Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.
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Sullivan, P. F., Kendler, K. S. & Neale, M. C. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187–1192 (2003)
Lichtenstein, P. et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373, 234–239 (2009)
Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genet. 45, 1150–1159 (2013)
Levinson, D. F. et al. Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. Am. J. Psychiatry 168, 302–316 (2011)
Sullivan, P. F., Daly, M. J. & O’Donovan, M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nature Rev. Genet. 13, 537–551 (2012)
Stone, J. L. et al. Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455, 237–241 (2008)
Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009)
Lee, S. H. et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genet. 44, 247–250 (2012)
Malhotra, D. & Sebat, J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148, 1223–1241 (2012)
Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 . Nature Genet. 43, 977–983 (2011)
Moreno-De-Luca, D. et al. Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. Am. J. Hum. Genet. 87, 618–630 (2010)
Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013)
Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012)
Need, A. C. et al. Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia. Am. J. Hum. Genet. 91, 303–312 (2012)
Crowley, J. J. et al. Deep resequencing and association analysis of schizophrenia candidate genes. Mol. Psychiatry 18, 138–140 (2013)
Takata, A. et al. A population-specific uncommon variant in GRIN3A associated with schizophrenia. Biol. Psychiatry 73, 532–539 (2013)
Timms, A. E. et al. Support for the N-methyl-d-aspartate receptor hypofunction hypothesis of schizophrenia from exome sequencing in multiplex families. JAMA Psychiatry 3, 1–9 (2013)
Girard, S. L. et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nature Genet. 43, 860–863 (2011)
Xu, B. et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nature Genet. 44, 1365–1369 (2012)
de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012)
Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012)
Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012)
Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012)
O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012)
Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012)
Keinan, A. & Clark, A. G. Recent explosive human population growth has resulted in an excess of rare genetic variants. Science 336, 740–743 (2012)
Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011)
Liu, L. et al. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet. 9, e1003443 (2013)
Kiezun, A. et al. Exome sequencing and the genetic basis of complex traits. Nature Genet. 44, 623–630 (2012)
Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature http://dx.doi.org/10.1038/nature12929 (this issue)
Linderholm, K. R. et al. Increased levels of kynurenine and kynurenic acid in the CSF of patients with schizophrenia. Schizophr. Bull. 38, 426–432 (2012)
Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nature Genet. 43, 969–976 (2011)
Bayés, À. et al. Characterisation of the proteome, diseases and evolution of the human postsynaptic density. Nature Neurosci. 14, 19–21 (2011)
Dolmetsch, R. E., Xu, K. & Lewis, R. S. Calcium oscillations increase the efficiency and specificity of gene expression. Nature 392, 933–936 (1998)
Yasuda, R., Sabatini, B. L. & Svoboda, K. Plasticity of calcium channels in dendritic spines. Nature Neurosci. 6, 948–955 (2003)
Müller, C. S. et al. Quantitative proteomics of the Cav2 channel nano-environments in the mammalian brain. Proc. Natl Acad. Sci. USA 107, 14950–14957 (2010)
Komiyama, N. H. et al. SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the complex with postsynaptic density 95 and NMDA receptor. J. Neurosci. 22, 9721–9732 (2002)
Berryer, M. H. et al. Mutations in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum. Mutat. 34, 385–394 (2013)
Betancur, C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 1380, 42–77 (2011)
Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011)
Callan, M. A. & Zarnescu, D. C. Heads-up: new roles for the fragile X mental retardation protein in neural stem and progenitor cells. Genesis 49, 424–440 (2011)
Ascano, M., Jr et al. FMRP targets distinct mRNA sequence elements to regulate protein expression. Nature 492, 382–386 (2012)
Migaud, M. et al. Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein. Nature 396, 433–439 (1998)
Fernández, E. et al. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol. Syst. Biol. 5, 269 (2009)
Nithianantharajah, J. et al. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nature Neurosci. 16, 16–24 (2013)
Steward, O. & Worley, P. F. Selective targeting of newly synthesized Arc mRNA to active synapses requires NMDA receptor activation. Neuron 30, 227–240 (2001)
Waltereit, R. et al. Arg3.1/Arc mRNA induction by Ca2+ and cAMP requires protein kinase A and mitogen-activated protein kinase/extracellular regulated kinase activation. J. Neurosci. 21, 5484–5493 (2001)
Albrechtsen A Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler Transform. Bioinformatics 25, 1754–1760 (2009)
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)
