Infertility is a heterogeneous condition, with genetic causes thought to underlie a substantial fraction of cases. Genome sequencing is becoming increasingly important for genetic diagnosis of diseases including idiopathic infertility; however, most rare or minor alleles identified in patients are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling. To determine the consequences of these variants in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXO1, FKBP6, MCM9, M1AP, MEI1, MSH4 and SEPT12 by generating genome-edited mouse models. Nine variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction (PPI) in the yeast two hybrid (Y2H) assay. Though these genes are essential for normal meiosis or spermiogenesis in mice, only one variant, observed in the MCM9 gene of a male infertility patient, compromised fertility or gametogenesis in the mouse models. To explore the disconnect between predictions and outcomes, we compared pathogenicity calls of missense variants made by ten widely used algorithms to 1) those annotated in ClinVar and 2) those evaluated in mice. All the algorithms performed poorly in terms of predicting the effects of human missense variants modeled in mice. These studies emphasize caution in the genetic diagnoses of infertile patients based primarily on pathogenicity prediction algorithms and emphasize the need for alternative and efficient in vitro or in vivo functional validation models for more effective and accurate VUS description to either pathogenic or benign categories.
Keywords: CRISPR/Cas9; infertility; meiosis; pathogenicity prediction algorithms; variants of uncertain significance.