Abstract
By analyzing the whole-exome sequences of 4,264 schizophrenia cases, 9,343 controls and 1,077 trios, we identified a genome-wide significant association between rare loss-of-function (LoF) variants in SETD1A and risk for schizophrenia (P = 3.3 × 10−9). We found only two heterozygous LoF variants in 45,376 exomes from individuals without a neuropsychiatric diagnosis, indicating that SETD1A is substantially depleted of LoF variants in the general population. Seven of the ten individuals with schizophrenia carrying SETD1A LoF variants also had learning difficulties. We further identified four SETD1A LoF carriers among 4,281 children with severe developmental disorders and two more carriers in an independent sample of 5,720 Finnish exomes, both with notable neuropsychiatric phenotypes. Together, our observations indicate that LoF variants in SETD1A cause a range of neurodevelopmental disorders, including schizophrenia. Combining these data with previous common variant evidence, we suggest that epigenetic dysregulation, specifically in the histone H3K4 methylation pathway, is an important mechanism in the pathogenesis of schizophrenia.
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Acknowledgements
We thank the thousands of patients who participated in these studies. We thank H. Firth and D. FitzPatrick for discussions. The UK10K project was funded by Wellcome Trust grant WT091310. The DDD Study is funded by HICF-1009-003. The DDD and the INTERVAL sequencing studies are funded by Wellcome Trust grant WT098051. T.S. is supported by the Williams College Dr. Herchel Smith Fellowship. P.F.S. is supported by NIH R01 MH077139. A.P. is supported by Academy of Finland grants 251704 and 286500, NIMH U01MH105666 and the Sigrid Juselius Foundation. The work at Cardiff University was funded by Medical Research Council (MRC) Centre (G0801418) and Program Grants (G0800509). The key groups of the Sequencing Initiative Suomi (SISu) project are from the Universities of Eastern Finland, Oulu and Helsinki and The Institute for Health and Welfare, Finland, Lund University, The Wellcome Trust Sanger Institute, University of Oxford, The Broad Institute, University of Michigan, Washington University in St. Louis and University of California, Los Angeles (UCLA). The SiSu project is coordinated in the Institute for Molecular Medicine Finland at the University of Helsinki. Participants in INTERVAL were recruited with the active collaboration of NHS Blood and Transplant England, which has supported fieldwork and other elements of the trial. DNA extraction and genotyping was funded by the National Institute of Health Research (NIHR RP-PG-0310-1004), the NIHR BioResource and the NIHR Cambridge Biomedical Research Centre. The academic coordinating center for INTERVAL was supported by core funding from NIHR Blood and Transplant Research Unit in Donor Health and Genomics, UK Medical Research Council (G0800270) and British Heart Foundation (SP/09/002). M.I.K. was supported by Instrumentarium Science Foundation, Finland; Finnish Foundation for Cardiovascular Research; Orion Research Foundation and the University of Eastern Finland, Saastamoinen Foundation.
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T.S., S.S.G., E.L.C., D.G., M.E.H., M.C.O'D. A.P., M.J.O. and J.C.B. conceived and designed the experiments. S.S.G., E.L.C. and E.P. performed the experiments. T.S., D. Curtis, S.M.P., L.C., J.M. and H.C. performed the statistical analysis. T.S., M.I.K., S.M.P., L.C., J.M., H.C., G.B. and E.R. analyzed the data. M.I.K., S.M.P., J. Suvisaari, D.B., G.B., O.P., D. Collier, M.J.D., J.D., N.B.F., M.J., G.K., J.K., O.K., P.H., C.M.H., M.M., S.A.M., P.M., A.M.M., A.M., J.S.M., C.M., W.O., T.P., D.R., J. Sambrook, P.S., D.S.C., J.V., J.T.R.W., H.W. and P.F.S. contributed reagents, materials and analysis tools. T.S., P.F.S., M.E.H., M.J.O. and J.C.B. wrote the paper. J. Suvisaari, D.B., M.A., M.B., T.C., D. Collier, N.C., M.D., A.F., S.J., C.I., J.L., R.M.M., R.N.-E., T.P. and D.S.C. recruited patients.
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P.F.S. consults for Pfizer, Inc. M.E.H. is a shareholder of, and consultant for, Congenica Ltd.
Integrated supplementary information
Supplementary Figure 1 Density plots of sequence coverage in the UK10K, INTERVAL and DDD data sets.
