Article series: Applications of next-generation sequencing

Genetic studies in intellectual disability and related disorders

Journal name:
Nature Reviews Genetics
Volume:
17,
Pages:
9–18
Year published:
DOI:
doi:10.1038/nrg3999
Published online

Abstract

Genetic factors play a major part in intellectual disability (ID), but genetic studies have been complicated for a long time by the extreme clinical and genetic heterogeneity. Recently, progress has been made using different next-generation sequencing approaches in combination with new functional readout systems. This approach has provided novel insights into the biological pathways underlying ID, improved the diagnostic process and offered new targets for therapy. In this Review, we highlight the insights obtained from recent studies on the role of genetics in ID and its impact on diagnosis, prognosis and therapy. We also discuss the future directions of genetics research for ID and related neurodevelopmental disorders.

At a glance

Figures

  1. Increase of genes linked to isolated ID and ID-associated disorders.
    Figure 1: Increase of genes linked to isolated ID and ID-associated disorders.

    Graphical overview of the increase in gene discovery for isolated intellectual disability (ID) and ID-associated disorders over time, specified by the type of inheritance. Vertical dashed lines represent the introduction of genomic microarrays (red) and next-generation sequencing (NGS)-based technologies (orange) for the detection of new ID genes. From this figure it is clear that we have not reached any saturation in ID disease gene identification, except perhaps for X-linked forms of ID. Supplementary information S1 (table) lists all genes shown in this figure, along with their respective ID phenotype.

  2. Diagnostic yield for ID over time.
    Figure 2: Diagnostic yield for ID over time.

    Graphical overview of the diagnostic yield for moderate to severe intellectual disability (ID) (excluding Down syndrome, which represents 6–8% of all ID) over time. Solid line indicates the mean of published studies, and the shaded background indicates the lower and upper boundaries of reported diagnostic yields. In the 1970s, conventional karyotyping became a routine diagnostic test and provided a conclusive diagnosis in 3–6.5% of ID cases. The diagnostic yield increased by 6–10% after the introduction of both Sanger sequencing and targeted fluorescence in situ hybridization (FISH) in the 1990s120. At the beginning of this century, genomic microarrays were introduced, increasing the diagnostic yield by another 15–23%25, 32. The introduction of whole-exome sequencing in 2010 and onwards added a diagnostic yield of 24–33%4, 7, 10, and a first pilot study using whole-genome sequencing added a further 26% in 2014 (Ref. 5), accumulating to an overall diagnostic yield of 55–70% for moderate to severe ID. Interestingly, a higher diagnostic yield has been observed for moderate to severe ID (IQ score <50) compared with mild ID (IQ score 50–70)120, 121, 122. As an example, subtelomeric aberrations explain 0.5% of mild ID and 7.4% of moderate to severe ID. Since the introduction of genomic microarray technology, the diagnostic yield per category of ID is less well documented. This is also the case for differences in diagnostic yield between males and females.