We are grateful for the participation of all subjects contributing to this research, and to the collection team that worked to recruit them: E. Flordal-Thelander, A.-B. Holmgren, M. Hallin, M. Lundin, A.-K. Sundberg, C. Pettersson, R. Satgunanthan-Dawoud, S. Hassellund, M. Rådstrom, B. Ohlander, L. Nyrén and I. Kizling. We acknowledge funding support from National Institutes of Health (NIH)/National Institute of Mental Health (NIMH) ARRA Grand Opportunity grant NIMH RC2 MH089905 (S.M.P., P.S.), the Sylvan Herman Foundation, the Stanley Center for Psychiatric Research, the Stanley Medical Research Institute, NIH/National Human Genome Research Institute (NHGRI) grant U54HG003067 (E.S.L.), NIH/NIMH grant R01 MH095088 (S.J.H.), NIH/NIMH grant R01 MH091115 (S.J.H.), the Tau Consortium (S.J.H.), NIH/NIMH grant R01 MH099126 (S.M.P.), NIH/NHGRI grant R01 HG005827 (S.M.P.), NIH/NIMH grant R01 MH077139 (P.F.S.), NIH/NIMH grant R01 MH095034 (P.S.), NIH/NIMH grant T32 MH017119 (L.D.), the Friedman Brain Institute at Mount Sinai School of Medicine, the Karolinska Institutet, Karolinska University Hospital, the Swedish Research Council, an ALF grant from Swedish County Council, the Söderström Königska Foundation, the Netherlands Scientific Organization (NWO 645-000-003), the Wellcome Trust, Genes to Cognition Program, The Medical Research Council and European Union projects GENCODYS no. 241995, EUROSPIN no. 242498 and SYNSYS no. 242167 (E.F., M.O.C., N.H.K., J.S.C., S.G.N.G.). Work at the Icahn School of Medicine at Mount Sinai was also supported by the Institute for Genomics and Multiscale Biology (including computational resources and staff expertise provided by the Department of Scientific Computing). The funders had no role in study design, execution, analysis or manuscript preparation.
The authors declare no competing financial interests.
Extended data figures and tables
a, Multidimensional scaling plot of ancestry in the full Swedish GWAS sample, in which each point represents one individual; the left panel superimposes HapMap CEU and Finnish samples and the right panel highlights (in green) the subset of the full Swedish sample for whom we have exome sequence data. b, Quantile–quantile plot for gene-based SKAT results (MAF < 5% coding variants). Similar, or more conservative, profiles were obtained for other subsets of variants. c, Case enrichment of rare (MAF < 0.1%) and singleton disruptive mutations for the constituent sets of the primary/schizophrenia gene set (top panel in green) and the secondary (autism/intellectual disability) gene set (bottom panel in orange). The primary set is enriched in cases (MAF < 0.1%; disruptive mutations, P = 10−4; singletons, P = 8 × 10−4; significant after correction for multiple testing) whereas the autism/intellectual disability set shows only a modest trend (P = 0.04 for MAF < 0.1% and P = 0.03 for singletons) and is not significant after correction. x axis represents –log10(P); OR, odds ratio. Number of genes is for total in the set (whether or not they had a rare variant).
a, Individual gene-ranking of composite set genes. Genes are ranked by their case burden of rare disruptive mutations, from left to right, for the composite set. The squares along the bottom indicate to which sets each gene belongs. The red and blue triangles represent case and control counts for each gene. The lines above represent the statistical significance of the best test for this set: that is, the significance of the top K genes, evaluated by permutation. The black line represents results for the real data (disruptive MAF < 0.1% composite set analysis). The orange line represents the dummy condition, in which we artificially constructed a set in which the number of genes, statistical enrichment, odds ratio and case/control counts were similar to the real composite set. However, this set included the 25 top-ranked genes from individual gene-based tests (disruptive MAF < 0.1% variants), with the remainder selected at random. The profile of the best test line is markedly different between the real and dummy gene sets (note: truncated at P = 0.0001 reflecting the number of permutations performed). Whereas the dummy P value climbs quickly and then drops to the final aggregate result, the true composite set line continues to climb after 200 genes, indicating that many genes with a single disruptive mutation contribute to the observed set enrichment (rather than a relatively small proportion of the 1,796 genes accounting for the majority of the signal, as in the dummy set). b, Phenotypic characteristics of cases carrying mutations. Relationship between clinical and demographic measures in schizophrenia cases in relation to carrying one or more composite set disruptive risk alleles (MAF < 0.1%). Hospital Discharge Registry (HDR) data (ICD9 codes) were available on 979 of the 990 case carriers. All P values (uncorrected) are two-sided from a case-only joint logistic regression of carrier status (one or more risk alleles) on all admission and demographic variables including year of first and last admissions. The four pairs of columns represent analyses in which we varied the way in which the HDR admission data were represented (for drug abuse, general medication condition, epilepsy and intellectual disability). # admissions, independent variables are the untransformed number of admissions; >X admissions, independent variable is binary 0/1 variable representing whether individuals had more than X admissions. Of all clinical/demographic measures considered, we observed a nominally significant increased likelihood that cases carrying a disruptive allele in the composite set have increased rates of secondary diagnoses of intellectual disability compared to other cases (based on HDR ICD9 codes).
