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Biological insights from 108 schizophrenia-associated genetic loci
Open accessAbstract
Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.
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“Conservative estimates are that 10 -20% of people with ASD harbour a de novo rare point mutation or CNV contributing to their presentation (34, 49,50).”
From Autism spectrum disorder (2020) · cites this paper as [50]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In ASD, a single de novo more risk than the cumulative effect of the top decile of polygenic risk for schizophrenia 47,50”
From Autism spectrum disorder (2020) · cites this paper as [50]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Large-scale genome-wide association studies (GWAS) have identified approximately 160 regions of the genome harboring common variants robustly associated with the diagnosis of schizophrenia, with evidence for a substantial polygenic component in signals that individually fall below genome-wide levels of significance (Pardiñas et al., 2018; Ripke et al., 2014)”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as Ripke et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Significant correlations were found for several traits including schizophrenia 15 ( 𝑟 𝐺 = 0.211, p = 1.03 x 10 -5 ) and measures of cognitive ability, especially educational attainment 20 ( 𝑟 𝐺 = 0.199, p = 2.56 x 10 -9 )”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The scientific basis of this imbalance has been demonstrated theoretically, in simulations, and empirically across many traits and diseases11–22.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [18]
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“Mounting evidence suggests a link between GRAMD1b and various neurodevelopmental disorders, including schizophrenia and intellectual disability (Schizophrenia Working Group of the Psychiatric Genomics C, 2014; Reuter et al, 2017; Santos‐Cortez et al, 2018; Thyme et al, 2019).”
From Molecular basis of accessible plasma membrane cholesterol recognition by the GRAM domain of GRAMD1b (2021) · cites this paper as Schizophrenia Working Group of the Psychiatric Genomics C, 2014
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“Genetic association studies (Ripke et al., 2014) as well as epidemiological studies addressing the impact of the environment (van Os, Kenis, & Rutten, 2010) on disease burden, have not yet explained the non-complete genetic correlation between monozygotic twins in conditions such as schizophrenia (SCZ) (41–65%), Bipolar Disorder (BD) (~60%) (Craddock, O'Donovan, & Owen, 2005) or Major Depression (”
From Can epigenetics shine a light on the biological pathways underlying major mental disorders? (2022) · cites this paper as Ripke et al., 2014
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“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 [2]
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“A relevant example is evident in genomics research, in which data pooling has, e.g., enabled knowledge of disease-related loci to be expanded substantially [e.g.Lambert et al. (2013); Manolio et al. (2008); Schizophrenia Working Group of the Psychiatric Genomics (2014)], findings which would not have been possible without the sample sizes offered through data-sharing.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Schizophrenia Working Group 2014
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“However, the absolute number of cannabis users needed to stop using cannabis in order to prevent one case of schizophrenia per year has been estimated at approximately 5000 for heavy users among men, and 10,000 to 15,000 among women (67).”
From Association Between Cannabis and Psychosis: Epidemiologic Evidence (2016) · cites this paper as [67]
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“Research in other fields has benefited substantially from datasharing initiatives.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Schizophrenia Working Group 2014
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“However, the major ethical and scientific challenge surrounding clinical implementation is that they are many-fold more accurate in European ancestry individuals than others.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [18]
Experimental — accuracy not yet measuredIn the citing paper
“Additionally, associations have been found between schizophrenia and the arginine and serine rich coiled-coil 1 (RSRC1) 28 and myocyte enhancer factor 2C (MEF2C) 21 genes, both of which we found to be putatively associated 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 [21]
In the citing paper
“many high-risk SZ genes identified by GWAS studies are highly enriched in mid-frontal lobe 161”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [161]
In the citing paper
“Converging lines of genetic, post-mortem, and pre-clinical evidence suggest immune dysregulation may play a role in the pathogenesis of both schizophrenia 63–67 and depression, 68, 69 and it is hypothesised that immune alterations are seen only in a proportion of these patients (immune/inflammatory subgroups) and are linked to poor treatment response. 67, 70–72”
From Treatment resistance in psychiatry: state of the art and new directions (2022) · cites this paper as [64]
In the citing paper
“one association (rs3768644; chr2:72.3Mb) is no longer supported11”
From Mapping genomic loci implicates genes and synaptic biology in schizophrenia (2022) · cites this paper as [11]
In the citing paper
“Large-scale genome-wide association studies have also identified associations between immune system-related genes and psychiatric conditions [15, 16].”
