Meta-Analysis

. 2022 Sep;54(9):1320-1331.

doi: 10.1038/s41588-022-01104-0. Epub 2022 Aug 18.

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

Jack M Fu^#^{1

2

3}, F Kyle Satterstrom^#^{2

4

5}, Minshi Peng^#⁶, Harrison Brand^#^{1

2

3

7}, Ryan L Collins^{1

2

3

8}, Shan Dong⁹, Brie Wamsley¹⁰, Lambertus Klei¹¹, Lily Wang^{2

8}, Stephanie P Hao^{1

3

7}, Christine R Stevens^{2

4

5}, Caroline Cusick⁴, Mehrtash Babadi¹², Eric Banks¹², Brett Collins^{13

14

15}, Sheila Dodge¹⁶, Stacey B Gabriel¹⁶, Laura Gauthier¹², Samuel K Lee¹², Lindsay Liang⁹, Alicia Ljungdahl⁹, Behrang Mahjani^{13

14

17}, Laura Sloofman^{13

14

15}, Andrey N Smirnov¹², Mafalda Barbosa^{15

18}, Catalina Betancur¹⁹, Alfredo Brusco^{20

21}, Brian H Y Chung²², Edwin H Cook²³, Michael L Cuccaro²⁴, Enrico Domenici²⁵, Giovanni Battista Ferrero²⁶, J Jay Gargus²⁷, Gail E Herman²⁸, Irva Hertz-Picciotto²⁹, Patricia Maciel³⁰, Dara S Manoach³¹, Maria Rita Passos-Bueno³², Antonio M Persico³³, Alessandra Renieri^{34

35

36}, James S Sutcliffe^{37

38}, Flora Tassone^{29

39}, Elisabetta Trabetti⁴⁰, Gabriele Campos³², Simona Cardaropoli²⁶, Diana Carli²⁶, Marcus C Y Chan²², Chiara Fallerini^{34

35}, Elisa Giorgio²⁰, Ana Cristina Girardi³², Emily Hansen-Kiss⁴¹, So Lun Lee²², Carla Lintas⁴², Yunin Ludena²⁹, Rachel Nguyen²⁷, Lisa Pavinato²⁰, Margaret Pericak-Vance²⁴, Isaac N Pessah^{29

43}, Rebecca J Schmidt²⁹, Moyra Smith²⁷, Claudia I S Costa³², Slavica Trajkova²⁰, Jaqueline Y T Wang³², Mullin H C Yu²²; Autism Sequencing Consortium (ASC); Broad Institute Center for Common Disease Genomics (Broad-CCDG); iPSYCH-BROAD Consortium; David J Cutler⁴⁴, Silvia De Rubeis^{13

14

15

45}, Joseph D Buxbaum^{46

47

48

49

50

51}, Mark J Daly^{52

53

54

55

56

57}, Bernie Devlin⁵⁸, Kathryn Roeder^{59

60}, Stephan J Sanders⁶¹, Michael E Talkowski^{62

63

64

65

66}

Collaborators, Affiliations

Collaborators

Branko Aleksic, Mykyta Artomov, Elisa Benetti, Monica Biscaldi-Schafer, Anders D Børglum, Angel Carracedo, Andreas G Chiocchetti, Hilary Coon, Ryan N Doan, Montserrat Fernández-Prieto, Christine M Freitag, Sherif Gerges, Stephen Guter, David M Hougaard, Christina M Hultman, Suma Jacob, Miia Kaartinen, Alexander Kolevzon, Itaru Kushima, Terho Lehtimäki, Caterina Lo Rizzo, Nell Maltman, Marianna Manara, Gal Meiri, Idan Menashe, Judith Miller, Nancy Minshew, Matthew Mosconi, Norio Ozaki, Aarno Palotie, Mara Parellada, Kaija Puura, Abraham Reichenberg, Sven Sandin, Stephen W Scherer, Sabine Schlitt, Lauren Schmitt, Katja Schneider-Momm, Paige M Siper, Pål Suren, John A Sweeney, Karoline Teufel, Maria Del Pilar Trelles, Lauren A Weiss, Ryan Yuen

