New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation.

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation. A meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at the single-nucleotide polymorphism (SNP), gene and pathway levels. Coverage of genetic variants was increased compared with previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. Significant findings were investigated in NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) for replication. A total of 7062 950 SNPs were analyzed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (P=1.80e-08, ITGA9 (integrin α9)) and rs76191705 (P=2.59e-08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At the gene level, no consistent effect was found. At the pathway level, the Gene Ontology (GO) terms GO: 0005694 (chromosome) and GO: 0044427 (chromosomal part) were associated with improvement (corrected P=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (P=0.047), whereas rs76191705 was not. The two SNPs did not replicate in NEWMEDS. ITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, replication was partial. Further studies may help in clarifying their role.

Interaction between childhood maltreatment on immunogenetic risk in depression: Discovery and replication in clinical case-control samples.

Major depressive disorder (MDD) is a prevalent disorder with moderate heritability. Both MDD and interpersonal adversity, including childhood maltreatment, have been consistently associated with elevated inflammatory markers. We investigated interaction between exposure to childhood maltreatment and extensive genetic variation within the inflammation pathway (CRP, IL1b, IL-6, IL11, TNF, TNFR1, and TNFR2) in relation to depression diagnosis. The discovery RADIANT sample included 262 cases with recurrent DSM-IV/ICD-10 MDD, and 288 unaffected controls. The replication Münster cohort included 277 cases with DSM-IV MDD, and 316 unaffected controls. We identified twenty-five single nucleotide polymorphisms (SNPs) following multiple testing correction that interacted with childhood maltreatment to predict depression in the discovery cohort. Seven SNPs representing independent signals (rs1818879, rs1041981, rs4149576, rs616645, rs17882988, rs1061622, and rs3093077) were taken forward for replication. Meta-analyses of the two samples presented evidence for interaction with rs1818879 (IL6) (RD=0.059, SE=0.016, p<0.001), with the replication Münster sample approaching statistical significance in analyses restricted to recurrent MDD and controls following correction for multiple testing (q=0.066). The CRP locus (rs3093077) showed a similar level of evidence for interaction in the meta-analysis (RD=0.092, SE=0.029, p=0.002), but less compelling evidence in the replication sample alone (recurrent MDD q=0.198; all MDD q=0.126). Here we present evidence suggestive of interaction with childhood maltreatment for novel loci in IL-6 (rs1818879) and CRP (rs3093077), increasing risk of depression. Replication is needed by independent groups, targeting these specific variants and interaction with childhood maltreatment on depression risk.

Genetic and other risk factors for suicidal ideation and the relationship with depression.

BACKGROUND: There is a genetic contribution to the risk of suicide, but sparse prior research on the genetics of suicidal ideation. METHODS: Active and passive suicidal ideation were assessed in a Sri Lankan population-based twin registry (n = 3906 twins) and a matched non-twin sample (n = 2016). Logistic regression models were used to examine associations with socio-demographic factors, environmental exposures and psychiatric symptoms. The heritability of suicidal ideation was assessed using structural equation modelling. RESULTS: The lifetime prevalence of any suicidal ideation was 13.0% (11.7-14.3%) for men; 21.8% (20.3-23.2%) for women, with no significant difference between twins and non-twins. Factors that predicted suicidal ideation included female gender, termination of marital relationship, low education level, urban residence, losing a parent whilst young, low standard of living and stressful life events in the preceding 12 months. Suicidal ideation was strongly associated with depression, but also with abnormal fatigue and alcohol and tobacco use. The best fitting structural equation model indicated a substantial contribution from genetic factors (57%; CI 47-66) and from non-shared environmental factors (43%; CI 34-53) in both men and women. In women this genetic component was largely mediated through depression, but in men there was a significant heritable component to suicidal ideation that was independent of depression. CONCLUSIONS: These are the first results to show a genetic contribution to suicidal ideation that is independent of depression outside of a high-income country. These phenomena may be generalizable, because previous research highlights similarities between the aetiology of mental disorders in Sri Lanka and higher-income countries.

Genetic effects influencing risk for major depressive disorder in China and Europe.

Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log10 Bayes Factor=8.08) but failed to replicate in an independent European sample (P=0.911). Gene-set enrichment analyses indicate enrichment of genes involved in neuronal development and axonal trafficking. We successfully demonstrate a partially shared polygenic basis of MDD in East Asian and European populations. Taken together, these findings support a complex etiology for MDD and possible population differences in predisposing genetic factors, with important implications for future genetic studies.

Machine learning, statistical learning and the future of biological research in psychiatry.

Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other 'omic' measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.

Analysis of the effects of depression associated polymorphisms on the activity of the BICC1 promoter in amygdala neurones.

The Bicaudal C Homolog 1 (BICC1) gene, which encodes an RNA binding protein, has been identified by genome wide association studies (GWAS) as a candidate gene associated with major depressive disorder (MDD). We explored the hypothesis that MDD associated single-nucleotide polymorphisms (SNPs) affected the ability of cis-regulatory elements within intron 3 of the BICC1 gene to modulate the activity of the BICC1 promoter region. We initially established that the BICC1 promoter drove BICC1 mRNA expression in amygdala, hippocampus and hypothalamus. Intriguingly, we provide evidence that MDD associated polymorphisms alter the ability of the BICC1 promoter to respond to PKA signalling within amygdala neurones. Considering the known role of amygdala PKA pathways in fear learning and mood these observations suggest a possible mechanism through which allelic changes in the regulation of the BICC1 gene in amygdala neurones may contribute to mood disorders. Our findings also suggest a novel direction for the identification of novel drug targets and the design of future personalised therapeutics.The Pharmacogenomics Journal advance online publication, 6 October 2015; doi:10.1038/tpj.2015.62.

Polygenic interactions with environmental adversity in the aetiology of major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene-environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD. METHOD: The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them. RESULTS: PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10(-6)). SLEs and CT were also associated with MDD status (p = 2.19 × 10(-4) and p = 5.12 × 10(-20), respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples. CONCLUSIONS: CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene-environment interactions in complex traits.

Familiality and SNP heritability of age at onset and episodicity in major depressive disorder.

BACKGROUND: Strategies to dissect phenotypic and genetic heterogeneity of major depressive disorder (MDD) have mainly relied on subphenotypes, such as age at onset (AAO) and recurrence/episodicity. Yet, evidence on whether these subphenotypes are familial or heritable is scarce. The aims of this study are to investigate the familiality of AAO and episode frequency in MDD and to assess the proportion of their variance explained by common single nucleotide polymorphisms (SNP heritability). METHOD: For investigating familiality, we used 691 families with 2-5 full siblings with recurrent MDD from the DeNt study. We fitted (square root) AAO and episode count in a linear and a negative binomial mixed model, respectively, with family as random effect and adjusting for sex, age and center. The strength of familiality was assessed with intraclass correlation coefficients (ICC). For estimating SNP heritabilities, we used 3468 unrelated MDD cases from the RADIANT and GSK Munich studies. After similarly adjusting for covariates, derived residuals were used with the GREML method in GCTA (genome-wide complex trait analysis) software. RESULTS: Significant familial clustering was found for both AAO (ICC = 0.28) and episodicity (ICC = 0.07). We calculated from respective ICC estimates the maximal additive heritability of AAO (0.56) and episodicity (0.15). SNP heritability of AAO was 0.17 (p = 0.04); analysis was underpowered for calculating SNP heritability of episodicity. CONCLUSIONS: AAO and episodicity aggregate in families to a moderate and small degree, respectively. AAO is under stronger additive genetic control than episodicity. Larger samples are needed to calculate the SNP heritability of episodicity. The described statistical framework could be useful in future analyses.

Investigation of blood mRNA biomarkers for suicidality in an independent sample.

Changes in the blood expression levels of SAT1, PTEN, MAP3K3 and MARCKS genes have been reported as biomarkers of high versus low suicidality state (Le-Niculescu et al.). Here, we investigate these expression biomarkers in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study, of patients with major depressive disorder on a 12-week antidepressant treatment. Blood gene expression levels were available at baseline and week 8 for patients who experienced suicidal ideation during the study (n=20) versus those who did not (n=37). The analysis is well powered to detect the effect sizes reported in the original paper. Within either group, there was no significant change in the expression of these four genes over the course of the study, despite increasing suicidal ideation or initiation of antidepressant treatment. Comparison of the groups showed that the gene expression did not differ between patients with or without treatment-related suicidality. This independent study does not support the validity of the proposed biomarkers.

