PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including De Novo Variants, with Trio Whole Genome Sequencing and Data Reanalysis
Version 1
: Received: 9 August 2023 / Approved: 14 August 2023 / Online: 14 August 2023 (09:36:37 CEST)
Version 2
: Received: 29 September 2023 / Approved: 9 October 2023 / Online: 10 October 2023 (05:00:59 CEST)
How to cite:
Bar, O.; Vahey, E.; Mintz, M.; Frye, R.E.; Boles, R.G. High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including De Novo Variants, with Trio Whole Genome Sequencing and Data Reanalysis. Preprints2023, 2023081002. https://doi.org/10.20944/preprints202308.1002.v2
Bar, O.; Vahey, E.; Mintz, M.; Frye, R.E.; Boles, R.G. High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including De Novo Variants, with Trio Whole Genome Sequencing and Data Reanalysis. Preprints 2023, 2023081002. https://doi.org/10.20944/preprints202308.1002.v2
Bar, O.; Vahey, E.; Mintz, M.; Frye, R.E.; Boles, R.G. High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including De Novo Variants, with Trio Whole Genome Sequencing and Data Reanalysis. Preprints2023, 2023081002. https://doi.org/10.20944/preprints202308.1002.v2
APA Style
Bar, O., Vahey, E., Mintz, M., Frye, R.E., & Boles, R.G. (2023). High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including <em>De Novo</em> Variants, with Trio Whole Genome Sequencing and Data Reanalysis. Preprints. https://doi.org/10.20944/preprints202308.1002.v2
Chicago/Turabian Style
Bar, O., Richard Eugene Frye and Richard Gregory Boles. 2023 "High Sensitivity for Detection of Primary Diagnostic Variants in Autism Spectrum Disorder, Including <em>De Novo</em> Variants, with Trio Whole Genome Sequencing and Data Reanalysis" Preprints. https://doi.org/10.20944/preprints202308.1002.v2
Abstract
Autism spectrum disorder (ASD) is a common condition with lifelong implications and a strong hereditary component suggesting genetic underpinnings. The last decade has seen dramatic improvements in DNA sequencing and related bioinformatics and databases.
We analyzed the raw DNA sequencing files on the Variantyx® bioinformatics platform for the last 50 ASD patients evaluated with trio whole genome sequencing (trio-WGS). “Qualified” variants were defined as coding, rare, and evolutionarily conserved. Primary Diagnostic Variants (PDV) additionally were in genes directly linked to ASD and matched clinical correlation.
A PDV was identified in 34/50 (68%) of cases, including 25 (50%) cases with heterozygous de novo and 10 (20%) with inherited variants. De novo variants in genes directly associated with ASD were far more likely to be Qualifying than non-Qualifying versus a control group of genes not associated with ASD (P = 0.0002, odds ratio 29), validating that most are indeed disease related. Only 14/34 (41%) of PDV cases had the variant listed on the laboratory report, and reanalysis increased diagnostic yield from 28% to 68%. Variants that we assigned as PDVs yet not on the report were predominately de novo in genes not yet reported as ASD associated. Many subjects both with and without a PDV had inherited Qualifying variants in known ASD-associated genes, suggesting polygenic inheritance. Thirty-three subjects (66%) had treatment recommendation(s) based on DNA analyses.
Our results demonstrate high yield of trio-WGS for revealing molecular diagnoses in ASD that is greatly enhanced by re-analyzing DNA sequencing files. In contrast to previous reports, de novo variants dominate the findings, mostly representing novel conditions. This has implications to the cause and rising prevalence of autism.
Keywords
autism; diagnostic yield; DNA sequencing; novel disorders
Subject
Medicine and Pharmacology, Pediatrics, Perinatology and Child Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
