Genome-Wide Association Study of Major Recurrent Depression in the UK Population

Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
American Journal of Psychiatry (Impact Factor: 12.3). 08/2010; 167(8):949-57. DOI: 10.1176/appi.ajp.2010.09091380
Source: PubMed


Studies of major depression in twins and families have shown moderate to high heritability, but extensive molecular studies have failed to identify susceptibility genes convincingly. To detect genetic variants contributing to major depression, the authors performed a genome-wide association study using 1,636 cases of depression ascertained in the U.K. and 1,594 comparison subjects screened negative for psychiatric disorders.
Cases were collected from 1) a case-control study of recurrent depression (the Depression Case Control [DeCC] study; N=1346), 2) an affected sibling pair linkage study of recurrent depression (probands from the Depression Network [DeNT] study; N=332), and 3) a pharmacogenetic study (the Genome-Based Therapeutic Drugs for Depression [GENDEP] study; N=88). Depression cases and comparison subjects were genotyped at Centre National de Génotypage on the Illumina Human610-Quad BeadChip. After applying stringent quality control criteria for missing genotypes, departure from Hardy-Weinberg equilibrium, and low minor allele frequency, the authors tested for association to depression using logistic regression, correcting for population ancestry.
Single nucleotide polymorphisms (SNPs) in BICC1 achieved suggestive evidence for association, which strengthened after imputation of ungenotyped markers, and in analysis of female depression cases. A meta-analysis of U.K. data with previously published results from studies in Munich and Lausanne showed some evidence for association near neuroligin 1 (NLGN1) on chromosome 3, but did not support findings at BICC1.
This study identifies several signals for association worthy of further investigation but, as in previous genome-wide studies, suggests that individual gene contributions to depression are likely to have only minor effects, and very large pooled analyses will be required to identify them.

