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Evidence for 28 genetic disorders discovered by combining healthcare and research data

Abstract

De novo mutations in protein-coding genes are a well-established cause of developmental disorders1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent–offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

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Fig. 1: Results of DeNovoWEST analysis.
Fig. 2: Properties of the novel genes.
Fig. 3: Factors that influence power to detect DD-associated genes.

Data availability

Sequence and variant-level data and phenotypic data for the DDD study data are available from the European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/) with study ID EGAS00001000775. The RadboudUMC sequence and variant-level data cannot be made available through the EGA owing to the nature of consent for clinical testing. To access the data, please contact C.G. (christian.gilissen@radboudumc.nl) with a request. Data sharing will be dependent on patient consent, diagnostic status of the patient, the type of request and the potential benefit to the patient. GeneDx data cannot be made available through the EGA owing to the nature of consent for clinical testing. GeneDx-referred patients are consented for aggregate, deidentified research and subject to US HIPAA privacy protection. As such, we are not able to share patient-level BAM or VCF data, which are potentially identifiable without a HIPAA Business Associate Agreement. Access to the deidentified aggregate data used in this analysis is available upon request to GeneDx. GeneDx has contributed deidentified data to this study to improve clinical interpretation of genomic data, in accordance with patient consent and in conformance with the ACMG position statement on genomic data sharing (details are provided in the Supplementary Note). Clinically interpreted variants and associated phenotypes from the DDD study are available through DECIPHER (https://decipher.sanger.ac.uk). Clinically interpreted variants from RUMC are available from the Dutch national initiative for sharing variant classifications (https://www.vkgl.nl/nl/diagnostiek/vkgl-datashare-database) as well as LOVD (https://databases.lovd.nl/shared/variants), where they are listed with ‘VKGL-NL_Nijmegen’ as the owner. Clinically interpreted variants from GeneDx are deposited in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar) under accession number 26957 (https://www.ncbi.nlm.nih.gov/clinvar/submitters/26957/). Previously described datasets were from the Genome Aggregation Database (gnomAD v2.1.1; https://gnomad.broadinstitute.org/), The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov) and the Developmental Disorders Genotype-Phenotype Database (DDG2P; https://www.ebi.ac.uk/gene2phenotype/downloads).

Code availability

The DeNovoWEST method is available on GitHub (https://github.com/queenjobo/DeNovoWEST) along with code to recreate all of the figures in the manuscript (https://doi.org/10.5281/zenodo.3909398). Code to run the Phenopy method is also available on GitHub (https://github.com/GeneDx/phenopy).

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Acknowledgements

We thank the families and their clinicians for their participation and engagement, and our colleagues who assisted in the generation and processing of data. Inclusion of RadboudUMC data was in part supported by the Solve-RD project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 779257. This work was in part financially supported by grants from the Netherlands Organization for Scientific Research (917-17-353 to C.G.). The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003). This study makes use of DECIPHER, which is funded by the Wellcome Trust. The full acknowledgements can be found at www.ddduk.org/access.html. The DDD study authors acknowledges the work of R. Kelsell. Finally, we acknowledge the contribution of an esteemed DDD clinical collaborator, M. Bitner-Glindicz, who died during the course of the study.

Author information

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Contributions

J.K., K.E.S., L.W., K.J.A., M.E.H., C.G. and K.R. contributed to the generation of figures and writing of the manuscript. J.K., K.E.S., L.W., Z.Z., K.J.A., R.Y.E., G.G., S.H.L., H.C.M., J.F.M., E.d.B., R.P., M.R.F.R. and H.G.Y. contributed to the generation and quality control of data. J.K., K.E.S., L.W., Z.Z., K.J.A., R.I.T., J.F.M., P.J.S., P.D., E.J.G., N.H., J.L., I.M., A.Y. and K.R. developed methods, contributed data or performed analyses. H.C.M., L.E.L.M.V., J.J., C.F.W., H.G.B., H.V.F., D.R.F., J.C.B., M.E.H., C.G. and K.R. provided experimental and analytical supervision. M.E.H., C.G. and K.R. provided project supervision.

Corresponding author

Correspondence to Matthew E. Hurles.

Ethics declarations

Competing interests

Z.Z., K.J.A., R.I.T., J.J. and K.R. are employees of GeneDx. J.J. and K.R. are shareholders of OPKO. M.E.H. is a co-founder of, consultant to and holds shares in Congenica, a genetics diagnostic company.

Additional information

Peer review information Nature thanks Ipsita Agarwal, James Lupski, Shamil Sunyaev and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Exploring the remaining number of DD genes.

a, Number of significant genes after downsampling the full cohort and running the enrichment test of DeNovoWEST. b, The likelihood of the observed distribution of de novo PTV mutations was modelled. This model varies the numbers of remaining haploinsufficient (HI) DD genes and PTV enrichment in those remaining genes. The 50% credible interval is shown in red and the 90% credible interval is shown in orange. Note that the median PTV enrichment in genes that are significant and known to operate through a loss-of-function mechanism (as indicated by an arrow) is 39.7.

Extended Data Table 1 Recurrent mutations

Supplementary information

Supplementary Information

This file contains Supplementary Methods and descriptions of Supplementary Analyses, Supplementary Figures 1-14, descriptions of Supplementary Tables 1-3, Supplementary Tables 4-9 and Supplementary References. It also contains Supplementary notes detailing the DDD consortia members.

Reporting Summary

mutations from 31,058 individuals with developmental disorders

Supplementary Table 1 De novo mutations from 31,058 individuals with developmental disorders. For every de novo mutation, we provide: proband ID (‘id’), chromosome (‘chrom’), position in GRCh37 (‘pos’), the reference allele (‘ref’), the alternative allele (‘alt’), the VEP consequence of the mutation (‘consequence’), the HGNC symbol (‘symbol’), the centre which sequence the proband (‘study’), the fraction of reads that are from the alternative allele (‘altprop_child’), and the HGNC ID (‘hgnc_id’).

Results of DeNovoWEST

Supplementary Table 2 . Results from analysis on the full cohort and on the undiagnosed subset, along with gene-level DNM counts per consequence. Column headers are described within the file.

Novel genes

Supplementary Table 3 . For each of the 28 novel genes in this analysis, we determined if it had an associated phenotype in OMIM, any publications about an association between mutations in that gene and developmental disorders, and whether it was significant in a study of inherited and de novo mutations in autism spectrum disorders21 (“sig_ASD”) or a metaanalysis of de novo mutations in individuals with neurodevelopmental disorders22 (“sig_meta”).

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Kaplanis, J., Samocha, K.E., Wiel, L. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020). https://doi.org/10.1038/s41586-020-2832-5

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