Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genome sequencing identifies major causes of severe intellectual disability


Severe intellectual disability (ID) occurs in 0.5% of newborns and is thought to be largely genetic in origin1,2. The extensive genetic heterogeneity of this disorder requires a genome-wide detection of all types of genetic variation. Microarray studies and, more recently, exome sequencing have demonstrated the importance of de novo copy number variations (CNVs) and single-nucleotide variations (SNVs) in ID, but the majority of cases remain undiagnosed3,4,5,6. Here we applied whole-genome sequencing to 50 patients with severe ID and their unaffected parents. All patients included had not received a molecular diagnosis after extensive genetic prescreening, including microarray-based CNV studies and exome sequencing. Notwithstanding this prescreening, 84 de novo SNVs affecting the coding region were identified, which showed a statistically significant enrichment of loss-of-function mutations as well as an enrichment for genes previously implicated in ID-related disorders. In addition, we identified eight de novo CNVs, including single-exon and intra-exonic deletions, as well as interchromosomal duplications. These CNVs affected known ID genes more frequently than expected. On the basis of diagnostic interpretation of all de novo variants, a conclusive genetic diagnosis was reached in 20 patients. Together with one compound heterozygous CNV causing disease in a recessive mode, this results in a diagnostic yield of 42% in this extensively studied cohort, and 62% as a cumulative estimate in an unselected cohort. These results suggest that de novo SNVs and CNVs affecting the coding region are a major cause of severe ID. Genome sequencing can be applied as a single genetic test to reliably identify and characterize the comprehensive spectrum of genetic variation, providing a genetic diagnosis in the majority of patients with severe ID.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Study design and diagnostic yield in patients with severe ID per technology.
Figure 2: Detected duplication of a chromosome 4 region into the X-chromosomal IQSEC2 gene.
Figure 3: Pie chart showing role of de novo mutations in severe ID.

Accession codes

Data deposits

Data included in this manuscript have been deposited at the European Genome-phenome Archive ( under accession number EGAS00001000769.


  1. Ropers, H. H. Genetics of early onset cognitive impairment. Annu. Rev. Genomics Hum. Genet. 11, 161–187 (2010)

    Article  CAS  Google Scholar 

  2. Mefford, H. C., Batshaw, M. L. & Hoffman, E. P. Genomics, intellectual disability, and autism. N. Engl. J. Med. 366, 733–743 (2012)

    Article  CAS  Google Scholar 

  3. de Vries, B. B. et al. Diagnostic genome profiling in mental retardation. Am. J. Hum. Genet. 77, 606–616 (2005)

    Article  CAS  Google Scholar 

  4. Vissers, L. E. et al. A de novo paradigm for mental retardation. Nature Genet. 42, 1109–1112 (2010)

    Article  CAS  Google Scholar 

  5. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012)

    Article  CAS  Google Scholar 

  6. de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012)

    Article  CAS  ADS  Google Scholar 

  7. Lupski, J. R. et al. Whole-genome sequencing in a patient with Charcot–Marie–Tooth neuropathy. N. Engl. J. Med. 362, 1181–1191 (2010)

    Article  CAS  Google Scholar 

  8. Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010)

    Article  CAS  ADS  Google Scholar 

  9. Michaelson, J. J. et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431–1442 (2012)

    Article  CAS  Google Scholar 

  10. Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012)

    Article  CAS  ADS  Google Scholar 

  11. Jiang, Y. H. et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am. J. Hum. Genet. 93, 249–263 (2013)

    Article  CAS  Google Scholar 

  12. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012)

    Article  ADS  Google Scholar 

  13. Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012)

    Article  CAS  Google Scholar 

  14. Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012)

    Article  CAS  ADS  Google Scholar 

  15. Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012)

    Article  CAS  ADS  Google Scholar 

  16. Epi4K Consortium & Epilepsy Phenome/Genome Project De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013)

  17. Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013)

    Article  CAS  Google Scholar 

  18. Xu, B. et al. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nature Genet. 43, 864–868 (2011)

    Article  CAS  Google Scholar 

  19. Girard, S. L. et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nature Genet. 43, 860–863 (2011)

    Article  CAS  Google Scholar 

  20. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013)

