics approval was provided by the Social Sciences Institutional Review Board, UW-Madison.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement WLS public release data is available for download at http://www.ssc.wisc.edu/wlsresearch. Information on obtaining WLS genotypic data is available at this site. All WLS data is available free of charge.
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.