Abstract
We conducted a genome-wide association study in a large population of infertile men due to unexplained spermatogenic failure (SPGF). More than seven million genetic variants were analysed in 1,274 SPGF cases and 1,951 unaffected controls from two independent European cohorts. Two genomic regions were associated with the most severe histological pattern of SPGF, defined by Sertoli cell-only (SCO) phenotype, namely the MHC class II gene HLA-DRB1 (rs1136759, P = 1.32E-08, OR = 1.80) and an upstream locus of VRK1 (rs115054029, P = 4.24E-08, OR = 3.14), which encodes a protein kinase involved in the regulation of spermatogenesis. The SCO-associated rs1136759 allele (G) determines a serine in the position 13 of the HLA-DRβ1 molecule located in the antigen-binding pocket. Overall, our data support the notion of unexplained SPGF as a complex trait influenced by common variation in the genome, with the SCO phenotype likely representing an immune-mediated condition.
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Introduction
According to recent estimations, the global prevalence of infertility has increased considerably during the last decades regardless of the socio-demographic index1. Specifically, up to 50 million couples worldwide currently require medical assistance for reproduction, with around half of such cases being related to male factor infertility2,3. Male infertility can be due either to an obstruction of the post-testicular tract or to non-obstructive causes4. Two extreme manifestations of the latter are non-obstructive azoospermia (NOA) and severe oligozoospermia (SO), which are characterised by a severe spermatogenic failure (SPGF) leading to a reduction in the number of spermatozoa in the ejaculate (very low concentration of spermatozoa in SO and complete lack of sperm in NOA)2.
Many SO patients eventually father a biological child following the isolation of viable seminal spermatozoa and subsequent intracytoplasmic sperm injection (ICSI)5. Although this simple procedure may not be applicable to azoospermic cases, there is still a chance for men suffering from this condition to benefit from the current in vitro fertilisation techniques by undergoing a testicular sperm extraction (TESE) from a testis biopsy6. The overall pregnancy outcomes following TESE depend on the degree of histological abnormalities, which include hypospermatogenesis (HS, production of an extremely low number of sperm cells), maturation arrest (MA, incomplete differentiation of the germline), and Sertoli cell-only (SCO, total absence of germ cells in the seminiferous tubules). NOA patients with a histopathological diagnosis of HS have a considerably higher probability of a successful TESE when compared to those diagnosed with MA or incomplete SCO (with the latter having the poorest success rates)7. TESE is currently regarded as the gold standard procedure not only for sperm cell retrieval in NOA cases but also in order to obtain a conclusive histological diagnosis. However, approximately half of the TESE performed will eventually be unsuccessful in retrieving viable spermatozoa for ICSI. To that extent, having a non-invasive diagnostic test which could be able to predict sperm retrieval outcomes would be beneficial for the clinical management of NOA cases6.
Known genetic causes of SPGF include karyotype anomalies (e.g. Klinefelter syndrome), microdeletions of the azoospermia factor (AZF [MIM 415000]) regions in the Y-chromosome, and point mutations in master regulator genes for spermatogenesis4. However, thus far, a genetic cause can only be established in about 20% of infertile men due to SPGF, being the origin of the infertility of the remaining cases defined as unexplained (idiopathic)8. In this regard, increasing evidence clearly suggests that common variants in the genome, such as single-nucleotide polymorphisms (SNP), may play a relevant role in the development of this form of male infertility by unbalancing the molecular network that controls the spermatogenic process2,9.
Over the past decade, genome-wide association studies (GWASs), in which hundreds of thousands to millions of genetic colourblindnessvariants across the genome are interrogated in a hypothesis-free fashion, have allowed to gain a valuable knowledge about the genetic component of many complex diseases and traits10,11. Nevertheless, the field of SPGF research has yet to have benefited to its fullest potential from the fast progress achieved during the golden era of GWASs, likely due to the fact that most efforts have been dedicated to identifying high-penetrance rare mutations through targeted sequencing methods9. In this context, only three GWASs of SPGF have been performed to date, i.e. a pilot study in a population of European descent in 2009 and two well-powered studies in Asians in 2011 and 201212,13,14. The first study did not yield consistent results due to the lack of statistical power to detect signals with a robust effect, as only 92 infertile men due to SPGF (including 52 SO and 40 NOA patients) and 80 normozoospermic controls were analysed for 370,000 SNPs12. Conversely, the other two Asian GWASs of SPGF, together with an additional follow-up study from one of the research groups (in which thousands of individuals were included)15, identified several risk variants for NOA susceptibility at the genome-wide level of significance. The SPGF-associated loci known to date at this significance threshold map within eight genomic regions encompassing protein arginine methyltransferase 6 (PRMT6 [MIM 608274]), peroxisome biogenesis factor 10 (PEX10 [MIM 602859]), SRY-box 5 (SOX5 [MIM 604975]), major histocompatibility complex, class II, DR-alpha (HLA-DRA [MIM 142860]), butyrophilin-like protein 2 (BTNL2 [MIM 606000]), CDC42-binding protein kinase, alpha (CDC42BPA [MIM 603412]), interleukin 17A (IL17A [MIM 603149]), and actin-binding LIM protein family, member 1 (ABLIM1 [MIM 602330])13,14,15. However, most of these genetic associations with NOA have not been replicated in independent studies and the histological phenotypes are yet to be analysed9,16.
Considering the above, we established an international collaborative effort with the aim to substantially improve the current knowledge on the genetic basis of SPGF by conducting a powerful GWAS in a large case-control cohort of European ancestry. Likewise, taking advantage of the high SNP coverage that the major histocompatibility region (MHC) has in the current genotyping arrays, we also decided to specifically interrogate this genomic region at the protein sequence level.
Results
Testing for association with disease susceptibility in the discovery phase
In a first attempt to identify genetic polymorphisms that could be involved in the development of the different patterns of SPGF, we performed case-control comparisons between the different established study groups and the control population in the Iberian cohort. Association signals at the genome-wide level of significance were detected in two haplotype blocks including the SNPs rs186420734, associated with TESEneg (P = 2.95E−08, OR = 11.34, 95% CI = 4.80–26.76), and rs9271527, associated with SCO (P = 2.41E−08, OR = 2.38, 95% CI = 1.75–3.22) (Table 1 and Supplementary Fig. 1). According to Open Targets, the genes functionally implicated by these variants were follicle-stimulating hormone receptor (FSHR [MIM 136435]) for rs186420734 and several MHC class II genes, including HLA-DRB1 (MIM 142857) and HLA-DRA, for rs9271527.
