Initiative for standardization of reporting genetics of male infertility

Abstract

ABSTRACTThe number of publications on research of male infertility is increasing. Technologies used in research of male infertility generate complex results and various types of data that need to be appropriately managed, arranged, and made available to other researchers for further use. In our previous study, we collected over 800 candidate loci for male fertility in seven mammalian species. However, the continuation of the work towards a comprehensive database of candidate genes associated with different types of idiopathic human male infertility is challenging due to fragmented information, obtained from a variety of technologies and various omics approaches. Results are published in different forms and usually need to be excavated from the text, which hinders the gathering of information. Standardized reporting of genetic anomalies as well as causative and risk factors of male infertility therefore presents an important issue. The aim of the study was to collect examples of diverse genomic loci published in association with human male infertility and to propose a standardized format for reporting genetic causes of male infertility. From the currently available data we have selected 75 studies reporting 186 representative genomic loci which have been proposed as genetic risk factors for male infertility. Based on collected and formatted data, we suggested a first step towards unification of reporting the genetics of male infertility in original and review studies. The proposed initiative consists of five relevant data types: 1) genetic locus, 2) race/ethnicity, number of participants (infertile/controls), 3) methodology, 4) phenotype (clinical data, disease ontology, and disease comorbidity), and 5) reference. The proposed form for standardized reporting presents a baseline for further optimization with additional genetic and clinical information. This data standardization initiative will enable faster multi-omics data integration, database development and sharing, establishing more targeted hypotheses, and facilitating biomarker discovery.