Making sense of missense variants in autism

Autism spectrum disorder (ASD) is phenotypically and genetically heterogeneous, and the relevance of de novo missense variants to neurodevelopmental outcomes remains unclear. A new study by Koire et al. suggests genes and pathways that could be involved in ASD. These authors assessed bias toward a predicted fitness impact among rare de novo missense variants in functionally related genes. For both established and unexpected genes, the computationally predicted evolutionary impact of these variants correlated with patient IQ. This study suggests a role for this rare variant class in ASD and presents a generalizable approach toward elucidating the genotype-phenotype relationships in complex diseases.


Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

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Supplementary Material


Fig. S1. Stratification of de novo missense variants using gene-centric network analysis.
Fig. S2. Relationship between de novo evolutionary action score burden and patient IQ for prioritized and deprioritized gene groups.
Fig. S3. Relationship between patient IQ and genic tolerance to mutation (RVIS).
Fig. S4. Relationship between evolutionary action burden and phenotype cannot be explained by the number of de novo missense variants of interest in a patient.
Fig. S5. Comparison of prioritized de novo missense variants and genotype-phenotype analysis in male versus female probands.
Fig. S6. Enrichment of prioritization status in genes with high pLI scores and in brain-expressed genes.
Fig. S7. Effect of prioritization status on the relationship between genotype and phenotype in SFARI autism gene list.
Fig. S8. Effect of threshold variation on high and low weighted evolutionary action burden group comparisons.
Fig. S9. Updated PubMed search for associating our prioritization to ASD.
Fig. S10. Quality control of de novo variant calls.
Table S1. Proband de novo missense variants.
Table S2. Healthy sibling de novo missense variants.
Table S3. GO pathway definitions.
Table S4. GO pathway bias in patients and matched siblings.
Table S5. Gene annotation with prioritization status.
Table S6. IQ and total weighted evolutionary action burden for male patients with at least one missense variant in a prioritized gene.
Table S7. Variant annotation with evolutionary action, RVIS, IQ, and gene prioritization status for variants in male patients with available phenotypic data.
Table S8. Genotype-phenotype relationship of de novo missense variants within gene sets, separated by prioritization group and metric used to estimate genic tolerance to mutation.
Table S9. Genotype-phenotype relationship of de novo missense variants within gene sets, separated by prioritization group and metric used to define phenotype.
Table S10. IQ correlation to mutation burden as measured by six prediction methods.
Table S11. IQ correlation to mutation burden as measured by evolutionary action for different gene sets.
References (5961)


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Published In

Science Translational Medicine
Volume 13 | Issue 594
May 2021

Submission history

Received: 10 April 2020
Accepted: 1 March 2021


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We are grateful to all of the families at the participating Simons Simplex Collection sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren, and E. Wijsman). We appreciate obtaining access to phenotypic data from SFARI. Funding: This work is supported by the NIH (grant numbers GM079656-8, DE025181, GM066099, and AG061105 to O.L.), the Oskar Fischer Foundation (to O.L.), the NSF (grant number DBI1356569 to O.L.), and the Defense Advance Research Project Agency (grant number N66001-15-C-4042 to O.L). A.K. is supported by the Baylor College of Medicine Comprehensive Cancer Training Program grant number RP160283, the Baylor Research Advocates for Student Scientists, and the McNair MD/PhD Scholars program. Author contributions: A.K. conceptualized and planned the project with advice from Y.W.K., S.J.W., C.B., P.K., and O.L. Data acquisition was performed by A.K., C.B., and O.L. De novo variant calling and quality assessment were performed by A.K. and C.B., and processing of data to score variants with the evolutionary action method was performed by P.K. Network and gene set enrichment analysis of genes affected by de novo variants was performed by A.K. and S.J.W. Time-stamped analysis and comparisons to other variant prediction methods were performed by Y.W.K. and A.K. Remaining analyses including pathway-based prioritization of de novo missense variants, genotype-phenotype correlations, and additional validation analyses were performed by A.K. The manuscript was written by A.K., C.B., P.K., and O.L., and all authors commented on the manuscript. Competing interests: O.L., P.K., and A.K. are coinventors on U.S. patent no. 10886005B2 entitled “Identifying genes associated with a phenotype.” Data and materials availability: All data associated with this study are present in the paper or the Supplementary Materials. Data for generating the figures are in the Supplementary Tables. Researchers can obtain the underlying SSC population dataset described in this study ( by request to



Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.
Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA.
Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, TX, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Division of Carrier Screening and Prenatal Testing, SEMA4, Stamford, CT, USA.
Stephen J. Wilson
Department of Biochemistry, Baylor College of Medicine, Houston, TX, USA.
Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Department of Biochemistry, Baylor College of Medicine, Houston, TX, USA.

Funding Information

Baylor University: GM079656-8
Baylor University: DBI1356569
Baylor University: N66001-15-C-4042
Defense Advance Research Project Agency: N66001-15-C-4042
Defense Advance Research Project Agency: RP160283
Oskar Fischer Foundation
McNair MD/PhD Scholars program
Baylor Research Advocates for Student Scientist


Corresponding author. Email: [email protected]

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