Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression
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Abstract
Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype. A genome-wide association meta-analysis of individuals with clinically assessed or self-reported depression identifies 44 independent risk loci. Comparisons with other psychiatric disorders and candidate gene analyses provide new insights into major depressive disorder.
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A GWAS meta-analysis of MDD published in 2018 identified 44 depression-associated loci and integrated genetic and mQTL data from two large studies to identify nine risk variants controlling local DNA methylation in blood.
“A GWAS meta-analysis of MDD published in 2018 identified 44 depression-associated loci and integrated genetic and mQTL data from two large studies to identify nine risk variants controlling local DNAm in blood.”
From Epigenetics and depression (2019) · cites this paper as [49]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA subset of the risk variants for MDD could be responsible for the differentially methylated regions observed in patients with MDD.
“This overlap indicates that a subset of the risk variants for MDD could be responsible for the DMRs observed in patients with MDD.”
From Epigenetics and depression (2019) · cites this paper as [49]
The Results section, under “Implications from integration of functional genomic data” (page not available), reports strong evidence that nine major-depression-associated SNPs control local DNA methylation. It does not establish that those SNPs overlap with, or are responsible for, differentially methylated regions observed in patients with MDD. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSome of the nine mQTLs are located near genes encoding miRNAs; however, the function of these variants and DMRs must still be assessed.
“Interestingly, some of the nine mQTLs are located near genes encoding miRNAs; however, the function of these variants and DMRs must still be assessed.”
From Epigenetics and depression (2019) · cites this paper as [49]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe major haplotype outside Africa was found significantly associated with greater susceptibility to major depression and other related psychiatric disorders and with higher values for self-reported helping behavior.
“The major haplotype outside Africa was found significantly associated with greater susceptibility to major depression and other related psychiatric disorders [55,56] as well as with higher values for self-reported helping behavior [57].”
From Human genetic adaptation related to cellular zinc homeostasis (2023) · cites this paper as [56]
The Results section reports 44 loci significantly associated with major depression, and the Discussion reports positive genetic correlations with other psychiatric disorders. Neither passage identifies a major haplotype outside Africa or shows that such a haplotype is associated with susceptibility to those disorders. The helping-behavior claim is attached to a different citation [57] and is not assessed here. (LLM, conf=0.83)
Experimental — accuracy not yet measuredGWAS have discovered and successfully replicated genome-wide associations of common gene variants for schizophrenia and major depression.
“large consortia that have discovered and successfully replicated genome-wide associations of common gene variants (single nucleotide polymorphisms, SNPs) for several disorders including schizophrenia [2] and major depression [3–6].”
From Neandertal introgression partitions the genetic landscape of neuropsychiatric disorders and associated behavioral phenotypes (2022) · cites this paper as [3]
Postmortem studies of patients with MDD have shown reduced densities of glial cells in the prefrontal cortex, hippocampus and amygdala.
“and postmortem studies of patients with MDD have shown reduced densities of glial cells in the prefrontal cortex (PFC), hippocampus and amygdala. 19”
From Major depressive disorder: hypothesis, mechanism, prevention and treatment (2024) · cites this paper as [19]
The Results section on functional genomic data discusses gene-expression enrichment in cortex and cites MRI findings, but does not report glial-cell densities. A search for postmortem glial density in the prefrontal cortex, hippocampus, and amygdala found no passage in our copy addressing the claimed reductions; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measuredDRD2 appeared in all 14 GO gene-sets and in prior depression studies.
“DRD2 was included in all 14 of the putative Gene Ontology Consortium gene-sets identified (but not the somatosensory pyramidal neurons gene-set) and has been identified in previous studies of depression5, 9, 22.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
The consistent direction of all 102 variants across contributing studies and replication suggests they are robust depression associations.
“All of the 102 variants associated with depression had an equivalent direction of allelic effect across the three studies5, 8, 9 that contributed to the meta-analysis and the replication sample (Supplementary Table 1), suggesting that these variants represent robust associations with depression.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
The study reaffirmed the genetic correlation between schizophrenia and depression reported by Wray and colleagues.
“The current study further reaffirms the genetic correlation between schizophrenia and depression observed by Wray, et al. 9.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
Several genetic correlations matched earlier reports by Wray and Howard, including age at menarche (rG = −0.12).