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
² Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁶ Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.
⁷ Pediatric Surgical Research Laboratories, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
⁸ Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
⁹ Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
¹⁰ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹¹ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹² Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹³ Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁴ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁵ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁶ Genomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁷ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
¹⁸ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁹ Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Institut de Biologie Paris Seine, Paris, France.
²⁰ Department of Medical Sciences, University of Torino, Turin, Italy.
²¹ Medical Genetics Unit, 'Città della Salute e della Scienza' University Hospital, Turin, Italy.
²² Department of Pediatrics and Adolescent Medicine, Duchess of Kent Children's Hospital, The University of Hong Kong, Hong Kong Special Administrative Region, China.
²³ Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
²⁴ The John P Hussman Institute for Human Genomics, The University of Miami Miller School of Medicine, Miami, FL, USA.
²⁵ Department of Cellular, Computational and Integrative Biology, , University of Trento, Trento, Italy.
²⁶ Department of Public Health and Pediatrics, University of Torino, Turin, Italy.
²⁷ Center for Autism Research and Translation, University of California Irvine, Irvine, CA, USA.
²⁸ The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
²⁹ MIND (Medical Investigation of Neurodevelopmental Disorders) Institute, University of California Davis, Davis, CA, USA.
³⁰ Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal.
³¹ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
³² Centro de Pesquisas sobre o Genoma Humano e Células tronco, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil.
³³ Interdepartmental Program 'Autism 0-90', 'Gaetano Martino' University Hospital, University of Messina, Messina, Italy.
³⁴ Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
³⁵ Medical Genetics, , University of Siena, Siena, Italy.
³⁶ Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
³⁷ Department of Molecular Physiology & Biophysics and Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA.
³⁸ Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA.
³⁹ Department of Biochemistry and Molecular Medicine, University of California Davis, School of Medicine, Sacramento, CA, USA.
⁴⁰ Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Verona, Italy.
⁴¹ Department of Diagnostic and Biomedical Sciences, University of Texas Health Science Center at Houston, School of Dentistry, Houston, TX, USA.
⁴² Service for Neurodevelopmental Disorders, University Campus Bio-medico of Rome, Rome, Italy.
⁴³ Department of Molecular Biosciences, University of California Davis, School of Veterinary Medicine, Davis, CA, USA.
⁴⁴ Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.
⁴⁵ Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴⁶ Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁷ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁸ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁹ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵⁰ Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵¹ Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁵ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁶ Harvard Medical School, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁷ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. mjdaly@atgu.mgh.harvard.edu.
⁵⁸ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. devlinbj@upmc.edu.
⁵⁹ Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
⁶⁰ Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
⁶¹ Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA. Stephan.Sanders@ucsf.edu.
⁶² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁴ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁵ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁶ Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.

^# Contributed equally.

PMID: 35982160
PMCID: PMC9653013
DOI: 10.1038/s41588-022-01104-0

Meta-Analysis

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

Jack M Fu et al. Nat Genet. 2022 Sep.

. 2022 Sep;54(9):1320-1331.

doi: 10.1038/s41588-022-01104-0. Epub 2022 Aug 18.

Authors

Collaborators

Branko Aleksic, Mykyta Artomov, Elisa Benetti, Monica Biscaldi-Schafer, Anders D Børglum, Angel Carracedo, Andreas G Chiocchetti, Hilary Coon, Ryan N Doan, Montserrat Fernández-Prieto, Christine M Freitag, Sherif Gerges, Stephen Guter, David M Hougaard, Christina M Hultman, Suma Jacob, Miia Kaartinen, Alexander Kolevzon, Itaru Kushima, Terho Lehtimäki, Caterina Lo Rizzo, Nell Maltman, Marianna Manara, Gal Meiri, Idan Menashe, Judith Miller, Nancy Minshew, Matthew Mosconi, Norio Ozaki, Aarno Palotie, Mara Parellada, Kaija Puura, Abraham Reichenberg, Sven Sandin, Stephen W Scherer, Sabine Schlitt, Lauren Schmitt, Katja Schneider-Momm, Paige M Siper, Pål Suren, John A Sweeney, Karoline Teufel, Maria Del Pilar Trelles, Lauren A Weiss, Ryan Yuen

