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Cited by 1,483Genetic Associations and EpidemiologySchizophrenia research and treatmentGenetic Mapping and Diversity in Plants and Animals

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Open access
Lee, Phil H., Anttila, Verneri, Won, Hyejung, Feng, Yen-Chen A., Rosenthal, Jacob, Zhu, Zhaozhong, Tucker-Drob, Elliot M., Nivard, Michel G. +598 more·2019·Cell
DOI 10.1016/j.cell.2019.11.020PMC PMC7077032
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Abstract

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

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Crossref · 10.1016/j.cell.2019.11.020

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  • Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 4

    Thirty-five loci (25.7%) were novel genome-wide significant associations.

    “while 35 loci (25.7%) represented novel genome-wide significant associations.”
    Quote in the paperSubstantial claim
  • Findingown data
    § SUMMARY · p. 3

    A cross-disorder meta-analysis found 109 loci linked to at least two psychiatric disorders.

    “Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders”
    Quote in the paperSubstantial claim
  • Findingown data
    § SUMMARY · p. 3

    The pleiotropic loci lie within genes with increased brain expression across the lifespan, starting in the second prenatal trimester.

    “The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester”
    Quote in the paperSubstantial claim
  • Findingown data
    § Genetic Correlations among Eight Neuropsychiatric Disorders Indicate Three Genetic Factors · p. 4

    The first genetic factor was composed mainly of AN, OCD, and more weakly TS.

    “The first factor consisted primarily of disorders characterized by compulsive/perfectionistic behaviors, specifically AN, OCD, and, more weakly, TS.”
    Quote in the paperSubstantial claim
  • Findingown data
    § Genetic Correlations among Eight Neuropsychiatric Disorders Indicate Three Genetic Factors · p. 4

    The third genetic factor was characterized by early-onset neurodevelopmental disorders ASD, ADHD, and TS, as well as MD.

    “and the third factor by three early-onset neurodevelopmental disorders (ASD, ADHD, TS) as well as MD.”
    Quote in the paperSubstantial claim
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    Findingown data
    § SUMMARY · p. 3

    Genetic correlation analyses showed a meaningful structure among the eight disorders, with three groups of interrelated disorders.

    “Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders.”
    Quote in the paperSubstantial claim

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    Findingcitation
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 4

    Conditional analysis within the 136 loci found 10 additional independently associated SNPs, giving 146 independent lead SNPs in total.

    “Within these 136 loci, multi-SNP-based conditional analysis (Yang et al., 2012) identified 10 additional SNPs with independent associations, resulting in a total of 146 independent lead SNPs”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Yang et al., 2012 source

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Enrichment analyses using genes preferentially expressed in specific cortical regions suggested that pleiotropic loci were over-represented among genes expressed in the frontal cortex, while non-pleiotropic loci were enriched in the occipital cortex (FDR q < 0.05).

    “Enrichment analyses using the genes preferentially expressed in specific cortical regions suggested that pleiotropic loci were over-represented among genes expressed in the frontal cortex, while non-pleiotropic loci were enriched in the occipital cortex (FDR q &lt;0.05; Figure 5C).”
    Quote in the paperSubstantial claim

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    Findingown data
    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    Two loci had opposite directional effects between SCZ and ASD (rs9329221, rs2921036).

    “two loci between SCZ and ASD (rs9329221, rs2921036)”
    Quote in the paperSubstantial claim

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    Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    Eleven of the 23 lead SNPs mapped to an intron of a protein-coding gene, and seven more had at least one protein-coding gene within 100 kb.

    “Eleven of the 23 lead SNPs map to the intron of a protein-coding gene, and seven additional lead SNPs had at least one protein-coding gene within 100 kb.”
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    Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    DCC expression peaks during early prenatal development.

    “Gene expression data indicate that DCC expression peaks during early prenatal development (Figure S3).”
    Quote in the paperNot a substantial claim

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 7

    More than 41% of genes associated with genome-wide significant loci, both pleiotropic and non-pleiotropic, were intolerant of loss-of-function mutations (pLI ≥ 0.9), which is highly unlikely by chance (Fisher's exact p = 4.90 × 10^-8).

    “More than 41% of the genes associated with our genome-wide significant loci, both pleiotropic and non-pleiotropic, were intolerant of loss of function mutations (pLI score R 0.9); this is highly unlikely to occur by chance (Fisher's exact p = 4.90 3 10 8 ).”
    Quote in the paperSubstantial claim

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 7

    The 109 pleiotropic risk loci were enriched for genes involved in neurogenesis (p = 9.67 × 10^-6), regulation of nervous system development (p = 3.41 × 10^-5), and neuron differentiation (p = 3.30 × 10^-5).

