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Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

Nature volume 562, pages 268–271 (2018)Cite this article

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Abstract

There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants1. However, patients with the same genetic defect can have different clinical presentations2,3,4, and some individuals who carry known disease-causing variants can appear unaffected5. Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic.

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Fig. 1: Outline of analysis exploring the contribution of common variants to risk of severe neurodevelopmental disorders.
Fig. 2: Patients recruited to the DDD study have diverse phenotypes.
Fig. 3: Genetic correlations between neurodevelopmental disorder risk (6,987 cases and 9,270 controls) against nineteen other traits.

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Data availability

The raw genotype data, post-quality-control genotype data and discovery GWAS summary statistics generated and/or analysed during the current study are available through European Genome-phenome Archive, under EGAS00001000775. This study makes use of data generated by the DECIPHER community: a full list of centres that contributed to the generation of the data is available from http://decipher.sanger.ac.uk, and via email from decipher@sanger.ac.uk. Information on how to access the data from the UKHLS can be found on the ‘Understanding Society’ website, at https://www.understandingsociety.ac.uk/.

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Acknowledgements

We thank the families involved in the DDD study for their participation and patience, the DDD study clinicians, research nurses and clinical scientists in the recruiting centres for their hard work on behalf of families, M. Niemi for help making Fig. 1 and V. Warrier for useful discussions. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between Wellcome and the Department of Health, and the Wellcome Sanger Institute (grant number WT098051). The views expressed in this publication are those of the author(s) and not necessarily those of Wellcome or the Department of Health. The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. This study makes use of data generated by the DECIPHER community. Funding for the project was provided by the Wellcome Trust. We used data from ‘Understanding Society: The UK Household Longitudinal Study’, which is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council (grant number ES/M008592/1). The data were collected by NatCen and the genome-wide scan data were analysed by the Wellcome Trust Sanger Institute. Data governance was provided by the METADAC data access committee, funded by ESRC, Wellcome and MRC (grant number MR/N01104X/1). Australian controls from the Brisbane Longitudinal Twin Study were collected and genotyped with grants from the National Health and Medical Research Council. We thank A. Pardiñas for producing the PGC-CLOZUK summary statistics without the Australian controls.

Reviewer information

Nature thanks D. Arking, C. Lewis and S. Ripke for their contribution to the peer review of this work.

Author information

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Mari E. K. Niemi, Hilary C. Martin, Daniel L. Rice, Giuseppe Gallone, Martin Kelemen, Jeremy McRae, Elizabeth J. Radford, Helen V. Firth, Matthew E. Hurles & Jeffrey C. Barrett

  2. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Scott Gordon, Kerrie McAloney & Nicholas G. Martin

  3. Department of Paediatrics, University of Cambridge, Cambridge, UK

    Elizabeth J. Radford

  4. Department of Genetics and Molecular Pathology, SA Pathology, Women’s and Children’s Hospital, Adelaide, South Australia, Australia

    Sui Yu

  5. Adelaide Medical School and Robinson Research Institute, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia

    Jozef Gecz

  6. South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia

    Jozef Gecz

  7. University of Exeter Medical School, Institute of Biomedical and Clinical Science, RILD, Royal Devon & Exeter Hospital, Exeter, UK

    Caroline F. Wright

  8. MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Western General Hospital, Edinburgh, UK

    David R. Fitzpatrick

  9. Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

    Helen V. Firth

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  1. Mari E. K. Niemi
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Contributions

J.C.B., C.F.W., D.R.F., H.V.F. and M.E.H. designed the study. M.E.K.N., H.C.M., D.L.R., G.G., M.K., J.M. and E.J.R. contributed to data analysis. S.Y., J.G. and N.G.M. performed data collection for the Australian cohort. K.M. and S.G. prepared data from the Australian cohort. M.E.K.N., H.C.M. and J.C.B. wrote the paper. H.C.M. and J.C.B. supervised the analyses and J.C.B. supervised the project.

Corresponding author

Correspondence to Jeffrey C. Barrett.