Considering the strong genetic association observed between the MHC system and the SCO phenotype in our discovery cohort, we decided to conduct a more comprehensive analysis of this genomic region by inferring multiallelic SNPs, classical HLA alleles, and polymorphic amino acid positions (Supplementary Data 1). The top SCO-associated peak was observed in the MHC class II, with the SNP rs1136759 showing the strongest signal (P = 3.04E−08, OR = 2.33, 95% CI = 1.73–3.15) (Table 1 and Supplementary Data 2). This SNP is located in the coding region of the HLA-DRB1 gene and it determines a serine in position 13 of the encoded protein (which also showed the same effect and statistical significance in the analysis), which lies in the antigen-binding pocket (Supplementary Data 2 and Fig. 1). This amino acid defines the HLA-DRB1 13 haplotype, which represented the most associated MHC classical allele with SCO in our study cohort (P = 3.86E−05, OR = 2.19, 95% CI = 1.51–3.17) (Supplementary Data 2). No additional associations with any of the SPGF patterns analysed were observed at the genome-wide significance level (Supplementary Fig. 1).
The position 13 of the HLA-DRβ1 subunit is highlighted in green (a). A magnified molecular representation of the residues conferring risk (serine) and protection (arginine) to Sertoli cell-only phenotype is also shown (b, c).
Replication phase in an independent population
In order to evaluate the consistency of our results in Iberians in an independent European population, we generated genome-wide genotyping data in a case-control cohort from Germany. This new analysis yielded no significant genetic association of the FSHR region with TESEneg (rs186420734: P = 0.98, OR = 1.02, 95% CI = 0.22–4.60) (Table 1). Consequently, the significant P-value observed in the TESEneg vs. controls comparison in the Iberian population was lost in the meta-analysis including both studies (rs186420734: PMETA = 1.37E−06, OR = 6.29, 95% CI = 2.98–13.27) (Table 1 and Supplementary Fig. 2), which showed a high heterogeneity between the ORs (Q = 6.5E−03). However, it should be noted that the lowest P-value across this genomic region in the German dataset was observed for rs28410762 (P = 2.79E−04, OR = 0.34, 95% CI = 0.19–0.61), which is located nearby the association peak in Iberians (49,399,835 and 49,429,854 in chromosome 2 for rs186420734 and rs28410762, respectively) and it is in LD with it, according to the 1KGPh3 EUR data (D’ = 1.00, r2 = 0.0027). On the other hand, a second suggestive peak of association with TESE outcome inside the FSHR gene was observed separately in each study as well as in the meta-analysis (top signal: rs77472631, PMETA = 2.95E−05, OR = 3.18, 95% CI = 1.85–5.47) (Supplementary Fig. 2). In this case, the effect size was homogenous between populations (Q = 0.96).
On the contrary, the SCO-specific association signal with the MHC class II region observed in the Iberian population was replicated in the German dataset at the nominal level for this phase (rs1136759/HLA-DRβ1 Ser13: P = 2.38E−02, OR = 1.39, 95% CI = 1.05–1.86) (Table 1 and Supplementary Data 2). Although some heterogeneity in the ORs was observed between studies (Q = 0.015), consistent OR directions (towards risk) of the minor allele (G)/associated residue (Ser) were observed in both populations (ORIBERIANS = 2.33, ORGERMANS = 1.39). Therefore, the meta-analysis by the means of the inverse variance method confirmed this association at the genome-wide level of significance (PMETA [INV VAR] = 4.62E−08, OR = 1.78, 95% CI = 1.45–2.19) (Table 1). The lowest P-value in the meta-analysis amongst the classical MHC alleles was also observed for HLA-DRB1 13 (P = 8.07E−07, OR = 1.96, 95% CI = 1.50–2.56) (Supplementary Data 2).
In order to carry out dependency analyses in the combined population, we decided to conduct another meta-analysis using logistic regression analysis assuming an additive model adjusted by the 10 first PCs and the country of origin. A slightly more significant association between SCO and rs1136759/HLA-DRβ1 Ser13 was observed with this method (PMETA [LOG REG] = 1.32E−08, OR = 1.80, 95% CI = 1.47–2.21) (Supplementary Data 2). As observed in the discovery phase, conditioning by the top signal also decreased substantially the statistical significance of class II suggestive signals (Fig. 2, Supplementary Data 2).
a Unconditioned test of the MHC region. b Results of the MHC region after conditioning on HLA-DRβ1 Ser13. The −log10 of the combined logistic regression test P-values are plotted against their physical chromosomal position. A red/blue colour gradient was used to represent the effect size of each analysed variant (red for risk and blue for protection). The red line represents the genome-wide level of significance (P < E−08).
Similarly, when we tested the possible influence of the polymorphic amino acid positions in SCO predisposition in the combined dataset by the means of a likelihood-ratio test, the most associated position was HLA-DRβ1 13 (P = 2.90E−07). The effect sizes of the six possible residues that can be present at this amino acid position are shown in Table 2. Consistent with the above, the statistical significance of most positions was considerably reduced when conditioning on HLA-DRβ1 13, which supported the causality of this amino acid position (Supplementary Data 3 and Supplementary Fig. 3).
Genome-wide meta-analysis of discovery and replication studies
Taking advantage of the availability of GWAS data for the replication cohort, we aimed to identify possible additional association signals by performing a much more powerful genome-wide combined analysis using the inverse variance method (Supplementary Fig. 1).
A new genetic association at the genome-wide level of significance was observed between the SCO phenotype and a group of SNPs in complete LD with rs115054029 (PMETA [INV VAR] = 4.24E−08, OR = 3.14, 95% CI = 2.09–4.74) (Table 1 and Fig. 3). In this case, the ORs were consistent between studies, with no significant heterogeneity observed (ORIBERIANS = 3.05, ORGERMANS = 3.38, Q = 0.82) (Table 1). The nearest gene to this haplotype is vaccinia-related kinase 1 (VRK1, MIM [602168]), which encodes a member of the VRK family of serine/threonine protein kinases playing a crucial role in regulating the cell cycle17.
Data for the Iberian discovery cohort (a), the German replication cohort (b), and the combined cohort (c) are shown. The −log10 of the P-values from the logistic regression tests and the inverse variance method are plotted against their physical chromosomal position. A red/blue colour gradient was used to represent the effect size of each analysed variant (red for risk and blue for protection). The red line represents the genome-wide level of significance (P < 5E−08).
