The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability (original) (raw)

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Acknowledgements

We gratefully thank all participants in these studies. We thank T. Touloupoulou, M. Picchioni, C. Nosarti, F. Gaughran, and O. Howes for contributing clinical data used in this study. The UK10K project was funded by Wellcome Trust grant WT091310. The INTERVAL sequencing studies are funded by Wellcome Trust grant WT098051. T.S. is supported by the Williams College Dr. Herchel Smith Fellowship. A.P. is supported by Academy of Finland grants 251704 and 286500, NIMH grant U01MH105666, and the Sigrid Juselius Foundation. The work at Cardiff University was funded by Medical Research Council (MRC) Centre (G0801418) and Program (G0800509) grants. P.F.S. gratefully acknowledges support from the Swedish Research Council (Vetenskapsrådet, award D0886501). Creation of the Sweden schizophrenia study data was supported by NIMH grant R01 MH077139 and the Stanley Center of the Broad Institute. 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 were funded by the National Institute of Health Research (NIHR), the NIHR BioResource, and the NIHR Cambridge Biomedical Research Centre. The academic coordinating center for INTERVAL was supported by core funding from the following: the NIHR Blood and Transplant Research Unit in Donor Health and Genomics, the UK MRC (G0800270), and the British Heart Foundation (SP/09/002). For the CNV analysis, we would like to acknowledge the contribution of data from outside sources: (i) Genetic Architecture of Smoking and Smoking Cessation accessed through dbGaP (study accession phs000404.v1.p1). Funding support for genotyping, which was performed at the Center for Inherited Disease Research (CIDR), was provided by 1 X01 HG005274-01. CIDR is fully funded through a federal contract from the National Institutes of Health to the Johns Hopkins University, contract number HHSN268200782096C. Assistance with genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies (GENEVA) Coordinating Center (U01 HG004446). Funding support for collection of data sets and samples was provided by the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392) and the University of Wisconsin Transdisciplinary Tobacco Use Research Center (P50 DA019706 and P50 CA084724). (ii) High-Density SNP Association Analysis of Melanoma: Case–Control and Outcomes Investigation (dbGaP study accession phs000187.v1.p1). Research support to collect data and develop an application to support this project was provided by 3P50CA093459, 5P50CA097007, 5R01ES011740, and 5R01CA133996. (iii) Genetic Epidemiology of Refractive Error in the KORA Study (dbGaP study accession phs000303.v1.p1). Principal investigators: D. Stambolian (University of Pennsylvania) and H.E. Wichmann (Institut für Humangenetik, Helmholtz Zentrum München; National Eye Institute, National Institutes of Health). Funding was provided by R01 EY020483 from the National Institutes of Health. (iv) WTCCC2 study. Samples were downloaded from EGA (http://www.ebi.ac.uk/ega/) and include samples from the National Blood Donors Cohort (EGAD00000000024) and samples from the 1958 British Birth Cohort (EGAD00000000022). Funding for these projects was provided by the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z), the Wellcome Trust (072894/Z/03/Z, 090532/Z/09/Z, and 075491/Z/04/B), and NIMH grants (MH 41953 and MH083094).

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Authors and Affiliations

1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
   Tarjinder Singh & Jeffrey C Barrett
2. Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
   James T R Walters, Elliott Rees, Georg Kirov, Michael C O'Donovan & Michael J Owen
3. Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
   Mandy Johnstone & Douglas Blackwood
4. University College London Genetics Institute, University College London, London, UK
   David Curtis
5. Centre for Psychiatry, Barts and the London School of Medicine and Dentistry, London, UK
   David Curtis
6. National Institute for Health and Welfare, Helsinki, Finland
   Jaana Suvisaari & Minna Torniainen
7. Institute of Psychiatry, King's College London, London, UK
   Conrad Iyegbe, Robin M Murray & Marta Di Forti
8. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
   Andrew M McIntosh
9. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
   Daniel Geschwind & Michael Gandal
10. Division of Psychiatry, University College London, London, UK
    Elvira Bramon
11. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    Christina M Hultman
12. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Pamela Sklar
13. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
    Aarno Palotie
14. Program in Medical and Population Genetics and Genetic Analysis Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
    Aarno Palotie
15. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    Patrick F Sullivan
16. Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
    Patrick F Sullivan