Extended Data Figure 3 Stratified enrichment analysis P values by developmental trajectory of expression in brain (BrainSpan and Human Brain Transcriptome (HBT) data sets).
a, Uncorrected P values for a set of exploratory analyses in which we stratified genes in the enrichment analyses by their developmental profile of brain expression. We used four schemes to classify genes as ‘brain expressed’ and/or ‘biased’ with respect to prenatal or postnatal expression (see Supplementary Information section 6 for details). We merged data on the hippocampus and dorsolateral prefrontal cortex for the BrainSpan classifications; to mirror the classification of Xu et al.19 we kept separate these two groupings for the HBT data set. Results presented for MAF < 0.1% disruptive variants; similar results were obtained for singletons with the exception that the ‘K4’ prenatal enrichment signals were no longer significant. In general, the most consistent enrichment across variant classes, classification schemes and brain regions emerges for postnatally biased genes with high brain expression. b, Analysis of exome variants by developmental expression trajectory in human brain. Genes are grouped by cluster analysis of human postmortem brain expression into eight developmental trajectories, using RNA-sequencing data from the BrainSpan project. The top row gives the number of genes per cluster and the cluster centres in log2-scaled RPKM (reads per kilobase per million) values; solid and dotted solid lines indicate dorsolateral prefrontal cortex (DLPFC) and hippocampus (HPC), respectively. The bottom two rows show enrichment in the current study, relative to the exome-wide average, for singleton disruptive mutations in cases compared to controls, either subsetting all genes by expression profile (first row), or considering only genes in the composite set (second row). In both cases, we only observed nominally (P < 0.01) significant enrichment for genes that are postnatally biased. By contrast, a list of genes with loss-of-function (LoF) de novo mutations (compiled and reported in Fromer et al.30) shows strong enrichment for prenatal bias (see Fromer et al.30 for details on how de novo enrichment was calculated). Alternative approaches to classifying genes as prenatally or postnatally biased led to similar conclusions (Supplementary Information section 6).
This file contains Supplementary Methods and Supplementary References. (PDF 747 kb)
Genes and variants in highly enriched genesets: Details for specific variants for the genes in the small but highly enriched genesets, that showed large odds ratios (>5): ARC, PSD-95, calcium channel genes, Müller et al. Cav2 genes as well as disruptive KYNU variants. The variants from the four genesets are all singleton disruptive mutations, corresponding to the genes listed in ED Table 2. The (a) and (b) in the phenotype column indicate that the same individual carried two of these variants. (That the same cases carries two of these alleles is not necessarily surprising: assuming across cases equal and independent probabilities for carrying each allele, of the 49 case unique variants listed in this table, there is a 37% chance that at least one individual carries two of the 49 alleles, and a 7% chance that at least two individuals carry multiple alleles, based on 2,536 total cases.) The genotype meta-information fields are from GATK: AD = allele depth (number of reference, alternate reads); DP = total high quality reads used in calling; GQ = genotype quality, (max 99); PL = phred-scaled genotype likelihoods (reference, heterozygote, homozygote). (XLSX 39 kb)
Genes selected for the composite set: Source indicated: PSD = any PSD gene; FMRP-target = Darnell et al. FRMP target; SCZ de novo = nonsynonymous de novo mutation in Xu et al., Girard et al. or Fromer et al.; Calcium channel = voltage-gated calcium channel. (XLSX 97 kb)
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Purcell, S., Moran, J., Fromer, M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014). https://doi.org/10.1038/nature12975
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