Although several suggestive signals were observed, the analyses of the remaining SPGF groups did not produce any additional significant results (Supplementary Fig. 1).
Additional evidence of the consistency of the Sertoli cell-only phenotype associations
The exclusion criteria for participating in this study considered known causes of male infertility that can be assessed during the clinical routine. Regarding the congenital causes, those include karyotype analysis and screening for Y chromosome microdeletions. However, the presence of high-penetrance point mutations in key genes for spermatogenesis is not usually evaluated. As a consequence, it is likely that our study cohort contained some patients of SPGF whose aetiology could be explained by a single-gene mutation. Therefore, in order to evaluate the consistency of the observed SCO genetic associations, we decided to repeat the SCO analysis after removing cases with potential monogenic causes of their infertility. With that aim, we followed a validated workflow18 to detect the presence of rare coding variants in genes with known mutations associated with SCO, accordingly with both the “Male Infertility Genomic Consortium (IMIGC) database” and the “Infertility Disease Database (IDDB)”19,20. This method allowed us to identify 32 carriers of rare variants located in the exons of 40 SCO-associated genes. Interestingly, despite the evident reduction in the statistical power of this new genetic association test, the analysis of our GWAS data after removing such individuals produced even more significant results for both the HLA region (rs1136759: P = 1.04E−08, OR = 1.90, 95% CI = 1.52–2.36) and the VRK1 locus (P = 3.91E−08, OR = 3.36, 95% CI = 2.18–5.18).
Overlap of functional annotations with VRK1 variants
According to the variant-to-Gene (V2G) pipeline of Open Targets (which considers evidence of functionality such as QTL experiments, chromatin interaction experiments, in silico functional predictions, and distance between the variant and each gene’s canonical transcription start site), all the SCO-associated SNPs in chromosome 14 were annotated as being functionally implicated in VRK1. To characterise the possible functional impact of this genomic region on SCO susceptibility, we identified all variants in high LD (r2 > 0.8) with the rs115054029 haplotype in the European population of the 1KGPh3 project, considering all proxies equally as candidates for exerting the pathogenic effect, as in the previous studies21.
Interestingly, overlap with different regulatory marks was observed for most proxies in multiple tissues (Supplementary Data 4). It should be noted that according to the ENCODE testis assays ENCFF651APG and ENCFF300WML, the proxies rs148465384 and rs17770386 (r2 = 1 and 0.97 with the lead SNP rs115054029, respectively) overlap with a protein binding site for the polymerase II, RNA, subunit A (POLR2A [MIM 180660]), and rs78543559 (r2 = 1 with the lead SNP rs115054029) is located in a CCCTC-binding factor (CTCF [MIM 604167]) site in the adult testis. Out of these three SNPs, rs17770386 showed a CADD value = 11.61, which predicts a high probability of deleteriousness.
In addition, accordingly, with position weight matrix (PWM) data generated from ENCODE transcription factor binding experiments22, rs76150492 (r2 = 1 with the lead rs115054029) was predicted to modify the binding site of the protein encoded by paired box gene 5 (PAX5 [MIM 167414]), which is reported to play a relevant role in spermatogenesis23.
The possible effect of the rs115054029 haplotypic block on the deregulation of VRK1 function is consistent with the expression data of this gene reported in the Human Protein Atlas portal24,25, which includes data from GTEx and Single Cell Expression Atlas projects26,27, amongst others. In this regard, this gene shows an abundant expression in the testis tissue, specifically within the seminiferous ducts (Supplementary Fig. 4). At the cellular level, spermatogonia and spermatocytes show the most enhanced mRNA expression of VRK1 amongst all cell types analysed (Supplementary Fig. 4), thus suggesting a possible role of its encoded protein in the first stages of the spermatogenic process.
Functional annotation enrichment analysis of the grey zones
Functional annotation enrichment analysis is a powerful strategy to identify relevant cell types or tissues for a particular trait. Therefore, we evaluated the possible enrichment of DHS hotspots within the grey zone of the GWAS results (defined as the signals with P-values ranging from 1E−05 to 5E−08) for SPGF and the different histological subsets/TESE outcomes. No statistically significant enrichment was observed for any of the analysed subgroups either in the Iberian or German cohorts separately. However, the analysis of the summary stats for the meta-analysis showed a significant DHS hotspot enrichment in SCO. Strikingly, such enrichment was specific for blood-related samples, namely CD19+ primary cells, CD20+ cells, foetal spleen, CD19+ primary cells, and GM06990 lymphoblastoid cell line (Fig. 4). The DHS enrichments detected in the analysis of the remaining combined subgroups did not reach the statistical significance (Supplementary Figs. 5–7).
The radial axis represents the enrichment (OR) for each of the analysed cell types that are sorted by tissue along the outside edge of the plot. Boxes forming the edge are coloured by tissue. Enrichment is calculated for the GWAS P-value threshold P < 1E−05. Dots in the inner ring of the outer circle denote significant GARFIELD enrichment after multiple-testing correction for the number of effective annotations and are coloured with respect to the tissue cell type tested (font size of tissue labels reflects the number of cell types from that tissue).
Previously reported associations with non-obstructive azoospermia in our dataset
Finally, we checked in our dataset the statistical significance of non-MHC loci that have been described to be associated with NOA at the genome-wide level of significance (±0.5 Mbp 3’ and 5’ of the reported SNP) in previous studies performed in populations of Asian descent9. The effect size and P-value of both the reported association signals and the top signals observed in our combined GWAS accordingly with NOA and the extreme phenotype SCO for each region are summarised in Supplementary Data 5 and 6, respectively. Regional Manhattan plots of each genomic region are also available in Supplementary Figs. 8–10. Amongst the six analysed SNPs, only the rs13206743 variant, located in the IL17A genomic region at chromosome 6, showed evidence of association with NOA at the 5% significance level under the additive model (P = 2.32E−03), with an effect of the minor allele similar to that reported in the original Chinese study (OR = 1.20 in the present GWAS vs. OR = 1.35 in the study by Hu et al.15). However, suggestive P-values were detected across most genomic regions (Supplementary Data 6 and Supplementary Figs. 8–10).