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
² Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁶ Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.
⁷ Pediatric Surgical Research Laboratories, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
⁸ Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
⁹ Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
¹⁰ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹¹ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹² Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹³ Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁴ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁵ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁶ Genomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁷ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
¹⁸ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁹ Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Institut de Biologie Paris Seine, Paris, France.
²⁰ Department of Medical Sciences, University of Torino, Turin, Italy.
²¹ Medical Genetics Unit, 'Città della Salute e della Scienza' University Hospital, Turin, Italy.
²² Department of Pediatrics and Adolescent Medicine, Duchess of Kent Children's Hospital, The University of Hong Kong, Hong Kong Special Administrative Region, China.
²³ Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
²⁴ The John P Hussman Institute for Human Genomics, The University of Miami Miller School of Medicine, Miami, FL, USA.
²⁵ Department of Cellular, Computational and Integrative Biology, , University of Trento, Trento, Italy.
²⁶ Department of Public Health and Pediatrics, University of Torino, Turin, Italy.
²⁷ Center for Autism Research and Translation, University of California Irvine, Irvine, CA, USA.
²⁸ The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
²⁹ MIND (Medical Investigation of Neurodevelopmental Disorders) Institute, University of California Davis, Davis, CA, USA.
³⁰ Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal.
³¹ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
³² Centro de Pesquisas sobre o Genoma Humano e Células tronco, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil.
³³ Interdepartmental Program 'Autism 0-90', 'Gaetano Martino' University Hospital, University of Messina, Messina, Italy.
³⁴ Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
³⁵ Medical Genetics, , University of Siena, Siena, Italy.
³⁶ Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
³⁷ Department of Molecular Physiology & Biophysics and Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA.
³⁸ Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA.
³⁹ Department of Biochemistry and Molecular Medicine, University of California Davis, School of Medicine, Sacramento, CA, USA.
⁴⁰ Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Verona, Italy.
⁴¹ Department of Diagnostic and Biomedical Sciences, University of Texas Health Science Center at Houston, School of Dentistry, Houston, TX, USA.
⁴² Service for Neurodevelopmental Disorders, University Campus Bio-medico of Rome, Rome, Italy.
⁴³ Department of Molecular Biosciences, University of California Davis, School of Veterinary Medicine, Davis, CA, USA.
⁴⁴ Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.
⁴⁵ Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴⁶ Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁷ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁸ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁴⁹ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵⁰ Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵¹ Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joseph.buxbaum@mssm.edu.
⁵² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁵ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁶ Harvard Medical School, Boston, MA, USA. mjdaly@atgu.mgh.harvard.edu.
⁵⁷ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. mjdaly@atgu.mgh.harvard.edu.
⁵⁸ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. devlinbj@upmc.edu.
⁵⁹ Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
⁶⁰ Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
⁶¹ Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA. Stephan.Sanders@ucsf.edu.
⁶² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁴ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁵ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. MTALKOWSKI@mgh.harvard.edu.
⁶⁶ Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA. MTALKOWSKI@mgh.harvard.edu.

^# Contributed equally.

PMID: 35982160
PMCID: PMC9653013
DOI: 10.1038/s41588-022-01104-0

Abstract

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

PubMed Disclaimer

Conflict of interest statement

Competing interests

C.M.F. has been a consultant to Desitin and Roche and receives royalties for books on ASD, ADHD, and MDD. S.J.S. has been a consultant for, and receives funding for research from, BioMarin. J.D.B. and M.E.T. consult for BrigeBio Pharma. M.E.T. receives research funding and/or reagents from Illumina Inc., Levo Therapeutics, and Microsoft Inc. All other authors had no competing interests.

Figures

**Fig. 1 |. Overview of SNV/indel and CNV rates in ASD by mode of inheritance and constraint.**
a, The ASD cohort consisted of 49,049 family-based samples (15,036 cases) and 14,188 case-control samples (5,591 cases). One sample was a proband in one trio and a mother in another. b, The relative difference in PTV frequency between cases and unaffected controls (top) and average per sample variant count in unaffected controls (bottom) across inheritance classes (color) and LOEUF deciles (5,446 genes in top three deciles of LOEUF). Using a binomial test, cases were enriched for PTVs among the most constrained genes (lower LOEUF deciles), which weakened as negative selection against PTVs was relaxed (higher LOEUF deciles). c, Equivalent analyses were performed for missense variants annotated by MPC score and synonymous variants. Synonymous variants were not enriched in cases or controls, as evaluated via binomial tests. d, Benchmarking of the GATK-gCNV exome CNV discovery pipeline compared against WGS on overlapping samples achieved a sensitivity of 86% and PPV of 90% for rare CNVs (<1% site frequency) that spanned more than two captured exons (red line). e, The relative difference in variant frequency between cases and controls for deletions. Using binomial tests, we found that the enrichment of deletions (Del.) overlapping genes in the lowest LOEUF decile were stronger than PTVs in the same LOEUF deciles. f, Equivalent analysis for duplications (Dup.) demonstrated a similar pattern of enrichment compared with deletions but with more subtle relative differences. Statistical tests in b, c, e and f were two-sided binomial tests with 95% confidence interval error bars shown, P values (not corrected for multiple tests) and sample sizes are given in Supplementary Table 22.