From Inflammatory ratios as predictors of length of hospitalization in psychiatric patients: A multicenter study (2025) · cites this paper as [15]
In the citing paper
“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 [47]
In the citing paper
“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 [47]
In the citing paper
“Schizophrenia GWAS also identified a common variant at GRIN2A (OR = 1.057,P = 1.57x 10 - 10 ), providing an allelic series in which different perturbation of gene function results in severity of disease risk (Figure 3A) 5 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [5]
In the citing paper
“Efforts in the past decade are beginning to generate a more comprehensive view of the genetic architecture for schizophrenia, composed of common variants of small effects, large CNVs with elevated frequencies driven by genomic instability, and now, URVs of large effect implicating individual genes (Figure 6A) 5,7 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [5]
In the citing paper
“Following earlier reports studying heritability enrichment in GTEx tissues 5,30 , we found that genes with the highest specific expression in brain regions showed the strongest enrichment of risk URVs, most significantly in the human frontal cortex (P = 1.63 x 10 -8 ) and with limited signal in the other tissue types (Figure S19, Table S8, S9).”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [5]
In the citing paper
“Schizophrenia GWAS also identified a common variant at GRIN2A (OR = 1.057, P = 1.57 × 10−10), providing an allelic series in which different perturbation of gene function results in severity of disease risk (Figure 3A)8.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [8]
In the citing paper
“It is well-established that schizophrenia has a substantial genetic component with contributions from across the allele frequency spectrum8–11.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [8]
In the citing paper
“Pathway analyses of common variants have prioritized disease-relevant tissues and cell types, and in some cases, independently recapitulating known biology8,30,31.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [8]
In the citing paper
“Efforts in the past decade are beginning to generate a more comprehensive view of the genetic architecture for schizophrenia, composed of common variants of small effects, large CNVs with elevated frequencies driven by genomic instability, and now, URVs of large effect implicating individual genes (Figure 6A)8,10.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [8]
In the citing paper
“Following earlier reports studying heritability enrichment in GTEx tissues8,31, we found that genes with the highest specific expression in brain regions showed the strongest enrichment of risk URVs, most significantly in the human frontal cortex (P = 1.63 × 10−8) and with limited signal in the other tissue types (Extended Data Figure 4, Table S8, S9).”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [8]
In the citing paper
“We identified two loci (Supplementary Fig. 14) with statistically significant associations (p<10−6) replicating a couple of common variants associations previously observed for this cohort50: a single variant rs281766 on chromosome 2 in the UTR5 of genes TYW5 and C2orf47”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [24]
In the citing paper
“We observed a nominally significant enrichment in genes overlapping regions near common variants associated with schizophrenia24 (OR=1.37; 95% CI=1.09–1.73; P = 0.027) (Fig. 2) hinting at some degree of convergence.”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [24]
In the citing paper
“Similarly to rare variants enrichment, common variants associated with schizophrenia are localized near XLID genes CNKSR2 and NLGN4X, both of which escape X inactivation, as well as non-XLID gene PJA124.”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [24]
In the citing paper
“While a predictor based on common variants24,109 explained 15% of the variance in schizophrenia liability in this cohort”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [24]
In the citing paper
“as exemplified by a study by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) which identified 108 independent schizophrenia associated loci 14.”
From Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects (2017) · cites this paper as [14]
In the citing paper
“including rare copy number variants2 and >100 common variants3.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Indeed, GWAS SNPs associated with SCZ risk occur more often than expected by chance in cis-regulatory functional genomic elements, such as enhancers or eQTL SNPs3,21–24.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“As expected3, SCZ cases inherited an increased number of common variant alleles previously associated with SCZ risk (P = 1.6 × 10−8, Supplementary Fig. 1D).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Of the 108 SCZ GWAS loci previously reported3, 73 harbor cis-eQTL SNPs for one or more genes (FDR ≤ 5%).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“For that gene, the expected magnitude of differential expression fold change will be on the order of the allele frequency differences seen in the recent large Psychiatric Genomic Consortium SCZ genetic association study (~1–2%)3, precisely what is observed in the CMC data.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“We also slightly modified the Sherlock source code, omitting the exclusion criterion for SNPs (and genes whose expression is associated with those SNPs) that could not be found in the 1000 Genomes data, which encompassed only 49,612 [4.4%] of the 1,127,447 eSNPs also found in the PGC SCZ2 GWAS data 3.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Over 100 genetic loci harbor schizophrenia associated variants”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Of the 108 SCZ GWAS loci previously reported3, 73 harbor cis-eQTL SNPs for one or more genes (FDR ≤ 5%).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“As expected3, SCZ cases inherited an increased number of common variant alleles previously associated with SCZ risk (P = 1.6 × 10−8, Supplementary Fig. 1D).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“only recently have specific genetic risk factors been conclusively identified, including rare copy number variants2 and >100 common variants3”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Indeed, GWAS SNPs associated with SCZ risk occur more often than expected by chance in cis-regulatory functional genomic elements, such as enhancers or eQTL SNPs3,21–24.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“Prior analyses using brain eQTL datasets derived from older technologies have pointed to less than a handful of such associations3.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“For that gene, the expected magnitude of differential expression fold change will be on the order of the allele frequency differences seen in the recent large Psychiatric Genomic Consortium SCZ genetic association study (~1–2%)3, precisely what is observed in the CMC data.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [3]
In the citing paper
“In addition, recently reported genetic vulnerabilities for CSPG genes, including PTPRZ1 and neurocan (Buxbaum et al., 2008; Muhleisen et al., 2012; Ripke and Schizophrenia Working Group of the Psychiatric Genomics, 2014), may contribute to decrease CSPG synthesis in SZ.”
From Losing the sugar coating: Potential impact of perineuronal net abnormalities on interneurons in schizophrenia (2015) · cites this paper as Ripke and Schizophrenia Working Group of the Psychiatric Genomics, 2014
In the citing paper
“Due to overlapping samples between the ASD and PGC GWAS, INRICH analyses against the PGC schizophrenia study [19] was restricted to the noSWM3 ASD GWAS including the worldwide dataset minus the Swedish PAGES sample. This set was analysed in isolation and exceeded the experiment-wise correction (P = .008), with 19 of the 82 blocks included in the analysis overlapping these annotations.”
In the citing paper
“Previously, when comparing the results of an early iteration of the PGC schizophrenia GWAS (PGC1) [40], which is of similar size to that of reported here for the PGC ASD GWAS, to more recent and larger PGC schizophrenia GWAS (PGC2) [19], we noted that of the association signals 5 × 10−8 ≤ P < 10−4 in the schizophrenia PGC1 study, over 10% (20 of 183 independent loci) were subsequently reported as”
In the citing paper
“We again observe evidence of an overlap with schizophrenia, with a 2.5-fold enrichment in heritability for those markers within the PGC schizophrenia GWS loci (P = 0.021).”
In the citing paper
“After removing significant loci (+/−1 Mb) previously reported in the PGC schizophrenia analyses, we observed 12 new GWS loci (see Table 3; Additional file 14 and Additional file 15).”
In the citing paper
“A PGC schizophrenia GWAS [40], which was of comparable size to this, yielded 5 GWS loci and 183 non-GWS loci at P < 10−4. In a follow-up study [19], 20 of these markers were elevated beyond GWS; these were not limited to the most highly ranked markers, as the newly GWS markers ranged from 7 to 188 in the original study.”
In the citing paper
“When considering the GWS loci reported in the PGC schizophrenia GWAS, 118 of the markers pass QC in the ASD WWW GWAS sample. Eleven of these 118 schizophrenia-associated markers are also associated with ASD at P ≤ .05 (Pr(K > = 11/118) = 0.035).”
In the citing paper
“Due to overlapping samples between the ASD and PGC GWAS, INRICH analyses against the PGC schizophrenia study [19] was restricted to the noSWM3 ASD GWAS including the worldwide dataset minus the Swedish PAGES sample.”
In the citing paper
“Several reports have now confirmed an association between the LTCC CACNA1C gene and neurodevelopmental disorders25,58.”
From Neuronal defects in a human cellular model of 22q11.2 deletion syndrome (2020) · cites this paper as [58]
In the citing paper
“We found that noncoding SNVs associated with schizophrenia38 (n = 80, P < 5 × 10−8, see Supplementary Methods) preferentially colocalize with class 3 loops anchored by activity-decommissioned enhancers”
From Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression (2020) · cites this paper as [38]
In the citing paper
“A recent genome-wide association study (GWAS) implicated serine racemase (SRR) and GRIN2A as risk genes for schizophrenia 21 as well as other genes functionally up-and downstream of NMDAR activity 21,22 .”
