A comprehensive study of common and rare genetic variants in spermatogenesis-related loci identifies new risk factors for idiopathic severe spermatogenic failure

Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility? Our findings revealed a significant association between SPGF and the gene and identified three novel genes ( , , and ) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition. SPGF is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWASs) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort. We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 20 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics. This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (i) variant-wise association analyses using logistic regression models, (ii) gene-wise association analyses using combined multivariate and collapsing burden tests, and (iii) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients. The variant-wise analyses revealed an association between SPGF and -rs12347237 ( = 4.15E-06, odds ratio = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of cod