    Article  CAS  Google Scholar 

  21. Rippey, C. et al. Formation of chimeric genes by copy-number variation as a mutational mechanism in schizophrenia. Am. J. Hum. Genet. 93, 697–710 (2013)

    Article  CAS  Google Scholar 

  22. Biesecker, L. G. & Spinner, N. B. A genomic view of mosaicism and human disease. Nature Rev. Genet. 14, 307–320 (2013)

    Article  CAS  Google Scholar 

  23. The ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)

    Article  ADS  Google Scholar 

  24. Bell, J. B. D., Sistermans, E. & Ramsden, S. C. Practice guidelines for the Interpretation and Reporting of Unclassified Variants (UVs) in Clinical Molecular Genetics (The UK Clinical Molecular Genetics Society and the Dutch Society of Clinical Genetic Laboratory Specialists, 2007)

    Google Scholar 

  25. Berg, J. S., Khoury, M. J. & Evans, J. P. Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet. Med. 13, 499–504 (2011)

    Article  Google Scholar 

  26. Vulto-van Silfhout, A. T. et al. Clinical significance of de novo and inherited copy-number variation. Hum. Mutat. 34, 1679–1687 (2013)

    Article  CAS  Google Scholar 

  27. Hehir-Kwa, J. Y., Pfundt, R., Veltman, J. A. & de Leeuw, N. Pathogenic or not? Assessing the clinical relevance of copy number variants. Clin. Genet. 84, 415–421 (2013)

    Article  CAS  Google Scholar 

  28. Kolehmainen, J. et al. Cohen syndrome is caused by mutations in a novel gene, COH1, encoding a transmembrane protein with a presumed role in vesicle-mediated sorting and intracellular protein transport. Am. J. Hum. Genet. 72, 1359–1369 (2003)

    Article  CAS  Google Scholar 

  29. Carnevali, P. et al. Computational techniques for human genome resequencing using mated gapped reads. J. Comput. Biol. 19, 279–292 (2012)

    Article  CAS  MathSciNet  Google Scholar 

  30. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012)

    Article  CAS  ADS  Google Scholar 

Download references


We thank R. Drmanac, K. Albers, J. Goeman, D. Lugtenberg and P. N. Robinson for useful discussions, and M. Steehouwer, P. de Vries and W. Nillesen for technical support. This work was in part financially supported by grants from the Netherlands Organization for Scientific Research (912-12-109 to J.A.V., A.S. and B.B.A.d.V., 916-14-043 to C.G., 916-12-095 to A.H., 907-00-365 to T.K. and SH-271-13 to C.G. and J.A.V.) and the European Research Council (ERC Starting grant DENOVO 281964 to J.A.V.).

Author information

Authors and Affiliations



Laboratory work: M.K., I.M.J., T.B., A.H., L.E.L.M.V. Clinical investigation: B.W.M.v.B., M.H.W., B.B.A.d.V., T.K., H.G.B. Data analysis: C.G., J.Y.H.-K., D.T.T., M.v.d.V., R.T. Generation of ID gene list: C.G., A.S., R.P., H.G.Y., T.K., L.E.L.M.V. Data interpretation: L.E.L.M.V., R.P., H.G.Y. Study design: J.A.V., H.G.B., R.L., R.K. Supervision of the study: H.G.B., L.E.L.M.V., J.A.V. Manuscript writing: C.G., J.Y.H.-K., H.G.B., L.E.L.M.V., J.A.V.

Corresponding author

Correspondence to Joris A. Veltman.

Ethics declarations

Competing interests

R.L., R.K. and R.T. are employees of Complete Genomics Inc.

Extended data figures and tables

Extended Data Figure 1 Boxplots of rare missense burden in different gene sets.

Boxplots showing the difference in tolerance for rare missense variation in the general population. The vertical axis shows the distribution for each gene set of the number of rare (<1% in NHLBI Exome Sequencing Project) missense variants divided by the number of rare synonymous variants. From left to right the following gene sets are depicted: all 18,424 RefSeq genes, 170 loss-of-function tolerant genes from ref. 30, all 528 known ID genes (Supplementary Table 10), all 628 candidate ID genes (Supplementary Table 11), 9 known ID genes in which de novo mutations were identified in this study (Supplementary Table 8), and 10 candidate ID genes in which de novo mutations were identified in this study (Supplementary Table 8).