“Many of these genetic correlations are similar to those reported by Wray, et al. 9 and Howard, et al. 5, including earlier age at menarche (rG = -0.12, s.e = 0.02).”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
Wray and colleagues' PGC meta-analysis of 35 cohorts (n = 461,134) found 44 loci associated with a spectrum of depression phenotypes.
“Finally, a meta-analysis of 35 cohorts (total n = 461,134; 130,664 cases and 330,470 controls) conducted by Wray, et al. 9 (PGC) found 44 loci that were significantly associated with a spectrum of depression phenotypes, some obtained from structured clinical interview and others based on broader criteria.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
The TCF4 coding region contained two independent depression-associated variants and has been reported in earlier depression studies.
“The TCF4 gene coding region (Supplementary Figure 12) is also noteworthy as it contained two independently-associated variants for depression and has been identified in previous studies of depression 5, 9, 29.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
Sample sizes were not pre-determined statistically, though they resemble those in earlier reports.
“No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications 5, 8, 9.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [9]
MDD-associated genes overlap with genes that harbor rare de novo variation in ASD and developmental disorders.
“MDD-associated genes overlap with genes that harbor rare de novo variation in ASD and developmental disorders (DD), suggesting that a recently reported genetic correlation between MDD and ASD GWAS43 may also apply for rare variation.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [43]
Several prospective studies suggest that depression prior to completing education predicts substantially reduced income-earnings in adulthood after adjusting for level of educational attainment.
“Several prospective studies suggest that depression prior to completing education predicts substantially reduced income-earnings in adulthood after adjusting for level of educational attainment. 230,231”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [230]
The explanatory power of currently available polygenic risk scores for addictive disorders lags behind those for schizophrenia or major depression.
“It is true that a large number of risk alleles are involved, and that the explanatory power of currently available polygenic risk scores for addictive disorders lags behind those for e.g., schizophrenia or major depression [47,48].”
From Addiction as a brain disease revised: why it still matters, and the need for consilience (2021) · cites this paper as [48]
Power analyses indicate that additional SNPs associated with AD are likely to have small effect sizes, smaller than schizophrenia and more consistent with more common psychiatric disorders (e.g. major depression).
“Power analyses indicate that additional SNPs associated with AD are likely to have small effect sizes, smaller than schizophrenia47 and more consistent with more common psychiatric disorders (e.g. major depression48).”
From Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders (2018) · cites this paper as [48]
The DCC region has previously been associated with MD and neuroticism.
“and has been previously associated with both MD and neuroticism (Turley et al., 2018; Wray et al., 2018).”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Wray et al., 2018
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Self-citationSparse whole-genome sequencing identifies two loci for major depressive disorder
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Self-citationGWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment
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Self-citationAssociation analyses of 249,796 individuals reveal 18 new loci associated with body mass index
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Self-citationPsychiatric Genomics: An Update and an Agenda
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Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing
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Self-citationMeta-analysis of genome-wide association studies of anxiety disorders
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Self-citationThe Clinical Course of Unipolar Major Depressive Disorders
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An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
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Self-citationA genome-wide association meta-analysis identifies new childhood obesity loci
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Self-citationCell-Type-Specific Alternative Splicing Governs Cell Fate in the Developing Cerebral Cortex
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Self-citationThe splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain
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Self-citationA genome-wide association study of anorexia nervosa
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Self-citationCytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes
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Self-citationSelected SALM (Synaptic Adhesion-Like Molecule) Family Proteins Regulate Synapse Formation
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Self-citationGenetic identification Of brain cell types underlying schizophreniaPreprint
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Self-citationHigh loading of polygenic risk in cases with chronic schizophrenia
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Self-citationIgLON cell adhesion molecule Kilon is a crucial modulator for synapse number in hippocampal neurons
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Novel Association of Genetic Markers Affecting CYP2A6 Activity and Lung Cancer Risk
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Self-citationGenetic contributions to self-reported tiredness
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Self-citationHUGIn: Hi-C Unifying Genomic Interrogator
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Self-citationNeuron-specific SALM5 limits inflammation in the CNS via its interaction with HVEM
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Self-citationKernel Generative Topographic Mapping
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Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404 · see citing works
Malhi, G. S., & Mann, J. J. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31948-2 · Cited by 4,099 · see citing works
Identification of common genetic risk variants for autism spectrum disorder
Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, Grove, J., BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0344-8 · Cited by 2,421 · see citing works
Self-citationHoward, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., Coleman, J. R. I., Hagenaars, S. P., Ward, J., Wigmore, E. M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0326-7 · Cited by 2,565 · see citing works
Self-citationSatterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321 · see citing works
“18 We used results from six GWAS datasets: ASD, schizophrenia, major depressive disorder, and 1 attention deficit hyperactivity disorder (ADHD), which are all positively genetically correlated 2 with ASD and with each other; educational attainment, which is positively correlated with ASD 3 and negatively correlated wi…”
MethodSelf-citationADHD Working Group of the Psychiatric Genomics Consortium (PGC), Demontis, D., Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium, 23andMe Research Team, Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., Baldursson, G., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0269-7 · Cited by 2,203 · see citing works
Self-citationPolygenic prediction via Bayesian regression and continuous shrinkage priors
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A., & Smoller, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-09718-5 · Cited by 1,922 · see citing works
Genome-wide association study identifies 30 loci associated with bipolar disorder
eQTLGen Consortium, Stahl, E. A., BIOS Consortium, the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0397-8 · Cited by 1,519 · see citing works
Self-citationGenomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders
Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.020 · Cited by 1,381 · see citing works
“The eight disorders included AN (Duncan et al., 2017) ASD (Grove et al., 2019), ADHD (Demontis et al., 2019), BIP (Stahl et al., 2019), MD (Wray et al., 2018), OCD (International Obsessive Compulsive Disorder Foundation Genetics Collaborative [IOCDF-GC] and OCD Collaborative Genetics Association…”
BackgroundSelf-citationTranscriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
Self-citationFirth, J., Solmi, M., Wootton, R. E., Vancampfort, D., Schuch, F. B., Hoare, E., Gilbody, S., Torous, J., Teasdale, S. B., Jackson, S. E., et al. (2020) · World Psychiatry · doi:10.1002/wps.20773 · Cited by 1,148 · see citing works
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
Self-citationTime for united action on depression: a Lancet–World Psychiatric Association Commission
Herrman, H., Patel, V., Kieling, C., Berk, M., Buchweitz, C., Cuijpers, P., Furukawa, T. A., Kessler, R. C., Kohrt, B. A., Maj, M., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(21)02141-3 · Cited by 1,104 · see citing works
SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse
Koopmans, F., van Nierop, P., Andres-Alonso, M., Byrnes, A., Cijsouw, T., Coba, M. P., Cornelisse, L. N., Farrell, R. J., Goldschmidt, H. L., Howrigan, D. P., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.002 · Cited by 938 · see citing works
Self-citationMajor depressive disorder: hypothesis, mechanism, prevention and treatment
Cui, L., Li, S., Wang, S., Wu, X., Liu, Y., Yu, W., Wang, Y., Tang, Y., Xia, M., & Li, B. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01738-y · Cited by 1,061 · see citing works
“19,27,28 In addition, rare copy number variants are also identi fi ed to be related to MDD risk, there may be three copy number variants (CNV) loci associated with Prader-Willis syndrome: 1q21.”
Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R. E., McIntosh, A. M., Deary, I. J., et al. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0566-x · Cited by 958 · see citing works
Self-citationDuman, R. S., Sanacora, G., & Krystal, J. H. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.013 · Cited by 1,026 · see citing works
Analysis of polygenic risk score usage and performance in diverse human populations
Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11112-0 · Cited by 1,038 · see citing works
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science · doi:10.1126/science.aat7615 · Cited by 810 · see citing works
Self-citation23and Me Research Team, Karlsson Linnér, R., eQTLgen Consortium, International Cannabis Consortium, Social Science Genetic Association Consortium, Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0309-3 · Cited by 756 · see citing works
Self-citationThe genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works
Self-citationBrain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
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Revisiting the Stress Concept: Implications for Affective Disorders
McEwen, B. S., & Akil, H. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.0733-19.2019 · Cited by 686 · see citing works
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Self-citationComparative genetic architectures of schizophrenia in East Asian and European populations
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Lam, M., Indonesia Schizophrenia Consortium, Genetic REsearch on schizophreniA neTwork-China and the Netherlands (GREAT-CN), Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0512-x · Cited by 655 · see citing works
Self-citationBorder, R., Johnson, E. C., Evans, L. M., Smolen, A., Berley, N., Sullivan, P. F., & Keller, M. C. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18070881 · Cited by 629 · see citing works
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Self-citation23andMe Research Team, Walters, R. K., Polimanti, R., Johnson, E. C., McClintick, J. N., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0275-1 · Cited by 632 · see citing works
Self-citationMalhi, G. S., Bell, E., Bassett, D., Boyce, P., Bryant, R., Hazell, P., Hopwood, M., Lyndon, B., Mulder, R., Porter, R., et al. (2021) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867420979353 · Cited by 614 · see citing works
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Self-citationDefining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452 · see citing works
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Prospective biomarkers of major depressive disorder: A systematic review and meta-analysis
Kennis, M., Gerritsen, L., van Dalen, M., Williams, A., Cuijpers, P., & Bockting, C. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0585-z · Cited by 435 · see citing works
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Self-citationLuciano, M., Hagenaars, S. P., Davies, G., Hill, W. D., Clarke, T.-K., Shirali, M., Harris, S. E., Marioni, R. E., Liewald, D. C., Fawns-Ritchie, C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0013-8 · Cited by 391 · see citing works
Self-citationLarge-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior
Ganna, A., Verweij, K. J. H., Nivard, M. G., Maier, R., Wedow, R., Busch, A. S., Abdellaoui, A., Guo, S., Sathirapongsasuti, J. F., 23andMe Research Team, et al. (2019) · Science · doi:10.1126/science.aat7693 · Cited by 406 · see citing works
Self-citationFrom Basic Science to Clinical Application of Polygenic Risk Scores: A Primer
Wray, N. R., Lin, T., Austin, J., McGrath, J. J., Hickie, I. B., Murray, G. K., & Visscher, P. M. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3049 · Cited by 367 · see citing works
Self-citationGender Differences in Depression: Biological, Affective, Cognitive, and Sociocultural Factors
Hyde, J. S., & Mezulis, A. H. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000230 · Cited by 385 · see citing works
Machine learning in major depression: from classification to treatment outcome prediction
Gao, S., Calhoun, V. D., & Sui, J. (2018) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13048 · Cited by 370 · see citing works
Minimal phenotyping yields genome-wide association signals of low specificity for major depression
MDD Working Group of the Psychiatric Genomics Consortium, Cai, N., Revez, J. A., Adams, M. J., Andlauer, T. F. M., Breen, G., Byrne, E. M., Clarke, T.-K., Forstner, A. J., Grabe, H. J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0594-5 · Cited by 344 · see citing works
Self-citationDeep learning in mental health outcome research: a scoping review
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“For example, dozens of genetic loci have been discovered for schizophrenia ( > 100 loci) 50 , bipolar disorder (30 loci) 51 and major depressive disorders (44 loci) 52 , whereas for antisocial behavior 53 , AUDs 54 and opioid use disorders 8 , only a very small number of loci have been discovered.”
BackgroundCommon genetic variants contribute to risk of rare severe neurodevelopmental disorders
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Self-citationBerk, M., Köhler‐Forsberg, O., Turner, M., Penninx, B. W. J. H., Wrobel, A., Firth, J., Loughman, A., Reavley, N. J., McGrath, J. J., Momen, N. C., et al. (2023) · World Psychiatry · doi:10.1002/wps.21110 · Cited by 336 · see citing works
Multivariate genome-wide analyses of the well-being spectrum
BIOS consortium, Baselmans, B. M. L., Social Science Genetic Association Consortium, Jansen, R., Ip, H. F., van Dongen, J., Abdellaoui, A., van de Weijer, M. P., Bao, Y., Smart, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0320-8 · Cited by 320 · see citing works
Self-citationSingle-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
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Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
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Self-citationSpruit, A., Goos, L., Weenink, N., Rodenburg, R., Niemeyer, H., Stams, G. J., & Colonnesi, C. (2020) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-019-00299-9 · Cited by 280 · see citing works
“Genetic markers have been associated with depression symptoms and disorders (Hyde et al. 2016; Wray et al. 2018).”
