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.
“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]
The “Overlap with rare mutations” section discusses CNVs associated with ASD and genes with deleterious de novo mutations. It does not estimate that 10–20% of people with ASD carry such a variant or establish that those variants contribute to their individual presentations. No page number is available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn ASD, a single de novo germ-line heterozygous loss-of-function point mutation can convey more risk than the cumulative effect of the top decile of polygenic risk for schizophrenia.
“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]
The Polygenic risk score profiling section reports that the highest schizophrenia risk-score decile had greater odds of affected status than the lowest decile. The Overlap with rare mutations section notes overlap with ASD-associated de novo mutations, but the paper does not estimate the risk from a single ASD loss-of-function mutation or compare it with the schizophrenia polygenic-risk decile. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLarge-scale 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.
“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
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 measuredSignificant genetic correlations were found between ASD and schizophrenia (rG=0.211, p=1.03 x 10^-5) and educational attainment (rG=0.199, p=2.56 x 10^-9).
“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]
The “Overlap with rare mutations” section reports significant overlap between schizophrenia GWAS regions and genes with de novo mutations in ASD (P=0.035). It does not report the claimed genetic correlation (rG=0.211, p=1.03 × 10^-5). In the citing sentence, educational attainment has a separate citation, so that estimate is not attributed to this paper. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe scientific basis of the imbalance in PRS accuracy has been demonstrated theoretically, in simulations, and empirically across many traits and diseases.
“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]
The Polygenic risk score profiling section reports that schizophrenia risk-score prediction depends on the target sample, and it evaluates prediction in three samples. It does not demonstrate a theoretical or simulation-based explanation for an imbalance in PRS accuracy, or show that imbalance across many traits and diseases. (LLM, conf=0.79)
Experimental — accuracy not yet measured
Genetic association and epidemiological studies have not yet explained the non-complete genetic correlation between monozygotic twins in schizophrenia (41-65%), Bipolar Disorder (~60%), and Major Depression (~40%).
“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
GWAS 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 [2]
Data pooling in genomics research has enabled knowledge of disease-related loci to be expanded substantially.
“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
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.
“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]
The opening passage discusses schizophrenia risk and genetic causes, while the Discussion concerns genome-wide association findings; neither addresses cannabis cessation. Searches for the claimed prevention estimates, heavy cannabis use, and sex-specific figures found no relevant passage in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredResearch in other fields has benefited substantially from data-sharing initiatives.
“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
The Contribution section says this paper makes schizophrenia results and some genotype data available, and the Discussion describes the success of its schizophrenia GWAS. Neither passage says that research in other fields benefited substantially from data-sharing initiatives. Searches for data sharing, collaboration, and benefits in other fields found no passage establishing that claim. (LLM, conf=0.74)
Experimental — accuracy not yet measuredPRS are many-fold more accurate in European ancestry individuals than in others, and this disparity is the major ethical and scientific challenge surrounding clinical implementation.
“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]
The Polygenic risk score profiling section (page unavailable) reports limited sensitivity and specificity and cautions against using the scores as a predictive test. The 108 independent associated loci section (page unavailable) identifies European- and East Asian-ancestry samples, but the passages found do not compare score accuracy across ancestries or call such a disparity the major ethical and scientific challenge. Searches for ancestry-related prediction differences and clinical implementation found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measuredRSRC1 and MEF2C have been linked to schizophrenia and were both putatively associated with depression here.
“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]
Many high-risk SZ genes identified by GWAS are highly enriched in mid-frontal lobe.
“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]
Immune dysregulation may contribute to the pathogenesis of schizophrenia and depression, and immune alterations are hypothesised to occur in a subgroup linked to poor treatment response.
“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]
One association reported in 2014, rs3768644 at chr2:72.3Mb, is no longer supported.
“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]
Large-scale genome-wide association studies have identified associations between immune system-related genes and psychiatric conditions.
“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]
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 [47]
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 [47]
Schizophrenia GWAS 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.
“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]
The genetic architecture of schizophrenia is composed of common variants of small effects, large CNVs with elevated frequencies driven by genomic instability, and URVs of large effect implicating individual genes.
“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]
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.
“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]
Schizophrenia GWAS identified a common variant at GRIN2A (OR=1.057, P=1.57e-10), providing an allelic series where different perturbation of gene function results in severity of disease risk.
“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]
Schizophrenia has a substantial genetic component with contributions across the allele frequency spectrum.
“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]
Pathway analyses of common variants have prioritized disease-relevant tissues and cell types and sometimes recapitulated known biology.
“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]
Recent efforts have begun to generate a more comprehensive view of schizophrenia genetic architecture, comprising common variants of small effect, large copy-number variants with elevated frequencies driven by genomic instability, and ultra-rare variants of large effect implicating individual genes.
“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]
Genes with highest specific expression in brain regions show strongest enrichment of risk URVs, most significantly in human frontal cortex (P=1.63e-8), with limited signal in other tissues.
“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]
The variant rs281766 on chromosome 2 is significantly associated with schizophrenia in this cohort (p<10^-6).
