Abstract

Male infertility affects approximately 17% of all men and represents a complex disorder in which not only semen parameters such as sperm motility, morphology, and number of sperm are highly variable, but also testicular phenotypes range from normal spermatogenesis to complete absence of germ cells. Genetic factors significantly contribute to the disease but chromosomal aberrations, mostly Klinefelter syndrome, and microdeletions of the Y-chromosome have remained the only diagnostically and clinically considered genetic causes. Monogenic causes remain understudied and, thus, often unidentified, leaving the majority of the male factor couple infertility pathomechanistically unexplained. This has been changing mostly because of the introduction of exome sequencing that allows the analysis of multiple genes in large patient cohorts. As a result, pathogenic variants in single genes have been associated with non-syndromic forms of all aetiologic sub-categories in the last decade. This review highlights the contribution of exome sequencing to the identification of novel disease genes for isolated (non-syndromic) male infertility by presenting the results of a comprehensive literature search. Both, reduced sperm count in azoospermic and oligozoospermic patients, and impaired sperm motility and/or morphology, in asthenozoospermic and/or teratozoospermic patients are highly heterogeneous diseases with well over 100 different candidate genes described for each entity. Applying the standardized evaluation criteria of the ClinGen gene curation working group, 70 genes with at least moderate evidence to contribute to the disease are highlighted. The implementation of these valid disease genes in clinical exome sequencing is important to increase the diagnostic yield in male infertility and, thus, improve clinical decision-making and appropriate genetic counseling. Future advances in androgenetics will continue to depend on large-scale exome and genome sequencing studies of comprehensive international patient cohorts, which are the most promising approaches to identify additional disease genes and provide reliable data on the gene-disease relationship.

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