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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

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.citation	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.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.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	journal article	es_ES
dc.date.updated	2021-09-14T13:03:09Z
dc.identifier.doi	10.3390/jpm11070609
dc.identifier.idgrec	147839
dc.rights.accessRights	open access	es_ES