Abstract
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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Acknowledgements
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.
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Project leadership: S.M.P., J.L.M., P.F.S., S.A.M., C.M.H., P.S. Sample collection and phenotyping: A.K., P.K.E.M., P.F.S., K.C., J.L.M., C.M.H. Sample processing and data management: K.C., D.R., M.F., J.L.M. Sequencing and variant calling: M.d.P., E.B., K.S., K.G., T.F., S.G. Primary statistical analysis: D.R., M.F., S.M.P. Additional analyses: L.D., E.S., G.G., S.J.H., N.S., P.R., C.O’D., S.E.B. Determination of synaptic gene sets: S.J.H., E.F., M.O.C., N.H.K., J.S.C., S.G.N.G. Interpretation of main findings: S.M.P., E.M.S., E.S.L., S.J.H., M.F., P.F.S., S.A.M., P.S. Primary drafting of manuscript: S.M.P., P.F.S., S.A.M., E.S.L., P.S. Production and approval of the final manuscript: all authors.
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Extended data figures and tables
Extended Data Figure 1 Ancestry and association summaries.
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).
Extended Data Figure 2 Genic and phenotypic subset analyses for the composite set.
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).
Supplementary information
Supplementary Information
This file contains Supplementary Methods and Supplementary References. (PDF 747 kb)
Supplementary Table 1
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)
Supplementary Table 2
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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DOI: https://doi.org/10.1038/nature12975
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