BackgroundThe future of rodent models in depression research
Gururajan, A., Reif, A., Cryan, J. F., & Slattery, D. A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0221-6 · Cited by 269 · see citing works
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Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
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Self-citationCortisol and major depressive disorder-translating findings from humans to animal models and Back
Nandam, L. S., Brazel, M., Zhou, M., & Jhaveri, D. J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00974 · Cited by 265 · see citing works
Environmental exposures and depression: biological mechanisms and epidemiological evidence
van den Bosch, M., & Meyer-Lindenberg, A. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040218-044106 · Cited by 254 · see citing works
Genetic correlates of social stratification in Great Britain
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Self-citationPenner-Goeke, S., & Binder, E. B. (2019) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2019.21.4/ebinder · Cited by 254 · see citing works
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Psychedelics in Psychiatry: Neuroplastic, Immunomodulatory, and Neurotransmitter Mechanisms
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Qi, G., & Chatterjee, N. (2019) · Nature Communications · doi:10.1038/s41467-019-09432-2 · Cited by 207 · see citing works
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Self-citationColeman, J. R. I., Gaspar, H. A., Bryois, J., Byrne, E. M., Forstner, A. J., Holmans, P. A., de Leeuw, C. A., Mattheisen, M., McQuillin, A., Whitehead Pavlides, J. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.015 · Cited by 199 · see citing works
Self-citationUsing genetics for social science
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Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex
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Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
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An atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185 · see citing works
Self-citationTaylor, M. J., Martin, J., Lu, Y., Brikell, I., Lundström, S., Larsson, H., & Lichtenstein, P. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3652 · Cited by 174 · see citing works
A comparative atlas of single-cell chromatin accessibility in the human brain
Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173 · see citing works
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Self-citationSex-Specific Role for the Long Non-coding RNA LINC00473 in Depression
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- Authors
- eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., Agerbo, E., Air, T. M., Andlauer, T. M. F., Bacanu, S.-A., Bækvad-Hansen, M., Beekman, A. F. T., Bigdeli, T. B., Binder, E. B., Blackwood, D. R. H., Bryois, J., Buttenschøn, H. N., Bybjerg-Grauholm, J., Cai, N., Castelao, E., Christensen, J. H., Clarke, T.-K., Coleman, J. I. R., Colodro-Conde, L., Couvy-Duchesne, B., Craddock, N., Crawford, G. E., Crowley, C. A., Dashti, H. S., Davies, G., Deary, I. J., Degenhardt, F., Derks, E. M., Direk, N., Dolan, C. V., Dunn, E. C., Eley, T. C., Eriksson, N., Escott-Price, V., Kiadeh, F. H. F., Finucane, H. K., Forstner, A. J., Frank, J., Gaspar, H. A., Gill, M., Giusti-Rodríguez, P., Goes, F. S., Gordon, S. D., Grove, J., Hall, L. S., Hannon, E., Hansen, C. S., Hansen, T. F., Herms, S., Hickie, I. B., Hoffmann, P., Homuth, G., Horn, C., Hottenga, J.