The past decade has seen remarkable progress in identifying genes that, when impacted by deleterious coding variation, confer high likelihood for autism spectrum disorder (ASD), intellectual disability and other associated developmental disorders.
The past decade has seen remarkable progress in identifying genes that, when impacted by deleterious coding variation, confer high likelihood for autism spectrum disorder , intellectual disability and other associated developmental disorders.
However, most underlying gene discovery efforts have focused on individuals of European ancestry, limiting insights into genetic liability across diverse populations. To help address this, the Genomics of Autism in Latin American Ancestries Consortium was formed, presenting here the largest sequencing study of autism in Latin American individuals . We identified 35 genome-wide significant for 38 complex traits are largely similar across local ancestries, in agreement with other studies, including a recent analysis showing thatWe considered whether the observed similarity in deleterious variant burden between AMR-assigned and EUR-assigned individuals could reflect the influence of EUR admixture within AMR genomes. In principle, local ancestry inference would allow mapping of individual variants to ancestral tracts, enabling a more granular test of whether such variants preferentially arise on EUR versus non-EUR backgrounds. However, current LAI methods require dense haplotypic data across the genome, typically from WGS. The sparse and uneven coverage of exome data poses considerable challenges for LAI, and performance has been shown to decline substantially in this context. Moreover, because much of our gene discovery relies on de novo rather than inherited variants, the signal is unlikely to be biased by local ancestry tracts, and we also confirmed that variant and gene discovery is clearly driven by the large proportion of individuals with modest overall EUR ancestry . Still, we acknowledge that this is a potential limitation of the study and a valuable direction for future work in cohorts with whole-genome data where LAI can be reliably determined. Using clinical genetics software platforms, we confirm the overall translatability of clinical genetic approaches when focusing on rare deleterious variation; however, we also reveal differences in the rate of P/LP variants between AMR and non-AMR individuals and between EUR and non-EUR individuals. The causes driving differences in rates of P/LP need to be better understood, as this is a limitation that complicates the interpretation of our analyses. A recent study focusing on pediatric patients with serious neurologic, cardiac or immunologic conditions reported similar diagnostic yield for genome sequencing in European Americans and Latin Americans ; however, yields were lower and inconclusive results were higher in African Americans. In that study, genome sequencing was carried out by commercial diagnostic laboratories, making use of a proprietary pipeline that incorporates variant databases; the degree to which proprietary algorithms and the degree to which reliance on previously observed variation influenced the higher rate of inconclusive results cannot be determined. Analysis of pathogenic variation in the All of Us Research Program, which integrates data from a diverse cohort to identify genetic differences across ancestries, further highlights the disparities in variant classification across populations. The study examined P/LP variants in a modest number of genes with actionable findings, showing differences as a function of ancestry, with 42% fewer pathogenic variants identified in Latin American versus EUR individuals . Neptune relies heavily on variants identified in prior curated data, which will bias the findings in diverse populations. Consistent with this, analyses of the GALA cohort using Neptune show lower rates of findings compared to non-AMR samples. Our results suggest that with a focus on deleterious de novo variation, use of prior results is less necessary, and others have shown that even highly curated variant databases include false-positive findings that can lead to incorrect information to subsequent families. Where possible, we recommend minimizing reliance on previously reported pathogenic variants. In addition, to further improve genetic testing results across diverse populations, our results show that it is of key importance to use allele frequency from all relevant populations, as we have done here. We should, however, recognize the limitations inherent in our study and in any study that focuses on ancestries beyond EUR and a few other commonly characterized populations. For instance, we focused on de novo variants and their interpretation in AMR populations. Variants called de novo in our sample, and within subjects, are likely a mixture of true and false positives. For populations not deeply characterized for genetic variation, it is reasonable to expect elevation in the false-positive rate, simply because we do not know the frequencies of variants therein