Copy number variants and therapeutic response to antidepressant medication in major depressive disorder.

It would be beneficial to find genetic predictors of antidepressant response to help personalise treatment of major depressive disorder (MDD). Rare copy number variants (CNVs) have been implicated in several psychiatric disorders, including MDD, but their role in antidepressant response has yet to be investigated. CNV data were available for 1565 individuals with MDD from the NEWMEDS (Novel Methods leading to New Medications in Depression and Schizophrenia) consortium with prospective data on treatment outcome with either a serotonergic or noradrenergic antidepressant. No association was seen between the presence of CNV (rare or common), the overall number of CNVs or genomic CNV 'burden' and antidepressant response. Specific CNVs were nominally associated with antidepressant response, including 15q13.3 duplications and exonic NRXN1 deletions. These were associated with poor response to antidepressants. Overall burden of CNVs is unlikely to contribute to personalising antidepressant treatment. Specific CNVs associated with antidepressant treatment require replication and further study to confirm their role in the therapeutic action of antidepressant.

DNA methylation in interleukin-11 predicts clinical response to antidepressants in GENDEP.

Transcriptional differences in interleukin-11 (IL11) after antidepressant treatment have been found to correspond to clinical response in major depressive disorder (MDD) patients. Expression differences were partly mediated by a single-nucleotide polymorphism (rs1126757), identified as a predictor of antidepressant response as part of a genome-wide association study. Here we attempt to identify whether DNA methylation, another baseline factor known to affect transcription factor binding, might also predict antidepressant response, using samples collected from the Genome-based Therapeutic Drugs for Depression project (GENDEP). DNA samples from 113 MDD individuals from the GENDEP project, who were treated with either escitalopram (n=80) or nortriptyline (n=33) for 12 weeks, were randomly selected. Percentage change in Montgomery-Åsberg Depression Rating Scale scores between baseline and week 12 were utilized as our measure of antidepressant response. The Sequenom EpiTYPER platform was used to assess DNA methylation across the only CpG island located in the IL11 gene. Regression analyses were then used to explore the relationship between CpG unit methylation and antidepressant response. We identified a CpG unit predictor of general antidepressant response, a drug by CpG unit interaction predictor of response, and a CpG unit by rs1126757 interaction predictor of antidepressant response. The current study is the first to investigate the potential utility of pharmaco-epigenetic biomarkers for the prediction of antidepressant response. Our results suggest that DNA methylation in IL11 might be useful in identifying those patients likely to respond to antidepressants, and if so, the best drug suited to each individual.

Body mass index, but not FTO genotype or major depressive disorder, influences brain structure.

Obesity and major depressive disorder (MDD) are highly prevalent and often comorbid health conditions. Both are associated with differences in brain structure and are genetically influenced. Yet, little is known about how obesity, MDD, and known risk genotypes might interact in the brain. Subjects were 81 patients with MDD (mean age 48.6 years) and 69 matched healthy controls (mean age 51.2 years). Subjects underwent 1.5T magnetic resonance imaging, genotyping for the fat mass and obesity associated (FTO) gene rs3751812 polymorphism, and measurements for body mass index (BMI). We conducted a whole brain voxelwise analysis using tensor-based morphometry (TBM) to examine the main and interaction effects of diagnosis, BMI and FTO genotype. Significant effects of BMI were observed across widespread brain regions, indicating reductions in predominantly subcortical and white matter areas associated with increased BMI, but there was no influence of MDD or FTO rs3751812 genotype. There were no significant interaction effects. Within MDD patients, there was no effect of current depressive symptoms; however the use of antidepressant medication was associated with reductions in brain volume in the frontal lobe and cerebellum. Obesity affects brain structure in both healthy participants and MDD patients; this influence may account for some of the brain changes previously associated with MDD. BMI and the use of medication should ideally be measured and controlled for when conducting structural brain imaging research in MDD.

Association at SYNE1 in both bipolar disorder and recurrent major depression.

