CoChat
Sign Up
Cited by 216Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental DisordersGenomic variations and chromosomal abnormalities

The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.

Genome-wide characteristics of de novo mutations in autism

Open access
Yuen, Ryan KC, Merico, Daniele, Cao, Hongzhi, Pellecchia, Giovanna, Alipanahi, Babak, Thiruvahindrapuram, Bhooma, Tong, Xin, Sun, Yuhui, Cao, Dandan, Zhang, Tao, Wu, Xueli, Jin, Xin, Zhou, Ze, Liu, Xiaomin, Nalpathamkalam, Thomas, Walker, Susan, Howe, Jennifer L, Wang, Zhuozhi, MacDonald, Jeffrey R, Chan, Ada JS, D’Abate, Lia, Deneault, Eric, Siu, Michelle T, Tammimies, Kristiina, Uddin, Mohammed, Zarrei, Mehdi, Wang, Mingbang, Li, Yingrui, Wang, Jun, Wang, Jian, Yang, Huanming, Bookman, Matt, Bingham, Jonathan, Gross, Samuel S, Loy, Dion, Pletcher, Mathew, Marshall, Christian R, Anagnostou, Evdokia, Zwaigenbaum, Lonnie, Weksberg, Rosanna, Fernandez, Bridget A, Roberts, Wendy, Szatmari, Peter, Glazer, David, Frey, Brendan J, Ring, Robert H, Xu, Xun, Scherer, Stephen W·2016·npj Genomic Medicine
DOI 10.1038/npjgenmed.2016.27PMC PMC4980121
Open this paper in CoChat — free accountOpen PDF

Abstract

De novo mutations (DNMs) are important in autism spectrum disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole-genome sequencing (WGS) of 200 ASD parent–child trios and characterised germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (P=4.2×10−10). However, when clustered DNMs (those within 20 kb) were found in ASD, not only did they mostly originate from the mother (P=7.7×10−13), but they could also be found adjacent to de novo copy number variations where the mutation rate was significantly elevated (P=2.4×10−24). By comparing with DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (P=8.0×10−9; odds ratio=1.84), of which 15.6% (P=4.3×10−3) and 22.5% (P=7.0×10−5) were non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, regulatory sequences involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD-risk and epigenetic genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the aetiology of ASD.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/npjgenmed.2016.27