and which variants are relatively more common. For this reason, more of the variation called de novo is likely to be inherited variation., elevate false negatives within these populations, including genomic variation important for phenotypes like autism, which is another limitation of our study. The combination of these three quantities—true positives, false positives and false negatives—determines the total variation that we observe. Based on our results, which show similar patterns to those observed in EUR studies, we can conclude that the vast majority of our results arise from true positives. Nonetheless, we should not conclude that populations are all the same when it comes to calling de novo variation. Indeed, we can be confident that they are not, given what we know about increased genetic diversity in African populationsand the impact that cryptic structural variation and singleton events have on the reliability of calling ultra-rare variation. Only through deeper genetic studies can we expect completely comparable results to those of EUR population samples, ameliorating the above issues. In conclusion, our observations are consistent with the neurobiology of autism being shared across ancestries and provide support for the translatability of autism clinical genetic approaches across ancestries.GALA comprises multiple sites from North, Central and South America recruiting AMR participants for studies on the genetic architecture of autism. Study procedures were approved by the institutional review board of the Program for the Protection of Human Subjects at Mount Sinai . Informed consent was obtained from the parents or legal guardians of all study participants. Study procedures for participant enrollment were approved by the Program for the Protection of Human Subjects at Mount Sinai , the University of California, Davis IRB and the University of Miami IRB . Two cohorts were collected previously: study procedures for participant enrollment in Costa Rica were approved under the guidelines of the Ministry of Health of Costa Rica, the Ethical Committee of the National Children’s Hospital in San Jose and the IRB at Mount Sinai, as described previously; and The Autism Simplex Collection , which included an estimated 12% of individuals of Latin American ancestry, was recruited across 13 sites in North America and Europe, as described previously, with local IRB oversight and all consents reviewed before depositing biospecimens and data to the National Institutes of Health repository. For clarity, we use ‘ASD’ to refer to individuals who received a clinical diagnosis according to the procedure outlined below and ‘autism’ elsewhere. ASD diagnoses are based on expert clinical evaluations using, 5th Edition criteria, incorporating all available data, including standardized assessments. Participants can be any age. Individuals with a known genetic condition are excluded from analyses. Once a diagnosis of ASD is confirmed, the individual and their parents contribute a sample for genetic analyses. If both parents are not available, collection of other biological family members is encouraged . Participating sites generally also collect additional clinical and family history information.The Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, located in New York City, is the main coordinating site within the GALA Consortium. AMR individuals make up almost 30% of the population of New York City. Affected individuals undergo a full diagnostic ASD workup and receive additional assessments, including a cognitive test, adaptive behavior measure, medical checklist and behavioral checklists. Participating families receive $100 USD in compensation.The Human Genome and Stem Cell Research Center at the Universidade de São Paulo in Brazil has over 20 years of experience in clinical and molecular research in autism, with more than 2,000 families seen. Brazil has a multiethnic admixed population, including African and Amerindian ancestry. The HUG-CELL conducts research in human and medical genetics of rare diseases, providing genetic counseling services and genetic tests for the population. A team of psychiatrists, psychologists and neurologists completes a formal ASD diagnostic workup prior to obtaining samples for genetic testing from the individual and their family members. Financial compensation for participation is not permitted at this site; however, individuals who meet clinical criteria are offered free fragile X testing.The Centro de Investigaciones Genéticas en Enfermedades Humanas at the Universidad de los Andes in Bogotá, Colombia, in close collaboration with the Instituto Colombiano del Sistema Nervioso, Clínica Montserrat, focuses on unraveling the prevalence and characteristics of autism within the Colombian population. Through ASD referrals, the impact of CIGEn extends beyond Bogotá, reaching out to other cities throughout Colombia , with the aim of including families from diverse backgrounds. Financial compensation is not offered for participation.The Children’s Psychiatric Hospital ‘Juan N. Navarro’ , which is part