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Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder

The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in ge...

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Autores principales: Kainer, David, Templeton, Alan R., Prates, Erica T., Jacboson, Daniel, Allan, Euan R.O., Climer, Sharlee, Garvin, Michael R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634371/
https://www.ncbi.nlm.nih.gov/pubmed/36340933
http://dx.doi.org/10.1016/j.xhgg.2022.100150
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author Kainer, David
Templeton, Alan R.
Prates, Erica T.
Jacboson, Daniel
Allan, Euan R.O.
Climer, Sharlee
Garvin, Michael R.
author_facet Kainer, David
Templeton, Alan R.
Prates, Erica T.
Jacboson, Daniel
Allan, Euan R.O.
Climer, Sharlee
Garvin, Michael R.
author_sort Kainer, David
collection PubMed
description The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in genomic information may reside in rare variants, interaction among variants (epistasis), or cryptic structural variation (SV) and may provide mechanisms that underlie ASD. Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes. Most are in non-coding genic space and are over-represented in expression quantitative trait loci, suggesting that they affect gene regulation, which we confirm with their overlap of differentially expressed genes in postmortem brain tissue of ASD individuals. We then identify an SV in the GRIK2 gene that alters RNA splicing and a regulatory region of the ACMSD gene in the kynurenine pathway as significantly associated with a non-verbal ASD phenotype, supporting our hypothesis that these currently excluded loci can provide a clearer mechanistic understanding of ASD. Finally, we use an explainable artificial intelligence approach to define subgroups demonstrating their use in the context of precision medicine.
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spelling pubmed-96343712022-11-05 Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder Kainer, David Templeton, Alan R. Prates, Erica T. Jacboson, Daniel Allan, Euan R.O. Climer, Sharlee Garvin, Michael R. HGG Adv Article The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in genomic information may reside in rare variants, interaction among variants (epistasis), or cryptic structural variation (SV) and may provide mechanisms that underlie ASD. Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes. Most are in non-coding genic space and are over-represented in expression quantitative trait loci, suggesting that they affect gene regulation, which we confirm with their overlap of differentially expressed genes in postmortem brain tissue of ASD individuals. We then identify an SV in the GRIK2 gene that alters RNA splicing and a regulatory region of the ACMSD gene in the kynurenine pathway as significantly associated with a non-verbal ASD phenotype, supporting our hypothesis that these currently excluded loci can provide a clearer mechanistic understanding of ASD. Finally, we use an explainable artificial intelligence approach to define subgroups demonstrating their use in the context of precision medicine. Elsevier 2022-10-06 /pmc/articles/PMC9634371/ /pubmed/36340933 http://dx.doi.org/10.1016/j.xhgg.2022.100150 Text en © 2022 Oak Ridge National Laboratory, The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Kainer, David
Templeton, Alan R.
Prates, Erica T.
Jacboson, Daniel
Allan, Euan R.O.
Climer, Sharlee
Garvin, Michael R.
Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title_full Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title_fullStr Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title_full_unstemmed Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title_short Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
title_sort structural variants identified using non-mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634371/
https://www.ncbi.nlm.nih.gov/pubmed/36340933
http://dx.doi.org/10.1016/j.xhgg.2022.100150
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