-J., Hougaard, D. M., Hu, M., Hyde, C. L., Ising, M., Jansen, R., Jin, F., Jorgenson, E., Knowles, J. A., Kohane, I. S., Kraft, J., Kretzschmar, W. W., Krogh, J., Kutalik, Z., Lane, J. M., Li, Y., Li, Y., Lind, P. A., Liu, X., Lu, L., MacIntyre, D. J., MacKinnon, D. F., Maier, R. M., Maier, W., Marchini, J., Mbarek, H., McGrath, P., McGuffin, P., Medland, S. E., Mehta, D., Middeldorp, C. M., Mihailov, E., Milaneschi, Y., Milani, L., Mill, J., Mondimore, F. M., Montgomery, G. W., Mostafavi, S., Mullins, N., Nauck, M., Ng, B., Nivard, M. G., Nyholt, D. R., O’Reilly, P. F., Oskarsson, H., Owen, M. J., Painter, J. N., Pedersen, C. B., Pedersen, M. G., Peterson, R. E., Pettersson, E., Peyrot, W. J., Pistis, G., Posthuma, D., Purcell, S. M., Quiroz, J. A., Qvist, P., Rice, J. P., Riley, B. P., Rivera, M., Saeed Mirza, S., Saxena, R., Schoevers, R., Schulte, E. C., Shen, L., Shi, J., Shyn, S. I., Sigurdsson, E., Sinnamon, G. B. C., Smit, J. H., Smith, D. J., Stefansson, H., Steinberg, S., Stockmeier, C. A., Streit, F., Strohmaier, J., Tansey, K. E., Teismann, H., Teumer, A., Thompson, W., Thomson, P. A., Thorgeirsson, T. E., Tian, C., Traylor, M., Treutlein, J., Trubetskoy, V., Uitterlinden, A. G., Umbricht, D., Van der Auwera, S., van Hemert, A. M., Viktorin, A., Visscher, P. M., Wang, Y., Webb, B. T., Weinsheimer, S. M., Wellmann, J., Willemsen, G., Witt, S. H., Wu, Y., Xi, H. S., Yang, J., Zhang, F., Arolt, V., Baune, B. T., Berger, K., Boomsma, D. I., Cichon, S., Dannlowski, U., de Geus, E. C. J., DePaulo, J. R., Domenici, E., Domschke, K., Esko, T., Grabe, H. J., Hamilton, S. P., Hayward, C., Heath, A. C., Hinds, D. A., Kendler, K. S., Kloiber, S., Lewis, G., Li, Q. S., Lucae, S., Madden, P. F. A., Magnusson, P. K., Martin, N. G., McIntosh, A. M., Metspalu, A., Mors, O., Mortensen, P. B., Müller-Myhsok, B., Nordentoft, M., Nöthen, M. M., O’Donovan, M. C., Paciga, S. A., Pedersen, N. L., Penninx, B. W. J. H., Perlis, R. H., Porteous, D. J., Potash, J. B., Preisig, M., Rietschel, M., Schaefer, C., Schulze, T. G., Smoller, J. W., Stefansson, K., Tiemeier, H., Uher, R., Völzke, H., Weissman, M. M., Werge, T., Winslow, A. R., Lewis, C. M., Levinson, D. F., Breen, G., Børglum, A. D., & Sullivan, P. F.
- Affiliations
- The University of Queensland (AU)
- Broad Institute (US)
- Aarhus University (DK)
- Amsterdam Public Health (NL)
- The University of Adelaide (AU)
- Virginia Commonwealth University (US)
- Emory University (US)
- Statens Serum Institut (DK)
- European Bioinformatics Institute (GB)
- Centre Hospitalier Universitaire Vaudois (CH)
- King's College London (GB)
- QIMR Berghofer Medical Research Institute (AU)
- University of North Carolina at Chapel Hill (US)
- University of Bonn (DE)
- University of British Columbia (CA)
- Harvard University (US)
- Trinity College Dublin (IE)
- Johns Hopkins University (US)
- University of Exeter (GB)
- The University of Sydney (AU)
- F. Hoffmann-La Roche AG (CH)
- Universitätsmedizin Greifswald (DE)
- Cleveland Clinic (US)
- Pfizer (United States) (US)
- Max Planck Institute of Psychiatry (DE)
- Kaiser Permanente (US)
- University of Southern California (US)
- Brigham and Women's Hospital (US)
- University of Copenhagen (DK)
- SIB Swiss Institute of Bioinformatics (CH)
- Case Western Reserve University (US)
- University of Oxford (GB)
- Queensland University of Technology (AU)
- University of Tartu (EE)
- GGZ inGeest (NL)
- Washington University in St. Louis (US)
- Universidad de Granada (ES)
- University Medical Center Groningen (NL)
- National Cancer Institute (US)
- University of Iceland (IS)
- University of Glasgow (GB)
- deCODE Genetics (Iceland) (IS)
- Heidelberg University (DE)
- Oslo University Hospital (NO)
- 23andMe (United States) (US)
- University of Cambridge (GB)
- Mental Health Services (DK)
- University of Münster (DE)
- University of Basel (CH)
- University of Trento (IT)
- University of Freiburg (DE)
- Centre for Addiction and Mental Health (CA)
- Karolinska Institutet (SE)
- Aarhus University Hospital (DK)
- University of Liverpool (GB)
- Copenhagen University Hospital (DK)
- University of Iowa (US)
- National Institutes of Health (US)
- Dalhousie University (CA)
- Corresponding author
- Wray, Naomi R.
- Sullivan, Patrick F.
- ORCID
- Wray, Naomi R.
- Ripke, Stephan
- Mattheisen, Manuel
- Trzaskowski, Maciej
- Byrne, Enda M.
- Abdellaoui, Abdel
- Adams, Mark J.
- Agerbo, Esben
- Air, Tracy M.
- Bacanu, Silviu-Alin
- Bigdeli, Tim B.