of the Psychiatric Care Services of the Mexican Government’s Ministry of Health, provides professional care for minors with mental health, psychiatric and behavioral problems. As the largest teaching center in child and adolescent psychiatry in Mexico, it performs diverse biomedical and clinical research activities. One of the main lines of research focuses on autism, in collaboration with the Genetics Department at the National Institute of Psychiatry Ramón de la Fuente Muñíz . The samples from Mexico are being sequenced and were not included in the current analyses. Financial compensation is not provided at this site, in accordance with ethics committee requirements.The Centro Ann Sullivan del Perú is a non-profit center in Lima, Peru, that serves individuals with varying abilities and their families. The center specializes in helping individuals with ASD. GALA investigators from the Seaver Autism Center traveled to Lima to perform 40 psychiatric evaluations, aid in ASD diagnostics and collect blood samples from individuals with ASD and their families. Behavioral surveys were carried out for all participants, and ASD and attention-deficit/hyperactivity disorder diagnoses were made using DSM-5 criteria. Financial compensation was not offered; instead, participating individuals received their clinical evaluation results.The Childhood Autism Risks from Genetics and the Environment cohort is a population-based case−control study collected in California at the University of California, Davis, Center for Children’s Environmental Health laboratories with the intent of addressing the impact of environmental exposures on riskThe John P. Hussman Institute for Human Genomics at the University of Miami, located in Miami, Florida, recruits families through clinical referrals and lay organizations, providing services to families with ASD. Upwards of 70% of the Miami population identifies as AMR. The diagnostic workup included the Autism Diagnostic Interview-Revised and assessment of adaptive behavior. Discrepancies between ADI-R and clinical findings were resolved using additional clinical measures, including the Autism Diagnostic Observation Schedule .The founder population of the Central Valley of Costa Rica originated at the end of the 16th century from the intermarriage of 86 Spanish families and Indigenous Americans. The population was geographically isolated until the late 19th century; therefore, the current inhabitants are estimated to descend from fewer than 1,000 founders. A genetic study on autism in the CVCR was initiated in 2003, and affected individuals were ascertained using the translated Spanish versions of the ADI-R and the ADOS as well as assessment of intellectual abilities and adaptive behaviorTASC was a collaboration among 13 sites in North America and Western Europe funded by the National Alliance for Autism Research, now Autism Speaks, and the National Institute of Mental Health. As detailed previously, more than 1,700 individuals with ASD confirmed with extensive prospective assessment, as well as additional family members including parents, completed this study. Individuals within this study were sequenced, and those who were of AMR ancestry were included in these analyses.The Autism Research Program at the Kaiser Permanente Northern California Division of Research was established in 2002 by Senior Research Scientist Lisa Croen. The program focuses on research identifying genetic and environmental factors associated with autism and understanding patterns of detection, diagnosis and utilization of health services for individuals with ASD across the lifespan. The ARP created the Autism Family Biobank, a repository including genetic, medical and environmental information from more than 1,000 individuals with ASD and their two biological parents, who donated blood or saliva between 2015 and 2017. This collection is representative of the diverse population served by KPNC, an integrated healthcare system. The samples from Kaiser Permanente are being sequenced and were not included in the current analyses. Participants receive $15 USD per biospecimen, and families receive an additional $15 USD upon completion of the parent surveys.; and additional AMR samples from the new release of SPARK . The current freeze includes trio data from 14,359 AMR samples, including 4,450 affected individuals and 1,459 typically developing siblings and case−control data from 267 cases and 801 controls. https://gnomad.broadinstitute.org/news/2021-09-using-the-gnomad-ancestry-principal-components-analysis-loadings-and-random-forest-classifier-on-your-dataset/ ). Specifically, each of three jointly called datasets, derived from unpublished GALA sequencing, Fu et al. and SPARK , was merged with the Human Genome Diversity Project + 1000 Genomes Project subset of gnomAD, and principal component analysis was performed in the joint dataset after they had been restricted to 5,000 ancestry-informative single-nucleotide polymorphisms. A random forest classifier was trained on the HGDP + 1KG reference samples using the first 10 principal