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 to 25 percentage 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 rares variants to promote precision medicine in fertility preservation programs. A case-control WES study including 118 patients and 32 controls was performed in which only non-synonymous rare variants less than 5 percentage minor allele frequency and coverage over 100x were considered. A profile of 66 variants of uncertain significance was used for training an unsupervised machine learning model to separate cases from controls and stratify the population into two subtypes of OF. This is the first study linking OF to the accumulation of rare variants and generates a new potential taxonony supporting application of this approach for precision medicine in fertility preservation.