Discussion
We performed a genome-wide screening of around 7 million common variants in a large European cohort of well-characterised infertile men, comprising a total of 1274 patients diagnosed with SPGF of unexplained origin (772 NOA and 502 SO) and 1951 unaffected controls. The only available GWAS of this condition on this ancestry was published in 2009, which included a modest number of genetic variants and a small study cohort12. Therefore, we consider that our study provides an important contribution to the current knowledge on the genetic basis of SPGF, since the European population used in the previous study was underpowered, and the data on Asian populations were not analysed according to specific phenotypic patterns12,13,14,15.
We were able to identify VRK1 as a potential susceptibility locus for SCO, which represents the most severe manifestation of SPGF28. However, it is important to note that this association was not detected in the discovery phase but in the meta-analysis of both study cohorts. Consequently, additional replication studies in independent populations are definitively needed before establishing VRK1 as a firm SCO gene. VRK1 encodes a serine/threonine protein kinase that plays a pivotal role in the regulation of the cell cycle by phosphorylating relevant transcription factors for cell proliferation such as the tumour protein p53 (MIM 191170), histones, and different proteins involved in DNA damage response pathways29,30,31,32,33. Indeed, overexpression of VRK1 has been observed in many types of tumours, as it is directly implicated in the entry of the G1 phase of the cell cycle, chromatin condensation, Golgi fragmentation, and assembly of the nuclear envelope34.
The human testis represents the structure with the highest expression of VRK1 amongst all the tissues analysed in the GTEx project27. At the single-cell level, VRK1 expression has been restricted to actively dividing cells of the testis (mainly spermatogonia and primary spermatocytes)35. In this context, the association between SCO and the VRK1 region described here is consistent with previous studies on animal models of VRK1 defficiency, including Caenorhabditis elegans, Drosophila melanogaster, and Mus musculus, all three characterised by mitotic defects with resultant infertility36,37,38. Regarding the latter, mice containing hypomorphic alleles of this gene showed reduced testis size with a progressive loss of cellularity within the seminiferous tubules and absence of spermatogenesis with increasing postnatal age. Interestingly, by 11 weeks of age, these Vrk1-deficient mice developed an SCO-like phenotype, with the tubules comprising only one basal layer of Sertoli cells36. Therefore, it is likely that the SCO-associated genotypes in the upstream vicinity of the VRK1 locus identified in our study cohort increase SPGF risk by altering the correct regulation of this gene. Functional experiments focused on this genomic region may shed more light on this assumption.
On the other hand, our results reinforce the hypothesis of a crucial involvement of the MHC class II region in SPGF predisposition leading to NOA. In this sense, studies performed in Japan at the beginning of the present century reported a strong contribution of the classical MHC alleles HLA-DRB1*1302 and DQB1*0604 to NOA risk, independently from the presence of Y-chromosome microdeletions39,40. Later on, the two GWASs performed in Chinese populations, and the follow-up study of one of them, also highlighted this genomic region as the top associated signal with NOA across the whole genome13,14,15. Additional evidence of the major involvement of the MHC class II in NOA was also generated by two recent studies, including an independent meta-analysis and a fine-mapping of this region using GWAS data, both from Han Chinese, in which the haplotype HLA-DRB1*1302 was confirmed as a molecular marker for NOA41,42.
No previous studies have specifically interrogated the MHC contribution to SPGF susceptibility under a European genetic architecture9. With that aim, we inferred classical MHC alleles and polymorphic amino acid positions using an imputation method that has been thoroughly validated during the last decade using different approaches43,44,45. In fact, this same imputation pipeline was recently used by Huang et al. to fine-map this genomic region using GWAS data from NOA patients of Asian descent42. Interestingly, our analysis in Europeans showed a significant association of the MHC region specifically with the most severe NOA phenotype (defined by SCO) instead of with NOA as a whole. The SNP variant rs1136759*G and its encoded residue in position 13 of the HLA-DRβ1 subunit (serine), were significantly overrepresented in the SCO group compared to healthy controls in both the discovery phase and in the meta-analysis. Some heterogeneity in the effect sizes on SCO was observed between the Iberian and German populations. However, in both cases, the reference alleles (rs1136759*G and HLA-DRβ1 Ser13) showed risk ORs, and the combined analysis by logistic regression adjusted by PCs and country of origin (and, thus, controlling for possible population effects) yielded even more significant results (P = 1.32E−08, OR = 1.80) than those obtained by the inverse variance method (P = 4.62E−08, OR = 1.78). Moreover, all of the observed effects on SCO predisposition within the MHC class II region were eliminated after conditioning either on rs1136759*G/HLA-DRβ1 Ser13 in the independent variant test or on position HLA-DRβ1 13 in the omnibus test. All these pieces of evidence point clearly towards a firm association.
The amino acid HLA-DRβ1 Ser13 defines the HLA-DRB1*13 classical haplotypes, which also showed a strong genetic effect on SCO in our study. Therefore, the relevant role of the HLA-DRB1 gene in NOA reported in Asians seems to be limited to the SCO phenotype in Europeans. A possible explanation for this observation could be that the NOA cohorts included in the Asian studies were enriched in SCO patients. However, the clinical characterisation of such populations was not included in the original publications and, therefore, we can only speculate at this point. We did not detect any significant genetic effect on SPGF within the MHC class I region, as reported in the Asian population studies42, and a power issue could not be ruled out in this case. Moreover, since our subphenotype analyses were performed with considerably lower study cohorts, this may represent the main limitation of our study. Similarly, although most of the SPGF subtypes analysed here were correlated (that is, SPGF comprised all infertile men, NOA included all subgroups except SO, and TESEneg was composed mostly of SCO and MA individuals), we did not account for possible multiple testing effects due to the subphenotype analyses of our cohort, which may also represent a major caveat because the reported associations are close to the genome-wide significance level (P < 5E−08).
