In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes

Abstract Infertility is a highly heterogeneous condition, with genetic causes estimated to be involved in approximately half of the cases. High-throughput sequencing (HTS) approaches are becoming an increasingly important tool for genetic diagnosis of diseases, including idiopathic infertility. Nevertheless, most rare or minor alleles revealed by HTS are classified as 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 segregating polymorphisms in key fertility genes, we functionally evaluated 8 missense variants in the genes ANKRD31, BRDT, DMC1, EXOI, FKBP6, MSH4 and SEPT12 by generating genome-edited mouse models. Six variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction in the yeast 2 hybrid assay. Even though these genes are known to be essential for normal meiosis or spermiogenesis in mice, none of the tested human variants compromised fertility or gametogenesis in the mouse models. These results should be useful for genetic diagnoses of infertile patients, but they also underscore the need for more effective VUS categorization. To this end, we evaluated the performance of 10 widely used pathogenicity prediction algorithms in classifying missense variants within fertility-related genes from two sources: 1) the ClinVar database, and 2) those functionally tested in mice. We found that all the algorithms performed poorly in terms of predicting phenotypes of mouse-modeled variants. These studies highlight the importance of functional validation of potential infertility-causing genetic variants.