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.
Highlights
For genomic study of SNVs associated with Ovarian failure (OF), we focused on variants absent in the IGSR with a low minor allele frequency (MAF) (
Given the unique intra-variability of each individual and that finding variants shared by several individuals is complex, to ensure that the accumulation of variants was predictive of OF, we identified variants shared by at least 10% of cases and not present in controls
In addition to identifying variants in genes not previously associated with fertility, we identified three variants affecting genes previously associated with OF: mutS homolog 3 (MSH3), gamma-glutamyltransferase 1 (GGT1), and aquaporin 8 (AQP8)
Summary
Ovarian failure (OF) is characterised by accelerated attrition of the ovarian follicle reserve, amenorrhea, dramatic hypoestrogenism, and elevated gonadotropin levels, but these manifestations differ depending on aetiology [1,2,3]. Anti-Müllerian hormone (AMH) and follicle stimulating hormone (FSH) measurements aid OF diagnosis [13,14]. Both are limited as predictive biomarkers because modest increases or decreases in AMH are difficult to detect and do not characterise subtypes of OF; FSH has less sensitivity than AMH and depends on the day of the cycle in which the sample is obtained [15,16,17]. As for other conditions [18], high-throughput genomics data may help discern subtypes and stages of OF
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