Authors

1. Tarjinder Singh
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2. James T R Walters
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3. Mandy Johnstone
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4. David Curtis
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5. Jaana Suvisaari
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6. Minna Torniainen
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7. Elliott Rees
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8. Conrad Iyegbe
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9. Douglas Blackwood
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10. Andrew M McIntosh
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11. Georg Kirov
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12. Daniel Geschwind
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13. Robin M Murray
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14. Marta Di Forti
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15. Elvira Bramon
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16. Michael Gandal
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17. Christina M Hultman
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18. Pamela Sklar
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19. Aarno Palotie
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20. Patrick F Sullivan
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21. Michael C O'Donovan
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22. Michael J Owen
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23. Jeffrey C Barrett
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Consortia

INTERVAL Study

UK10K Consortium

Contributions

T.S. and J.C.B. conceived and designed the experiments. T.S. performed the statistical analysis. T.S., J.T.R.W., M.J., D.C., J.S., M.T., E.R., and P.F.S. analyzed the data. T.S., J.T.R.W., M.J., J.S., M.T., E.R., C.I., D.B., A.M.M., G.K., D.G., R.M.M., M.D.F., E.B., M.G., C.M.H., P.S., A.P., M.C.O'D., M.J.O., and J.C.B. contributed reagents, materials, or analysis tools. T.S., D.C., M.J.O., and J.C.B. wrote the manuscript.

Corresponding authors

Correspondence toTarjinder Singh or Jeffrey C Barrett.

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The authors declare no competing financial interests.

Additional information

Integrated supplementary information

Supplementary Figure 1 The use of frequency and size cutoffs in CNV gene set enrichment tests to reduce genomic inflation.

Quantile–quantile plots were generated based on P values from CNV enrichment tests of random gene sets, using different MAF cutoffs (<0.1%, 1%) and CNV size cutoffs (removing the top 5% and 10% of CNVs overlapping the most genes). Each dot represents a different gene set. The 95% CI assuming uniformly distributed P values is displayed as the gray shaded area. The genomic inflation factor (λ) is provided for each distribution. Inflation followed the reasonable null distribution when more stringent MAF thresholds and size cutoffs were applied (see MAF < 0.1% and removing the 10% of CNVs overlapping the most genes).

Supplementary Figure 2 Quantile–quantile plots of P values from enrichment tests of 1,766 gene sets.

Top left, case–control SNVs from whole-exome sequence data. Top right, de novo mutations from 1,077 trios. Bottom left, case–control CNVs. Bottom right, meta-analysis P values from Fisher’s method (dark blue). Tailored enrichment tests were applied to each variant type (Online Methods). Each dot represents a different gene set. The 95% CI assuming uniformly distributed P values is displayed as the gray shaded area. The genomic inflation factor (λ) is provided for each distribution. General inflation of P values from tests of disruptive variants (loss-of-function and CNVs) was observed, but no inflation was observed for tests of synonymous variants. Damaging missense, missense variants with CADD Phred > 15.

Supplementary Figure 3 Quantile–quantile plots of P values from enrichment tests of random gene sets.

Top left, case–control SNVs from whole-exome sequence data. Top right, de novo mutations from 1,077 trios. Bottom left, case–control CNVs. Bottom right, meta-analysis P values from Fisher’s method (dark blue). Genes were randomly sampled from the genome to create gene sets with the same size distribution as the 1,766 tested gene sets. Each dot represents a different gene set. The 95% CI assuming uniformly distributed P values is displayed as the gray shaded area. Tailored enrichment tests were applied to each variant type (Online Methods). The genomic inflation factor (λ) is provided for each distribution. No inflation of test statistics was observed in the meta-analysis P values. Damaging missense, missense variants with CADD Phred > 15.

Supplementary Figure 4 Enrichment of de novo mutations in genes with near-complete depletion of truncating variants across schizophrenia and neurodevelopmental disorders.