    “The 109 pleiotropic risk loci were enriched for genes involved in neurogenesis (gene-set enrichment p = 9.67 3 10 6 ), regulation of nervous system development (p = 3.41 3 10 5 ), and neuron differentiation (p = 3.30 3 10 5 )”
    Quote in the paperSubstantial claim

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 7

    The 146 lead SNPs showed significant enrichment of genes expressed in brain (beta = 0.123, SE = 0.0109, p = 1.22 × 10^-29) and pituitary (beta = 0.0916, SE = 0.0136, p = 8.74 × 10^-12), but not in other GTEx tissues.

    “Overall, they showed significant enrichment of genes expressed in the brain ( beta = 0.123, SE = 0.0109, enrichment p = 1.22 3 10 29 ) and pituitary ( beta = 0.0916, SE = 0.0136, p = 8.74 3 10 12 ), but not in the other Genotype-Tissue Expression (GTEx) tissues.”
    Quote in the paperSubstantial claim

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Expression levels were associated with the degree of pleiotropy, with the pleiotropy > 2 group showing higher expression than the pleiotropy = 2 group (t test p < 2.10 × 10^-4) or non-pleiotropic risk loci (t test p < 2.2 × 10^-16).

    “Expression levels were associated with the degree of pleiotropy, with the pleiotropy &gt; 2 group showing higher gene expression than either the pleiotropy = 2 group (t test p &lt; 2.10 3 10 4 ) or non-pleiotropic risk loci (t test p &lt; 2.2 3 10 16 ).”
    Quote in the paperSubstantial claim

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    Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    ADHD, TS, OCD, and AN were associated with 16%, 14%, 11%, and 7% of the pleiotropic loci, respectively.

    “The other disorders, ADHD, TS, OCD, and AN featured associations in 16%, 14%, 11%, and 7% of the pleiotropic loci, respectively.”
    Quote in the paperSubstantial claim

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    Findingcitation
    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Disorder-specific lead SNPs and pleiotropic SNPs did not differ in minor allele frequencies, average heterozygosity, or predicted allele ages.

    “Neither did we find differences between disorder-specific lead SNPs and pleiotropic SNPs with respect to their minor allele frequencies, average heterozygosity, or predicted allele ages (Kiezun et al., 2013).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Kiezun et al., 2013 source

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Pleiotropic and non-pleiotropic SNPs did not differ in distance to nearest genes, distance to splicing sites, chromosome compositions, or predicted functional consequences of non-coding regulatory elements.

    “Pleiotropic and non-pleiotropic SNPs also did not differ in terms of the distance to nearest genes, distance to splicing sites, chromosome compositions, and predicted functional consequences of non-coding regulatory elements.”
    Quote in the paperSubstantial claim

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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Cell-type-specific analysis indicated that genes implicated in pleiotropic loci were mainly expressed in neurons (FDR q < 0.05) but not in glial cell types.

    “Cell-type-specific analysis indicated that genes implicated in pleiotropic loci were mainly expressed in neurons (FDR q &lt;0.05) but not in glial cell types.”
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    Findingcitation
    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    Brain Hi-C data indicated interaction of the associated region with the KDM7A promoter.

    “and KDM7A (encoding Lysine Demethylase 7A)”
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    Findingown data
    § Functional Characterization of Pleiotropic Risk Loci · p. 7

    Pleiotropic risk loci were enriched for genes involved in glutamate receptor signaling (p = 2.45 × 10^-6) and the voltage-gated calcium channel complex (p = 5.72 × 10^-4).

    “Pleiotropic risk loci also showed enrichment of genes involved in specific neurotransmitter-related pathwaysglutamate receptor signaling (p = 2.45 3 10 6 ) and voltagegated calcium channel complex (p = 5.72 3 10 4 )”
    Quote in the paperSubstantial claim

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    Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    Among single-disorder-specific loci, 81% were associated with SCZ and 16% with MD.

    “Of the single-disorderspecific loci, 81% and 16% were associated with SCZ and MD, respectively.”
    Quote in the paperNot a substantial claim

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    Findingown data
    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    Of the 109 pleiotropic loci, 76 were identified in individual-disorder GWAS and 33 were novel.

    “Of the 109 pleiotropic loci, 76 were identified in the GWAS of individual disorders, while the remaining 33 are novel.”
    Quote in the paperSubstantial claim

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    Findingown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    Among the 109 pleiotropic loci, 83% involved SCZ and 72% involved BIP.

    “Of the 109 pleiotropic loci, 83% and 72% involved SCZ and BIP, respectively.”
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    Findingown data
    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    RBFOX1 developmental gene expression gradually increased throughout the prenatal period.

    “In contrast to DCC , however, developmental gene-expression of RBFOX1 showed gradually increasing gene expression throughout the prenatal period (Figure S3).”
    Quote in the paperNot a substantial claim

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    Findingown data
    § DISCUSSION · p. 11

    Average expression of genes related to pleiotropic loci peaked in the second trimester and remained overexpressed throughout the lifespan.