- Binder, Elisabeth B.
- Bryois, Julien
- Buttenschøn, Henriette N.
- Cai, Na
- Castelao, Enrique
- Christensen, Jane Hvarregaard
- Clarke, Toni-Kim
- Craddock, Nick
- Crawford, Gregory E.
- Crowley, Cheynna A.
- Dashti, Hassan S.
- Davies, Gail
- Deary, Ian J.
- Degenhardt, Franziska
- Derks, Eske M.
- Dolan, Conor V.
- Dunn, Erin C.
- Eley, Thalia C.
- Eriksson, Nicholas
- Finucane, Hilary K.
- Forstner, Andreas J.
- Frank, Josef
- Gaspar, Héléna A.
- Gill, Michael
- Goes, Fernando S.
- Gordon, Scott D.
- Grove, Jakob
- Hall, Lynsey S.
- Hannon, Eilis
- Hansen, Christine Søholm
- Hansen, Thomas F.
- Herms, Stefan
- Hickie, Ian B.
- Hoffmann, Per
- Homuth, Georg
- Hottenga, Jouke-Jan
- Hougaard, David M.
- Hu, Ming
- Hyde, Craig L.
- Jansen, Rick
- Jin, Fulai
- Jorgenson, Eric
- Knowles, James A.
- Kohane, Isaac S.
- Kraft, Julia
- Kretzschmar, Warren W.
- Krogh, Jesper
- Kutalik, Zoltán
- Lane, Jacqueline M.
- Li, Yihan
- Li, Yun
- Lind, Penelope A.
- Liu, Xiaoxiao
- Lu, Leina
- MacIntyre, Donald J.
- MacKinnon, Dean F.
- Marchini, Jonathan
- Mbarek, Hamdi
- McGrath, Patrick
- McGuffin, Peter
- Medland, Sarah E.
- Mehta, Divya
- Middeldorp, Christel M.
- Milaneschi, Yuri
- Milani, Lili
- Mill, Jonathan
- Mondimore, Francis M.
- Montgomery, Grant W.
- Mostafavi, Sara
- Mullins, Niamh
- Nauck, Matthias
- Ng, Bernard
- Nivard, Michel G.
- Nyholt, Dale R.
- O’Reilly, Paul F.
- Owen, Michael J.
- Painter, Jodie N.
- Pedersen, Carsten Bøcker
- Peterson, Roseann E.
- Pettersson, Erik
- Peyrot, Wouter J.
- Pistis, Giorgio
- Purcell, Shaun M.
- Quiroz, Jorge A.
- Qvist, Per
- Rice, John P.
- Riley, Brien P.
- Rivera, Margarita
- Saxena, Richa
- Schoevers, Robert
- Schulte, Eva C.
- Shi, Jianxin
- Shyn, Stanley I.
- Sinnamon, Grant B. C.
- Smit, Johannes H.
- Steinberg, Stacy
- Stockmeier, Craig A.
- Streit, Fabian
- Strohmaier, Jana
- Tansey, Katherine E.
- Teismann, Henning
- Teumer, Alexander
- Thompson, Wesley
- Thomson, Pippa A.
- Thorgeirsson, Thorgeir E.
- Traylor, Matthew
- Treutlein, Jens
- Trubetskoy, Vassily
- Uitterlinden, André G.
- Umbricht, Daniel
- Viktorin, Alexander
- Visscher, Peter M.
- Wang, Yunpeng
- Webb, Bradley T.
- Weinsheimer, Shantel Marie
- Wellmann, Jürgen
- Willemsen, Gonneke
- Witt, Stephanie H.
- Wu, Yang
- Xi, Hualin S.
- Yang, Jian
- Zhang, Futao
- Arolt, Volker
- Baune, Bernhard T.
- Boomsma, Dorret I.
- Cichon, Sven
- Dannlowski, Udo
- DePaulo, J. Raymond
- Domenici, Enrico
- Domschke, Katharina
- Esko, Tõnu
- Grabe, Hans J.
- Hamilton, Steven P.
- Hayward, Caroline
- Heath, Andrew C.
- Hinds, David A.
- Kendler, Kenneth S.
- Kloiber, Stefan
- Lewis, Glyn
- Li, Qingqin S.
- Lucae, Susanne
- Magnusson, Patrik K.
- Martin, Nicholas G.