In any case, the fact that the MHC class II region reached the genome-wide statistical significance when analysing our less-powered SCO group compared to the larger NOA group gives an idea of the high impact of this region on the most extreme SPGF phenotype. In this regard, the position 13 of HLA-DRβ1 associated with SCO in our study is located in the binding groove of the HLA-DR molecule, being directly involved in the molecular interactions with the presented peptide, which implies a functional impact on T cell antigen recognition, either during early thymic development or peripheral immune responses46. Recent evidence also suggests that certain HLA-DRβ1 epitopes may increase the risk for autoimmune processes by favouring macrophage polarisation in an antigen-presenting-independent fashion47. Strikingly, this same amino acid position also represents one of the most relevant MHC positions in different immune-related diseases, including systemic lupus erythematosus, giant cell arteritis, rheumatoid arthritis, and type I diabetes, amongst others43,48,49,50. Indeed, there is firm evidence pointing to the immune response as a possible aetiological factor in SPGF. For example, (1) autoimmune responses against testicular structures and/or germ cells have been found to be associated with cryptorchidism (which may lead to SPGF), and patients of this condition carrying certain HLA-DRB1 haplotypes have been reported to show a higher production of anti-sperm antibodies, (2) infection and inflammation of the male genital tract is frequent in men diagnosed with male infertility, (3) acute or chronic inflammation may impair the testicular function through the inhibition of steroidogenesis and disturbance of the germ cell epithelium, (4) immune cell infiltrates associated with an exacerbated immune response have been observed in testicular biopsies from NOA patients, and (5) an expression signature comprising proinflammatory genes has been correlated with NOA51,52,53,54,55,56.
Therefore, the contribution of autoimmune processes to the extreme forms of SPGF like SCO should not be disregarded. Our data definitively support this idea and are consistent with the aetiological mechanism proposed by Gong et al., in which a chronic subclinical testicular inflammation may produce the release of novel self-peptides triggering autoimmunity through antigen-presentation to Th17 cells57. In fact, active chromatin regions in immune-related cell types and tissues are enriched with suggestive genetic associations with SCO (Fig. 4). Under this pathogenic scenario, it could be possible that the presence in the genome of some MHC class II genetic variants, such as rs1136759*G that implies a serine in the position 13 of HLA-DRβ1, may increase the probability of initiating such autoimmune response by favouring the presentation of more immunogenic peptides.
Finally, the association signal with unsuccessful TESE at the 5’ upstream region of FSHR detected in our discovery cohort was not replicated in the German population. In males, the follicle-stimulating hormone (FSH) is a major regulator of testis development and spermatogenesis through binding to its receptor (FSHR), which is located in the cell membrane of the Sertoli cells58,59. This pathway is also very relevant in female fertility, as it controls folliculogenesis and drives oocyte maturation60. Consequently, increasing evidence highlights these two genes as key players in the development of infertility. Although high-penetrant inactivating mutations of this signalling pathway are scarce, several SNPs in the genes encoding FSHR and the beta subunit of the ligand (FSHB) have been associated with unfavourable reproductive parameters in both female and male subjects (including SPGF cases) in a vast number of studies61,62,63,64,65,66,67,68,69,70,71,72. In addition, some of those SNPs have been also reported to influence the gene expression of FSHR/FSHB likely by modifying transcription factor binding sites in regulatory regions66,67,70,71,72,73. Therefore, a combined effect of both genes in male reproductive impairment has been proposed by integrating the transcriptional activity and the receptor sensitivity, which could be affected by common variations of the FSHB and FSHR genes, respectively74. Consistent with the above, stratification of patients accordingly to the risk genotypes of this pathway is being considered for improving the current FSH treatments of male infertility patients, which has been shown to improve sperm parameters in SPGF men75,76,77.
Taking all the above into consideration, we are confident in the consistency of the GWAS peak in the FSHR region detected in our analysis in Iberians accordingly with the TESE success. It can be speculated that there might be population-specific LD patterns that may link the associated rs186420734 SNP with the causal variant/s in the Iberian and German genetic backgrounds. Under this assumption, and considering that rs186420734 is a rare variant in the healthy population, a possible different tagger in Germans could not be detected due to a power limitation. This could be also the case with the seven previously reported non-MHC NOA hits at the genome-wide significance level in Asians, from which only IL17A rs13206743 was replicated here at the nominal level.
In conclusion, our results support the notion of unexplained SPGF as a complex trait influenced by common variation of the genome, with the added effect of risk genetic variants in an individual (mainly in non-coding regulatory regions) being critical for its development9. Moreover, the data presented here also suggest that SPGF (or NOA) is not a single disease from a genetic point of view, but a combination of different phenotypes that have only in common a critical failure of the spermatogenic process at different points; thus underpinning the importance of defining homogeneous study groups for elucidating its genetic basis. Therefore, there is still a long way to go until we may fully characterise the molecular network that underlies SPGF. Male infertility GWAS remain lagging behind many other fields and, indeed, much larger studies focused on specific SPGF phenotypes are still needed. An integrative approach will be also helpful in this challenging endeavour, considering the key role of the non-coding polymorphisms in SPGF predisposition and the intricate haplotype architecture of the genome. Hopefully, with time and effort, the increase in the understanding of these complex processes may help to develop more efficient diagnostic and prognostic tools that could anticipate both the diagnosis and TESE outcome before the analysis of a testis biopsy, thus preventing the NOA patients with extreme phenotypes from undergoing unnecessary surgeries.
Methods
Study population
Two independent case-control cohorts of European descent (including a discovery cohort from the Iberian Peninsula and a replication cohort from Germany) were analysed in this study, comprising a total of 1274 infertile men due to SPGF of unexplained origin (772 NOA and 502 SO patients) and 1951 unaffected controls. Informed written consent was signed by all participants before being enroled in the study and all DNA samples were irreversibly anonymised. The following procedures were in accordance with the tenets of the Declaration of Helsinki and received approval by the Ethics Committee “CEIM/CEI Provincial de Granada” (Andalusia, Spain) at the session held on January 26, 2021 (approval number: 1/21). Besides, each participating centre received ethical approval and complied with the requirements of their local regulatory authorities.
SPGF cases were recruited in different public health centres and private fertility clinics from Spain and Portugal, and at the Centre of Reproductive Medicine and Andrology, University Hospital Münster, Germany, following comprehensive selection criteria based on the approved guidelines for the management of infertile men by the American Urological Association (AUA)/American Society for Reproductive Medicine (ASRM), the Canadian Urological Association (CUA), and the World Health Organization (WHO, 2010)78,79,80. These criteria include a physical examination of male patients showing evidence of clinical infertility by revision of the medical history, genetic screening (including Y-chromosome microdeletions and karyotype analysis), endocrine profile (follicle stimulating hormone, luteinizing hormone and testosterone), and semen analysis. Patients with no signs of post-testicular ejaculatory duct obstruction were analysed to establish the diagnosis of SO (<5 million spermatozoa/mL semen) or NOA (total absence of sperm in the ejaculate after two high-speed centrifugation processes in two different semen samples).
