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Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases
BACKGROUND: Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interp...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515723/ https://www.ncbi.nlm.nih.gov/pubmed/34645491 http://dx.doi.org/10.1186/s13073-021-00965-0 |
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author | De La Vega, Francisco M. Chowdhury, Shimul Moore, Barry Frise, Erwin McCarthy, Jeanette Hernandez, Edgar Javier Wong, Terence James, Kiely Guidugli, Lucia Agrawal, Pankaj B. Genetti, Casie A. Brownstein, Catherine A. Beggs, Alan H. Löscher, Britt-Sabina Franke, Andre Boone, Braden Levy, Shawn E. Õunap, Katrin Pajusalu, Sander Huentelman, Matt Ramsey, Keri Naymik, Marcus Narayanan, Vinodh Veeraraghavan, Narayanan Billings, Paul Reese, Martin G. Yandell, Mark Kingsmore, Stephen F. |
author_facet | De La Vega, Francisco M. Chowdhury, Shimul Moore, Barry Frise, Erwin McCarthy, Jeanette Hernandez, Edgar Javier Wong, Terence James, Kiely Guidugli, Lucia Agrawal, Pankaj B. Genetti, Casie A. Brownstein, Catherine A. Beggs, Alan H. Löscher, Britt-Sabina Franke, Andre Boone, Braden Levy, Shawn E. Õunap, Katrin Pajusalu, Sander Huentelman, Matt Ramsey, Keri Naymik, Marcus Narayanan, Vinodh Veeraraghavan, Narayanan Billings, Paul Reese, Martin G. Yandell, Mark Kingsmore, Stephen F. |
author_sort | De La Vega, Francisco M. |
collection | PubMed |
description | BACKGROUND: Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00965-0. |
format | Online Article Text |
id | pubmed-8515723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85157232021-10-20 Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases De La Vega, Francisco M. Chowdhury, Shimul Moore, Barry Frise, Erwin McCarthy, Jeanette Hernandez, Edgar Javier Wong, Terence James, Kiely Guidugli, Lucia Agrawal, Pankaj B. Genetti, Casie A. Brownstein, Catherine A. Beggs, Alan H. Löscher, Britt-Sabina Franke, Andre Boone, Braden Levy, Shawn E. Õunap, Katrin Pajusalu, Sander Huentelman, Matt Ramsey, Keri Naymik, Marcus Narayanan, Vinodh Veeraraghavan, Narayanan Billings, Paul Reese, Martin G. Yandell, Mark Kingsmore, Stephen F. Genome Med Research BACKGROUND: Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00965-0. BioMed Central 2021-10-14 /pmc/articles/PMC8515723/ /pubmed/34645491 http://dx.doi.org/10.1186/s13073-021-00965-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research De La Vega, Francisco M. Chowdhury, Shimul Moore, Barry Frise, Erwin McCarthy, Jeanette Hernandez, Edgar Javier Wong, Terence James, Kiely Guidugli, Lucia Agrawal, Pankaj B. Genetti, Casie A. Brownstein, Catherine A. Beggs, Alan H. Löscher, Britt-Sabina Franke, Andre Boone, Braden Levy, Shawn E. Õunap, Katrin Pajusalu, Sander Huentelman, Matt Ramsey, Keri Naymik, Marcus Narayanan, Vinodh Veeraraghavan, Narayanan Billings, Paul Reese, Martin G. Yandell, Mark Kingsmore, Stephen F. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title | Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title_full | Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title_fullStr | Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title_full_unstemmed | Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title_short | Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
title_sort | artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515723/ https://www.ncbi.nlm.nih.gov/pubmed/34645491 http://dx.doi.org/10.1186/s13073-021-00965-0 |
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