From Linking early-life NMDAR hypofunction and oxidative stress in schizophrenia pathogenesis (2016) · cites this paper as [21]
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Citations
Cites
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067
Effectiveness of Antipsychotic Drugs in Patients with Chronic Schizophrenia
Lieberman, J. A., Stroup, T. S., McEvoy, J. P., Swartz, M. S., Rosenheck, R. A., Perkins, D. O., Keefe, R. S. E., Davis, S. M., Davis, C. E., Lebowitz, B. D., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa051688 · Cited by 5,892
The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions
Gottesman, I. I., & Gould, T. D. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.4.636 · Cited by 5,615
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
Self-citationCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821
Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease
The International IBD Genetics Consortium (IIBDGC), Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., Lee, J. C., Philip Schumm, L., Sharma, Y., et al. (2012) · Nature · doi:10.1038/nature11582 · Cited by 4,722
Self-citationSystematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716
(2013) · The Lancet · doi:10.1016/s0140-6736(12)62129-1 · Cited by 3,010
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- World Health Organization. The Global Burden of Disease: 2004 Update (WHO Press, 2008)
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Sorted by citations
Items 101–200 of 1,121
Wootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., Hemani, G., Jones, H. J., Zammit, S., Davey Smith, G., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719002678 · Cited by 587
The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium (2017) · Molecular Autism · doi:10.1186/s13229-017-0137-9 · Cited by 587
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586
Chromosome conformation elucidates regulatory relationships in developing human brain
Won, H., de la Torre-Ubieta, L., Stein, J. L., Parikshak, N. N., Huang, J., Opland, C. K., Gandal, M. J., Sutton, G. J., Hormozdiari, F., Lu, D., et al. (2016) · Nature · doi:10.1038/nature19847 · Cited by 584
iPSYCH-Broad Autism Group, Weiner, D. J., Psychiatric Genomics Consortium Autism Group, Wigdor, E. M., Ripke, S., Walters, R. K., Kosmicki, J. A., Grove, J., Samocha, K. E., Goldstein, J. I., et al. (2017) · Nature Genetics · doi:10.1038/ng.3863 · Cited by 516
Common brain disorders are associated with heritable patterns of apparent aging of the brain
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0471-7 · Cited by 575
What causes psychosis? An umbrella review of risk and protective factors
Radua, J., Ramella‐Cravaro, V., Ioannidis, J. P. A., Reichenberg, A., Phiphopthatsanee, N., Amir, T., Yenn Thoo, H., Oliver, D., Davies, C., Morgan, C., et al. (2018) · World Psychiatry · doi:10.1002/wps.20490 · Cited by 553
The dysconnection hypothesis (2016)
Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.07.014 · Cited by 561
van Rheenen, W., van der Spek, R. A. A., Bakker, M. K., van Vugt, J. J. F. A., Hop, P. J., Zwamborn, R. A. J., de Klein, N., Westra, H.-J., Bakker, O. B., Deelen, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00973-1 · Cited by 542
Duncan, L. E., Yılmaz, Z., Gaspar, H., Walters, R. K., Goldstein, J., Anttila, V., Bulik-Sullivan, B. K., Ripke, S., Thornton, L., Hinney, A., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16121402 · Cited by 570
Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases
Gusev, A., Lee, S. H., Trynka, G., Finucane, H., Vilhjálmsson, B. J., Xu, H., Zang, C., Ripke, S., Bulik-Sullivan, B., Stahl, E., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.10.004 · Cited by 544
Self-citationSchizophrenia Working Group of the Psychiatric Genomics Consortium, Loh, P.-R., Bhatia, G., Gusev, A., Finucane, H. K., Bulik-Sullivan, B. K., Pollack, S. J., de Candia, T. R., Lee, S. H., Wray, N. R., et al. (2015) · Nature Genetics · doi:10.1038/ng.3431 · Cited by 449
Marder, S. R., & Cannon, T. D. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1808803 · Cited by 571
Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality
Evans, D. M., & Smith, G. D. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-050016 · Cited by 529
Gazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525
Whitaker, K. J., Vértes, P. E., Romero-García, R., Váša, F., Moutoussis, M., Prabhu, G., Weiskopf, N., Callaghan, M. F., Wagstyl, K., Rittman, T., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601745113 · Cited by 519
Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex
Jaffe, A. E., Gao, Y., Deep-Soboslay, A., Tao, R., Hyde, T. M., Weinberger, D. R., & Kleinman, J. E. (2016) · Nature Neuroscience · doi:10.1038/nn.4181 · Cited by 511
The International League Against Epilepsy Consortium on Complex Epilepsies, Abou-Khalil, B., Auce, P., Avbersek, A., Bahlo, M., Balding, D. J., Bast, T., Baum, L., Becker, A. J., Becker, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07524-z · Cited by 482
Top 10 Replicated Findings From Behavioral Genetics
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691615617439 · Cited by 517
Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases
Haycock, P., Burgess, S., Nounu, A., Zheng, J., Okoli, G. N., Bowden, J., Wade, K. H., Timpson, N. J., Evans, D. M., Willeit, P., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2016.5945 · Cited by 517
Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models
Wolfers, T., Doan, N. T., Kaufmann, T., Alnæs, D., Moberget, T., Agartz, I., Buitelaar, J. K., Ueland, T., Melle, I., Franke, B., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2467 · Cited by 508
Genetic insights into the neurodevelopmental origins of schizophrenia
Birnbaum, R., & Weinberger, D. R. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.125 · Cited by 509
Pasman, J. A., Verweij, K. J. H., Gerring, Z. F., Stringer, S., Sanchez‐Roige, S., Treur, J. L., Abdellaoui, A., Nivard, M. G., Baselmans, B. M. L., Ong, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0206-1 · Cited by 505
Polygenic scores via penalized regression on summary statistics
Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X., & Sham, P. C. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22050 · Cited by 483
Human microglial state dynamics in Alzheimer’s disease progression
Sun, N., Victor, M. B., Park, Y. P., Xiong, X., Scannail, A. N., Leary, N., Prosper, S., Viswanathan, S., Luna, X., Boix, C. A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.037 · Cited by 462
Braun, P. R., Han, S., Hing, B., Nagahama, Y., Gaul, L. N., Heinzman, J. T., Grossbach, A. J., Close, L., Dlouhy, B. J., Howard, M. A., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0376-y · Cited by 467
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491
Self-citationSniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3869 · Cited by 490
Self-citationCasting a Wide Net: Role of Perineuronal Nets in Neural Plasticity
Sorg, B. A., Berretta, S., Blacktop, J. M., Fawcett, J. W., Kitagawa, H., Kwok, J. C. F., & Miquel, M. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2351-16.2016 · Cited by 454
Polygenic risk scores for schizophrenia and bipolar disorder predict creativity
Power, R. A., Steinberg, S., Björnsdóttir, G., Rietveld, C. A., Abdellaoui, A., Nivard, M. G., Johannesson, M., Galesloot, T. E., Hottenga, J., Willemsen, G. A. H. M., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4040 · Cited by 496
The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders
Smoller, J. W. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.266 · Cited by 516
Gene hunting in autism spectrum disorder: on the path to precision medicine
Geschwind, D. H., & State, M. W. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00044-7 · Cited by 513
Autism genetics: opportunities and challenges for clinical translation
Vorstman, J. A. S., Parr, J. R., Moreno‐De‐Luca, D., Anney, R., Nürnberger, J. I., & Hallmayer, J. F. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.4 · Cited by 517
Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data
Shi, H., Kichaev, G., & Pasaniuc, B. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.05.013 · Cited by 387
Dendritic structural plasticity and neuropsychiatric disease
Forrest, M. P., Parnell, E., & Penzes, P. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.16 · Cited by 464
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics
Chiou, J., Geusz, R. J., Okino, M.-L., Han, J. Y., Miller, M., Melton, R. L., Beebe, E., Benaglio, P., Huang, S., Korgaonkar, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03552-w · Cited by 447
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 461
Study, S. S., Singh, T., Study, I., Study, D., UK10 K Consortium, Kurki, M. I., Curtis, D., Purcell, S. M., Crooks, L., McRae, J., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4267 · Cited by 452
Tumor-suppressor genes that escape from X-inactivation contribute to cancer sex bias
Dunford, A. J., Weinstock, D. M., Savova, V., Schumacher, S. E., Cleary, J. P., Yoda, A., Sullivan, T. J., Hess, J. M., Gimelbrant, A. A., Beroukhim, R., et al. (2016) · Nature Genetics · doi:10.1038/ng.3726 · Cited by 471
Defining 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
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452