- McIntosh, Andrew M.
- Metspalu, Andres
- Mors, Ole
- Mortensen, Preben Bo
- Nordentoft, Merete
- Nöthen, Markus M.
- O’Donovan, Michael C.
- Paciga, Sara A.
- Pedersen, Nancy L.
- Penninx, Brenda W. J. H.
- Perlis, Roy H.
- Porteous, David J.
- Potash, James B.
- Preisig, Martin
- Rietschel, Marcella
- Schulze, Thomas G.
- Smoller, Jordan W.
- Tiemeier, Henning
- Uher, Rudolf
- Völzke, Henry
- Weissman, Myrna M.
- Werge, Thomas
- Winslow, Ashley R.
- Lewis, Cathryn M.
- Levinson, Douglas F.
- Breen, Gerome
- Børglum, Anders D.
- Sullivan, Patrick F.
- Venue
- Nature Genetics
- Published
- 2018-04-26
- Volume
- 50
- Issue
- 5
- Pages
- 668-681
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5934326
- OpenAlex
- W2738082385
- Cited by
- 3,005
- References
- 140
- Citations by year
- 2026: 183 · 2025: 323 · 2024: 407 · 2023: 406 · 2022: 360 · 2021: 407 · 2020: 400 · 2019: 355 · 2018: 143 · 2017: 15 · 2014: 1
- FWCI
- 197.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- VU Research Portal (publishedVersion, other-oa)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- SERVAL (Université de Lausanne) (acceptedVersion, other-oa)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- ResearchOnline at James Cook University (James Cook University) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- IRIS (submittedVersion)
- JuSER (Forschungszentrum Jülich) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- SUNY Digital Repository Support (State University of New York System) (submittedVersion, cc-by-nc-nd)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Amgen
- Pfizer
- GlaxoSmithKline
- Northwestern University
- South London and Maudsley NHS Foundation Trust
- Wellcome Trust (104036/Z/14/Z)
- QIMR Berghofer Medical Research Institute
- National Institute for Health and Care Research
- King's College London
- Bundesministerium für Bildung und Forschung (01GS0481, 01ER0816, NGFN-2, 03ZIK012, 01ZZ0403, FKZ 01GS0481, 01ES0811, 01ZZ0103, 01ZZ9603)
- Erasmus Medisch Centrum
- Lundbeckfonden (R155-2014-1724, R102‐A9118, R248‐2017‐2003, U01 MH109528)
- Karolinska Institutet
- Max-Planck-Gesellschaft
- Novo Nordisk
- Westfälische Wilhelms-Universität Münster
- Siemens Healthineers (01ZZ0103, 01ZZ0403, 01ZZ9603)
- H. Lundbeck A/S
- Universität Greifswald
- National Institutes of Health (1s10od018164-01, CA093459)
- Gillings School of Public Health
- Medical Research Council (MR/L023784/2, MR/K026992/1, MR/N015746/1, MC_PC_17228, MR/L010305/1, MC_UU_00007/10)
- NODAI Genome Research Center, Tokyo University of Agriculture
- National Heart, Lung, and Blood Institute (T32HL007901)
- National Institute of Mental Health (K01MH109772, U01MH046276, U01MH109528, U01MH085520, R01MH059566, U01MH046318, R01MH059571, R01MH060879, R01MH059565, U01MH109536, R01MH081800, R01MH060870, MH109532, U01MH094421, U01MH046289, U01MH079469, R01MH061675, R01MH059586, R01MH059588, R01MH067257, U01MH079470, U01MH109532, R01MH059587)
- National Institute on Drug Abuse (R01DA034076)
- National Human Genome Research Institute (R01HG009658)
- National Cancer Institute (P50CA097007, R01CA133996, P50CA093459)
- National Institute of General Medical Sciences (P30GM103328)
- National Institute of Neurological Disorders and Stroke
- National Institute of Environmental Health Sciences (R01ES011740)
- FP7 Ideas: European Research Council (294838)
- Cilag
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- major depressive disorder, genetic susceptibility loci, genetic architecture, educational attainment, body mass, schizophrenia, shared genetic etiology, antidepressant targets, gene splicing, clinical phenotype
- MeSH terms
- Major Depressive Disorder, Female, Humans, Male, Phenotype, Risk Factors, Schizophrenia, Case-Control Studies, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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