    “average expression of genes related to pleiotropic loci peaked in the second trimester and remained overexpressed throughout the lifespan.”
    Quote in the paperSubstantial claim

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    Findingown data
    § DISCUSSION · p. 12

    Genes associated with multiple psychiatric disorders are disproportionately associated with biological pathways related to neurodevelopment and exhibit distinctive gene expression patterns, with enhanced expression beginning in the second prenatal trimester and persistently elevated expression relative to less pleiotropic genes.

    “Finally, we found that genes associated with multiple psychiatric disorders are disproportionately associated with biological pathways related to neurodevelopment and exhibit distinctive gene expression patterns, with enhanced expression beginning in the second prenatal trimester and persistently elevated expression relative to less pleiotropic genes.”
    Quote in the paperSubstantial claim

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    Findingown data
    § DISCUSSION · p. 9

    The study identified 146 LD-independent lead SNPs associated with at least one of eight disorders, including 35 novel loci.

    “we identified 146 LD-independent lead SNPs associated with at least one disorder, including 35 novel loci.”
    Quote in the paperSubstantial claim

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    Findingown data
    § DISCUSSION · p. 11

    Consistent with prior research, the study found substantial pairwise genetic correlations across child- and adult-onset disorders and demonstrated clusters of genetically-related disorders.

    “Consistent with prior research (Brainstorm Consortium et al., 2018; CrossDisorder Group of the Psychiatric Genomics Consortium, 2013), we found substantial pairwise genetic correlations across child- and adult-onset disorders and extended these findings by demonstrating clusters of genetically-related disorders.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Brainstorm Consortium et al., 2018

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    • cites CrossDisorder Group of the Psychiatric Genomics Consortium, 2013

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    Findingown data
    § DISCUSSION · p. 9

    Of the 146 lead SNPs, 109 loci affect two or more disorders, although characterization of this pleiotropy is partly dependent on per-disorder sample size.

    “Of these, 109 loci were found to affect two or more disorders, although characterization of this pleiotropy is partly dependent on per-disorder sample size.”
    Quote in the paperSubstantial claim

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    Findingown data
    § DISCUSSION · p. 12

    The study identified 109 genomic loci with pleiotropic effects, of which 33 had not previously been associated with any of the individual disorders.

    “We also identified 109 genomic loci with pleiotropic effects, of which 33 had not previously been associated with any of the individual disorders.”
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    Findingnone given
    § DISCUSSION · p. 11

    These pleiotropic genes, on average, begin to come online by the second trimester of fetal development and exhibit differentially high expression thereafter.

    “These pleiotropic genes, on average, begin to come online by the second trimester of fetal development and exhibit differentially high expression thereafter.”
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    Findingown data
    § Supplemental Figures · p. 24

    In general, as statistical power increased, the number of identified cross-disorder SNPs also increased.

    “In general, as power increased, so did the number of identified SNPs.”
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    Findingown data
    § Factor analysis and genomic SEM · p. 19

    A model that included additional negative cross-loadings provided similar fit to the data and highly similar correlations across the genetic factors.

    “A model that included additional negative cross-loadings provided similar fit to the data and highly similar correlations across the genetic factors.”
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    Findingown data
    § Examination of the Impact of Sample Size Imbalance on Genetic Correlations and Genomic SEM. Results · p. 20

    In both the WLS and ML solutions, standard errors were smaller for loadings involving the better-powered GWAS phenotypes.

    “Note that, in both the WLS and the ML solution, the standard errors are smaller for the loadings involving the better-powered GWAS phenotypes, as we would expect.”
    Quote in the paperNot a substantial claim

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    Interpretationown data
    § Genetic Correlations among Eight Neuropsychiatric Disorders Indicate Three Genetic Factors · p. 4

    Most disorder pairs had significant genetic correlations, which suggests a complex higher-order genetic structure behind psychopathology.

    “Despite variation in magnitude, significant genetic correlations were apparent for most pairs of disorders, suggesting a complex, higher-order genetic structure underlying psychopathology”
    Quote in the paperSubstantial claim

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    Premisecitation
    § INTRODUCTION · p. 3

    Psychiatric disorders are highly polygenic, and common variation accounts for a large share of their heritability.

    “Psychiatric disorders are highly polygenic, with a large proportion of heritability contributed by common variation.”
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    • cites Sullivan et al., 2018 source

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    § INTRODUCTION · p. 3

    GWAS of SCZ, BIP, MD, and ADHD from the PGC and other groups have produced many risk loci.

    “Many risk loci have emerged from genome-wide association studies (GWAS) of, amongothers, schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), and attention-deficit/hyperactivity disorder (ADHD) from the Psychiatric Genomics Consortium (PGC) and other efforts”
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    • cites Sullivan et al., 2018 source

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    § INTRODUCTION · p. 3

    The 2013 cross-disorder analysis used 33,332 cases and 27,888 controls.

    “in a sample comprising 33,332 cases and 27,888 controls”
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    Interpretationreasoning
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 4

    The factor structure reflects average genome-wide effects and does not capture fine-grained cross-disorder effects at the level of genomic regions or individual loci.