Akbarian, S., Liu, C., Knowles, J. A., Vaccarino, F. M., Farnham, P. J., Crawford, G. E., Jaffe, A. E., Pinto, D., Dracheva, S., Geschwind, D. H., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4156 · Cited by 451
Inflammation Effects on Motivation and Motor Activity: Role of Dopamine
Felger, J. C., & Treadway, M. T. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.143 · Cited by 474
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G., Wald, L. L., Fischl, B. R., Rosen, B. R., Mair, R. W., Roffman, J. L., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.31 · Cited by 443
Briggs, J., Wolvetang, E. J., Mattick, J. S., Rinn, J. L., & Barry, G. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.045 · Cited by 456
Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS
Faust, T. E., Gunner, G., & Schafer, D. P. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00507-y · Cited by 477
Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia
Genovese, G., Fromer, M., Stahl, E. A., Ruderfer, D. M., Chambert, K., Landén, M., Moran, J. L., Purcell, S. M., Sklar, P., Sullivan, P. F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4402 · Cited by 444
Lo, M., Hinds, D. A., Tung, J. Y., Franz, C. E., Fan, C.-C., Wang, Y., Smeland, O. B., Schork, A. J., Holland, D., Kauppi, K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3736 · Cited by 462
Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia
Li, Z., Chen, J., Yu, H., He, L., Xu, Y., Zhang, D., Yi, Q., Li, C., Li, X., Shen, J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3973 · Cited by 442
Concepts, estimation and interpretation of SNP-based heritability
Yang, J., Zeng, J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2017) · Nature Genetics · doi:10.1038/ng.3941 · Cited by 455
Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource
Purcell, S. M., Manoach, D. S., Demanuele, C., Cade, B. E., Mariani, S., Cox, R., Panagiotaropoulou, G., Saxena, R., Pan, J. Q., Smoller, J. W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15930 · Cited by 439
Self-citationGenomic landscape of cutaneous T cell lymphoma
Choi, J., Goh, G., Walradt, T. J., Hong, B. S., Bunick, C. G., Chen, K., Bjornson, R. D., Maman, Y., Wang, T. L., Tordoff, J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3356 · Cited by 439
Qi, T., Wu, Y., Zeng, J., Zhang, F., Xue, A., Jiang, L., Zhu, Z., Kemper, K., Yengo, L., Zheng, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04558-1 · Cited by 427
Imaging the Role of Inflammation in Mood and Anxiety-related Disorders
Felger, J. C. (2017) · Current Neuropharmacology · doi:10.2174/1570159x15666171123201142 · Cited by 447
Frei, O., Holland, D., Smeland, O. B., Shadrin, A. A., Fan, C. C., Maeland, S., O’Connell, K. S., Wang, Y., Djurovic, S., Thompson, W. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10310-0 · Cited by 416
Duncan, L. E., Ratanatharathorn, A., Aiello, A. E., Almli, L. M., Amstadter, A. B., Ashley-Koch, A. E., Baker, D. G., Beckham, J. C., Bierut, L. J., Bisson, J., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.77 · Cited by 425
Genetics and genomics of psychiatric disease
Geschwind, D. H., & Flint, J. (2015) · Science · doi:10.1126/science.aaa8954 · Cited by 424
Jones, S. E., Tyrrell, J., Wood, A. R., Beaumont, R. N., Ruth, K. S., Tuke, M. A., Yaghootkar, H., Hu, Y., Teder-Laving, M., Hayward, C., et al. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1006125 · Cited by 388
Treatment resistance in psychiatry: state of the art and new directions
Howes, O. D., Thase, M. E., & Pillinger, T. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01200-3 · Cited by 413
The PRACTICAL Consortium, Day, F. R., kConFab Investigators, AOCS Investigators, Scotland, G., EPIC-InterAct Consortium, Study, L. C., Ruth, K. S., Thompson, D. J., Lunetta, K. L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3412 · Cited by 402
Yu, D., Sul, J. H., Tsetsos, F., Nawaz, M. S., Huang, A. Y., Zelaya, I., Illmann, C., Osiecki, L., Darrow, S. M., Hirschtritt, M. E., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18070857 · Cited by 410
Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank
Szustakowski, J. D., Balasubramanian, S., Kvikstad, E., Khalid, S., Bronson, P. G., Sasson, A., Wong, E., Liu, D., Wade Davis, J., Haefliger, C., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00885-0 · Cited by 401
Large-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
Self-citationPollak, T. A., Lennox, B., Müller, S., Benros, M. E., Prüß, H., van Elst, L. T., Klein, H. C., Steiner, J. H., Frodl, T., Bogerts, B., et al. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30290-1 · Cited by 417
