Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder
Open accessAbstract
INTRODUCTION: The DNA of protein-coding genes is transcribed into mRNA, which is translated into proteins. The “coding genome” describes the DNA that contains the information to make these proteins and represents ~1.5% of the human genome. Newly arising de novo mutations (variants observed in a child but not in either parent) in the coding genome contribute to numerous childhood developmental disorders, including autism spectrum disorder (ASD). Discovery of these effects is aided by the triplet code that enables the functional impact of many mutations to be readily deciphered. In contrast, the “noncoding genome” covers the remaining ~98.5% and includes elements that regulate when, where, and to what degree protein-coding genes are transcribed. Understanding this noncoding sequence could provide insights into human disorders and refined control of emerging genetic therapies. Yet little is known about the role of mutations in noncoding regions, including whether they contribute to childhood developmental disorders, which noncoding elements are most vulnerable to disruption, and the manner in which information is encoded in the noncoding genome. RATIONALE: Whole-genome sequencing (WGS) provides the opportunity to identify the majority of genetic variation in each individual. By performing WGS on 1902 quartet families including a child affected with ASD, one unaffected sibling control, and their parents, we identified ~67 de novo mutations across each child’s genome. To characterize the functional role of these mutations, we integrated multiple datasets relating to gene function, genes implicated in neurodevelopmental disorders, conservation across species, and epigenetic markers, thereby combinatorially defining 55,143 categories. The scope of the problem—testing for an excess of de novo mutations in cases relative to controls for each category—is challenging because there are more categories than families. RESULTS: Comparing cases to controls, we observed an excess of de novo mutations in cases in individual categories in the coding genome but not in the noncoding genome. To overcome the challenge of detecting noncoding association, we used machine learning tools to develop a de novo risk score to look for an excess of de novo mutations across multiple categories. This score demonstrated a contribution to ASD risk from coding mutations and a weaker, but significant, contribution from noncoding mutations. This noncoding signal was driven by mutations in the promoter region, defined as the 2000 nucleotides upstream of the transcription start site (TSS) where mRNA synthesis starts. The strongest promoter signals were defined by conservation across species and transcription factor binding sites. Well-defined promoter elements (e.g., TATA-box) are usually observed within 80 nucleotides of the TSS; however, the strongest ASD association was observed distally, 750 to 2000 nucleotides upstream of the TSS. CONCLUSION: We conclude that de novo mutations in the noncoding genome contribute to ASD. The clearest evidence of noncoding ASD association came from mutations at evolutionarily conserved nucleotides in the promoter region. The enrichment for transcription factor binding sites, primarily in the distal promoter, suggests that these mutations may disrupt gene transcription via their interaction with enhancer elements in the promoter region, rather than interfering with transcriptional initiation directly. Promoter regions in autism.: De novo mutations from 1902 quartet families are assigned to 55,143 annotation categories, which are each assessed for autism spectrum disorder (ASD) association by comparing mutation counts in cases and sibling controls. A de novo risk score demonstrated a noncoding contribution to ASD driven by promoter mutations, especially at sites conserved across species, in the distal promoter or targeted by transcription factors.
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Genome sequencing leaves most ASD cases genetically unsolved.
“even its application leaves the majority of ASD cases “genetically unsolved”2,19–21.”
From Genome-wide detection of tandem DNA repeats that are expanded in autism (2020) · cites this paper as [19]
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- Authors
- An, J.-Y., Lin, K., Zhu, L., Werling, D. M., Dong, S., Brand, H., Wang, H. Z., Zhao, X., Schwartz, G. B., Collins, R. L., Currall, B. B., Dastmalchi, C., Dea, J., Duhn, C., Gilson, M. C., Klei, L., Liang, L., Markenscoff-Papadimitriou, E., Pochareddy, S., Ahituv, N., Buxbaum, J. D., Coon, H., Daly, M. J., Kim, Y. S., Marth, G. T., Neale, B. M., Quinlan, A. R., Rubenstein, J. L., Sestan, N., State, M. W., Willsey, A. J., Talkowski, M. E., Devlin, B., Roeder, K., & Sanders, S. J.
- Affiliations
- University of California, San Francisco (US)
- Carnegie Mellon University (US)
- Broad Institute (US)
- Massachusetts General Hospital (US)
- Harvard University (US)
- University of Pittsburgh (US)
- Yale University (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of Utah (US)
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA.
- Corresponding author
- An, Joon-Yong
- Lin, Kevin
- Zhu, Lingxue
- Werling, Donna M.
- Talkowski, Michael E.
- Devlin, Bernie
- Roeder, Kathryn
- Sanders, Stephan J.
- ORCID
- An, Joon-Yong
- Lin, Kevin
- Zhu, Lingxue
- Werling, Donna M.
- Dong, Shan
- Brand, Harrison
- Zhao, Xuefang
- Schwartz, Grace B.
- Collins, Ryan L.
- Currall, Benjamin B.
- Dastmalchi, Claudia
- Dea, Jeanselle
- Duhn, Clif
- Gilson, Michael C.
- Klei, Lambertus
- Liang, Lindsay
- Markenscoff-Papadimitriou, Eirene
- Pochareddy, Sirisha
- Ahituv, Nadav
- Buxbaum, Joseph D.
- Coon, Hilary
- Daly, Mark J.
- Kim, Young Shin
- Marth, Gabor T.
- Neale, Benjamin M.
- Quinlan, Aaron R.
- Rubenstein, John L.
- Sestan, Nenad
- State, Matthew W.
- Willsey, A. Jeremy
- Talkowski, Michael E.
- Devlin, Bernie
- Roeder, Kathryn
- Sanders, Stephan J.
- Venue
- Science
- Published
- 2018-12-14
- Volume
- 362
- Issue
- 6420
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6432922
- OpenAlex
- W2903631495
- Cited by
- 316
- References
- 55
- Citations by year
- 2026: 20 · 2025: 34 · 2024: 45 · 2023: 28 · 2022: 41 · 2021: 63 · 2020: 53 · 2019: 30
- FWCI
- 21.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (U01 MH105575, U01 MH100239-03S1, R01 MH110928, R37 MH057881, R01 MH094400, R01 MH109901, R01 MH049428, U01 MH111661, R01 MH115957)
- Simons Foundation (385027, 346042, 402281, 513631, 388196, 385110, 573206, 575097, 574598)
- National Human Genome Research Institute (R01 HG006693, R01 HG009141)
- National Institute of General Medical Sciences (R01 GM124355)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD081256)
- Beatrice and Samuel A. Seaver Foundation
- 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 › Genomics and Rare Diseases
- Keywords
- autism spectrum disorder, de novo mutations, promoter polymorphism, non-coding genome, transcription start sites, transcription factor binding sites, TATA box, species conservation, epigenetic modifications, neurodevelopmental disorders, machine learning, genetic risk factors, gene expression regulation, model interpretability, chromatin accessibility
- MeSH terms
- Autism Spectrum Disorder, Binding Sites, DNA Mutational Analysis, Humans, Mutation, Pedigree, Promoter Regions, Genetic, Risk, Transcription Factors, Genetic Variation, Conserved Sequence, Genetic Loci
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 ›
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