**Fig. 2 |. Contribution of CNVs to ASD by mechanism and genomic location.**
a, CNVs included deletions (DEL) or duplications (DUP) of genomic segments and involved a subset of recurrent sites known as GD loci. GDs mediated by NAHR harbored recurrent breakpoints localized to flanking segmental duplications (Seg. Dup.), whereas non-NAHR GDs did not. b, De novo CNVs were highly enriched in affected cases compared with unaffected offspring (Fisher’s exact test) and the effect size was greater than that observed in de novo PTVs or de novo missense variants (logistic regression). c, ORs for de novo GD CNVs in probands compared with unaffected siblings, a subset of which have no observed de novo CNVs in unaffected individuals in this cohort (for example, 16p11.2 deletions, 15q11.2-q13 duplications). d, Analysis of all GDs (de novo and inherited) in ASD cases compared with GDs in a population-based cohort (UKBB) discovered using GATK-gCNV with identical parameters, with LOESS-smoothed bands of the 95% confidence interval of the OR in gray. e, Parent-of-origin analysis of de novo CNVs using binomial tests showed maternal bias for NAHR-mediated CNVs at GD regions, which was most pronounced for the 16p11.2 GD as previously described. Statistical tests in b and c were Fisher’s exact test with 95% confidence interval plotted as error bars, P values (not corrected for multiple tests) and sample sizes are located in Supplementary Table 22; statistical tests in d were Fisher’s exact test of carrier status in 13,786 unique ASD cases and 143,532 unique UK biobank controls, P values (not corrected for multiple tests) are located in Supplementary Table 10; statistical tests in e were binomial test with 95% confidence interval plotted as error bars, sample sizes and P values (not corrected for multiple tests) are located in Supplementary Table 22.

**Fig. 3 |. Integrating variant types and inheritance classes improves association power and reveals mutational biases within candidate genes.**
a, Our new implementation of the TADA model included de novo, case-control and rare inherited modules for each variant type: PTV, MisB, MisA, deletion and duplication. We leveraged information from ASD probands as well as unaffected siblings in evaluating the effect of de novo variants. b, The evidence of ASD association contributed by each variant type for each of the 72 ASD genes with FDR ≤ 0.001. Some genes were associated predominantly with missense variants and duplications (for example, *PTEN* and *SLC6A1*), suggesting mechanisms other than haploinsufficiency. c, Applying TADA to our aggregated ASD dataset yielded 72 genes at FDR ≤ 0.001, compared with 32 and 19 genes at the same threshold in previous studies on a subset of the samples^,. Our expanded TADA model improved the integration of available evidence of association and increased gene discovery at equivalent statistical thresholds on the same datasets. d, We quantified the relative contribution of variant class and mode of inheritance to these 72 ASD-associated genes, demonstrating that de novo PTVs and MisB variants represented the strongest contributions to the association signals. e, Association evidence (BF) was predominantly driven by de novo variants. The statistical test used in b was the extended TADA model.

**Fig. 4 |. Relative contribution of evidence types in ASD risk genes.**
a, The relative evidence of ASD association in the extended TADA model (log₁₀BF) for the 72 ASD risk genes (FDR ≤ 0.001) shown for likely loss-of-function mechanism (PTVs and deletions) on the x axis versus variants that may act via alternative mechanisms (missense variants and duplications) on the y axis. b, Plot of the relative association evidence from de novo (y axis) versus inherited (x axis) variation for the 72 ASD risk genes. c, Evidence for ASD association for the gene *PLXNA1* was derived from de novo and inherited missense variants localized to the Plexin domain at the C-terminus of the Plexin-A1 protein. Statistical test in c was the transmission disequilibrium test. Obs/exp, observed/expected.

