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

Margarita Rivera^1,10  Peter McGuffin⁵  Martin Preisig⁴  Bertram Müller-Myhsok^1,14  Cathryn M Lewis^1,11  Anne E Farmer⁵  Ian W Craig⁵  Gerard Waeber³  Peter Vollenweider³  Rudolf Uher^1,12  Marcella Rietschel^1,13  John Rice⁹  Michael J Owen^1,15  Ole Mors¹  Wolfgang Maier^1,17  Susanne Lucae^1,14  Zoltan Kutalik²  Ania Korszun⁶  Ian Jones^1,18  Lisa Jones^1,16  Florian Holsboer^1,14  Michael Gill⁷  Nick Craddock^1,18  Sven Bergmann²  Stefan Kloiber^1,14  Christiane Wolf^1,14  Tanguy Corre²  Darina Czamara^1,14  Gerome Breen^1,10  Chi-Fa Hung⁸ 

[1] Research Department P, Aarhus University Hospital, Skovagervej 2, Risskov, DK-8240, Denmark;Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland;Division of Internal Medicine, CHUV, Rue du Bugnon 21, 1011, Lausanne, Switzerland;Department of Psychiatry, Lausanne University Hospital, Prilly-Lausanne, 1008, Switzerland;MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK;Barts and The London School of Medicine and Dentistry, Queen Mary’s University of London, London E1 2AD, UK;Department of Psychiatry, Trinity Centre for Health Sciences, Dublin 8, Ireland;Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan;Department of Psychiatry, Washington University School of Medicine, St Louis 63130, MO, USA;National Institute for Health Research Biomedical Research Centre for Mental Health at the Maudsley and Institute of Psychiatry, King’s College London, London, UK;Department of Medical and Molecular Genetics, School of Medicine, King’s College London, 8th Floor, Tower Wing, Guys Hospital, London SE1 9RT, UK;Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia B3H 3J5, NS, Canada;Central Institute of Mental Health, Mannheim, 68159, Germany;Max-Planck-Institute of Psychiatry, Kraepelinstraße 2, Munich, 80804, Germany;MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Henry Wellcome Building, Heath Park, Cardiff CF14 4XN, UK;Department of Psychiatry, School of Clinical and Experimental Medicine, University of Birmingham, Birmingham B15 2TT, UK;Department of Psychiatry, University of Bonn, Bonn, 53127, Germany;MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK

关键词: Obesity;    Major depressive disorder;    Genetic risk score;    Body mass index;   

Others  :  1174838
DOI  :  10.1186/s12916-015-0334-3

 received in 2014-12-05, accepted in 2015-03-24,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Hung et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

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