STUDY QUESTION
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?
SUMMARY ANSWER
Our findings revealed a significant association between SPGF and the SHOC1 gene, and identified three novel genes (PCSK4, AP3B1, and DLK1) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition.
WHAT IS KNOWN ALREADY
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 (NGS) methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWAS) 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.
STUDY DESIGN, SIZE, DURATION
We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 6 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.
PARTICIPANTS/MATERIALS, SETTING, METHODS
This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (1) variant-wise association analyses using logistic regression models, (2) gene-wise association analyses using combined multivariate and collapsing (CMC) burden tests, and (3) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients.
MAIN RESULTS AND THE ROLE OF CHANCE
The variant-wise analyses revealed an association between SPGF and SHOC1-rs12347237 (p = 4.15E-06, OR = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (PCSK4, AP3B1, and DLK1) were identified as novel relevant players in human male infertility using the gene-wise burden test approach (p < 5.56E-04). Furthermore, we linked a total of 32 potentially pathogenic and recessive coding variants of the selected genes to 35 different cases.
LARGE SCALE DATA
Publicly available via GWAS catalog (Accession number: GCST90239721).
LIMITATIONS, REASONS FOR CAUTION
The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies with larger sample sizes are essential to replicate our results. Additionally, the specific roles of the identified variants in the pathogenic mechanisms of SPGF should be assessed through functional experiments.
WIDER IMPLICATIONS OF THE FINDINGS
Our findings highlight the benefit of using GWAS genotyping to screen for both common and rare variants potentially implicated in idiopathic cases of SPGF, whether due to complex or monogenic causes. The discovery of novel genetic risk factors for SPGF and the elucidation of the underlying genetic causes provide new perspectives for personalized medicine and reproductive counselling.
STUDY FUNDING/COMPETING INTEREST(S)
This work was supported by the Spanish Ministry of Science and Innovation through the Spanish National Plan for Scientific and Technical Research and Innovation (PID2020-120157RB-I00) and the Andalusian Government through the research projects of “Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)” (ref. PY20_00212) and “Proyectos de Investigación aplicada FEDER-UGR 2023” (ref. C-CTS-273-UGR23). SGM was funded by the previously mentioned projects (ref. PY20_00212 and PID2020-120157RB-I00). AGJ was funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro” (grant ref. FPU20/02926). IPATIMUP integrates the i3S Research Unit, which is partially supported by the Portuguese Foundation for Science and Technology (FCT), financed by the European Social Funds (COMPETE-FEDER) and National Funds (projects PEstC/SAU/LA0003/2013 and POCI-01-0145-FEDER-007274). SS is supported by FCT funds (10.54499/DL57/2016/CP1363/CT0019), ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, and is also partially supported by the Portuguese Foundation for Science and Technology (UIDP/00009/2020 and UIDB/00009/2020). SLarriba received support from Instituto de Salud Carlos III (grant: DTS18/00101], co-funded by FEDER funds/European Regional Development Fund (ERDF)-a way to build Europe-), and from “Generalitat de Catalunya” (grant 2021SGR052). SLarriba is also sponsored by the “Researchers Consolidation Program” from the SNS-Dpt. Salut Generalitat de Catalunya (Exp. CES09/020). All authors declare no conflict of interest related to this study.