Genome-wide association studies (GWAS) have identified a number of loci that have strong support for their association with bipolar disorder (BD). The Psychiatric Genome-Wide Association Study (GWAS) Consortium Bipolar Disorder Working Group (PGC-BD) meta-analysis of BD GWAS data sets and replication samples identified evidence (P=6.7 × 10⁻7, odds ratio (OR)=1.147) of association with the risk of BD at the polymorphism rs9371601 within SYNE1, a gene which encodes nesprin-1. Here we have tested this polymorphism in an independent BD case (n=1527) and control (n=1579) samples, and find evidence for association (P=0.0095) with similar effect sizes to those previously observed in BD (allelic OR=1.148). In a combined (meta) analysis of PGC-BD data (both primary and replication data) and our independent BD samples, we found genome-wide significant evidence for association (P=2.9 × 10⁻8, OR=1.104). We have also examined the polymorphism in our recurrent unipolar depression cases (n=1159) and control (n=2592) sample, and found that the risk allele was associated with risk for recurrent major depression (P=0.032, OR=1.118). Our findings add to the evidence that association at this locus influences susceptibility to bipolar and unipolar mood disorders.

The current state of play on the molecular genetics of depression.

BACKGROUND: It has been well established that both genes and non-shared environment contribute substantially to the underlying aetiology of major depressive disorder (MDD). A comprehensive overview of genetic research in MDD is presented. Method Papers were retrieved from PubMed up to December 2011, using many keywords including: depression, major depressive disorder, genetics, rare variants, gene-environment, whole genome, epigenetics, and specific candidate genes and variants. These were combined in a variety of permutations. RESULTS: Linkage studies have yielded some promising chromosomal regions in MDD. However, there is a continued lack of consistency in association studies, in both candidate gene and genome-wide association studies (GWAS). Numerous factors may account for variable results including the use of different diagnostic approaches, small samples in early studies, population stratification, epigenetic phenomena, copy number variation (CNV), rare variation, and phenotypic and allelic heterogeneity. The conflicting results are also probably, in part, a consequence of environmental factors not being considered or controlled for. CONCLUSIONS: Each research group has to identify what issues their sample may best address. We suggest that, where possible, more emphasis should be placed on the environment in molecular behavioural genetics to identify individuals at environmental high risk in addition to genetic high risk. Sequencing should be used to identify rare and alternative variation that may act as a risk factor, and a systems biology approach including gene-gene interactions and pathway analyses would be advantageous. GWAS may require even larger samples with reliably defined (sub)phenotypes.

Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder.

Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750,000 high-quality genetic markers on a combined sample of ∼14,000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17,700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63,000 case-control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.

Estimating the heritability of reporting stressful life events captured by common genetic variants.

BACKGROUND: Although usually thought of as external environmental stressors, a significant heritable component has been reported for measures of stressful life events (SLEs) in twin studies. Method We examined the variance in SLEs captured by common genetic variants from a genome-wide association study (GWAS) of 2578 individuals. Genome-wide complex trait analysis (GCTA) was used to estimate the phenotypic variance tagged by single nucleotide polymorphisms (SNPs). We also performed a GWAS on the number of SLEs, and looked at correlations between siblings. RESULTS: A significant proportion of variance in SLEs was captured by SNPs (30%, p = 0.04). When events were divided into those considered to be dependent or independent, an equal amount of variance was explained for both. This 'heritability' was in part confounded by personality measures of neuroticism and psychoticism. A GWAS for the total number of SLEs revealed one SNP that reached genome-wide significance (p = 4 × 10-8), although this association was not replicated in separate samples. Using available sibling data for 744 individuals, we also found a significant positive correlation of R 2 = 0.08 in SLEs (p = 0.03). CONCLUSIONS: These results provide independent validation from molecular data for the heritability of reporting environmental measures, and show that this heritability is in part due to both common variants and the confounding effect of personality.

Genome-wide association analysis of copy number variation in recurrent depressive disorder.

