Identification and Functional Validation of Human Infertility Alleles

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 DESCRIPTION (provided by applicant): It is thought that nearly half of infertility cases have a genetic basis. Despite extensive knowledge gained from gene knockouts in mice, the genetic causes for the vast majority of idiopathic human infertilities are unknown. Traditional methods for studying inheritance, such as GWAS or linkage analysis, have been confounded by heterogeneity of infertility phenotypes and hundreds of genes involved in reproduction. Finally, we do not know the proportion of cases in which genetically-based infertilities are caused by de novo mutations vs. inheritance of alleles segregating in the human population. This project proposes a new, comprehensive, and multidisciplinary approach to this problem that will screen and identify nonsynonymous single nucleotide polymorphisms (nsSNPs) that functionally disrupt gametogenesis. The complementary expertise of two laboratories - one which has developed massively parallel in silico and in vitro methods to predict and validate disease-causing nsSNPs, and the other a leader in discovery and characterization of infertility genes using mouse mutant models - will together be brought together to bear on the problem. The Specific Aims are to: 1) Use computational approaches and high-throughput in vitro assays to identify nsSNPs in human infertility genes that are likely to disrupt protein function. These alleles will be precisely modeed in mice using CRISPR/Cas genome editing. 2) Phenotype the mouse models to identify those SNPs which impact gametogenesis and fertility. Overall, ~125 mouse models corresponding to nsSNPs in >300 known reproduction genes will be made. Already, 2 of 5 nsSNPs tested caused infertility. Establishing a database of experimentally-validated benign and deleterious SNPs in reproduction genes will revolutionize and empower the human reproductive genetics field as we enter the era of personalized medicine by improving the identification of causative alleles in exome-sequenced patients.