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Competing interests

M.E.H. is a co-founder of, consultant to and holds shares in Congenica, a genetics diagnostic company. J.C.B. is an employee of Genomics plc.

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Extended data figures and tables

Extended Data Fig. 1 Ancestry principal components analysis of UK and Australian samples.

a, b, Reference samples (n = 2,504) from 1000 Genomes Phase 3, coloured by the five super-populations, used for a projection PCA of UK cohorts (DDD and UKHLS) (a) or Australian cohorts (b). c, d, All DDD cases (discovery n = 11,304 and from trios n = 930) (c) and all Australian cases (n = 2,283) (d) from their respective projection PCA with 1000 Genomes. Case samples from individuals with European ancestry are plotted in red and non-Europeans in grey. e, f, All UKHLS controls (n = 10,396) (e) and all Australian controls (n = 4,274) (f) from their respective projection PCA with 1000 Genomes. Control samples from individuals with European ancestry are plotted in blue and non-Europeans in grey. All cases and controls coloured in grey (in c–f) were excluded from analysis owing to non-European ancestry. UK cohorts are plotted after removal of samples that failed quality control, and Australian cohorts before removal of samples that failed quality control.

Extended Data Fig. 2 Discovery GWAS of neurodevelopmental disorder risk.

a, Manhattan plot of discovery GWAS of neurodevelopmental disorder risk, with 6,987 DDD cases and 9,270 ancestry-matched UKHLS controls (both for individuals with European ancestry), using 4,134,438 variants, MAF ≥ 5%, chromosomes 1–22. P values were from a two-tailed χ2 distribution. Red line represents the threshold for genome-wide significance (P = 5 × 10−8). b, Quantile–quantile plot of discovery GWAS of neurodevelopmental disorder risk. Red line represents the expected values under the null hypothesis.

Extended Data Fig. 3 Ancestry principal components analysis of samples from the UK and Australian (principal components 2–5).

Reference samples (n = 2,504) from 1000 Genomes Phase 3—coloured by the five super-populations—are plotted on the left hand side, from projection PCAs with UK cohorts. Middle panels show the principal components plotted for DDD cases (discovery n = 10,556 and from trios n = 911) (UK samples) and Australian cases (n = 2,283). Red, case samples from individuals with European ancestry. Grey, samples from individuals of non-European ancestry; these individuals were excluded from analyses. Right-hand panels show principal components for UKHLS controls (n = 10,396) (UK samples) and Australian controls (n = 4,274). Blue, control samples from individuals with European ancestry. Grey, samples from individuals of non-European ancestry; these individuals were excluded from analyses. UK cohorts are plotted after removal of samples that failed quality control, and Australian cohorts before removal of samples that failed quality control.

Extended Data Table 1 Proportions of patients with a neurodevelopmental disorder who have at least one HPO term that belongs to a particular organ-system category
Full size table
Extended Data Table 2 Genetic correlations between neurodevelopmental disorder risk and a range of traits, calculated using the LD score method
Full size table
Extended Data Table 3 Polygenic score analyses comparing 1,266 Australian cases of neurodevelopmental disorders and 1,688 controls
Full size table
Extended Data Table 4 Polygenic score analyses comparing patients from the DDD with an exome diagnosis (n = 1,127) against undiagnosed patients (n = 2,479)
Full size table
Extended Data Table 5 Polygenic score analyses in patients from the DDD for measured traits
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1 and 2. Supplementary Table 1: Summary information for samples and variants genotyped on different DNA chips. This file contains summary information about all the data used in this study (UK and Australian cohorts), including genotyping chip information, and sample and variant numbers before and after quality control. Supplementary Table 2: Enrichment of neurodevelopmental disorder risk heritability in different cell type groups and overlapping functional categories. This file contains the results from the neurodevelopmental disorder risk GWAS (6,987 cases and 9,270 controls) partitioned SNP heritability analysis using LD score method.

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Niemi, M.E.K., Martin, H.C., Rice, D.L. et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562, 268–271 (2018). https://doi.org/10.1038/s41586-018-0566-4

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