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

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

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dc.contributor.author Henarejos Castillo, Ismael
dc.contributor.author Aleman, Alejandro
dc.contributor.author Martinez Montoro, Begoña
dc.contributor.author Gracia Aznárez, Francisco Javier
dc.contributor.author Sebastián León, Patricia
dc.contributor.author Romeu Villarroya, Mónica
dc.contributor.author Remohí Giménez, José
dc.contributor.author Patiño García, Ana
dc.contributor.author Royo, Pedro
dc.contributor.author Alkorta Arangurun, Gorka
dc.contributor.author Díaz Gimeno, Patricia
dc.date.accessioned 2021-09-14T13:03:08Z
dc.date.available 2021-09-14T13:03:08Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10550/80302
dc.description.abstract 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.
dc.relation.ispartof Journal Of Personalized Medicine, 2021, vol. 11, num. 7
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Henarejos Castillo, Ismael Aleman, Alejandro Martinez Montoro, Begoña Gracia Aznárez, Francisco Javier Sebastián León, Patricia Romeu Villarroya, Mónica Remohí Giménez, José Patiño García, Ana Royo, Pedro Alkorta Arangurun, Gorka Díaz Gimeno, Patricia 2021 Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure Journal Of Personalized Medicine 11 7
dc.subject Genoma humà
dc.subject Ginecologia
dc.title Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
dc.type info:eu-repo/semantics/article
dc.date.updated 2021-09-14T13:03:09Z
dc.identifier.doi https://doi.org/10.3390/jpm11070609
dc.identifier.idgrec 147839

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