A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

TitreA genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.
Publication TypeJournal Article
Year of Publication2015
AuthorsHung, CF, Breen, G, Czamara, D, Corre, T, Wolf, C, Kloiber, S, Bergmann, S, Craddock, N, Gill, M, Holsboer, F, Jones, L, Jones, I, Korszun, A, Kutalik, Z, Lucae, S, Maier, W, Mors, O, Owen, MJ, Rice, J, Rietschel, M, Uher, R, Vollenweider, P, Waeber, G, Craig, IW, Farmer, AE, Lewis, CM, Müller-Myhsok, B, Preisig, M, McGuffin, P, Rivera, M
JournalBmc Medicine
Volume13
Pagination86
Date Published04/2015
URLhttp://my.unil.ch/serval/document/BIB_6B8A30726C04.pdf
DOI10.1186/s12916-015-0334-3
ISSN1741-7015 (Electronic)
Abstract

BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD.

METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity.

RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results.

CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

Notes

Publication types: Journal ArticlePublication Status: epublish

Alternate URL

http://www.ncbi.nlm.nih.gov/pubmed/25903154?dopt=Abstract

WOS ID (UT)

000353257600001

Alternate JournalBMC Med
Citation Key / SERVAL IDserval:BIB_6B8A30726C04
Peer reviewRefereed
PubMed ID25903154
PubMed Central IDPMC4407390
Grant ListG0701420 / / Medical Research Council / United Kingdom
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