    “The factor structure described above is based on average effects across the genome, but does not address more finegrained cross-disorder effects at the level of genomic regions or individual loci.”
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    Interpretationown data
    § SUMMARY · p. 3

    Genetic factors for psychiatric disorders cross diagnostic boundaries, which points to substantial pleiotropy among the contributing loci.

    “Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci.”
    Quote in the paperSubstantial claim

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    Study design and conductown data
    § RESULTS · p. 3

    All participants were of self-identified European ancestry, and this was supported by principal component analysis of genome-wide data.

    “All study participants were of self-identified European ancestry, which was supported by principal component analysis of genome-wide data.”
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    Premisecitation
    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    NOX4 is a major source of superoxide production in human brain and promotes neural stem cell growth.

    “As a member of the NOX family genes that encode subunits of NADPH oxidase, NOX4 is a major source of superoxide production in human brain and a promoter of neural stem cell growth (Kuroda et al., 2014; Topchiy et al., 2013).”
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    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    BRAF contributes to the MAP kinase signal transduction pathway and plays a role in postsynaptic responses of hippocampal neurons.

    “BRAF , which contributes to the MAP kinase signal transduction pathway and plays a role in postsynaptic responses of hippocampal neurons (Grantyn and Grantyn, 1973)”
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    Interpretationown data
    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    The meta-analysis signal colocalizes with brain eQTLs for DCC (FDR q = 2.27 × 10^-5), which supports DCC as a plausible candidate gene.

    “The signal in our meta-analysis colocalizes with brain eQTLs for DCC (eQTL association FDR q = 2.27 3 10 5 ), supporting DCC as a plausible candidate gene”
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    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    KDM7A plays a central role in the nervous system and midbrain development.

    “KDM7A (encoding Lysine Demethylase 7A), which plays a central role in the nervous system and midbrain development (Horton et al., 2010; Qi et al., 2010; Tsukada et al., 2010).”
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    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    The DCC product guides axonal growth during neurodevelopment and acts as a master regulator of midline crossing and white matter projections.

    “The product of DCC plays a key role in guiding axonal growth during neurodevelopment and serves as a master regulator of midline crossing and white matter projections (Bendriem and Ross, 2017).”
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    § Figure 2. Results of Cross-Disorder MetaAnalysis and Candidate Gene Mapping · p. 6

    RBFOX1 encodes a splicing regulator mainly expressed in neurons and known to target genes important for neuronal development, including NMDA receptor 1 and voltage-gated calcium channels.

    “RBFOX1 (also called A2BP1 ) encodes a splicing regulator mainly expressed in neurons and known to target several genes important to neuronal development, including NMDA receptor 1 and voltage-gated calcium channels (Gandal et al., 2018; Gehman et al., 2011; Hamada et al., 2015).”
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    § Functional Characterization of Pleiotropic Risk Loci · p. 8

    Prior work using model organism knockouts indicated that risk loci affecting multiple disorders might be subject to stronger selection than those affecting one disorder.

    “Previous studies of model organisms using gene knock-out experiments suggested that pleiotropic risk loci may undergo stronger selection than non-pleiotropic loci (Hill and Zhang, 2012).”
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    § Cross-Disorder Meta-Analysis Identifies 109 Pleiotropic Loci · p. 5

    The DCC region has previously been associated with MD and neuroticism.

    “and has been previously associated with both MD and neuroticism (Turley et al., 2018; Wray et al., 2018).”
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    • cites Turley et al., 2018 source

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    Study design and conductown data
    § DISCUSSION · p. 11

    These results are based on average expression profiles, and not all individual gene expression patterns follow this pattern.

    “These results are based on average expression profiles, and not all individual gene expression patterns follow this pattern.”
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    Interpretationown data
    § DISCUSSION · p. 9

    The results indicate substantial pairwise genetic correlation between multiple disorders and a higher-level genetic structure pointing to broader domains underlying genetic risk to psychopathology.

    “Overall, these results indicate a substantial pairwise genetic correlation between multiple disorders along with a higher-level genetic structure that point to broader domains underlying genetic risk to psychopathology.”
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    § DISCUSSION · p. 11

    The method used to detect cross-phenotype association is one of several approaches.

    “the method we applied to detect cross-phenotype association, which combines an all-subsets fixed-effects GWAS meta-analysis with a Bayesian method for evaluating the best-fit configuration of genotype-phenotype associations, is one of several approaches (Solovieff et al., 2013).”
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    § DISCUSSION · p. 10

    DCC is a master regulator of axon guidance through interactions with netrin-1 and draxin.

    “Prior studies showed that DCC is a master regulator of axon guidance (through its interactions with netrin-1 and draxin (Liu et al., 2018).”
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    • cites Liu et al., 2018 source

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    Interpretationreasoning
    § DISCUSSION · p. 11

    Some of the non-pleiotropic loci may have additional effects on psychiatric phenotypes not included in the meta-analysis and/or on non-psychiatric phenotypes.

