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Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure

Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10–25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to...

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Autores principales: Henarejos-Castillo, Ismael, Aleman, Alejandro, Martinez-Montoro, Begoña, Gracia-Aznárez, Francisco Javier, Sebastian-Leon, Patricia, Romeu, Monica, Remohi, Jose, Patiño-Garcia, Ana, Royo, Pedro, Alkorta-Aranburu, Gorka, Diaz-Gimeno, Patricia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305607/
https://www.ncbi.nlm.nih.gov/pubmed/34199109
http://dx.doi.org/10.3390/jpm11070609
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author Henarejos-Castillo, Ismael
Aleman, Alejandro
Martinez-Montoro, Begoña
Gracia-Aznárez, Francisco Javier
Sebastian-Leon, Patricia
Romeu, Monica
Remohi, Jose
Patiño-Garcia, Ana
Royo, Pedro
Alkorta-Aranburu, Gorka
Diaz-Gimeno, Patricia
author_facet Henarejos-Castillo, Ismael
Aleman, Alejandro
Martinez-Montoro, Begoña
Gracia-Aznárez, Francisco Javier
Sebastian-Leon, Patricia
Romeu, Monica
Remohi, Jose
Patiño-Garcia, Ana
Royo, Pedro
Alkorta-Aranburu, Gorka
Diaz-Gimeno, Patricia
author_sort Henarejos-Castillo, Ismael
collection PubMed
description Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10–25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to identify a blood-based gene variant profile using accumulation of rare variants to promote precision medicine in fertility preservation programs. A case–control (n = 118, n = 32, respectively) WES study was performed in which only non-synonymous rare variants <5% minor allele frequency (MAF; in the IGSR) and coverage ≥ 100× were considered. A profile of 66 variants of uncertain significance was used for training an unsupervised machine learning model to separate cases from controls (97.2% sensitivity, 99.2% specificity) and stratify the population into two subtypes of OF (A and B) (93.31% sensitivity, 96.67% specificity). Model testing within the IGSR female population predicted 0.5% of women as subtype A and 2.4% as subtype B. This is the first study linking OF to the accumulation of rare variants and generates a new potential taxonomy supporting application of this approach for precision medicine in fertility preservation.
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spelling pubmed-83056072021-07-25 Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure Henarejos-Castillo, Ismael Aleman, Alejandro Martinez-Montoro, Begoña Gracia-Aznárez, Francisco Javier Sebastian-Leon, Patricia Romeu, Monica Remohi, Jose Patiño-Garcia, Ana Royo, Pedro Alkorta-Aranburu, Gorka Diaz-Gimeno, Patricia J Pers Med Article Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10–25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to identify a blood-based gene variant profile using accumulation of rare variants to promote precision medicine in fertility preservation programs. A case–control (n = 118, n = 32, respectively) WES study was performed in which only non-synonymous rare variants <5% minor allele frequency (MAF; in the IGSR) and coverage ≥ 100× were considered. A profile of 66 variants of uncertain significance was used for training an unsupervised machine learning model to separate cases from controls (97.2% sensitivity, 99.2% specificity) and stratify the population into two subtypes of OF (A and B) (93.31% sensitivity, 96.67% specificity). Model testing within the IGSR female population predicted 0.5% of women as subtype A and 2.4% as subtype B. This is the first study linking OF to the accumulation of rare variants and generates a new potential taxonomy supporting application of this approach for precision medicine in fertility preservation. MDPI 2021-06-27 /pmc/articles/PMC8305607/ /pubmed/34199109 http://dx.doi.org/10.3390/jpm11070609 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Henarejos-Castillo, Ismael
Aleman, Alejandro
Martinez-Montoro, Begoña
Gracia-Aznárez, Francisco Javier
Sebastian-Leon, Patricia
Romeu, Monica
Remohi, Jose
Patiño-Garcia, Ana
Royo, Pedro
Alkorta-Aranburu, Gorka
Diaz-Gimeno, Patricia
Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title_full Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title_fullStr Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title_full_unstemmed Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title_short Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
title_sort machine learning-based approach highlights the use of a genomic variant profile for precision medicine in ovarian failure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305607/
https://www.ncbi.nlm.nih.gov/pubmed/34199109
http://dx.doi.org/10.3390/jpm11070609
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