In schizophrenia, ASD, and severe neurodevelopmental disorders, de novo mutations were enriched in a subset of genes intolerant of loss-of-function variants, with no excess of polygenic burden in the remaining genes. To generate 95% CIs and P values, the rates of de novo mutations in affected trios (1,077 schizophrenia trios, 4,038 trios with ASD, and 1,133 trios with severe neurodevelopmental disorders) were compared against the rate in unaffected control trios (2,038 trios) using Poisson exact tests. Plotted P values are from the Poisson test of loss-of-function mutations. Damaging missense, missense variants with CADD Phred > 15.

Supplementary Figure 5 Enrichment of damaging rare variants in genes ordered and grouped by the degree of loss-of-function intolerance in schizophrenia, ASD, and severe neurodevelopmental disorders.

(a) Schizophrenia cases compared to controls for rare SNVs and indels. (b) Rates of de novo mutation in schizophrenia, ASD, and severe neurodevelopmental disorder probands as compared to control probands. Genes are ordered by their degree of loss-of-function intolerance (pLI score) and grouped into six categories: the 10% with the highest pLI score, the top 10–20% as ranked by pLI score, 20–40% as ranked by pLI score, and so on. Calculation of the 95% CIs and P values for the trio data followed the same method as in Supplementary Figure 4. A significant enrichment of rare, damaging variants was only observed in the 20% of genes with the highest pLI score, while no signal was observed in the remaining genes. Error bars are 95% CIs of the estimates. Damaging missense, missense variants with CADD Phred > 15.

Supplementary Figure 6 Non-random sampling of genes in the 1,766 tested gene sets.

(a) Genes are ranked and plotted based on the number of gene sets to which they belong. The top 1,000 genes were over-represented in gene sets from public databases, and genes outside the top 5,000 genes were under-represented. (b) Distribution of overlap coefficients with the set of loss-of-function-intolerant genes. The overlap coefficients between each of the 1,766 discovery gene sets and the set of loss-of-function-intolerant genes were calculated. The overlap coefficients between randomly sampled gene sets and loss-of-function-intolerant genes were similarly computed. These values are displayed as two density plots. The overlap coefficient is a similarity measure defined as where X and Y are sets of genes.

Supplementary Figure 7 Heat map of overlap coefficients calculated between the 35 significant gene sets (FDR < 5%).

The overlap coefficients of the 35 gene sets enriched for rare coding variants conferring risk for schizophrenia were computed and are clustered and displayed as a heat map. The overlap coefficient is a similarity measure defined as where X and Y are sets of genes. The overlap coefficients between each gene set and the set of loss-of-function-intolerant genes are displayed as rounded values. See Supplementary Table 2 and the Supplementary Note for more information on each gene set.

Supplementary Figure 8 Summary of cognition and educational attainment data available for the schizophrenia whole-exome data set.

(a) Individuals diagnosed with schizophrenia (cases). (b) Individuals without a diagnosis of schizophrenia (controls). Information on population is also provided (UK, Finland, and Sweden).

Supplementary Figure 9 Enrichment of rare loss-of-function variants in loss-of-function-intolerant genes after excluding known developmental disorder–associated genes in schizophrenia cases stratified by information on cognitive function as compared to controls.

The P values shown were calculated using the variant threshold method comparing the burden of loss-of-function variants between the corresponding cases and controls. Error bars represent the 95% CIs of the point estimates. Damaging missense, missense variants with CADD Phred > 15.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Note. (PDF 2042 kb)

Supplementary Table 1

Full results from enrichment analyses of 1,766 gene sets. (XLSX 111 kb)

Supplementary Table 2

Gene sets enriched for rare coding variants conferring risk for schizophrenia at FDR < 5%. (XLSX 14 kb)

Supplementary Table 3

Results from enrichment analyses of FDR < 5% gene sets, conditional on brain-expressed and ExAC loss-of-function-intolerant genes. (XLSX 13 kb)

Supplementary Table 4

Results from enrichment analyses of rare loss-of-function variants in loss-of-function-intolerant genes and developmental disorder–associated genes comparing schizophrenia cases stratified by information on cognitive function and matched controls. (XLSX 10 kb)

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Singh, T., Walters, J., Johnstone, M. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability.Nat Genet 49, 1167–1173 (2017). https://doi.org/10.1038/ng.3903

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• Received: 29 June 2016
• Accepted: 01 June 2017
• Published: 26 June 2017
• Issue Date: 01 August 2017
• DOI: https://doi.org/10.1038/ng.3903