    “Thus, some of the ''non-pleiotropic'' loci may have additional effects on psychiatric phenotypes that were not included in our meta-analysis and/or on non-psychiatric phenotypes.”
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    Study design and conductown data
    § Summary-data-based meta-analysis · p. 20

    The standardized genomic inflation factor for the meta-analysis was close to one, and the LDSC intercept was substantially less than the genomic control inflation factor, indicating that the increased mean chi-square statistics in the cross-disorder meta-analysis are mainly due to polygenicity rather than stratification or other confounding biases.

    “Standardized genomic inflation factor ( l 1000) for the meta-analysis result was close to one. LDSC intercept was substantially less than l GC (0.79 versus 1.55), suggesting that the increase in mean c 2 statistics in the cross-disorder meta-analysis is mainly due to polygenicity and not due to stratification or other confounding biases.”
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    Study design and conductown data
    § Examination of the Impact of Sample Size Imbalance on Genetic Correlations and Genomic SEM. Results · p. 20

    Re-running Genomic SEM with a maximum likelihood estimator that ignores differing precisions produced results consistent with the primary weighted least-squares analysis, and the nontrivial standardized factor loadings of MD on two of the three factors were evident in both solutions, making it unlikely that they are an artifact of its large sample size.

    “As shown in Data S1 .4, the results were consistent with those from the primary analysis reported in the main text that is based on a Weighted Least-squares (WLS) estimator, which does take into account the differing precisions of the genetic covariance estimates. Specifically, the nontrivial standardized factor loadings of MD on two of the three factors is evident in both the WLS and ML solutions”
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    Interpretationreasoning
    § Enrichment analysis using brain developmental, regional, and cell-type-specific data · p. 22

    The average expression values of each gene set represent the expression patterns of the disease risk genes as a group, so individual genes may show different patterns.

    “It should be noted that the average expression values of each gene set correspond to representative expression patterns of the disease risk genes, so individual genes may behave differently.”
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    Premisecitation
    § Examination of the Impact of Sample Size Imbalance on Genetic Correlations and Genomic SEM. Results · p. 20

    Sample size influences the precision (standard error) of a genetic correlation estimate but not the magnitude of the estimate itself.

    “while sample size will affect the precision of a genetic correlation estimate (ie standard error) it should not affect the magnitude of the estimate itself (Bulik-Sullivan et al., 2015).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Bulik-Sullivan et al., 2015 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation
    § Examination of the Impact of Sample Size Imbalance on Genetic Correlations and Genomic SEM. Results · p. 20

    These estimates are generally consistent with previously reported ones from studies with much smaller sample sizes, except for ASD.

    “however, these estimates are generally consistent with previously reported ones when sample sizes are much smaller (except for ASD) (Brainstorm Consortium et al., 2018) (Cross-Disorder Group of the Psychiatric Genomics et al., 2013)”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Brainstorm Consortium et al., 2018

      Not checked in the free run.

    • cites Cross-Disorder Group of the Psychiatric Genomics et al., 2013

      Not checked in the free run.

55 of 55

  • The paper relies on this without support.

    The nature and mechanisms of pleiotropic effects across psychiatric disorders are not yet clear.

    “However, the nature and mechanisms of these pleiotropic effects remain unclear.”

  • The authors state this as an assumption.

    Effects are either present or absent in studies.

    “This estimation, known as the mvalue, relies on two assumptions, 1) effects are either present or absent in studies, and 2) if they are present, they are similarly sized across studies.”

  • The authors state this as an assumption.

    If effects are present, they are similarly sized across studies.

    “This estimation, known as the mvalue, relies on two assumptions, 1) effects are either present or absent in studies, and 2) if they are present, they are similarly sized across studies.”
3 of 3

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Schizophrenia control counts differ

    Minor

    Table 1 lists 43,456 schizophrenia controls, while the methods list 43,546 for the European-ancestry cohorts. The two counts differ by 90 people.

  • Anorexia nervosa control counts differ

    Minor

    Table 1 lists 10,983 anorexia nervosa controls, but the methods list 10,982 and say all participants from that study were included in the current analyses.

2 of 2

Cites

Flagged first, then by citations

4 of the 90 references couldn’t be matched to a known paper.

