Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

Hyde, Craig; Nagle, Michael W.; Tian, Chao; Chen, Xing; Paciga, Sara A.; Wendland, Jens R.; Tung, Joyce Y.; Hinds, David A.; Perlis, Roy H.; Winslow, Ashley R.

doi:10.1038/ng.3623

Cited by 716 publications

(795 citation statements)

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“…A second possible explanation is that SSRIs treatment response likely represents a complex, highly polygenic trait, influenced by a large number of genes with small effects and interacting environmental factors. Increasing the sample size for the current pharmacogenomics studies seems to be the only available option to be successful in this process, as previously been observed in MDD GWAS [59]. Genetic variants associated with MDD were undetectable for smaller sample sizes (< 200,000) [59].…”

Section: Maoamentioning

confidence: 80%

“…To date, GWAS approaches have identified 18 loci contributing to the risk of MDD [20,24,59] and over 8 loci associated with BPD [55,63,84,92,106], including shared genetic loci located in the CACNA1C, CACNB2, AS3MT, ITIH3, and CCDC68 genes [22]. An extension of the GWAS approaches such as polygenic score analysis [87], bivariate restricted maximum likelihood (REML) in genome-wide complex trait analysis (GCTA) [21] and linkage disequilibrium (LD) score regression have strongly suggested that mood disorders are polygenic in nature, in that multiple genetic variants interacting with environmental factors contribute to the development of the diseases [87].…”

Section: Introductionmentioning

confidence: 99%

“…Candidate gene and genome-wide association studies (GWASs) have identified a list of candidate genes for MDD [20,24,35,59] and BPD [55,84,92,106]. To date, GWAS approaches have identified 18 loci contributing to the risk of MDD [20,24,59] and over 8 loci associated with BPD [55,63,84,92,106], including shared genetic loci located in the CACNA1C, CACNB2, AS3MT, ITIH3, and CCDC68 genes [22].…”

Section: Introductionmentioning

confidence: 99%

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Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

2017

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Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.

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Section: Maoamentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

2017

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“…Investigators of an ongoing large-scale GWAS of major depressive disorder (N = 368,890) in the 23andMe cohort shared association results for the loci identified in our DS and neuroticism analyses (Online Methods and Supplementary Table 15) 21 . Because the depression sample overlaps with our SWB sample, we did not request a lookup of the SWB-associated SNPs.…”

Section: Lookup Of Ds and Neuroticism Lead-snpsmentioning

confidence: 99%

“…We did not request results for the SNPs identified in the SWB or proxy-phenotype analyses, since these were both conducted in samples that overlap with 23andMe's depression sample. For details on association models, quality-control filters, and the ascertainment of depression status, we refer to the companion study 21 . The p-values we report are based on standard errors that have been inflated by the square by the intercept from an LD score regression 10 .…”

Section: Bayesian Credibility Analysesmentioning

confidence: 99%

Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism

Okbay

Baselmans

Neve

et al. 2015

Preprint

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We conducted genome-wide association studies of three phenotypes: subjective wellbeing (SWB; N = 298,420), depressive symptoms (DS; N = 161,460), and neuroticism (N = 170,910). We identified three variants associated with SWB, two with DS, and eleven with neuroticism, including two inversion polymorphisms. The two DS loci replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (| | . ) strengthen the overall credibility of the findings, and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal/pancreas tissues are strongly enriched for association.Subjective well-being (SWB)-as measured by survey questions on life satisfaction, positive affect, or happiness-is a major topic of research within psychology, economics, and epidemiology. Twin studies have found that SWB is genetically correlated with depression (characterized by negative affect, anxiety, low energy, bodily aches and pains, pessimism, and other symptoms) and neuroticism (a personality trait characterized by easily experiencing negative emotions such as anxiety and fear) 1-3 . Depression and neuroticism have received much more attention than SWB in genetic-association studies, but the discovery of associated genetic variants with either of them has proven elusive 4,5 .In this paper, we report a series of separate and joint analyses of SWB, depressive symptoms (DS), and neuroticism. Our primary analysis is a genome-wide association study (GWAS) of SWB based on data from 59 cohorts (N = 298,420). This GWAS identifies three loci associated with SWB at genome-wide significance (p < 5×10 -8 ). We supplement this primary analysis with auxiliary GWAS meta-analyses of DS (N = 180,866) and neuroticism (N = 170,910), performed by combining publicly available summary statistics from published studies with new genome-wide analyses of additional data. In these auxiliary analyses we identify two loci associated with DS and eleven with neuroticism, including two inversion polymorphisms. In depression data from an independent sample (N = 368,890), both DS associations replicate (p = 0.004 and p = 0.015).In our two joint analyses, we exploit the high genetic correlation between SWB, DS, and neuroticism (i) to evaluate the credibility of the 16 genome-wide significant associations across the three phenotypes, and (ii) to identify novel associations (beyond those identified by the GWAS). For (i), we investigate whether our three SWB-associated SNPs "quasi-replicate" by testing them for association with DS and neuroticism. We similarly examine the quasi-replication record of the DS and neuroticism loci by testing them for association with SWB. We find that the quasi-replication record closely matches what would be expected given our statistical power if none of the genome-wide significant associations were chance findings. These results strengthen the credibility of (most of) the original associations. For (ii), we u...

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Underperforming Big Ideas in Biomedical Research

Miller

Sittig

2017

JAMA

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Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

Cited by 716 publications

References 37 publications

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism

Underperforming Big Ideas in Biomedical Research

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