Per-sample sequence coverage was calculated and summarised from exome sequencing data generated in the UK10K (N = 4,734 in batch 0, and N = 562 in batch 1), INTERVAL (N = 4,502), and DDD (N = 1,972) data sets. The UK10K data set was separated into two sequencing batches. Top: sample mean coverage; Middle: percentage of Gencode v19 coding bases covered at 10x or more in each sample; Bottom: percentage of Gencode v19 coding bases covered at 20x or more in each sample.
Supplementary Figure 2 Variant metrics in the UK10K and INTERVAL data sets after each variant filtering step.
Box plots of per-sample heterozygote count (nHets), non-reference homozygote count (nNonRefHom), transition/transversion rate (TiTv), number of singletons (nSiS), and number of indels (nIndels) following each variant QC step. Variant metrics were summarised across all samples in the UK10K and INTERVAL data sets. Raw: no variant QC steps applied; Cov7: restricting to variants with at least 7x mean coverage; VQSR: GATK variant calibration using default parameters; Miss: filter for excess missingness; HWE: filter for deviation from Hardy-Weinberg equilibrium; GTfilt: filter for low alternate allele read depth, and abnormal allelic balance; GQmean: filter for low genotype quality; LCR: exclude variants in low-complexity regions. See Online Methods on more information on each step of variant QC.
Supplementary Figure 3 Variant counts summarized according to variant class and sequencing batch in the UK10K, INTERVAL, Finnish and Swedish data sets.
Box plots of per-sample variant counts in the UK10K, INTERVAL, Finnish, and Swedish data sets. All samples included in our meta-analysis are represented in the figure. The UK10K data sets was sub-divided according to sequencing batches (batch 0 and batch 1), and sample ancestry (UK and Finnish). The Finnish control data sets was separated by study of origin (Metsim, Finrisk, and Sanger controls). The Swedish case-control data set was separated into two sequencing batches. Differences exist in total variant counts between the UK, Finnish, and Swedish collections, likely reflecting differences in sequencing depth, capture reagents, sequencing protocol, read alignment, and variant calling. However, variant counts and population genetics metrics were consistent between cases and controls within each population group.
Supplementary Figure 4 Distributions of TiTv and frameshift/in-frame ratios in the UK10K, INTERVAL, Finnish and Swedish data sets.
Box plot of sample TiTv (left) and violin plot of sample frameshift-to-inframe ratio (right) in the UK10K, INTERVAL, Finnish, and Swedish data sets. All samples included in our meta-analysis are represented in the figure. See Supplementary Figure 3 for the legend, and a description of each batch and sub-study. Following sample and variant QC, the per-sample transition/transversion rate was comparable between all populations (mean ~3.25).
Supplementary Figure 5 Principal component analysis of UK and Finnish samples in our UK10K schizophrenia data set.
Principal components were estimated using 1000 Genomes samples, onto which we projected our cases and controls. We verified if samples had the same population ancestry (UK, Finnish or Swedish) as reported in the sample manifests, and excluded individuals who were of non-European ancestry. Thresholds for sample inclusion and exclusion are shown as dashed lines in each plot. Our analyses incorporated this information to perform permutations within each population (UK, Finnish, and Swedish) to control for ancestry and batch-specific differences. Top left: Population structure of all UK10K samples, with 1000 Genomes populations used as bases. We restricted our analyses to individuals of European ancestry; Bottom left: PCA plot of individuals of non-Finnish European ancestry in the UK10K data set with 1000 Genomes European populations used as bases. Samples not within the UK cluster (center of the plot) were excluded from analysis; Top right: PCA plot of individuals of Finnish ancestry in the UK10K data set. Samples not in the Finnish cluster (right of plot) were excluded from analysis. The three-letter symbols describing each population originate from nomenclature in the 1000 Genomes Project.
Supplementary Figure 6 Manhattan plot of the rare variant association analysis of LoF variants in 4,264 cases and 9,343 controls.
We tested for an excess of LoF variants within 18,271 genes using Fisher’s exact test. – log10P-values were plotted against the chromosomal location (mid-point) of each gene. We showed results from three allele frequency thresholds (singletons, < 0.1% and < 0.5%) for aggregating rare variants. No gene exceeded the exome-wide significant threshold of P = 1.25 x 10-6 (red line).
Supplementary Figure 7 Q-Q plots of the rare variant association analysis of LoF variants in 4,264 cases and 9,343 controls.
We tested for an excess of LoF variants within 18,271 genes using Fisher’s exact test, and plotted the ordered – log10P-values against transformed P-values sampled from the uniform distribution. The Q-Q plots for gene burden tests with minor allele frequency cut-offs of 0.1% and 0.5% followed an expected null distribution. The Q-Q plot for the burden test of singleton variants still showed deflation because the per-gene counts are too low and the data does not meet the asymptotic requirements of the statistical test. We included P-values from informative tests in which genes have at least one case LoF count.