Cited by

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Authors
Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., Wang, M. M.-J., Yu, D., Stahl, E. A., Walters, R. K., Anney, R. J. L., Duncan, L. E., Ge, T., Adolfsson, R., Banaschewski, T., Belangero, S., Cook, E. H., Coppola, G., Derks, E. M., Hoekstra, P. J., Kaprio, J., Keski-Rahkonen, A., Kirov, G., Kranzler, H. R., Luykx, J. J., Rohde, L. A., Zai, C. C., Agerbo, E., Arranz, M. J., Asherson, P., Bækvad-Hansen, M., Baldursson, G., Bellgrove, M., Belliveau, R. A., Buitelaar, J., Burton, C. L., Bybjerg-Grauholm, J., Casas, M., Cerrato, F., Chambert, K., Churchhouse, C., Cormand, B., Crosbie, J., Dalsgaard, S., Demontis, D., Doyle, A. E., Dumont, A., Elia, J., Grove, J., Gudmundsson, O. O., Haavik, J., Hakonarson, H., Hansen, C. S., Hartman, C. A., Hawi, Z., Hervás, A., Hougaard, D. M., Howrigan, D. P., Huang, H., Kuntsi, J., Langley, K., Lesch, K.-P., Leung, P. W. L., Loo, S. K., Martin, J., Martin, A. R., McGough, J. J., Medland, S. E., Moran, J. L., Mors, O., Mortensen, P. B., Oades, R. D., Palmer, D. S., Pedersen, C. B., Pedersen, M. G., Peters, T., Poterba, T., Poulsen, J. B., Ramos-Quiroga, J. A., Reif, A., Ribasés, M., Rothenberger, A., Rovira, P., Sánchez-Mora, C., Satterstrom, F. K., Schachar, R., Artigas, M. S., Steinberg, S., Stefansson, H., Turley, P., Walters, G. B., Werge, T., Zayats, T., Arking, D. E., Bettella, F., Buxbaum, J. D., Christensen, J. H., Collins, R. L., Coon, H., De Rubeis, S., Delorme, R., Grice, D. E., Hansen, T. F., Holmans, P. A., Hope, S., Hultman, C. M., Klei, L., Ladd-Acosta, C., Magnusson, P., Nærland, T., Nyegaard, M., Pinto, D., Qvist, P., Rehnström, K., Reichenberg, A., Reichert, J., Roeder, K., Rouleau, G. A., Saemundsen, E., Sanders, S. J., Sandin, S., St Pourcain, B., Stefansson, K., Sutcliffe, J. S., Talkowski, M. E., Weiss, L. A., Willsey, A. J., Agartz, I., Akil, H., Albani, D., Alda, M., Als, T. D., Anjorin, A., Backlund, L., Bass, N., Bauer, M., Baune, B. T., Bellivier, F., Bergen, S. E., Berrettini, W. H., Biernacka, J. M., Blackwood, D. H. R., Bøen, E., Budde, M., Bunney, W., Burmeister, M., Byerley, W., Byrne, E. M., Cichon, S., Clarke, T.-K., Coleman, J. R. I., Craddock, N., Curtis, D., Czerski, P. M., Dale, A. M., Dalkner, N., Dannlowski, U., Degenhardt, F., Di Florio, A., Elvsåshagen, T., Etain, B., Fischer, S. B., Forstner, A. J., Forty, L., Frank, J., Frye, M., Fullerton, J. M., Gade, K., Gaspar, H. A., Gershon, E. S., Gill, M., Goes, F. S., Gordon, S. D., Gordon-Smith, K., Green, M. J., Greenwood, T. A., Grigoroiu-Serbanescu, M., Guzman-Parra, J., Hauser, J., Hautzinger, M., Heilbronner, U., Herms, S., Hoffmann, P., Holland, D., Jamain, S., Jones, I., Jones, L. A., Kandaswamy, R., Kelsoe, J. R., Kennedy, J. L., Joachim, O. K., Kittel-Schneider, S., Kogevinas, M., Koller, A. C., Lavebratt, C., Lewis, C. M., Li, Q. S., Lissowska, J., Loohuis, L. M. O., Lucae, S., Maaser, A., Malt, U. F., Martin, N. G., Martinsson, L., McElroy, S. L., McMahon, F. J., McQuillin, A., Melle, I., Metspalu, A., Millischer, V., Mitchell, P. B., Montgomery, G. W., Morken, G., Morris, D. W., Müller-Myhsok, B., Mullins, N., Myers, R. M., Nievergelt, C. M., Nordentoft, M., Adolfsson, A. N., Nöthen, M. M., Ophoff, R. A., Owen, M. J., Paciga, S. A., Pato, C. N., Pato, M. T., Perlis, R. H., Perry, A., Potash, J. B., Reinbold, C. S., Rietschel, M., Rivera, M., Roberson, M., Schalling, M., Schofield, P. R., Schulze, T. G., Scott, L. J., Serretti, A., Sigurdsson, E., Smeland, O. B., Stordal, E., Streit, F., Strohmaier, J., Thorgeirsson, T. E., Treutlein, J., Turecki, G., Vaaler, A. E., Vieta, E., Vincent, J. B., Wang, Y., Witt, S. H., Zandi, P., Adan, R. A. H., Alfredsson, L., Ando, T., Aschauer, H., Baker, J. H., Bencko, V., Bergen, A. W., Birgegård, A., Perica, V. B., Brandt, H., Burghardt, R., Carlberg, L., Cassina, M., Clementi, M., Courtet, P., Crawford, S., Crow, S., Crowley, J. J., Danner, U. N., Davis, O. S. P., Degortes, D., DeSocio, J. E., Dick, D. M., Dina, C., Docampo, E., Egberts, K., Ehrlich, S., Espeseth, T., Fernández-Aranda, F., Fichter, M. M., Foretova, L., Forzan, M., Gambaro, G., Giegling, I., Gonidakis, F., Gorwood, P., Mayora, M. G., Guo, Y., Halmi, K. A., Hatzikotoulas, K., Hebebrand, J., Helder, S. G., Herpertz-Dahlmann, B., Herzog, W., Hinney, A., Imgart, H., Jiménez-Murcia, S., Johnson, C., Jordan, J., Julià, A., Kaminská, D., Karhunen, L., Karwautz, A., Kas, M. J. H., Kaye, W. H., Kennedy, M. A., Kim, Y.