Extended Data Figure 2 Structural variant involving STAG1 (patient 40).

ac, CNV identified using WGS in patient 40, including the STAG1 gene. a, Chromosome 3 profile (log2 test over reference (T/R) ratios) based on read-depth information for patient, father and mother. Black arrow points towards the de novo event in patient 40. b, Genic contents of deletion. Grey arrows show primers used to amplify the junction fragment. c, Details on the proximal and distal breakpoints, showing the ‘fragmented’ sequence at both ends. Breakpoints are provided in Extended Data Table 1.

Extended Data Figure 3 Structural variant involving SHANK3 (patient 5).

ac, CNV identified using WGS in patient 5, including the SHANK3 gene. a, Detail of chromosome 22 profile (log2 T/R ratios) based on read-depth information for patient, father and mother. Red dots in top panel show ratios indicating the de novo deletion in patient 5. b, Genic content of the deletion. c, Sanger validation for the junction fragment. Dotted vertical line indicates the breakpoint with sequence on the left side originating from sequence proximal to SHANK3 and on the right side sequence that originates from sequence distal to ACR. Breakpoints are provided in Extended Data Table 1.

Extended Data Figure 4 Single-exon deletion involving SMC1A (patient 48).

a, Schematic depiction of the deletion identified in patient 48 involving a single exon of SMC1A. Pink horizontal bar highlights the exon that was deleted in the patient. b, Details at the genomic level of the deletion including exon 16, with Sanger sequence validation of the breakpoints. Junction is indicated by a black vertical dotted line. Breakpoints are provided in Extended Data Table 1.

Extended Data Figure 5 Intra-exonic deletion involving MECP2 (patient 18).

a, Schematic depiction of the deletion identified in patient 18, which is located within exon 4 of MECP2. Initial Sanger sequencing in a diagnostic setting could not validate the deletion as the primers used to amplify exon 4 removed the primer-binding sites (FW2 and RV1 respectively). Multiplex ligation probe amplification (MLPA) analysis for CNV detection showed normal results as the MLPA primer-binding sites were located just outside of the deleted region. b, Combining primers FW1 and RV2 amplified the junction fragment, clearly showing the deletion within exon 4. Of note, the background underneath the Sanger sequence is derived from the wild-type allele. Breakpoints are provided in Extended Data Table 1.

Extended Data Figure 6 Confirmation of mosaic mutations in PIAS1, HIVEP2 and KANSL2.

ac, Approaches used to confirm the presence of mosaic mutations in PIAS1 (a), HIVEP2 (b) and KANSL2 (c). Images and read-depth information showing the base counts in the BAM files (left) indicated that the variants/wild-type allele were not in a 50%/50% distribution. Sanger sequencing (middle) then confirmed the variant to be present in the patient, and absent in the parents (data from parents not shown), again indicating that the mutation allele is underrepresented. Guided by these two observations, amplicon-based deep sequencing using Ion Torrent subsequently confirmed the mosaic state of the mutations (right). On the basis of deep sequencing, percentages of mosaicism for PIAS1, HIVEP2 and KANSL2 were estimated at 21%, 22% and 20%, respectively.

Extended Data Figure 7 Compound heterozygous structural variation affecting VPS13B (patient 12).

a, b, CNVs of VPS13B identified using WGS in patient 12. a, Schematic representation of VPS13B, with vertical bars indicating coding exons. In patient 12 two deletions were identified, one 122 kb in size which was inherited from his father, and another 2 kb in size, which was inherited from his mother and consisted only of a single exon. b, Both CNV junction fragments were subsequently validated using Sanger sequencing. Left, junction fragment from the paternally inherited deletion. Right, junction fragment from the maternally inherited deletion. Breakpoints are provided in Extended Data Table 1.

Extended Data Table 1 Large variants of potential clinical relevance identified using WGS and probability of exonic CNVs occurring in affected and control individuals for these loci
Extended Data Table 2 De novo SNVs of potential clinical relevance identified using WGS

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Tables 1-15 and Supplementary References. (PDF 3010 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gilissen, C., Hehir-Kwa, J., Thung, D. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing