Abstract
Infertility is a heterogeneous condition, with genetic causes estimated to be involved in approximately half of the cases. High-throughput sequencing (HTS) is becoming an increasingly important tool for genetic diagnosis of diseases including idiopathic infertility, however, most rare or minor alleles revealed by HTS 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 population polymorphisms in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXOI, 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 in the yeast 2 hybrid assay. Even though these genes are known to be essential for normal meiosis or spermiogenesis in mice, only one of the tested human variants (rs1460351219, encoding p.R581H in MCM9), which was observed in 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 present in ClinVar, and 2) those which have been evaluated in mice. We found that all the algorithms performed poorly in terms of predicting the effects of human missense variants that have been 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 vivo functional validation models for more effective and accurate VUS delineation to either pathogenic or benign categories.
Significance Although infertility is a substantial medical problem that affects up to 15% of couples, the potential genetic causes of idiopathic infertility have been difficult to decipher. This problem is complicated by the large number of genes that can cause infertility when perturbed, coupled with the large number of VUS that are present in the genomes of affected patients. Here, we present and analyze mouse modeling data of missense variants that are classified as deleterious by commonly-used pathogenicity prediction algorithms but which caused no detectible phenotype when introduced into mice by genome editing. We find that augmenting pathogenicity predictions with preliminary screens for biochemical defects substantially enhanced the proportion of prioritized variants that caused phenotypes in mice. The results emphasize that, in the absence of substantial improvements of in silico prediction tools or other compelling pre-existing evidence, in vivo analysis is crucial for confident attribution of infertility alleles.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Figures updated; author list updated; supplemental files updated.