Klimentidis, Y. C., Raichlen, D. A., Bea, J., Garcia, D. O., Wineinger, N. E., Mandarino, L. J., Alexander, G. E., Chen, Z., & Going, S. B. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0120-3 · Cited by 403
Developmental timing and critical windows for the treatment of psychiatric disorders
Marín, O. (2016) · Nature Medicine · doi:10.1038/nm.4225 · Cited by 374
Psychiatric genetics and the structure of psychopathology
Smoller, J. W., Andreassen, O. A., Edenberg, H. J., Faraone, S. V., Glatt, S. J., & Kendler, K. S. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0010-4 · Cited by 398
Morgan, S. E., Seidlitz, J., Whitaker, K. J., Romero-García, R., Clifton, N. E., Scarpazza, C., van Amelsvoort, T., Marcelis, M. C., van Os, J., Donohoe, G. J., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820754116 · Cited by 388
Association Between Cannabis and Psychosis: Epidemiologic Evidence
Gage, S. H., Hickman, M., & Zammit, S. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.08.001 · Cited by 379
Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z., & Bergmann, S. (2016) · Nature Methods · doi:10.1038/nmeth.3799 · Cited by 326
Signatures of negative selection in the genetic architecture of human complex traits
Zeng, J., de Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., Yap, C. X., Xue, A., Sidorenko, J., McRae, A. F., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0101-4 · Cited by 391
Self-citationA comprehensive transcriptional map of primate brain development
Bakken, T. E., Miller, J. A., Ding, S., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Dalley, R. A., Royall, J. J., Lemon, T. A., et al. (2016) · Nature · doi:10.1038/nature18637 · Cited by 387
Wang, A. K., & Miller, B. J. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx035 · Cited by 389
Schizotypy: looking back and moving forward
Kwapil, T. R., & Barrantes-Vidal, N. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu186 · Cited by 387
Pedersen, C. B., Bybjerg-Grauholm, J., Pedersen, M. G., Grove, J., Agerbo, E., Bækvad-Hansen, M., Poulsen, J. B., Hansen, C. S., McGrath, J. J., Als, T. D., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.196 · Cited by 338
Jiang, X., O’Reilly, P. F., Aschard, H., Hsu, Y., Richards, J. B., Dupuis, J., Ingelsson, E., Karasik, D., Pilz, S., Berry, D. J., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02662-2 · Cited by 389
Glutamate hypothesis in schizophrenia
Uno, Y., & Coyle, J. T. (2019) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12823 · Cited by 389
Auditory dysfunction in schizophrenia: integrating clinical and basic features
Javitt, D. C., & Sweet, R. A. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn4002 · Cited by 377
Sculpting Neural Circuits by Axon and Dendrite Pruning
Riccomagno, M. M., & Kolodkin, A. L. (2015) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100913-013038 · Cited by 382
Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population
iPSYCH-SSI-Broad Autism Group, Robinson, E. B., St Pourcain, B., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B., Grove, J., Maller, J., Samocha, K. E., Sanders, S. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3529 · Cited by 367
From 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
Merritt, K., Egerton, A., Kempton, M. J., Taylor, M. J., & McGuire, P. K. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.0442 · Cited by 367
The BrainSeq Consortium, Jaffe, A. E., Straub, R. E., Shin, J. H., Tao, R., Gao, Y., Collado-Torres, L., Kam-Thong, T., Xi, H. S., Quan, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0197-y · Cited by 364
Identification of 55,000 Replicated DNA Methylation QTL
McRae, A. F., Marioni, R. E., Shah, S. H., Yang, J., Powell, J. E., Harris, S. E., Gibson, J., Henders, A. K., Bowdler, L. M., Painter, J. N., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-35871-w · Cited by 360
Sugathan, A., Biagioli, M., Golzio, C., Erdin, S., Blumenthal, I., Manavalan, P., Ragavendran, A., Brand, H., Lucente, D., Miles, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405266111 · Cited by 342
Epigenetics of Stress-Related Psychiatric Disorders and Gene × Environment Interactions
Klengel, T., & Binder, E. B. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.036 · Cited by 353
Zhou, H., Sealock, J., Sanchez‐Roige, S., Clarke, T., Levey, D. F., Cheng, Z., Li, B., Polimanti, R. A., Kember, R. L., Vickers‐Smith, R., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0643-5 · Cited by 351
Steullet, P., Cabungcal, J.-H., Coyle, J., Didriksen, M., Gill, K., Grace, A. A., Hensch, T. K., LaMantia, A.-S., Lindemann, L., Maynard, T. M., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.47 · Cited by 360
PSD95: A synaptic protein implicated in schizophrenia or autism?
