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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-9634371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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