**Fig. 5 |. Integration of ASD and DD datasets.**
We performed meta-analysis of the ASD cohort with the 31,058 DD trios reported in Kaplanis et al. (n = 46,094 combined NDD cases). a, Relative difference of de novo PTVs in ASD and DD cohorts across deciles of constraint as measured by LOEUF. b, Relative difference of de novo missense variants in DD and ASD cohorts across categories of MPC scores. c, To explore overlap in association evidence across ASD and DD risk genes, we considered the 477 TADA-DD genes with FDR ≤ 0.05. We evaluated their P value distributions converted from TADA-ASD FDRs and observed nonuniformity suggesting that, in aggregate, 70.1% of these genes also had evidence of association with ASD. d, In the complementary analysis of the 185 TADA-ASD genes with FDR ≤ 0.05, we looked at their P value distributions converted from TADA-DD FDRs and again observed high nonuniformity, suggesting that, in aggregate, 86.6% of these genes had evidence of association with DD. e, Using PTV and MisB variant data, we devised a chi-squared statistic, denoted the C statistic, to measure if a gene has more observed variants in one cohort relative to the other. A mixture model was used to deconvolve the commingled distributions. f, We transformed the fitted mixture distribution into posterior probability for ASD enrichment. Using a cutoff of <0.01, we found 82 DD-predominant genes, while using a cutoff of >0.99 we found 36 ASD-predominant genes. Statistical tests: a,b, two-sided binomial test, with 95% confidence interval error bars shown; P values (not corrected for multiple tests) and sample sizes located in Supplementary Table 22; c,d, R v.3.5.3 package limma_3.38.3; e,f, mixture model.

**Fig. 6 |. Single-cell data reveals differential neuronal layers impacted by ASD and DD genes.**
a, A UMAP plot visualization after integrating two studies^, that provided single-cell gene expression of human fetal brains consisting of 37,000 cortical cells at 6–27 weeks postconception. Similar cell types from the two batches were grouped together while preserving cells unique to either study. See Supplementary Table 17 for unabbreviated cell type labels and classifications to ‘progenitor’ and ‘neuron’ types. b, Both ASD- and DD-predominant genes (right and left, respectively) were found to be enriched in interneurons and excitatory neurons compared to glial cells. Compared with DD-predominant genes, ASD-predominant genes were relatively more neuron-enriched than progenitor-enriched. The developmental trajectory of excitatory neurons was approximately recapitulated in the UMAP, starting with OPC and other progenitor cells and ending with maturing upper-layer enriched and deep layer excitatory neurons. For interneurons, InCGE and InMGE were precursors to InN. Statistical test used in b was Fisher’s exact test.

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Transient peripheral blood transcriptomic response to ketamine treatment in children with ADNP syndrome.
Buxbaum Grice AS, Sloofman L, Levy T, Walker H, Ganesh G, Rodriguez de Los Santos M, Amini P, Buxbaum JD, Kolevzon A, Kostic A, Breen MS. Buxbaum Grice AS, et al. Transl Psychiatry. 2024 Jul 25;14(1):307. doi: 10.1038/s41398-024-03005-8. Transl Psychiatry. 2024. PMID: 39054328 Free PMC article.
Context-dependent hyperactivity in syngap1a and syngap1b zebrafish models of SYNGAP1-related disorder.
Sumathipala SH, Khan S, Kozol RA, Araki Y, Syed S, Huganir RL, Dallman JE. Sumathipala SH, et al. Front Mol Neurosci. 2024 Jul 10;17:1401746. doi: 10.3389/fnmol.2024.1401746. eCollection 2024. Front Mol Neurosci. 2024. PMID: 39050824 Free PMC article.
Rare de novo damaging DNA variants are enriched in attention-deficit/hyperactivity disorder and implicate risk genes.
Olfson E, Farhat LC, Liu W, Vitulano LA, Zai G, Lima MO, Parent J, Polanczyk GV, Cappi C, Kennedy JL, Fernandez TV. Olfson E, et al. Nat Commun. 2024 Jul 12;15(1):5870. doi: 10.1038/s41467-024-50247-7. Nat Commun. 2024. PMID: 38997333 Free PMC article.
A de novo ARIH2 gene mutation was detected in a patient with autism spectrum disorders and intellectual disability.
Vinci M, Treccarichi S, Galati Rando R, Musumeci A, Todaro V, Federico C, Saccone S, Elia M, Calì F. Vinci M, et al. Sci Rep. 2024 Jul 9;14(1):15848. doi: 10.1038/s41598-024-66475-2. Sci Rep. 2024. PMID: 38982159 Free PMC article.

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References

1. Maenner MJ et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveill. Summ 70, 1–16 (2021). - PMC - PubMed
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1. Grove J et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet 51, 431–444 (2019). - PMC - PubMed
1. Satterstrom FK et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020). - PMC - PubMed
1. Kaplanis J et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020). - PMC - PubMed

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Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

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Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

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