-R., Klareskog, L., Klump, K. L., Knudsen, G. P. S., Landén, M., Le Hellard, S., Levitan, R. D., Li, D., Lichtenstein, P., Maj, M., Marsal, S., McDevitt, S., Mitchell, J., Monteleone, P., Monteleone, A. M., Munn-Chernoff, M. A., Nacmias, B., Navratilova, M., O’Toole, J. K., Padyukov, L., Pantel, J., Papezova, H., Rabionet, R., Raevuori, A., Ramoz, N., Reichborn-Kjennerud, T., Ricca, V., Roberts, M., Rujescu, D., Rybakowski, F., Scherag, A., Schmidt, U., Seitz, J., Slachtova, L., Slof-Op‘t Landt, M. C. T., Slopien, A., Sorbi, S., Southam, L., Strober, M., Tortorella, A., Tozzi, F., Treasure, J., Tziouvas, K., van Elburg, A. A., Wade, T. D., Wagner, G., Walton, E., Watson, H. J., Wichmann, H.-E., Woodside, D. B., Zeggini, E., Zerwas, S., Zipfel, S., Adams, M. J., Andlauer, T. F. M., Berger, K., Binder, E. B., Boomsma, D. I., Castelao, E., Colodro-Conde, L., Direk, N., Docherty, A. R., Domenici, E., Domschke, K., Dunn, E. C., Foo, J. C., de. Geus, E. J. C., Grabe, H. J., Hamilton, S. P., Horn, C., Hottenga, J.-J., Howard, D., Ising, M., Kloiber, S., Levinson, D. F., Lewis, G., Magnusson, P. K. E., Mbarek, H., Middeldorp, C. M., Mostafavi, S., Nyholt, D. R., Penninx, B. W., Peterson, R. E., Pistis, G., Porteous, D. J., Preisig, M., Quiroz, J. A., Schaefer, C., Schulte, E. C., Shi, J., Smith, D. J., Thomson, P. A., Tiemeier, H., Uher, R., van der Auwera, S., Weissman, M. M., Alexander, M., Begemann, M., Bramon, E., Buccola, N. G., Cairns, M. J., Campion, D., Carr, V. J., Cloninger, C. R., Cohen, D., Collier, D. A., Corvin, A., DeLisi, L. E., Donohoe, G., Dudbridge, F., Duan, J., Freedman, R., Gejman, P. V., Golimbet, V., Godard, S., Ehrenreich, H., Hartmann, A. M., Henskens, F. A., Ikeda, M., Iwata, N., Jablensky, A. V., Joa, I., Jönsson, E. G., Kelly, B. J., Knight, J., Konte, B., Laurent-Levinson, C., Lee, J., Lencz, T., Lerer, B., Loughland, C. M., Malhotra, A. K., Mallet, J., McDonald, C., Mitjans, M., Mowry, B. J., Murphy, K. C., Murray, R. M., O’Neill, F. A., Oh, S.-Y., Palotie, A., Pantelis, C., Pulver, A. E., Petryshen, T. L., Quested, D. J., Riley, B., Sanders, A. R., Schall, U., Schwab, S. G., Scott, R. J., Sham, P. C., Silverman, J. M., Sim, K., Steixner, A. A., Tooney, P. A., van Os, J., Vawter, M. P., Walsh, D., Weiser, M., Wildenauer, D. B., Williams, N. M., Wormley, B. K., Zhang, F., Androutsos, C., Arnold, P. D., Barr, C. L., Barta, C., Bey, K., Bienvenu, O. J., Black, D. W., Brown, L. W., Budman, C., Cath, D., Cheon, K.-A., Ciullo, V., Coffey, B. J., Cusi, D., Davis, L. K., Denys, D., Depienne, C., Dietrich, A., Eapen, V., Falkai, P., Fernandez, T. V., Garcia-Delgar, B., Geller, D. A., Gilbert, D. L., Grados, M. A., Greenberg, E., Grünblatt, E., Hagstrøm, J., Hanna, G. L., Hartmann, A., Hedderly, T., Heiman, G. A., Heyman, I., Hong, H. J., Huang, A., Huyser, C., Ibanez-Gomez, L., Khramtsova, E. A., Kim, Y. K., Kim, Y.-S., King, R. A., Koh, Y.-J., Konstantinidis, A., Kook, S., Kuperman, S., Leventhal, B. L., Lochner, C., Ludolph, A. G., Madruga-Garrido, M., Malaty, I., Maras, A., McCracken, J. T., Meijer, I. A., Mir, P., Morer, A., Müller-Vahl, K. R., Münchau, A., Murphy, T. L., Naarden, A., Nagy, P., Nestadt, G., Nestadt, P. S., Nicolini, H., Nurmi, E. L., Okun, M. S., Paschou, P., Piras, F., Piras, F., Pittenger, C., Plessen, K. J., Richter, M. A., Rizzo, R., Robertson, M., Roessner, V., Ruhrmann, S., Samuels, J. F., Sandor, P., Schlögelhofer, M., Shin, E.-Y., Singer, H., Song, D.-H., Song, J., Spalletta, G., Stein, D. J., Stewart, S. E., Storch, E. A., Stranger, B., Stuhrmann, M., Tarnok, Z., Tischfield, J. A., Tübing, J., Visscher, F., Vulink, N., Wagner, M., Walitza, S., Wanderer, S., Woods, M., Worbe, Y., Zai, G., Zinner, S. H., Sullivan, P. F., Franke, B., Daly, M. J., Bulik, C. M., Lewis, C. M., McIntosh, A. M., O’Donovan, M. C., Zheutlin, A., Andreassen, O. A., Børglum, A. D., Breen, G., Edenberg, H. J., Fanous, A. H., Faraone, S. V., Gelernter, J., Mathews, C. A., Mattheisen, M., Mitchell, K. S., Neale, M. C., Nurnberger, J. I., Ripke, S., Santangelo, S. L., Scharf, J. M., Stein, M. B., Thornton, L. M., Walters, J. T. R., Wray, N. R., Geschwind, D. H., Neale, B. M., Kendler, K. S., & Smoller, J. W.