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Available from: Ian W Craig, Oct 01, 2015
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    • "Deterministic genetic models for psychiatric illness have proved elusive. Genome-wide association studies (GWAS) have not translated into significant therapeutic gains for disorders such as depression, and aetiology from a genetic viewpoint remains opaque (Lewis et al. 2010; Ripke et al. 2013). Possible reasons for this include well-cited nosological difficulties (Casey et al. 2013) incorporating phenomenological, psychopathological and pathophysiological heterogeneity, small effect sizes of individual genes and overestimated heritability (McGuffin et al. 2007; Bohacek & Mansuy, 2013; Uher, 2014). "
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    ABSTRACT: Background: Hopes to identify genetic susceptibility loci accounting for the heritability seen in unipolar depression have not been fully realized. Family history remains the 'gold standard' for both risk stratification and prognosis in complex phenotypes such as depression. Meanwhile, the physiological mechanisms underlying life-event triggers for depression remain opaque. Epigenetics, comprising heritable changes in gene expression other than alterations of the nucleotide sequence, may offer a way to deepen our understanding of the aetiology and pathophysiology of unipolar depression and optimize treatments. A heuristic target for exploring the relevance of epigenetic changes in unipolar depression is the hypothalamic-pituitary-adrenal (HPA) axis. The glucocorticoid receptor (GR) gene (NR3C1) has been found to be susceptible to epigenetic modification, specifically DNA methylation, in the context of environmental stress such as early life trauma, which is an established risk for depression later in life. Method: In this paper we discuss the progress that has been made by studies that have investigated the relationship between depression, early trauma, the HPA axis and the NR3C1 gene. Difficulties with the design of these studies are also explored. Results: Future efforts will need to comprehensively address epigenetic natural histories at the population, tissue, cell and gene levels. The complex interactions between the epigenome, genome and environment, as well as ongoing nosological difficulties, also pose significant challenges. Conclusions: The work that has been done so far is nevertheless encouraging and suggests potential mechanistic and biomarker roles for differential DNA methylation patterns in NR3C1 as well as novel therapeutic targets.
    Psychological Medicine 09/2015; DOI:10.1017/S0033291715001555 · 5.94 Impact Factor
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    • ") appeared , not a single biological test was ready for inclusion in the DSM - 5 over three decades later ( Kapur et al . , 2012 ) . Large genome - wide asso - ciation studies have been unable to replicate genetic associations with depression diagnosis ( Lewis et al . , 2010 ; Shi et al . , 2011 ; Wray et al . , 2012 ; Daly et al . , 2013 ) or treatment response ( Tansey et al . , 2012 ) , and in a recent study with over 34000 subjects , no single locus reached genome - wide significance ( Hek et al . , 2013 ) . The high comorbidity rates of depression with other disorders such as generalized anxiety disord"
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    ABSTRACT: Major depression (MD) is a highly heterogeneous diagnostic category. Diverse symptoms such as sad mood, anhedonia, and fatigue are routinely added to an unweighted sum-score, and cutoffs are used to distinguish between depressed participants and healthy controls. Researchers then investigate outcome variables like MD risk factors, biomarkers, and treatment response in such samples. These practices presuppose that (1) depression is a discrete condition, and that (2) symptoms are interchangeable indicators of this latent disorder. Here I review these two assumptions, elucidate their historical roots, show how deeply engrained they are in psychological and psychiatric research, and document that they contrast with evidence. Depression is not a consistent syndrome with clearly demarcated boundaries, and depression symptoms are not interchangeable indicators of an underlying disorder. Current research practices lump individuals with very different problems into one category, which has contributed to the remarkably slow progress in key research domains such as the development of efficacious antidepressants or the identification of biomarkers for depression. The recently proposed network framework offers an alternative to the problematic assumptions. MD is not understood as a distinct condition, but as heterogeneous symptom cluster that substantially overlaps with other syndromes such as anxiety disorders. MD is not framed as an underlying disease with a number of equivalent indicators, but as a network of symptoms that have direct causal influence on each other: insomnia can cause fatigue which then triggers concentration and psychomotor problems. This approach offers new opportunities for constructing an empirically based classification system and has broad implications for future research.
    Frontiers in Psychology 03/2015; 6(306):1-11. DOI:10.3389/fpsyg.2015.00309 · 2.80 Impact Factor
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    • "Genetic risk profiles constructed using results from schizophrenia GWAS studies were shown to be significantly associated with case/control status in an independent bipolar dataset, indicating that they share common genetic risk alleles. Genome-wide association studies of MDD have not proven to be as successful in identifying genetic risk variants (Sullivan et al. 2009; Shi et al. 2011; Muglia et al. 2010; Shyn et al. 2011; Lewis et al. 2010; Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium 2013). This may reflect, in part, the phenotypic, genetic and environmental heterogeneity that characterize this disorder. "
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    ABSTRACT: The etiology of major depressive disorder (MDD) is likely to be heterogeneous, but postpartum depression (PPD) is hypothesized to represent a more homogenous subset of MDD. We use genome-wide SNP data to explore this hypothesis. We assembled a total cohort of 1,420 self-report cases of PPD and 9,473 controls with genome-wide genotypes from Australia, The Netherlands, Sweden and the UK. We estimated the total variance attributable to genotyped variants. We used association results from the Psychiatric Genomics Consortia (PGC) of bipolar disorder (BPD) and MDD to create polygenic scores in PPD and related MDD data sets to estimate the genetic overlap between the disorders. We estimated that the percentage of variance on the liability scale explained by common genetic variants to be 0.22 with a standard error of 0.12, p = 0.02. The proportion of variance (R 2) from a logistic regression of PPD case/control status in all four cohorts on a SNP profile score weighted by PGC-BPD association results was small (0.1 %) but significant (p = 0.004) indicating a genetic overlap between BPD and PPD. The results were highly significant in the Australian and Dutch cohorts (R 2 > 1.1 %, p < 0.008), where the majority of cases met criteria for MDD. The genetic overlap between BPD and MDD was not significant in larger Australian and Dutch MDD case/control cohorts after excluding PPD cases (R 2 = 0.06 %, p = 0.08), despite the larger MDD group affording more power. Our results suggest an empirical genetic evidence for a more important shared genetic etiology between BPD and PPD than between BPD and MDD.
    Archives of Women s Mental Health 07/2014; DOI:10.1007/s00737-014-0428-5 · 2.16 Impact Factor
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