Patients showing known causes of male infertility were excluded from the study. Consequently, as in other related genetic studies16,21,81, only those men with a normal history of testicular development with no evidence of either testicular (such as orchitis, testicular malformations, and obstruction of vas deferens) or karyotype/chromosome abnormalities were selected. The non-obstructive primary spermatogenic impairment was subsequently confirmed in around half of our SPGF cohort by the histological analysis of a testicular biopsy from those patients that decided to undergo assisted reproduction treatments involving TESE (including both conventional TESE and micro-TESE).
The pathological anatomy results from the biopsy were used to classify the SPGF patients into different subgroups according to the observed histological phenotypes, including HS (extremely low cell counts of the germline but with all stages of spermatogenesis/spermiogenesis observable in few testicular locations), MA (early maturation arrest either at spermatogonia or at primary spermatocyte stages of more than 90% of the germline), and SCO (total absence of germ cells in all seminiferous tubules). Furthermore, two additional subgroups of NOA were established based on the TESE outcome, as follows: TESEneg (if no viable sperm cell was retrieved from the biopsy) and TESEpos (including NOA patients with a successful sperm retrieval). SO patients were not considered for this classification because the TESE success rate associated with this form of infertility is close to 100%82. All the available information about the main clinical features of our study cohort is shown in Supplementary Table 1.
Generation of genotype data and quality controls
Genomic DNA samples obtained from peripheral blood mononuclear cells of every participant were genotyped at the genome-wide level using the Infinium™ Global Screening Array-24 v3.0 (GSA) in an iScan System (Illumina, Inc), following the manufacturer’s protocol. This is an advanced high-throughput genotyping platform that allows the genotyping of more than 700,000 carefully selected genetic variants, including tag polymorphisms, relevant markers for clinical research, and variants for quality control (such as ancestry informative markers). Thus, this system delivers a high genomic coverage ideal for imputation methods. The genotyping of the Iberian samples was conducted in the Human Genotyping Unit of the National Genotyping Centre (CEGEN) at the Spanish National Cancer Research Centre (Madrid, Spain), whereas that of the German samples was carried out in the Genomics Unit of the LIFE & BRAIN GmbH Biomedical & Scientific Technology Platform (Bonn, Germany). In both cases, the genotype calling was performed with the Genotyping Module (v.2.0) implemented in the GenomeStudio software (Illumina, Inc), and assigning the chromosome positions according to the Genome Reference Consortium Human Build 38 (GRCh38).
The genotype data was subject to stringent quality control (QC) measures using R and PLINK v.1.983. First, we removed all the genetic variants with a cluster separation < 0.4 and filtered out INDELs and rare variants with minor allele frequencies (MAF) < 0.01. Moreover, SNPs with call rates < 0.98 and those whose genotype distributions deviated from Hardy–Weinberg equilibrium (HWE) in controls (P < 0.001) were also excluded from further analyses. Regarding the QC of the recruited individuals, samples with <95% of successfully called SNPs and one subject per pair of first-degree relatives (identity by descent >0.4) were excluded. In addition, principal component (PC) analyses were conducted with a set of 2921 ancestry-informative markers included in the GSA chip, in order to detect and remove population outliers (>4 standard deviations from the cluster centroids of each population) using PLINK, R and the gcta64 software. Supplementary Fig. 11 showed the two first PCs plotted against each other for the samples that remained after the removal of population outliers.
Imputation methods
To maximise the genetic coverage of our data sets, we conducted SNP genotype imputation for chromosomes 1-22 and X on the genome build GRCh38, using the haplotype data of the ‘NHLBI Trans-OMICs for Precision Medicine’ (TOPMed) programme (freeze 5) as reference panel, in the Next-Generation Genotype Imputation Service of the TOPMed Imputation Server84. Eagle v.2.4. for haplotype phasing85 and minimac4 algorithms were applied for genotype imputation86. Due to the lack of a Y chromosome reference panel, we could not impute additional Y chromosome variants; therefore, only the directly genotyped SNPs were analysed.
Moreover, considering previously reported evidence regarding the possible role of the major histocompatibility (MHC) system in NOA predisposition42, we decided to carry out a more comprehensive interrogation of this genomic region in our study population. With that aim, we extracted the extended MHC region (from 29 to 34 Mbp in chromosome 6) from the non-imputed data and used the SNP2HLA method44, with a reference panel collected by the Type 1 Diabetes Genetics Consortium comprising 5,225 individuals of European origin87, to impute SNPs, classical MHC alleles at two- and four-digits, and polymorphic amino acid positions.
To ensure the high quality of the imputed data, only SNPs with a very reliable imputation quality metric (namely Rsq > 0.9 for minimac4 or posterior probability > 0.9 for SNP2HLA) were analysed (genotypes that did not reach the selected cut-off value were set to missing). Furthermore, the imputed data underwent also rigorous QC filters using PLINK and R, including the removal of singletons, rare variants (MAF < 0.01), and polymorphisms with call rates lower than 98%. SNPs whose genotype frequencies showed evidence of deviation from HWE (P < 0.001) were also excluded from further analyses.
Following the QC procedures, the final case-control data sets comprised 627 SPGF patients and 1027 unaffected controls from the Iberian Peninsula and 647 SPGF patients and 924 control participants from Germany. A total of 7,371,432 SNPs were analysed in the Iberian cohort and 7,536,533 SNPs in the German cohort. Regarding the comprehensive interrogation of the MHC region, the imputed data included 7258 SNPs, 424 classical alleles (at 2- and 4-digit coverage), and 1276 polymorphic amino acid variants from the human leucocyte antigen (HLA) genes HLA-A (MIM 142800), HLA-B (MIM 142830), HLA-C (MIM 142840), HLA-DPA1 (MIM 142880), HLA-DPB1 (MIM 142858), HLA-DQA1 (MIM 146880), HLA-DQB1 (MIM 604305), and HLA-DRB1 (MIM 142857).
Statistics and reproducibility
To determine the minimum effect sizes that could be detected in this study based on experimental design, power analysis estimations were calculated with the online tool of the Genetic Association Study (GAS) Power Calculator, which implements the methods described in Skol et al. assuming additive genetic effects88 (Supplementary Table 2).