  3. Genic overlap for neurodevelopmental disorders.
    Figure 3: Genic overlap for neurodevelopmental disorders.

    We collected de novo mutations of published patient–parent trio-based sequencing studies in neurodevelopmental disorders. All de novo mutations were re-annotated using our custom pipeline and grouped by phenotype: autism spectrum disorder (ASD; 2,683 patients)47, 116, epileptic encephalopathy (EE; 264 patients)43, intellectual disability (ID; 1,284 patients)4, 5, 7, 10 and schizophrenia (SCZ; 1,063 patients)44, 46, 49, 89, 117. To assess the significance for overlap for de novo loss-of-function (LoF) mutations between these four neurodevelopmental disorders, we carried out 10,000 simulations with the total number of identified de novo mutations in these studies, making use of the gene-specific mutation rates from a previous study56 a | The number of genes with overlapping de novo LoF mutations in two, three, and all four of the disorders, from 10,000 simulations, indicated as boxplots. Diamond symbols indicate the actual number of genes with de novo mutations across the neurodevelopmental disorders. There were significantly more genes with actual de novo LoF mutation for two and three disorders than expected by chance from the simulation studies (P <0.0001 and P = 0.0084 respectively), whereas no genes with de novo LoF mutations in all four disorders were identified. b | Venn diagram denoting the overlap for the actual number of genes with de novo LoF mutations shared between each of the disorders. The genes for which overlap in de novo LoF mutations between neurodevelopmental disorders has been identified are listed. Importantly, this does not imply that all of these mutations are relevant for these neurodevelopmental disorders. This is because some of these genes, such as TTN (titin), have a high mutation rate and therefore de novo LoF mutations are also observed in unaffected individuals. ADNP, activity-dependent neuroprotector homeobox; AHDC1, AT hook, DNA-binding motif, containing 1; ANKRD11, ankyrin repeat domain 11; ARID1B, AT-rich interactive domain 1B (SWI1-like); AUTS2, autism susceptibility candidate 2; BAZ2B, bromodomain adjacent to zinc finger domain, 2B; CAMK2A, calcium/calmodulin-dependent protein kinase II-α; CDAN1, codanin 1; CDC42BPB, CDC42 binding protein kinase beta (DMPK-like); CHD, chromodomain helicase DNA binding protein; CNOT3, CCR4-NOT transcription complex, subunit 3; CRYBG3, βγ-crystallin domain-containing 3; CTNNB1, catenin (cadherin-associated protein), β1, 88kDa; CUL3, cullin 3; DDX3X, DEAD (Asp-Glu-Ala-Asp) box helicase 3, X-linked; DYRK1A, dual-specificity Tyr-phosphorylation regulated kinase 1A; FOXP1, forkhead box P1; HIVEP3, HIV type I enhancer binding protein 3; IQSEC2, IQ motif and Sec7 domain 2; KMT2A, lysine (K)-specific methyltransferase 2A; LRP2, low-density lipoprotein receptor-related protein 2; MED13L, mediator complex subunit 13-like; MOV10, Mov10 RISC complex RNA helicase; NIN, ninein (GSK3B interacting protein); POGZ, pogo transposable element with ZNF domain; PPM1D, protein phosphatase, Mg2+/Mn2+ dependent, 1D; NBEA, neurobeachin; PHF7, PHD finger protein 7; RAI1, retinoic acid induced 1; SCN2A; sodium channel, voltage gated, type II α-subunit; SETBP1, SET binding protein 1; SMARCC2, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily c, member 2; ST3GAL6, ST3 β-galactoside α-2,3-sialyltransferase 6; STXBP1, syntaxin binding protein 1; SYNGAP1, synaptic RAS GTPase activating protein 1; TBR1, T-box brain gene 1; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMG-box); TNRC18, trinucleotide repeat containing 18; TRIP12, thyroid hormone receptor interactor 12; WAC, WW domain containing adaptor with coiled-coil; WDR45, WD repeat domain 45; YTHDC1, YTH domain containing 1; ZMYND11, zinc finger, MYND-type containing 11; ZNF292, zinc finger protein 292.

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Affiliations

  1. Department of Human Genetics, Donders Centre for Neuroscience, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands.

    • Lisenka E. L. M. Vissers,
    • Christian Gilissen &
    • Joris A. Veltman
  2. Department of Clinical Genetics, Maastricht University Medical Centre. Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.

    • Joris A. Veltman

Competing interests statement

The authors declare no competing interests.

Corresponding author

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Author details

  • Lisenka E. L. M. Vissers

    Lisenka E. L. M. Vissers obtained her Ph.D. in genetics at the Radboud University Medical Centre in Nijmegen, The Netherlands. She is an assistant professor and head of the translational genomics group in the Department of Human Genetics of the Radboud University Medical Hospital. Her work focuses on the optimization and implementation of high-throughput genomics technologies for the study of human genetic disease in routine genetic diagnostic care. Currently, she is actively involved in clinical utility studies with a focus on clinically and genetically heterogeneous disorders, in particular intellectual disability.

  • Christian Gilissen

    Christian Gilissen studied computer sciences and obtained a Ph.D. in genetics at the Radboud University Medical Centre in Nijmegen, The Netherlands, in 2012. He is now an assistant professor in bioinformatics in the Department of Human Genetics at the Radboud University Medical Hospital. His work focuses on developing bioinformatic methods for the implementation of new high-throughput genomic technologies in both research as well as routine clinical diagnostics. He has a special research interest in the interpretation of genomic variation in relation to developmental diseases, in particular intellectual disability.

  • Joris A. Veltman

    Joris A. Veltman received his Ph.D. in genetics from the University of Maastricht, the Netherlands, and was a postdoctoral fellow at the University of California San Francisco, USA. He is now Professor in Translational Genomics at the Radboud University Medical Centre in Nijmegen, The Netherlands, and Maastricht University Medical Centre. His work focuses on the identification and interpretation of genomic variation, with a particular interest in the role of rare de novo mutations in severe disorders affecting fitness. He is also actively involved in the optimization and implementation of these new genomics approaches in routine clinical diagnosis.

Supplementary information

PDF files

  1. Supplementary information S1 (table) (473 KB)

    Genes linked to isolated ID and ID-associated disorders

Additional data