Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways
Open accessPublished version of a preprint
Abstract
Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated ( P < 5 × 10 −8 ) across the three phenotypes. The direction of effect of these loci is consistently replicated in an independent sample, with 14 loci likely representing novel findings. Gene sets are enriched in excitatory neurotransmission, mechanosensory behaviour, post synapse, neuron spine and dendrite functions. Our findings suggest that broad depression is the most tractable UK Biobank phenotype for discovering genes and gene sets that further our understanding of the biological pathways underlying depression.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DRD2 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 [5]
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 [5]
Howard and colleagues' UK Biobank GWAS found 17 variants associated across three depression phenotypes (maximum n = 322,580).
“More recently, a genome-wide association analysis of UK Biobank by Howard, et al. 5 identified 17 variants associated across three depression phenotypes (maximum total n = 322,580; 113,769 cases and 208,811 controls).”
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 [5]
Howard and colleagues reported a strong genetic correlation (rG = 0.86, s.e. = 0.05) between broader self-declared depression and hospital-diagnosed MDD.
“The results of Howard, et al. 5 showed that there is a strong genetic correlation (rG = 0.86, s.e. = 0.05) between broader self-declared definitions of depression and clinically diagnosed major depressive disorder (MDD) within a hospital setting.”
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 [5]
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 [5]
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 [5]
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 [5]
CELF4 was the top finding for convergence between functional variant prioritization and multi-tissue TWAS results.
“The CUGBP Elav-Like Family Member 4 (CELF4) gene has been highlighted recently in an earlier precursor to this meta-analysis,8 and was our top finding for convergence between functional variant prioritization and multi-tissue TWAS results”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [8]
No statistical methods were used to pre-determine sample sizes, but the sample sizes were similar to those in previous publications.
“No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications. 8,10”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationSecond-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
Robust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541 · see citing works
Genetic Epidemiology of Major Depression: Review and Meta-Analysis
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000) · American Journal of Psychiatry · doi:10.1176/appi.ajp.157.10.1552 · Cited by 3,431 · see citing works
Self-citationeQTLGen, 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., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005 · see citing works
Self-citationA reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836 · see citing works
LocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707 · see citing works
Genome-wide association study identifies five new schizophrenia loci
The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011) · Nature Genetics · doi:10.1038/ng.940 · Cited by 1,919 · see citing works
Expansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850 · see citing works
Genome-wide association analysis identifies 13 new risk loci for schizophrenia
Multicenter Genetic Studies of Schizophrenia Consortium, Ripke, S., Psychosis Endophenotypes International Consortium, Wellcome Trust Case Control Consortium 2, O'Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., Akterin, S., Bergen, S. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2742 · Cited by 1,496 · see citing works
Self-citationPsychiatric GWAS Consortium Bipolar Disorder Working Group, Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., Edenberg, H. J., Nurnberger, J. I., Rietschel, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.943 · Cited by 1,344 · see citing works
Self-citationA mega-analysis of genome-wide association studies for major depressive disorder
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., Byrne, E. M., Blackwood, D. H. R., Boomsma, D. I., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.21 · Cited by 1,120 · see citing works
Self-citationEstimating Missing Heritability for Disease from Genome-wide Association Studies
Lee, S. H., Wray, N. R., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.02.002 · Cited by 1,064 · see citing works
Study, L. C., Okbay, A., Baselmans, B. M. L., De Neve, J.-E., Turley, P., Nivard, M. G., Fontana, M. A., Meddens, S. F. W., Linnér, R. K., Rietveld, C. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3552 · Cited by 1,013 · see citing works
Self-citationZheng, J., Erzurumluoglu, A. M., Elsworth, B. L., Kemp, J. P., Howe, L., Haycock, P. C., Hemani, G., Tansey, K., Laurin, C., Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw613 · Cited by 869 · see citing works
Sparse whole-genome sequencing identifies two loci for major depressive disorder
CONVERGE consortium, Cai, N., Bigdeli, T. B., Kretzschmar, W., Li, Y., Liang, J., Song, L., Hu, J., Li, Q., Jin, W., et al. (2015) · Nature · doi:10.1038/nature14659 · Cited by 864 · see citing works
Biological interpretation of genome-wide association studies using predicted gene functions
Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H.-J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms6890 · Cited by 787 · see citing works
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., Tung, J. Y., Hinds, D. A., Perlis, R. H., & Winslow, A. R. (2016) · Nature Genetics · doi:10.1038/ng.3623 · Cited by 784 · see citing works
Self-citationGenome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 606 · see citing works
Meta-analysis of genome-wide association studies of anxiety disorders
Otowa, T., Hek, K., Lee, M., Byrne, E. M., Mirza, S. S., Nivard, M. G., Bigdeli, T. B., Aggen, S. H., Adkins, D. E., Wolen, A. R., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.197 · Cited by 558 · see citing works
Gao, R., & Penzes, P. (2015) · Current Molecular Medicine · doi:10.2174/1566524015666150303003028 · Cited by 516 · see citing works
Targeting glutamate signalling in depression: progress and prospects
Murrough, J. W., Abdallah, C. G., & Mathew, S. J. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.16 · Cited by 482 · see citing works
Smıth, D. J., Nicholl, B. I., Cullen, B., Martin, D. J., Haq, Z. U., Evans, J. J., Gill, J. M. R., Roberts, B. A., Gallacher, J., Mackay, D., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075362 · Cited by 361 · see citing works
Self-citationAn excitatory synapse hypothesis of depression
Thompson, S. M., Kallarackal, A. J., Kvarta, M. D., Van Dyke, A. M., LeGates, T. A., & Cai, X. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.03.003 · Cited by 265 · see citing works
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
O'Dushlaine, C., Kenny, E., Heron, E., Donohoe, G., Gill, M., Morris, D., The International Schizophrenia Consortium, & Corvin, A. (2011) · Molecular Psychiatry · doi:10.1038/mp.2010.7 · Cited by 228 · see citing works
Duman, R. S. (2014) · Depression and Anxiety · doi:10.1002/da.22227 · Cited by 213 · see citing works
Polygenic dissection of major depression clinical heterogeneity
Milaneschi, Y., Lamers, F., Peyrot, W. J., Abdellaoui, A., Willemsen, G. A. H. M., Hottenga, J., Jansen, R., Mbarek, H., Dehghan, A., Lu, C., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.86 · Cited by 183 · see citing works
Cai, X., Kallarackal, A. J., Kvarta, M. D., Goluskin, S., Gaylor, K., Bailey, A. M., Lee, H.-K., Huganir, R. L., & Thompson, S. M. (2013) · Nature Neuroscience · doi:10.1038/nn.3355 · Cited by 180 · see citing works
A Genome-Wide Association Study of Depressive Symptoms
Hek, K., Demirkan, A., Lahti, J., Terracciano, A., Teumer, A., Cornelis, M. C., Amin, N., Bakshis, E., Baumert, J., Ding, J., et al. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.09.033 · Cited by 172 · see citing works
Hermesdorf, M., Berger, K., Baune, B. T., Wellmann, J., Ruscheweyh, R., & Wersching, H. (2016) · The Journal of Pain · doi:10.1016/j.jpain.2016.01.474 · Cited by 140 · see citing works
mGluR5 in the nucleus accumbens is critical for promoting resilience to chronic stress
Shin, S., Kwon, O., Kang, J. I., Kwon, S., Oh, S., Choi, J., Kim, C. H., & Kim, D. G. (2015) · Nature Neuroscience · doi:10.1038/nn.4028 · Cited by 125 · see citing works
Middeldorp, C. M., Hammerschlag, A. R., Ouwens, K. G., Groen‐Blokhuis, M. M., Pourcain, B. S., Greven, C. U., Pappa, I., Tiesler, C. M. T., Ang, W., Nolte, I. M., et al. (2016) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2016.05.025 · Cited by 123 · see citing works
Self-citationWray, N. R., Ripke, S., Mattheisen, M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/167577 · Cited by 62 · see citing works
Zeng, Y., Navarro, P., Shirali, M., Howard, D. M., Adams, M. J., Hall, L. S., Clarke, T., Thomson, P. A., Smith, B. H., Murray, A. D., et al. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.12.012 · Cited by 31 · see citing works
Self-citationHaplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits
Shirali, M., Knott, S., Pong‐Wong, R., Navarro, P., & Haley, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-23307-4 · Cited by 14 · see citing works
Self-citationHoward, D. M., Folkersen, L., Coleman, J. R. I., Adams, M. J., Glanville, K. P., Werge, T. M., Hagenaars, S. P., Han, B., Porteous, D. J., Campbell, A. I., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/134601 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [30]Howard, D. M. et al. The stratification of major depressive disorder into genetic subgroups. Preprint at bioRxiv10.1101/134601 (2017).
Cited by
Sorted by citations
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · 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
Howard, 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-citationDepression and obesity: evidence of shared biological mechanisms
Milaneschi, Y., Simmons, W. K., van Rossum, E. F. C., & Penninx, B. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0017-5 · Cited by 1,073 · see citing works
Major 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
Duman, R. S., Sanacora, G., & Krystal, J. H. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.013 · Cited by 1,026 · see citing works
Levey, D. F., Stein, M. B., Wendt, F. R., Pathak, G. A., Zhou, H., Aslan, M., Quaden, R., Harrington, K. M., Nuñez, Y. Z., Overstreet, C., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00860-2 · Cited by 422 · see citing works
Self-citationMinimal 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-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-citationAssociation of Rare Copy Number Variants With Risk of Depression
Kendall, K. M., Rees, E., Bracher-Smith, M., Legge, S., Riglin, L., Zammit, S., O’Donovan, M. C., Owen, M. J., Jones, I., Kirov, G., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0566 · Cited by 144 · see citing works
Davies, N. M., Hill, W. D., Anderson, E. L., Sanderson, E., Deary, I. J., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43990 · Cited by 135 · see citing works
Self-citationIndicators of mental disorders in UK Biobank—A comparison of approaches
Davis, K. A. S., Cullen, B., Adams, M., Brailean, A., Breen, G., Coleman, J. R. I., Dregan, A., Gaspar, H. A., Hübel, C., Lee, W., et al. (2019) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1796 · Cited by 111 · see citing works
Self-citationIncubation of depression: ECM assembly and parvalbumin interneurons after stress
Spijker, S., Koskinen, M.-K., & Riga, D. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.015 · Cited by 55 · see citing works
Tesfaye, M., Jaholkowski, P., Hindley, G. F. L., Shadrin, A. A., Rahman, Z., Bahrami, S., Lin, A., Holen, B., Parker, N., Cheng, W., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01212-4 · Cited by 46 · see citing works
Genomic prediction of depression risk and resilience under stress
Fang, Y., Scott, L., Song, P., Burmeister, M., & Sen, S. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0759-3 · Cited by 43 · see citing works
Depression and Osteoporosis: A Mendelian Randomization Study
He, B., Lyu, Q., Yin, L., Zhang, M., Quan, Z., & Ou, Y. (2021) · Calcified Tissue International · doi:10.1007/s00223-021-00886-5 · Cited by 44 · see citing works
Krissaane, I., De Niz, C., Gutiérrez-Sacristán, A., Korodi, G., Ede, N., Kumar, R., Lyons, J., Manrai, A., Patel, C., Kohane, I., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa068 · Cited by 28 · see citing works
H‐MAGMA, inheriting a shaky statistical foundation, yields excess false positives
Yurko, R., Roeder, K., Devlin, B., & G'Sell, M. (2021) · Annals of Human Genetics · doi:10.1111/ahg.12412 · Cited by 23 · see citing works
Multifactorial prediction of depression diagnosis and symptom dimensions
McNamara, M. E., Shumake, J., Stewart, R. A., Labrada, J., Alario, A., Allen, J. J. B., Palmer, R., Schnyer, D. M., McGeary, J. E., & Beevers, C. G. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.113805 · Cited by 23 · see citing works
- Authors
- Howard, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., Coleman, J. R. I., Alloza, C., Shen, X., Barbu, M. C., Wigmore, E. M., Gibson, J., 23andMe Research Team, Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McCreight, J. C., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. F., Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Hagenaars, S. P., Lewis, C. M., Ward, J., Smith, D. J., Sullivan, P. F., Haley, C. S., Breen, G., Deary, I. J., & McIntosh, A. M.