Affiliations
  • University of Kansas Medical Center (US)
  • Massachusetts General Hospital (US)
  • University of North Carolina at Chapel Hill (US)
  • Broad Institute (US)
  • Harvard University (US)
  • The University of Texas at Austin (US)
  • Icahn School of Medicine at Mount Sinai (US)
  • Cardiff University (GB)
  • Wuhan University of Technology (CN)
  • Umeå University (SE)
  • Heidelberg University (DE)
  • Universidade Federal de São Paulo (BR)
  • University of Illinois Chicago (US)
  • University of Helsinki (FI)
  • Mental Illness Research, Education and Clinical Centers (US)
  • Amsterdam Public Health (NL)
  • Stanford University (US)
  • University of Pennsylvania (US)
  • Radboud University Medical Center (NL)
  • Karolinska Institutet (SE)
  • Amsterdam Neuroscience (NL)
  • Charité – Universitätsmedizin Berlin (DE)
  • Aarhus University (DK)
  • Institut des Sciences Moléculaires (FR)
Corresponding author
  • Jordan, Jennifer
  • Smoller, Jordan W.
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2019-12
Volume
179
Issue
7
Pages
1469-1482.e11
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2019.11.020
PMC
PMC7077032
OpenAlex
W2995971356
Cited by
1,381
References
90
Citations by year
2026: 134 · 2025: 188 · 2024: 183 · 2023: 188 · 2022: 266 · 2021: 280 · 2020: 130 · 2019: 11 · 2014: 1
FWCI
103.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell (publishedVersion, other-oa)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
  • Lancaster EPrints (Lancaster University) (acceptedVersion)
  • ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
  • Worcester Research and Publications (University of Worcester) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • Työväentutkimus Vuosikirja (submittedVersion, other-oa)
  • Pure (University of Bath) (submittedVersion, cc-by-nc-nd)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • SUNY Digital Repository Support (State University of New York System) (submittedVersion, cc-by-nc-nd)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • HAL (submittedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
  • ORCA Online Research @Cardiff (Cardiff University) (submittedVersion, cc-by-nc-nd)
  • YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
  • Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
  • IRIS (submittedVersion)
  • JuSER (Forschungszentrum Jülich) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion, other-oa)
  • Site cant be reached (submittedVersion, cc-by)
  • ORBi (University of Liège) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • Research Portal (Queen's University Belfast) (submittedVersion)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
  • VU Research Portal (submittedVersion, other-oa)
  • SERVAL (Université de Lausanne) (submittedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
  • NOVA (University of Newcastle Australia) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Columbia Academic Commons (Columbia University)
Funders
National Institutes of Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
mental illness, pleiotropy, genetic correlation, shared genetic loci, anorexia nervosa, attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, Tourette syndrome, brain gene expression, brain development, psychiatric classification, risk prediction
MeSH terms
Mental Disorders, Humans, Genetic Predisposition to Disease, Quantitative Trait Loci, Genome-Wide Association Study, Neurogenesis, Genetic Pleiotropy

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

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.

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