All the case-control comparisons were performed with PLINK and R. In the first step, we tested for association using the imputed data of the discovery cohort (Iberian). Specifically, we compared all case groups (SPGF, NOA, SO, MA, HS, and TESEneg) against the group of unaffected controls using logistic regression on the best-guess genotypes (Rsq > 0.9), adding the 10 first PCs and the country of origin (Spain or Portugal) as covariates and assuming additive effects. If a subtype-specific genetic association was detected, cases showing such clinical phenotype/TESE outcome were also compared against those not showing it, to check whether the association was maintained after eliminating SPGF as a possible confounding variable.
With regards to the analysis of the MHC region, we tested SNPs, classical HLA alleles, and all possible combinations of amino acid residues per position by logistic regression as described above. For the positional model analysis, we established a null generalised linear model for each position including the 10 first PCs and the country of origin as covariates, which was compared on the basis of a χ2-based estimate to an alternative model including such covariates and all the possible residues at those positions (considered as conditioning factors)43.
Besides, considering the extensive linkage disequilibrium (LD) of this genomic region, dependency analyses were performed to identify independent genetic effects by step-wise logistic regression with conditioning by the top association signals (together with the 10 first PCs and the country of origin).
After evaluating the relevance of the results of the discovery phase, we decided to analyse an independent replication cohort from Germany following the same workflow described above for the discovery cohort.
Finally, since whole-genome genotype data were generated for both the discovery and the replication cohorts, we decided to conduct a combined analysis of both studies by the means of the inverse variance weighted meta-analysis under a fixed effects model; thus, increasing the statistical power to detect additional association signals. In this case, the possible heterogeneity of the effect sizes between the two analysed studies was evaluated using both I2 and Cochran’s Q tests. Additionally, we also performed a combined analysis of the MHC region (including both the discovery and the replication cohorts) by logistic regression on the best-guess genotypes (>0.9 probability) assuming an additive model with the 10 first PCs and the country of origin (Spain, Portugal, and Germany) as covariates, in order to allow an adequate evaluation of the dependency effects in the pooled dataset43.
Odds ratios (OR) and 95% confidence intervals (CI) were calculated for all the statistical analyses. The statistical significance was set at the genome-wide level (P < 5E−08) in the meta-analysis, provided that the P-value for each study separately was below 0.05 and the directionality of effect presented by the ORs was consistent between studies. The Manhattan plots were generated using an in-house R script, and the zooms of the associated regions were created with LocusZoom.js89. The 3D models of the HLA molecules were performed with the UCSF Chimera software90. The online tools provided by the GTEx27 and LDlink91 portals were used for figure generation together with custom R scripts.
In silico characterisation of the associated regions
In order to shed light on the possible pathogenic mechanisms involved in SPGF susceptibility, we decided to enrich our results with publicly available functional annotation data by using different bioinformatics approaches.
With that aim, we first used LDLink91 to identify all proxies of the associated variants outside the MHC region (r2 > 0.8) in the European population of the 1KGPh3. Then, we queried different databases and online tools to extract all the relevant information that could help us to elucidate the potential molecular and cellular mechanisms underlying the observed associations, including RegulomeDB92, Haploreg v.4.1.93, Open Targets Genetics94, SNPnexus95, GTEx27, Human Protein Atlas24,25, and ENCODE96, which integrate the datasets included in Ensembl, SIFT, Polyphen, CpG, Vista enhancers, miRbase, TarBase, TargetScan, miRNA Registry, snoRNA-LBME-DB, Roadmap, Ensembl regulatory build, CADD, DeepSEA, EIGEN, FATHMM, fitCons, FunSeq2 GWAVA, and REMM. The different predictive scores for functionality are described in Supplementary Tables 3 and 4.
Furthermore, the possible overlap of the associated variants and their proxies with regulatory regions in the testicular tissue was assessed by analysing the testis-specific assays in ENCODE96: DNase-seq hypersensitivity sites (ENCFF323BCL, ENCFF608KRZ); CTCF (ENCODE sample references: ENCFF300WML, ENCFF559LDF, ENCFF644JKD, ENCFF767LMP, ENCFF788RFY, ENCFF855EVV) and POLR2A (ENCFF535DHF, ENCFF651APG) protein ChIP-seqs; H3K4me3 (ENCFF286DAB, ENCFF509DBT), H3K4me1 (ENCFF316MJM), H3K27ac (ENCFF610XSK, ENCFF819NRA), H3K9me3 (ENCFF711LHL), and H3K27me3 (ENCFF881OHS) histone modification ChIP-seqs.
Finally, we also assessed the enrichment of the suggestive association signals (P < 1E−05) observed for the analysed phenotypes and the DNase I-hypersensitive sites (DHS hotspots) identified by ENCODE96 and the Roadmap Epigenomics project97 for all available cell types using GARFIELD98. In brief, GARFIELD performs a greedy LD-prunning, LD-based tagging, and functional annotation of the genetic variants included in the GWAS summary statistics. Functional annotation enrichment is quantified by the means of generalised linear models controlling for distance to the nearest TSS and number of LD proxies, and establishing different genome-wide significance thresholds. According to Bonferroni’s multiple testing correction based on the number of independent tests carried out, the significant threshold for enrichment in DHS hotspots was established at P-value < 2.6E−04, as recommended by Iotchkova et al.98.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The summary statistics of the combined GWAS analysed here (both NOA and SO) are available through the NHGRI-EBI GWAS Catalogue (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). Individual-level genotype data are not publicly available because they could compromise the privacy of participants and informed consent. The 3D structure of the HLA-DR molecule shown in Fig. 1 is based on the Protein Data Bank entry 3pdo, with a direct view of the peptide-binding groove. The source data behind the plots shown in Fig. 4 and Supplementary Figs. 5–7 is included in supplementary data 7. All other data are contained either in the article file and its supplementary information or available upon reasonable request to the corresponding authors.
References
Sun, H. et al. Global, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990–2017: results from a global burden of disease study, 2017. Aging 11, 10952–10991 (2019).
Tournaye, H., Krausz, C. & Oates, R. D. Novel concepts in the aetiology of male reproductive impairment. Lancet Diabetes Endocrinol. 5, 544–553 (2017).
Mascarenhas, M. N., Flaxman, S. R., Boerma, T., Vanderpoel, S. & Stevens, G. A. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 9, e1001356 (2012).
Krausz, C. & Riera-Escamilla, A. Genetics of male infertility. Nat. Rev. Urol. 15, 369–384 (2018).
Palermo, G. et al. Sperm characteristics and outcome of human assisted fertilization by subzonal insemination and intracytoplasmic sperm injection. Fertil. Steril. 59, 826–835 (1993).
