Background: Pathogenic variants in the mismatch repair (MMR) genes are the drivers of Lynch Syndrome; optimal variant interpretation is required for the management of suspected and confirmed cases. Given the onerous nature of extracting information related to genetic variants, literature searching tools which harness artificial intelligence may aid in retrieving information to allow optimum variant classification. Methods: In this study, we described the nature of discordance in a sample of 80 variants from a list of variants requiring updating by InSiGHT for ClinGen by comparing their existing InSiGHT classifications on ClinVar. Variants were searched for using a traditional method (Google Scholar) and literature searching tool (Mastermind Genomenon) independently. Descriptive statistics were used to compare: the number of articles before and after screening for relevance and the number of relevant articles unique to either method. Results: 916 articles were returned by both methods. Mastermind averaged four relevant articles per search, Google Scholar, three. Of relevant Mastermind articles, 193/308 (62.7%) were unique to it, compared to 87/202, (43.0%) for Google Scholar. All 6/80 (20%) variants with pathogenic or likely pathogenic InSiGHT classifications have newer VUS assertions on ClinVar. Conclusion: Mastermind on average returned a more relevant literature search. Google Scholar still found unique information, suggesting that Mastermind could play a complementary role.
The study of mutations that impact fertility has a catch-22. Fertility mutants are often lost since they cannot simply be propagated and maintained. This has hindered progress in understanding the genetics of fertility. In mice, several molecules are found to be required for the interactions between the sperm and egg, with JUNO and IZUMO1 being the only known receptor pair on the egg and sperm surface, respectively. In C. elegans, a total of 12 proteins on the sperm or oocyte have been identified to mediate their interactions. Majority of these genes were identified through mutants isolated from genetic screens. In this review, we summarize the several key screening strategies that led to the identification of fertility mutants in C. elegans and provide a perspective about future research using genetic approaches. Recently, advancements in new technologies such as high-throughput sequencing and Crispr-based genome editing tools have accelerated the molecular, cell biological, and mechanistic analysis of fertility genes. We review how these valuable tools advance our understanding of the molecular underpinnings of C. elegans fertilization and complement fertility research in humans and other species.