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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/35976

Title: A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder
Authors: Hung, Chi-Fa
Breen, Gerome
Czamara, Darina
Corre, Tangury
Wolf, Christiane
Kloiber, Stefan
Bergmann, Sven
Craddock, Nick
Gill, Michael
Holsboer, Florian
Jones, Lisa
Jones, Ian
Korszun, Ania
Kutalik, Zoltan
Lucae, Susanne
Maier, Wolfgang
Mors, Ole
Owen, Michael J.
Rice, John
Rietschel, Marcella
Uher, Rudolf
Vollenweider, Peter
Waeber, Gerard
Craig, Ian W.
Farmer, Anne E.
Lewis, Cathryn M.
Müller-Myhsok, Bertram
Preisig, Martin
McGuffin, Peter
Rivera Sánchez, Margarita
Issue Date: 2015
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; χ2 = 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; χ2 = 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.
Sponsorship: This study was funded by the Medical Research Council, UK. GlaxoSmithKline (G0701420) funded the DeNT study and were co-funders with the Medical Research Centre for the GWAS of the whole sample. The GENDEP study was funded by a European Commission Framework 6 grant, EC Contract Ref.: LSHB-CT- 2003-503428. This study presents independent research [part-] funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. The CoLaus/PsyCoLaus was funded by four grants from the Swiss National Science Foundation (#32003B-105993, #32003B-118308, #33CSC0-122661, and #139468), the Faculty of Biology and Medicine of Lausanne, and two grants from GlaxoSmithKline Clinical Genetics.
Publisher: Biomed Central
Keywords: Body mass index
Genetic risk score
Major depressive disorder
Obesity
URI: http://hdl.handle.net/10481/35976
ISSN: 1741-7015
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Hung, C.-F.; et al. A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder. BMC Medicine, 13:86 (2015). [http://hdl.handle.net/10481/35976]
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