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Sequence context of regions with de novo mutations. (a) Transition (Ti) to transversion (Tv) ratio of different kinds of de novo mutations found in: germline of ASD, germline of Dutch population controls, somatic events of ASD and lymphocyte-derived cell line (LCL) of ASD. (b) Sequence context of the base substitution mutation spectra for different de novo mutations. Each of the 96 mutated trinucleotides (mutated position at centre) from each cohort is represented in a heatmap (intensity of colour correspond to frequency of each mutation). The 5′ base to the mutated site is shown on the vertical axis, while the 3′ base is shown on the horizontal axis.
Figure 1 Sequence context of regions with de novo mutations. (a) Transition (Ti) to transversion (Tv) ratio of different kinds of de novo mutations found in: germline of ASD, germline of Dutch population controls, somatic events of ASD and lymphocyte-derived cell line (LCL) of ASD. (b) Sequence context of the base substitution mutation spectra for different de novo mutations. Each of the 96 mutated trinucleotides (mutated position at centre) from each cohort is represented in a heatmap (intensity of colour correspond to frequency of each mutation). The 5′ base to the mutated site is shown on the vertical axis, while the 3′ base is shown on the horizontal axis.CC BY · PubMed Central
Figure 2 Origins of de novo mutations in ASD. (a) Parent-of-origin of germline and somatic variants. Number of germline de novo SNVs and de novo indels derived from the father was significantly higher than that from the mother. On the other hand, there are significantly more clustered (within 20 kb) germline de novo mutations originating from the mother than from the father, while somatic mutations can be found in similar proportion from both parents. (b) Number of de novo SNVs found on the paternal allele increases with the age of father, but there is no correlation between the number of de novo SNVs found on the maternal allele and the age of the mother. (c) Distance between de novo mutations is shorter than expected for a subset of de novo mutations both between and within individuals. (d) Mutation rate is significantly higher than the background within 100 kb flanking the de novo CNVs.
Figure 2 Origins of de novo mutations in ASD. (a) Parent-of-origin of germline and somatic variants. Number of germline de novo SNVs and de novo indels derived from the father was significantly higher than that from the mother. On the other hand, there are significantly more clustered (within 20 kb) germline de novo mutations originating from the mother than from the father, while somatic mutations can be found in similar proportion from both parents. (b) Number of de novo SNVs found on the paternal allele increases with the age of father, but there is no correlation between the number of de novo SNVs found on the maternal allele and the age of the mother. (c) Distance between de novo mutations is shorter than expected for a subset of de novo mutations both between and within individuals. (d) Mutation rate is significantly higher than the background within 100 kb flanking the de novo CNVs.CC BY · PubMed Central
Figure 3 Functional impact of genome-wide damaging de novo mutations. (a) Damaging de novo mutations are significantly enriched in both coding (missense) and non-coding regions (splicingNeg, UTR3 and UTR5) in ASD compared with population controls. Definition of damaging tiers can be found in the Materials and methods. LOF, loss-of-function mutations; missense, missense mutations; splicingNeg, exon-skipping mutations predicted by SPANR; UTR, untranslated regions. Number of variants is indicated above each bar. Solid horizontal line indicates OR=1, and dash horizontal line represents P=0.05. (b) Non-coding de novo mutations in non-genic regions are significantly enriched in DNase I hypersensitive regions (DHS). Damaging de novo mutations predicted by ‘Deepbind loss; PCons>0’ are significantly enriched in ASD in general (All regions), but further enriched in DNase I hypersensitive regions and regions proximal to genes. DHS, DNase I hypersensitive sites; PCons, PhastCons; Tx, transcript. Number of variants is indicated above each bar. Solid horizontal line indicates OR=1, and dash horizontal line represents P=0.05. (c) Damaging de novo mutations are significantly enriched (false discovery rate ⩽0.25) in Gene Ontology defined pathways that are related to chromatin organisation, RNA processing and translation, synaptic transmission and others. Genes involved in the pathways are listed. Genes with DNMs in coding region are bolded. Asterisk represents gene that is found in more than one gene pathway.
Figure 3 Functional impact of genome-wide damaging de novo mutations. (a) Damaging de novo mutations are significantly enriched in both coding (missense) and non-coding regions (splicingNeg, UTR3 and UTR5) in ASD compared with population controls. Definition of damaging tiers can be found in the Materials and methods. LOF, loss-of-function mutations; missense, missense mutations; splicingNeg, exon-skipping mutations predicted by SPANR; UTR, untranslated regions. Number of variants is indicated above each bar. Solid horizontal line indicates OR=1, and dash horizontal line represents P=0.05. (b) Non-coding de novo mutations in non-genic regions are significantly enriched in DNase I hypersensitive regions (DHS). Damaging de novo mutations predicted by ‘Deepbind loss; PCons>0’ are significantly enriched in ASD in general (All regions), but further enriched in DNase I hypersensitive regions and regions proximal to genes. DHS, DNase I hypersensitive sites; PCons, PhastCons; Tx, transcript. Number of variants is indicated above each bar. Solid horizontal line indicates OR=1, and dash horizontal line represents P=0.05. (c) Damaging de novo mutations are significantly enriched (false discovery rate ⩽0.25) in Gene Ontology defined pathways that are related to chromatin organisation, RNA processing and translation, synaptic transmission and others. Genes involved in the pathways are listed. Genes with DNMs in coding region are bolded. Asterisk represents gene that is found in more than one gene pathway.CC BY · PubMed Central
Figure 4 Sample outliers identified by the Methylation Outlier Sample Test (MOST). (a) Sample 2-1276-003, carrying a de novo damaging heterozygous mutation at DNMT3A (p.R635W), was identified as an outlier sample in principle component (PC) 9 and 13. (b) Sample 2-0028-003, which carries a de novo frameshift mutation at ADNP (p.Q345fs), was identified as an outlier sample in PC9 and PC11 (and PC14 not shown). (c) Sample 2-1280-003 was identified as an outlier sample in PC 12. No de novo mutation in known epigene was found, but there is a maternal inherited rare damaging missense mutation at KMT5C (p.R205Q). (d) Functional enrichment of genes involved in the PC9 responsible for the sample outliers. Functions from negative loadings are in blue and that from positive loadings are in red.
Figure 4 Sample outliers identified by the Methylation Outlier Sample Test (MOST). (a) Sample 2-1276-003, carrying a de novo damaging heterozygous mutation at DNMT3A (p.R635W), was identified as an outlier sample in principle component (PC) 9 and 13. (b) Sample 2-0028-003, which carries a de novo frameshift mutation at ADNP (p.Q345fs), was identified as an outlier sample in PC9 and PC11 (and PC14 not shown). (c) Sample 2-1280-003 was identified as an outlier sample in PC 12. No de novo mutation in known epigene was found, but there is a maternal inherited rare damaging missense mutation at KMT5C (p.R205Q). (d) Functional enrichment of genes involved in the PC9 responsible for the sample outliers. Functions from negative loadings are in blue and that from positive loadings are in red.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 216