components and used to assign superpopulation/continental ancestry to individuals in our dataset. AMR ancestry classification was based on the predicted ancestry label assigned by the random forest model. Non-AMR cases included any individuals with ASD in ASC or SPARK releases who did not meet our criteria for genetically inferred AMR ancestry . Hail 0.2 was used to process the SPARK and unpublished GALA joint-genotyped variant call files . Multiallelic sites were split; variants were annotated using the Variant Effect Predictor ) were removed. Hail’s pc_relate function was used to confirm reported pedigrees and identify duplicate samples within and between datasets, which were removed. Sex was imputed using the impute_sex function, and genotype filters were applied as described in previous methodology) with variant frequencies from the non-neuro subset of gnomAD exomes version 2.1.1 as priors. Potential de novo variants were dropped if they were present at a frequency greater than 0.1% within the non-neuro subset of gnomAD version 2.1.1, gnomAD version 3.1.2, in any subpopulation of these gnomAD datasets or the dataset in which they were called. Variants were further excluded if they had ‘ExcessHet’ in the Filters field, exhibited a proband allele balance 6 in the total parents of the dataset were excluded as well. Hard filtering was applied according to GATK recommendations . Final counts of transmitted and non-transmitted alleles were produced for PTV, MisB, MisA . Trio and case−control datasets were analyzed separately, and GATK-gCNVwas used to detect CNVs. First, raw CRAM files were compressed into read counts that covered the annotated exons to serve as input data. Then, a PCA-based approach that combines density and distance-based clustering was employed on the observed read counts to organize batches of samples for parallel processing. GATK-gCNV was run on cohort mode analysis for 200 samples within the cluster identified through PCA, and the remaining samples were subjected to GATK-gCNV analysis using the case mode, with models specific to the cohort . For quality control, CNV calls were processed according to Fu et al.methodology; CNVs were retained if they had an allele frequency< 1% that spanned more than two captured exons. For homozygous deletions, the quality score threshold was set to the lesser of 400 or 10 times the number of intervals. For heterozygous deletions, the quality score threshold was set to the lesser of 100 or 10 times the number of intervals. For duplications, the quality score threshold was set to the lesser of 50 or four times the number of intervals. For sample-level quality control, samples were retained if the number of raw, autosomal CNV calls detected by GATK-gCNV did not exceed 200 and if the number of calls with quality score ≥ 20 did not exceed 35. After quality control, 291 probands, 25 typically developing siblings, 209 cases and 735 controls remained. A gene was considered impacted by a deletion if at least 10% of its non-redundant exons were overlapped by the deletion. For a duplication, a gene was considered impacted if at least 75% of its non-redundant exons were overlapped. Additionally, CNVs were annotated against a list of 79 curated genomic disorder loci , and a CNV call was classified as a genomic disorder CNV if it shared at least 50% reciprocal overlap with an annotated genomic disorder.was performed for three types of inheritance classes: de novo and duplication ), inherited and case−control variation. CNVs resulting from non-allelic homologous recombination were excluded, and only CNVs impacting fewer than nine constrained genes were retained as described, accounting for sample size and directly using relative risk priors from Fu et al. directly . Previously published mutation rates were adjusted to align with the observed variant counts in unaffected siblings for each variant type in the dataset, we compared the observed numbers of variants in the GALA cohort with the expected number of variants derived from TADA analysis in Fu. Although observed and expected counts may vary at the individual gene level, as expected for ultra-rare events, the overall observed and expected totals across all genes are well matched, supporting the consistency of signal with expectation ; this compares to 28,262 variants among the 13,030 non-AMR family-based cases, of which 20,750 were classified by Neptune. In AMR participants, 136 variants were classified as P/LP, representing 1.12% of all variants in these genes and 1.60% of all classified variants. In non-AMR participants, 344 variants were classified as P/LP, representing 1.22% of all variants and 1.66% of all classified variants. Examining the results from the perspective of the participants, in AMR we observed 2.73 variants in these genes per individual, of which 1.91 per individual could be classified by Neptune, and 0.031 per individual were classified as P/LP. The corresponding numbers were 2.17 variants, 1.59 Neptune classified variants and 0.026 P/LP variants per non-AMR individual. The results show that, on the variant level, the differences in AMR versus non-AMR participants trace in part to a reduced ability of Neptune to classify non-AMR variants . However, as also noted above, there are more variants per AMR participant , leading to an apparent lessening of impact in terms of P/LP variants per individual.As noted above, for genetic association analyses, the TADA framework was limited to autosomal genes with available mutation rates and LOEUF scores and considered only missense variants with an MPC score ≥ 1. By contrast, the clinical interpretation of variants included all autosomal or X-linked protein-truncating, synonymous and missense variants, regardless of gene annotation. In addition to applying the allele frequency cutoff of 0.1% , X-linked variants were subjected to an allele frequency cutoff of 0.1% in the male non-psychiatric subsets of gnomAD versions 2.1.1 and 3.1.2 and their subpopulations. This resulted in 20,571 de novo variants being included for clinical genetics annotation. Inherited variant analysis was restricted to a list of well-established X-linked genes implicated in autism and/or intellectual disability and/or with a broader NDD phenotype , without knowing the full spectrum of non-autism phenotypes in the participants. Hence, the results presented here ) as well as the local IRBs in Brazil, Colombia, Peru, Mexico, Kaiser Permanente and the CHARGE study . Written informed consent was obtained from all participants or from parents or legal guardians where necessary. Data collection adhered to relevant ethical and cultural standards, and compensation for participation varied by site as described above. Collaborations between institutions in the United States and Latin America were established to ensure equitable contributions across sites. Local investigators in Brazil, Colombia, Mexico and Perú were involved in data collection and authorship. Sex was recorded based on self-report at enrollment and confirmed with genetic information. Both male and female participants were included; however, sex-stratified analyses were not conducted, as the primary focus of this study was on de novo and rare variant burden across ancestry groups rather than sex differences. Participant ages varied by cohort, with probands typically enrolled during childhood or adolescence and parents as adults.All statistical analyses were performed using R , Hail and Python . Statistical methods are described in detail in the relevant sections of. Two-sided tests were used throughout unless otherwise specified. Multiple hypothesis testing was corrected using the Benjamini–Hochberg FDR procedure or Bonferroni correction as appropriate. Sample sizes were determined by the number of available participants meeting inclusion criteria in the ASC, GALA and SPARK cohorts; no statistical method was used to predetermine sample size. All available samples passing relatedness and quality control thresholds were included in the analyses. No data were otherwise excluded from the analyses. Because this study involved secondary analysis of existing human genomic data, randomization and blinding were not applicable. The investigators were not blinded to sample status during analyses. Scripts for computational analyses performed were deposited in a GitHub repository and on the National Human Genome Research Institute Genomic Data Science Analysis, Visualization and Informatics Lab-space under accession number phs002502.v1.p1 . All requests will be reviewed by the Mount Sinai Institutional Data Access Committee to ensure compliance with participant consent and IRB protocols. Reasonable requests will receive a response within 2−4 weeks. Summary variant counts, gene-level burden statistics and figure source data are available in the accompanyingAll software used in this study is publicly available at the cited references. The R code used to generate the TADA analysis and figures is available under the MIT license atZhou, X. et al. Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes.Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism.Abul-Husn, N. S. et al. Molecular diagnostic yield of genome sequencing versus targeted gene panel testing in racially and ethnically diverse pediatric patients.Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples.DeFelice, M. et al. Blended genome exome as a cost efficient alternative to deep whole genomes or arrays. Preprint atGoogle Scholar He, X. et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes.Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Miller, D. T. et al. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics .Hou, K. et al. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals.Saitou, M., Dahl, A., Wang, Q. & Liu, X. Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines.Maples, B. K., Gravel, S., Kenny, E. E. & Bustamante, C. D. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.Ciesielski, T. H., Sirugo, G., Iyengar, S. K. & Williams, S. M. Characterizing the pathogenicity of genetic variants: the consequences of context.Sharo, A. G., Zou, Y., Adhikari, A. N. & Brenner, S. E. ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden.Gomez, F., Hirbo, J. & Tishkoff, S. A. Genetic variation and adaptation in Africa: implications for human evolution and disease.McInnes, L. A. et al. A genetic study of autism in Costa Rica: multiple variables affecting IQ scores observed in a preliminary sample of autistic cases.McInnes, L. A. et al. The NRG1 exon 11 missense variant is not associated with autism in the Central Valley of Costa Rica.Mathews, C. A. et al. Genetic studies of neuropsychiatric disorders in Costa Rica: a model for the use of isolated populations.Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.Miller, D. T. et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics . GALA is currently supported by the National Institutes of Health , the Seaver Autism Center for Research and Treatment and the SWT and Seaver Foundations. GALA originated with sites from, and with support of, the ASC . ASC sites continue to support analyses of GALA studies, with additional analyses supported by MH128813. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by Clinical and Translational Science Awards grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this paper was also supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This study makes use of data generated by the DECIPHER community. A full list of centers that contributed to the generation of the data is available fromand via email from contact@deciphergenomics.org. DECIPHER is hosted by the EMBL-EBI, and funding for the DECIPHER project was provided by the Wellcome Trust . Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA Marina Natividad Avila, Seulgi Jung, Tess Levy, Laura G. Sloofman, Thariana Pichardo, Dalia Marquez, Alexander Kolevzon, Paige M. Siper, Silvia De Rubeis, Jennifer Foss-Feig, Erina Hara, Danielle Halpern, Yi Li, Catherine Sancimino, Renee Soufer, Jessica Zweifach, Brett Collins, Abraham Reichenberg, Sven Sandin, Laura Sloofman, Behrang Mahjani, Silvia De Rubeis & Joseph D. BuxbaumMarina Natividad Avila, Seulgi Jung, Tess Levy, Laura G. Sloofman, Thariana Pichardo, Dalia Marquez, Alexander Kolevzon, Paige M. Siper, Silvia De Rubeis, Jennifer Foss-Feig, Erina Hara, Danielle Halpern, Yi Li, Catherine Sancimino, Renee Soufer, Jessica Zweifach, Brett Collins, Abraham Reichenberg, Sven Sandin, Laura Sloofman, Behrang Mahjani, Silvia De Rubeis & Joseph D. Buxbaum Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Marina Natividad Avila, Seulgi Jung, Laura G. Sloofman, Mafalda Barbosa, Laura Sloofman, Behrang Mahjani & Joseph D. BuxbaumMarina Natividad Avila, Seulgi Jung, Tess Levy, Laura G. Sloofman, Thariana Pichardo, Silvia De Rubeis, Laura Sloofman, Silvia De Rubeis & Joseph D. BuxbaumMarina Natividad Avila, Seulgi Jung, Laura G. Sloofman, Laura Sloofman & Joseph D. Buxbaum The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Marina Natividad Avila, Seulgi Jung, Tess Levy, Laura G. Sloofman, Thariana Pichardo, Paige M. Siper, Silvia De Rubeis, Jennifer Foss-Feig, Mafalda Barbosa, Brett Collins, Abraham Reichenberg, Laura Sloofman, Behrang Mahjani, Silvia De Rubeis & Joseph D. BuxbaumF. Kyle Satterstrom, Jack M. Fu, Christine R. Stevens, Mykyta Artomov, Harrison Brand, Ryan L. Collins, Sherif Gerges, Aarno Palotie, Michael E. Talkowski & Mark J. DalyF. Kyle Satterstrom, Christine R. Stevens, Caroline M. Cusick, Mykyta Artomov, Sherif Gerges, Michael E. Talkowski & Mark J. Daly Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USACenter for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USADepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USADepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADivision of Research, Kaiser Permanente Northern, Pleasanton, CA, USACentro de Estudos do Genoma Humano e Células-Tronco, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brasil Gabriele S. Campos, Claudia I. S. Costa, Ana Cristina D. E. S. Girardi, Naila Lourenço, Jaqueline Y. T. Wang & Maria Rita Passos-BuenoRoberto Chaskel, Andrea del Pilar Lopez & Andrea del Pilar LopezRoberto Chaskel, Magdalena Fernandez & Eugenio Ferro John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USAThe Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USAFacultad de Ciencias, Universidad de los Andes, Bogotá, ColombiaMIND Institute, University of California, Davis, Davis, CA, USA Yunin Ludena, Isaac Pessah, Rebecca Schmidt, Irva Hertz-Picciotto, Flora Tassone, Isaac N. Pessah & Rebecca J. SchmidtRosa Oyama, Lizbeth Tolentino & Liliana MayoDepartamento de Genética, Subdirección de Investigaciones Clínicas, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz México, Ciudad de México, MexicoProgram in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USABehrang Mahjani The Alper Center for Neural Development and Regeneration, Icahn School of Medicine at Mount Sinai, New York, NY, USACatalina BetancurBranko Aleksic, Andreas G. Chiocchetti, Christine M. Freitag, Sabine Schlitt, Katja Schneider-Momm & Karoline TeufelMykyta Artomov, Harrison Brand, Ryan L. Collins & Sherif Gerges Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, ItalyMedical