Coley, A. A., & Gao, W. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.11.016 · Cited by 361
Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits
Shi, H., Mancuso, N., Spendlove, S. J., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.022 · Cited by 351
Werling, D. M., Brand, H., An, J.-Y., Stone, M. R., Zhu, L., Glessner, J. T., Collins, R. L., Dong, S., Layer, R. M., Markenscoff-Papadimitriou, E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0107-y · Cited by 293
The Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis
de la Torre-Ubieta, L., Stein, J. L., Won, H., Opland, C. K., Liang, D., Lu, D., & Geschwind, D. H. (2018) · Cell · doi:10.1016/j.cell.2017.12.014 · Cited by 353
RICOPILI: Rapid Imputation for COnsortias PIpeLIne
Lam, M., Awasthi, S., Watson, H. J., Goldstein, J., Panagiotaropoulou, G., Trubetskoy, V., Karlsson, R., Frei, O., Fan, C.-C., De Witte, W., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz633 · Cited by 315
The Fourth Law of Behavior Genetics
Chabris, C. F., Lee, J. J., Cesarini, D. A., Benjamin, D. J., & Laibson, D. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721415580430 · Cited by 377
Skene, N., & Grant, S. G. N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00016 · Cited by 335
Hannon, E., Dempster, E., Viana, J., Burrage, J., Smith, A. R., Macdonald, R., St Clair, D., Mustard, C., Breen, G., Therman, S., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1041-x · Cited by 349
Self-citationLane, J. M., Liang, J., Vlasac, I., Anderson, S. G., Bechtold, D. A., Bowden, J., Emsley, R., Gill, S., Little, M. A., Luik, A. I., et al. (2016) · Nature Genetics · doi:10.1038/ng.3749 · Cited by 346
de Moor, M. H. M., van den Berg, S. M., Verweij, K. J. H., Krueger, R. F., Luciano, M., Arias Vasquez, A., Matteson, L. K., Derringer, J., Esko, T., Amin, N., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0554 · Cited by 345
Karlsson Linnér, R., Mallard, T. T., Barr, P. B., Sanchez-Roige, S., Madole, J. W., Driver, M. N., Poore, H. E., de Vlaming, R., Grotzinger, A. D., Tielbeek, J. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00908-3 · Cited by 317
“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.”
BackgroundMinimal 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
Self-citation
Metadata
- Authors
- Schizophrenia Working Group of the Psychiatric Genomics Consortium
- Venue
- Nature
- Published
- 2014-07
- Volume
- 511
- Issue
- 7510
- Pages
- 421-427
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- 1476-4687
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- References
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- FWCI
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- U.S. Department of Veterans Affairs (I01BX001356, I01BX002395)
- Eli Lilly and Company
- Pfizer
- Massachusetts General Hospital
- Emory University
- Aarhus Universitet
- Virginia Commonwealth University
- Wellcome Trust
- Broad Institute
- National Institute for Health and Care Research (PDA/02/06/016)
- King's College London
- Cardiff University
- Trinity College Dublin
- Lundbeckfonden (R155-2014-1724)
- Karolinska Institutet
- Danmarks Tekniske Universitet
- Universitetet i Oslo
- Diakonhjemmet
- H. Lundbeck A/S
- National Institutes of Health (5u01mh094432-04)
- Stanley Center for Psychiatric Research, Broad Institute
- Medical Research Council (MR/L010305/1)
- National Institute of Mental Health (U01MH096296, R01MH077139, U01 MH094432, MH094421, R01MH100141, U01MH094421, K99MH101367, R01MH085548, R01MH040799, T32MH020030)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- schizophrenia, genetic susceptibility loci, genome-wide association study, polygenic inheritance, brain-expressed genes, glutamatergic neurotransmission, DRD2, immune system, etiology of schizophrenia, therapeutic target
- MeSH terms
- Alleles, Brain, Enhancer Elements, Genetic, Humans, Immunity, Mutation, Synaptic Transmission, Schizophrenia, Odds Ratio, Glutamic Acid, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, 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 ›
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