Dataset Information

A genome-wide association study of depressive symptoms.

ABSTRACT: BACKGROUND:Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms. METHODS:In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (p<1×10(-5)) was performed in five studies assessing depressive symptoms with other instruments. In addition, we performed a combined meta-analysis of all 22 discovery and replication studies. RESULTS:The discovery sample comprised 34,549 individuals (mean age of 66.5) and no loci reached genome-wide significance (lowest p = 1.05×10(-7)). Seven independent single nucleotide polymorphisms were considered for replication. In the replication set (n = 16,709), we found suggestive association of one single nucleotide polymorphism with depressive symptoms (rs161645, 5q21, p = 9.19×10(-3)). This 5q21 region reached genome-wide significance (p = 4.78×10(-8)) in the overall meta-analysis combining discovery and replication studies (n = 51,258). CONCLUSIONS:The results suggest that only a large sample comprising more than 50,000 subjects may be sufficiently powered to detect genes for depressive symptoms.

SUBMITTER: Hek K

PROVIDER: S-EPMC3845085 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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A genome-wide association study of depressive symptoms.

Hek Karin K Demirkan Ayse A Lahti Jari J Terracciano Antonio A Teumer Alexander A Cornelis Marilyn C MC Amin Najaf N Bakshis Erin E Baumert Jens J Ding Jingzhong J Liu Yongmei Y Marciante Kristin K Meirelles Osorio O Nalls Michael A MA Sun Yan V YV Vogelzangs Nicole N Yu Lei L Bandinelli Stefania S Benjamin Emelia J EJ Bennett David A DA Boomsma Dorret D Cannas Alessandra A Coker Laura H LH de Geus Eco E De Jager Philip L PL Diez-Roux Ana V AV Purcell Shaun S Hu Frank B FB Rimma Eric B EB Hunter David J DJ Jensen Majken K MK Curhan Gary G Rice Kenneth K Penman Alan D AD Rotter Jerome I JI Sotoodehnia Nona N Emeny Rebecca R Eriksson Johan G JG Evans Denis A DA Ferrucci Luigi L Fornage Myriam M Gudnason Vilmundur V Hofman Albert A Illig Thomas T Kardia Sharon S Kelly-Hayes Margaret M Koenen Karestan K Kraft Peter P Kuningas Maris M Massaro Joseph M JM Melzer David D Mulas Antonella A Mulder Cornelis L CL Murray Anna A Oostra Ben A BA Palotie Aarno A Penninx Brenda B Petersmann Astrid A Pilling Luke C LC Psaty Bruce B Rawal Rajesh R Reiman Eric M EM Schulz Andrea A Shulman Joshua M JM Singleton Andrew B AB Smith Albert V AV Sutin Angelina R AR Uitterlinden André G AG Völzke Henry H Widen Elisabeth E Yaffe Kristine K Zonderman Alan B AB Cucca Francesco F Harris Tamara T Ladwig Karl-Heinz KH Llewellyn David J DJ Räikkönen Katri K Tanaka Toshiko T van Duijn Cornelia M CM Grabe Hans J HJ Launer Lenore J LJ Lunetta Kathryn L KL Mosley Thomas H TH Newman Anne B AB Tiemeier Henning H Murabito Joanne J

Biological psychiatry 20130103 7

<h4>Background</h4>Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms.<h4>Methods</h4>In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Repli ...[more]

PMID: 23290196

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Project description:OBJECTIVE:Shared genetic background may explain phenotypic associations between depression and Type 2 diabetes (T2D). We aimed to study, on a genome-wide level, if genetic correlation and pleiotropic loci exist between depressive symptoms and T2D or glycemic traits. METHODS:We estimated single-nucleotide polymorphism (SNP)-based heritability and analyzed genetic correlation between depressive symptoms and T2D and glycemic traits with the linkage disequilibrium score regression by combining summary statistics of previously conducted meta-analyses for depressive symptoms by CHARGE consortium (N = 51,258), T2D by DIAGRAM consortium (N = 34,840 patients and 114,981 controls), fasting glucose, fasting insulin, and homeostatic model assessment of ?-cell function and insulin resistance by MAGIC consortium (N = 58,074). Finally, we investigated pleiotropic loci using a bivariate genome-wide association study approach with summary statistics from genome-wide association study meta-analyses and reported loci with genome-wide significant bivariate association p value (p < 5 × 10). Biological annotation and function of significant pleiotropic SNPs were assessed in several databases. RESULTS:The SNP-based heritability ranged from 0.04 to 0.10 in each individual trait. In the linkage disequilibrium score regression analyses, depressive symptoms showed no significant genetic correlation with T2D or glycemic traits (p > 0.37). However, we identified pleiotropic genetic variations for depressive symptoms and T2D (in the IGF2BP2, CDKAL1, CDKN2B-AS, and PLEKHA1 genes), and fasting glucose (in the MADD, CDKN2B-AS, PEX16, and MTNR1B genes). CONCLUSIONS:We found no significant overall genetic correlations between depressive symptoms, T2D, or glycemic traits suggesting major differences in underlying biology of these traits. However, several potential pleiotropic loci were identified between depressive symptoms, T2D, and fasting glucose, suggesting that previously established phenotypic associations may be partly explained by genetic variation in these specific loci.

Project description:BACKGROUND:Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology. RESULTS:Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1?kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression. CONCLUSIONS:Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

Dataset Information

A genome-wide association study of depressive symptoms.

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A genome-wide association study of depressive symptoms.

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