“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]
The authors observed nominally significant enrichment of dURVs in genes overlapping regions near common variants associated with schizophrenia (OR=1.37; 95% CI=1.09-1.73; P=0.027).
“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]
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 PJA1.
“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]
A common variant predictor explained 15% of the variance in schizophrenia liability in this cohort.
“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]
A PGC schizophrenia GWAS identified 108 independent schizophrenia-associated loci.
“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]
More than one hundred common genetic variants have been conclusively linked to schizophrenia risk.
“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]
Schizophrenia GWAS SNPs occur more often than chance in cis-regulatory functional genomic elements such as enhancers or eQTL SNPs.
“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]
Schizophrenia cases carried more common variant alleles previously associated with schizophrenia risk than controls, with P = 1.6 × 10−8.
“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]
Of the 108 previously reported schizophrenia GWAS loci, 73 contain cis-eQTL SNPs for one or more genes at FDR ≤ 5%.
“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]
For a gene whose differential expression is mainly driven by case-control allele frequency differences at an eQTL SNP, the expected fold-change magnitude is on the order of the allele frequency differences, about 1-2%, matching the CMC data.
“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]
Omitting the Sherlock exclusion for SNPs not in 1000 Genomes affected 49,612 (4.4%) of the 1,127,447 eSNPs also found in PGC SCZ2 GWAS data.
“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]
More than one hundred genetic loci contain variants associated with schizophrenia.
“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]
Of 108 schizophrenia GWAS loci, 73 harbor cis-eQTL SNPs for one or more genes at FDR ≤ 5%.
“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]
Schizophrenia cases inherited an increased number of common variant alleles previously associated with schizophrenia risk (P = 1.6 × 10−8).
“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]
Rare copy number variants and more than 100 common variants have been conclusively identified as genetic risk factors for schizophrenia.
“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]
GWAS SNPs associated with schizophrenia risk occur more often than expected by chance in cis-regulatory functional genomic elements, such as enhancers or eQTL SNPs.
“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]
Prior analyses using brain eQTL datasets derived from older technologies pointed to fewer than a handful of associations between schizophrenia GWAS risk loci and gene expression.
“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]
For a gene whose differential expression is mainly determined by case-control allele frequency difference at an eQTL SNP, the expected fold change is on the order of the allele frequency differences (~1-2%), matching the CommonMind Consortium data.
“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]
Genetic vulnerabilities in CSPG genes such as PTPRZ1 and neurocan may reduce CSPG synthesis in schizophrenia.
“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
INRICH analysis against the PGC schizophrenia study restricted to the noSWM3 ASD GWAS exceeded experiment-wise correction (P = .008), with 19 of 82 blocks overlapping these annotations.
“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 schizophrenia PGC1 study, over 10% (20 of 183 independent loci) of association signals with 5 × 10−8 ≤ P < 10−4 were subsequently reported as genome-wide significant in the schizophrenia PGC2 analysis.
“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”
LD Score partitioned heritability analysis showed a 2.5-fold enrichment in heritability for markers within the PGC schizophrenia GWS loci (P = 0.021).
“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).”
After removing significant loci previously reported in the PGC schizophrenia analyses, meta-analysis of PGC schizophrenia and PGC ASD GWAS identified 12 new genome-wide significant loci.
“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).”
The PGC schizophrenia GWAS yielded 5 genome-wide significant loci and 183 non-GWS loci at P < 10−4; in a follow-up study, 20 of these markers were elevated beyond genome-wide significance.
“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.”
When considering GWS loci from the PGC schizophrenia GWAS, 118 markers passed QC in the ASD WW GWAS; 11 of these were also associated with ASD at P ≤ .05 (Pr(K ≥ 11/118) = 0.035).
“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).”
INRICH analyses against the PGC schizophrenia study were restricted to the noSWM3 ASD GWAS due to overlapping samples between the ASD and PGC GWAS.
“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.”
Several reports have confirmed an association between the LTCC CACNA1C gene and neurodevelopmental disorders.
“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]
Noncoding single-nucleotide variants associated with schizophrenia (n = 80, P < 5 × 10−8) preferentially colocalize with class 3 loops anchored by activity-decommissioned enhancers.
“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]
Genetic evidence points to disturbed NMDAR signaling in schizophrenia: a GWAS implicated SRR and GRIN2A as risk genes and other genes functionally up- and downstream of NMDAR activity.
“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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- Authors
- Schizophrenia Working Group of the Psychiatric Genomics Consortium
- Venue
- Nature
- Published
- 2014-07
- Volume
- 511
- Issue
- 7510
- Pages
- 421-427
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4112379
- OpenAlex
- W2101357408
- Semantic Scholar
- 4d7a4017e4ebdf3380f5f84f759a51ffd3cba04d
- Cited by
- 7,577
- References
- 56
- Citations by year
- 2026: 196 · 2025: 336 · 2024: 452 · 2023: 497 · 2022: 657 · 2021: 763 · 2020: 826 · 2019: 856 · 2018: 767 · 2017: 812 · 2016: 713 · 2015: 563 · 2014: 133 · 2013: 1
- FWCI
- 566.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
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- UNC Libraries
- Funders
- 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
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