Large, rare copy number variants (CNVs) have been implicated in a variety of psychiatric disorders, but the role of CNVs in recurrent depression is unclear. We performed a genome-wide analysis of large, rare CNVs in 3106 cases of recurrent depression, 459 controls screened for lifetime-absence of psychiatric disorder and 5619 unscreened controls from phase 2 of the Wellcome Trust Case Control Consortium (WTCCC2). We compared the frequency of cases with CNVs against the frequency observed in each control group, analysing CNVs over the whole genome, genic, intergenic, intronic and exonic regions. We found that deletion CNVs were associated with recurrent depression, whereas duplications were not. The effect was significant when comparing cases with WTCCC2 controls (P=7.7 × 10(-6), odds ratio (OR) =1.25 (95% confidence interval (CI) 1.13-1.37)) and to screened controls (P=5.6 × 10(-4), OR=1.52 (95% CI 1.20-1.93). Further analysis showed that CNVs deleting protein coding regions were largely responsible for the association. Within an analysis of regions previously implicated in schizophrenia, we found an overall enrichment of CNVs in our cases when compared with screened controls (P=0.019). We observe an ordered increase of samples with deletion CNVs, with the lowest proportion seen in screened controls, the next highest in unscreened controls and the highest in cases. This may suggest that the absence of deletion CNVs, especially in genes, is associated with resilience to recurrent depression.

Non-steroidal anti-inflammatory drugs and efficacy of antidepressants in major depressive disorder.

BACKGROUND: It has been proposed that non-steroidal anti-inflammatory drugs (NSAIDs) may interfere with the efficacy of antidepressants and contribute to treatment resistance in major depressive disorder (MDD). This effect requires replication and a test of whether it is specific to serotonin-reuptake inhibiting (SRI) antidepressants. METHOD: We tested the effect of concomitant medication with NSAIDs on the efficacy of escitalopram, a SRI antidepressant, and nortriptyline, a tricyclic antidepressant, among 811 subjects with MDD treated for up to 12 weeks in the GENDEP study. Effects of NSAIDs on improvement of depressive symptoms were tested in mixed-effect linear models. Effects on remission were tested in logistic regression. Age, sex, baseline severity and centre of recruitment were considered as potential confounding factors. RESULTS: Ten percent (n=78) of subjects were taking NSAIDs during the antidepressant treatment. Older subjects were significantly more likely to take NSAIDs. After controlling for age, sex, centre of recruitment and baseline severity, concomitant medication with NSAIDs did not significantly influence the efficacy of escitalopram [ß=0.035, 95% confidence interval (CI) -0.145 to 0.215, p=0.704] or nortriptyline (ß=0.075, 95% CI -0.131 to 0.281, p=0.476). Although slightly fewer subjects who took NSAIDs reached remission [odds ratio (OR) 0.80, 95% CI 0.49-1.31, p=0.383], this non-significant effect was reversed after controlling for age, sex, baseline severity and recruitment centre effects (OR 1.04, 95% CI 0.61-1.77, p=0.882). CONCLUSIONS: NSAIDs are unlikely to affect the efficacy of SRI or other antidepressants. Concurrent use of NSAIDs and antidepressants does not need to be avoided.

Depressive disorder moderates the effect of the FTO gene on body mass index.

There is evidence that obesity-related disorders are increased among people with depression. Variation in the FTO (fat mass and obesity associated) gene has been shown to contribute to common forms of human obesity. This study aimed to investigate the genetic influence of polymorphisms in FTO in relation to body mass index (BMI) in two independent samples of major depressive disorder (MDD) cases and controls. We analysed 88 polymorphisms in the FTO gene in a clinically ascertained sample of 2442 MDD cases and 809 controls (Radiant Study). In all, 8 of the top 10 single-nucleotide polymorphisms (SNPs) showing the strongest associations with BMI were followed-up in a population-based cohort (PsyCoLaus Study) consisting of 1292 depression cases and 1690 controls. Linear regression analyses of the FTO variants and BMI yielded 10 SNPs significantly associated with increased BMI in the depressive group but not the control group in the Radiant sample. The same pattern was found in the PsyCoLaus sample. We found a significant interaction between genotype and affected status in relation to BMI for seven SNPs in Radiant (P<0.0057), with PsyCoLaus giving supportive evidence for five SNPs (P-values between 0.03 and 0.06), which increased in significance when the data were combined in a meta-analysis. This is the first study investigating FTO and BMI within the context of MDD, and the results indicate that having a history of depression moderates the effect of FTO on BMI. This finding suggests that FTO is involved in the mechanism underlying the association between mood disorders and obesity.