Genetics, University of Siena, Siena, ItalyMonica Biscaldi-Schafer & David M. HougaardAnders D. Børglum, Itaru Kushima & Norio OzakiDepartment of Medical Sciences, University of Torino, Turin, ItalyDepartment of Public Health and Pediatrics, University of Torino, Turin, ItalyGrupo de Medicina Xenómica, Centro de Investigación en Red de Enfermedades Raras , CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, SpainDepartment of Pediatrics and Adolescent Medicine, Duchess of Kent Children’s Hospital, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, ChinaDavid J. CutlerDepartment of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USACenter for Autism Research and Translation, University of California, Irvine, Irvine, CA, USAInstitute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USADepartment of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston, School of Dentistry, Houston, TX, USAMiia Kaartinen & Kaija PuuraDepartment of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FinlandDara S. ManoachNorio OzakiDepartment of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine Complutense University, Madrid, SpainDepartment of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USAJames S. SutcliffeProgram in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USAK.R., B.D., C.B. and J.D.B. conceived and designed the study. T.L., T.P., C.R.S., C.M.C., J.L.A., G.S.C., H.C., R.C., C.I.S.C., M.L.C., A.D.P.L., M.F., E.F., L.G., A.C.D.E.S.G., A.J.G., L.C.H., N.L., Y.L., D.N.-R., R.O., K.P.P., I.P., R.S., H.M.S., L.T., J.Y.T.W., L.A.-G., L.A.C., C.S.C.-F., I.H.-P., A.K., M.C.L., L.M., M.R.P.-B., M.A.P.-V., P.S., F.T., M.P.T., M.E.T., M.J.D. and J.D.B. contributed samples and generated data. J.D.B., C.B., B.D., B.M., S.D.R., L.K., L.S., J.M.F., F.K.S., S.J. and M.N.A. developed methodology and performed data analyses. J.D.B., B.D., C.B., E.H.C., T.L., L.S. and M.N.A. drafted and revised the paper. All authors reviewed and approved the final version of the paper. J.D.B. supervised the study.L.A.-G. is the main author of the CRIDI-ASD interview; she teaches the training course for the aforementioned instrument and receives payment for the training. The other authors declare no conflicts of interest.thanks Andres Moreno-Estrada and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editor: Anna Ranzoni, in collaboration with theThe figure describes the variant, genotype, and sample quality control steps that were implemented to process the raw, joint-genotyped VCFs and generate the de novo and inherited calls used for downstream analyses. Sample counts are tabulated before downstream ancestry filtering. Extended Data Fig. 2 Comparison of rare de novo variant counts per sample between ASD probands and unaffected siblings across different ancestries, normalized to synonymous variant rates. The average number of rare variants per sample –normalized by the synonymous de novo variant rate– is compared between ASD probands and their unaffected siblings for all ancestries , Admixed American , and non-Admixed American protein truncating variants in highly constrained genes and less constrained genes ; missense variants categorized by predicted functional severity ; and MPC< 1 and synonymous missense variants. Data are presented as mean values ± 95% confidence intervals. Statistical significance was assessed using two-sided z-tests comparing normalized de novo mutation rates between probands and siblings.Extended Data Fig. 3 Genic burden of PTVs across different ancestries in gnomAD v2.1.1 as a function of gene constraint. The sum of observed PTVs per ancestry is plotted, scaled to each population’s size and total gene coding sequence length within gnomAD LOEUF deciles. The plot includes African African American , Admixed American , East Asian , Non-Finnish European , and South Asian ancestries. LOEUF deciles represent levels of gene constraint, with lower deciles indicating more constrained genes.The proportional impact of each inheritance mode on the ASD-associated genes is shown at three false discovery rate thresholds: ≤0.1 thresholds: ≤0.1 the number of P/LP variants, all expressed per proband. AMR participants have more variants per individual compared to non-AMR participants, but a reduced ability of Neptune to classify variants in AMR contributes to a slightly lower proportion of P/LP variants per individual. Similar results are seen for non-EUR versus EUR. Data are presented as mean values ± 95% confidence intervals . Statistical analysis: pairwise two-sidedExtended Data Fig. 7 Lollipop diagrams illustrating variants identified in emerging autism-associated genes.individuals are marked with green, and variants found in DECIPHER are in purple. Figures were generated using the lollipop software package) inferred using a Random Forest classifier trained on 1000 Genomes + Human Genome Diversity Project reference populations. Most individuals display majority Admixed American ancestry. and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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