Related

See all

Metadata

Authors
Yuen, R. K., Merico, D., Cao, H., Pellecchia, G., Alipanahi, B., Thiruvahindrapuram, B., Tong, X., Sun, Y., Cao, D., Zhang, T., Wu, X., Jin, X., Zhou, Z., Liu, X., Nalpathamkalam, T., Walker, S., Howe, J. L., Wang, Z., MacDonald, J. R., Chan, A. J., D’Abate, L., Deneault, E., Siu, M. T., Tammimies, K., Uddin, M., Zarrei, M., Wang, M., Li, Y., Wang, J., Wang, J., Yang, H., Bookman, M., Bingham, J., Gross, S. S., Loy, D., Pletcher, M., Marshall, C. R., Anagnostou, E., Zwaigenbaum, L., Weksberg, R., Fernandez, B. A., Roberts, W., Szatmari, P., Glazer, D., Frey, B. J., Ring, R. H., Xu, X., & Scherer, S. W.
Affiliations
  • Hospital for Sick Children (CA)
  • BGI Group (China) (CN)
  • University of Toronto (CA)
  • Karolinska Institutet (SE)
  • Google (United States) (US)
  • Autism Speaks (US)
  • University of Alberta (CA)
  • Memorial University of Newfoundland (CA)
Corresponding author
Scherer, Stephen W
ORCID
Venue
npj Genomic Medicine
Publisher
Springer Science and Business Media LLC
Published
2016-08-03
Volume
1
Issue
1
Pages
160271-1602710
ISSN
2056-7944 (ISSN-L)
DOI
10.1038/npjgenmed.2016.27
PMC
PMC4980121
OpenAlex
W2484001856
Cited by
216
References
70
Citations by year
2026: 9 · 2025: 6 · 2024: 7 · 2023: 13 · 2022: 28 · 2021: 23 · 2020: 30 · 2019: 33 · 2018: 32 · 2017: 33 · 2016: 2
FWCI
21.21
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Genomic Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Autism Speaks
  • GlaxoSmithKline
  • Genome Canada
  • Ontario Brain Institute
  • National Alliance for Research on Schizophrenia and Depression
  • Stollery Children’s Hospital Foundation
  • Children's Hospital Foundation
  • Ontario Genomics
  • Government of Ontario
  • Ontario Genomics Institute
  • Sick Kids Foundation
  • University of Toronto
  • Vetenskapsrådet
  • Hospital for Sick Children
  • U.S. Public Health Service (140849-1)
  • Canadian Institutes of Health Research (140849_1)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Keywords
de novo mutations, autism spectrum disorder, germline mutations, somatic mutations, paternal age, clustered mutations, copy number variation, non-coding variants, untranslated regions, exon skipping, DNA methylation, DNMT3A, ADNP, mutation rate

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review