- Affiliations
- University of Edinburgh (GB)
- King's College London (GB)
- 23andMe (United States) (US)
- University of Glasgow (GB)
- University of North Carolina at Chapel Hill (US)
- Institute of Genetics and Cancer (GB)
- Corresponding author
- Howard, David M.
- ORCID
- Howard, David M.
- Adams, Mark J.
- Shirali, Masoud
- Clarke, Toni-Kim
- Marioni, Riccardo E.
- Davies, Gail
- Coleman, Jonathan R. I.
- Alloza, Clara
- Shen, Xueyi
- Barbu, Miruna C.
- Wigmore, Eleanor M.
- Gibson, Jude
- Alipanahi, Babak
- Auton, Adam
- Bryc, Katarzyna
- Elson, Sarah L.
- Fontanillas, Pierre
- Furlotte, Nicholas A.
- Hinds, David A.
- Litterman, Nadia K.
- McIntyre, Matthew H.
- Mountain, Joanna L.
- Pitts, Steven J.
- Sathirapongsasuti, J. Fah
- Shringarpure, Suyash
- Tung, Joyce Y.
- Vacic, Vladimir
- Wilson, Catherine H.
- Hagenaars, Saskia P.
- Lewis, Cathryn M.
- Ward, Joey
- Smith, Daniel J.
- Sullivan, Patrick F.
- Haley, Chris S.
- Breen, Gerome
- Deary, Ian J.
- McIntosh, Andrew M.
- Venue
- Nature Communications
- Published
- 2018-04-16
- Volume
- 9
- Issue
- 1
- Pages
- 1470-1470
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5902628
- OpenAlex
- W2798099314
- Cited by
- 612
- References
- 43
- Citations by year
- 2026: 29 · 2025: 84 · 2024: 86 · 2023: 102 · 2022: 66 · 2021: 82 · 2020: 80 · 2019: 63 · 2018: 18 · 2017: 2
- FWCI
- 37.33
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Funders
- South London and Maudsley NHS Foundation Trust
- Wellcome Trust (104036/Z/14/Z, 104036, 2016-2021)
- National Institute for Health and Care Research
- King's College London
- Lister Institute of Preventive Medicine
- Dr Mortimer and Theresa Sackler Foundation
- Centre for Cognitive Ageing and Cognitive Epidemiology (MR/K026992/1)
- Directorate for Biological Sciences
- Medical Research Council (MR/K026992/1, 104036/Z/14/Z, 11/NW/0382, MR/S015132/1, MC_PC_17228, MC_UU_00007/10)
- Biotechnology and Biological Sciences Research Council (MR/K026992/1)
- National Institute of Mental Health (U01MH109528)
- 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
- depression, major depressive disorder, genetic susceptibility loci, excitatory neurotransmission, postsynaptic function, polygenic traits, UK Biobank
- MeSH terms
- Depression, Depressive Disorder, Major, Major Depressive Disorder, Gene Expression Regulation, United Kingdom, Humans, Nerve Tissue Proteins, Synaptic Transmission, Phenotype, Synapses, Cohort Studies, Biomarkers, Linkage Disequilibrium, Biological Specimen Banks, Genetic Predisposition to Disease, International Classification of Diseases, Mechanotransduction, Cellular, Genome-Wide Association Study, Genetic Loci
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