Supplementary Figure 8 The robustness of the SETD1A result across reasonable parameters in the TADA model.
Because the TADA model depended heavily on the specification of its hyperparameters, we calculated the log q-value of SETD1A across different mean relative risk of de novo variants (
), mean relative risk of case-control variants (
), and numbers of true schizophrenia risk genes (k). Each vertical column is a different value for 
, and each horizontal facet is a different value for k. Our signal in SETD1A had a q-value < 0.01 across all reasonable parameters. Blue line: P = 0.05; red line: P = 0.01.
Supplementary Figure 9 Manhattan plot of the meta-analysis of de novo mutations and case-control variants in 1,077 trios, 4,264 cases and 9,343 controls.
De novo and case-control burden P-values were meta-analysed using Fisher's combined probability method. – log10P-values were plotted against the chromosomal location (mid-point) of each gene. A total of 18,271 genes were tested. Only SETD1A exceeded exome-wide significance, with P = 3.3 × 10−9. Red line: P = 1.25 × 10−6.
Supplementary Figure 10 Q-Q plot of the meta-analysis of de novo mutations and case-control variants in 1,077 trios, 4,264 cases and 9,343 controls.
De novo and case-control burden P-values were meta-analysed using Fisher's combined probability method, and the log10 P-values plotted against transformed P-values sampled from the uniform distribution. Because only a subset of genes had de novo LoF variants, Fisher’s method deflated the combined P-value of genes without any de novo information.
Supplementary Figure 11 Results from the minigene experiment assessing the impact of the exon 16 splice acceptor site variant.
A. Minigene constructs driving expression of exons 15, 16 (ref and alt), and 17 fused to GFP were transfected into HELA cells. RT-PCR analysis of cell lysates using primer pair 2, spanning exons 15, 16, and the intervening intron reveal a change in size of PCR products suggesting retention of the intervening intron in the construct containing the splice-acceptor deletion (panel A, "Exons 15-16", REF versus ALT). PCRs with primer pair 3, spanning the intron downstream of exon 16 show no change in band sizes (panel A, "Exons 16-17", REF versus ALT), suggesting this intron is correctly spliced out in both reference and alternate forms. B. Cartoon of genomic locus surrounding the exon 16 splice acceptor deletion. The predicted structure of reference (green) and deletion containing (red) transcripts are shown above and below genomic map. The red star indicates a predicted premature stop codon due to intron retention and resulting frame-shifted translation. C. Results from capillary sequencing of PCR products from panel A confirms intron retention in the splice acceptor deletion construct (panel C, "RNA", yellow box). This will result in a predicted frame-shifted translation of exon 16 (panel C, "PEP", red box), and a premature truncation of the protein 28 amino acids into exon 16 (red star). Downstream intron splicing was confirmed by capillary sequencing to be intact in both constructs.
Supplementary Figure 12 De novo microdeletion of a single copy of SETD1A identified in the DDD study.
A proband was identified to have a 650 kb deletion encompassed SETD1A and 29 other genes. The figure showing the deletion was generated using the UCSC Genome Browser (https://genome.ucsc.edu/).
Supplementary Figure 13 Mendelian disorders of epigenetic machinery at histone H3.
Writers (in green) add methyl groups at the specified residue of the histone tail, while erasers (in red) perform targeted demethylation. Disrupting variants in writers and erasers described in the figure result in well-known examples of dominant, highly penetrant disorders characterised by developmental delay and intellectual disability. Only the tail of histone H3 and its four key lysine residues are illustrated here. Alternate nomenclature: EHMT1 (also known as KMT1D), EZH2 (KMT6A), NSD1 (KMT3B), SETD1A (KMT2F).
Supplementary Figure 14 Sample size curves for detecting an increased risk of premorbid cognitive impairment in schizophrenia SETD1A LoF carriers.
We performed power calculations using a simple one-sided t-test to identify sample sizes required to show possible cognitive impairment in SETD1A schizophrenia carriers. Effect sizes d (0.5, 1), and allele frequencies (0.0001, 0.0005, 0.001) are varied to show their influence on statistical power. We assume a Type I error probability of 0.05. For these effect sizes and frequencies, a sample of tens of thousands of cases will be needed.
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Singh, T., Kurki, M., Curtis, D. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat Neurosci 19, 571–577 (2016). https://doi.org/10.1038/nn.4267
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DOI: https://doi.org/10.1038/nn.4267
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