Corona, G. et al. Sperm recovery and ICSI outcomes in men with non-obstructive azoospermia: a systematic review and meta-analysis. Hum. Reprod. Update 25, 733–757 (2019).
Su, L. M. et al. Testicular sperm extraction with intracytoplasmic sperm injection for nonobstructive azoospermia: testicular histology can predict success of sperm retrieval. J. Urol. 161, 112–116 (1999).
Tuttelmann, F., Ruckert, C. & Ropke, A. Disorders of spermatogenesis: Perspectives for novel genetic diagnostics after 20 years of unchanged routine. Medizinische. Genetik: Mitteilungsblatt des. Berufsverbandes Medizinische Genetik eV 30, 12–20 (2018).
Cervan-Martin M., Castilla J. A., Palomino-Morales R. J. & Carmona F. D. Genetic landscape of nonobstructive azoospermia and new perspectives for the clinic. J. Clin. Med. 9, 300 (2020).
Visscher, P. M. et al. 10 Years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
Aston, K. I. & Carrell, D. T. Genome-wide study of single-nucleotide polymorphisms associated with azoospermia and severe oligozoospermia. J. Androl. 30, 711–725 (2009).
Hu, Z. et al. A genome-wide association study in Chinese men identifies three risk loci for non-obstructive azoospermia. Nat. Genet. 44, 183–186 (2011).
Zhao, H. et al. A genome-wide association study reveals that variants within the HLA region are associated with risk for nonobstructive azoospermia. Am. J. Hum. Genet. 90, 900–906 (2012).
Hu, Z. et al. Association analysis identifies new risk loci for non-obstructive azoospermia in Chinese men. Nat. Commun. 5, 3857 (2014).
Cervan-Martin, M. et al. Effect and in silico characterization of genetic variants associated with severe spermatogenic disorders in a large Iberian cohort. Andrology 9, 1151–1165 (2021).
Valbuena, A., Sanz-Garcia, M., Lopez-Sanchez, I., Vega, F. M. & Lazo, P. A. Roles of VRK1 as a new player in the control of biological processes required for cell division. Cell Signal. 23, 1267–1272 (2011).
Lopez-Rodrigo, O., Bossini-Castillo, L., Carmona, F. D., Bassas, L. & Larriba, S. Genome-wide compound heterozygote analysis highlights DPY19L2 alleles in a non-consanguineous Spanish family with total globozoospermia. Reprod. Biomed. Online 45, 332–340 (2022).
Houston, B. J. et al. A systematic review of the validated monogenic causes of human male infertility: 2020 update and a discussion of emerging gene-disease relationships. Hum. Reprod. Update 28, 15–29 (2021).
Wu, J. et al. IDDB: a comprehensive resource featuring genes, variants and characteristics associated with infertility. Nucleic Acids Res. 49, D1218–D1224 (2021).
Cervan-Martin, M. et al. Evaluation of male fertility-associated loci in a European population of patients with severe spermatogenic impairment. J. Pers. Med. 11, 22 (2020).
Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).
Fang, F. et al. A PAX5-OCT4-PRDM1 developmental switch specifies human primordial germ cells. Nat. Cell Biol. 20, 655–665 (2018).
Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv. 7, eabh2169 (2021).
Papatheodorou, I. et al. Expression Atlas update: from tissues to single cells. Nucleic Acids Res. 48, D77–D83 (2020).
Consortium, G. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Ghanami Gashti, N., Sadighi Gilani, M. A. & Abbasi, M. Sertoli cell-only syndrome: etiology and clinical management. J. Assist. Reprod. Genet. 38, 559–572 (2021).
Lopez-Borges, S. & Lazo, P. A. The human vaccinia-related kinase 1 (VRK1) phosphorylates threonine-18 within the Mdm-2 binding site of the p53 tumour suppressor protein. Oncogene 19, 3656–3664 (2000).
Lopez-Sanchez, I. et al. VRK1 interacts with p53 forming a basal complex that is activated by UV-induced DNA damage. FEBS Lett. 588, 692–700 (2014).
Kang, T. H. et al. Mitotic histone H3 phosphorylation by vaccinia-related kinase 1 in mammalian cells. Mol. Cell. Biol. 27, 8533–8546 (2007).
Monsalve, D. M. et al. VRK1 phosphorylates and protects NBS1 from ubiquitination and proteasomal degradation in response to DNA damage. Biochim. Biophys. Acta 1863, 760–769 (2016).
Sevilla, A., Santos, C. R., Barcia, R., Vega, F. M. & Lazo, P. A. c-Jun phosphorylation by the human vaccinia-related kinase 1 (VRK1) and its cooperation with the N-terminal kinase of c-Jun (JNK). Oncogene 23, 8950–8958 (2004).
Campillo-Marcos, I., Garcia-Gonzalez, R., Navarro-Carrasco, E. & Lazo, P. A. The human VRK1 chromatin kinase in cancer biology. Cancer Lett. 503, 117–128 (2021).
Guo, J. et al. The dynamic transcriptional cell atlas of testis development during human puberty. Cell Stem Cell 26, 262–276.e264 (2020).
Wiebe, M. S., Nichols, R. J., Molitor, T. P., Lindgren, J. K. & Traktman, P. Mice deficient in the serine/threonine protein kinase VRK1 are infertile due to a progressive loss of spermatogonia. Biol. Reprod. 82, 182–193 (2010).
Cullen, C. F., Brittle, A. L., Ito, T. & Ohkura, H. The conserved kinase NHK-1 is essential for mitotic progression and unifying acentrosomal meiotic spindles in Drosophila melanogaster. J. Cell Biol. 171, 593–602 (2005).
Gorjanacz, M. et al. Caenorhabditis elegans BAF-1 and its kinase VRK-1 participate directly in post-mitotic nuclear envelope assembly. EMBO J. 26, 132–143 (2007).
Tsujimura, A. et al. Susceptibility gene for non-obstructive azoospermia located near HLA-DR and -DQ loci in the HLA class II region. Hum. Genet. 110, 192–197 (2002).
Takao, T. et al. Susceptibility gene for non-obstructive azoospermia in the HLA class II region: correlations with Y chromosome microdeletion and spermatogenesis. Int. J. Androl. 27, 37–41 (2004).
Zou, S. et al. Association and meta-analysis of HLA and non-obstructive azoospermia in the Han Chinese population. Andrologia 49, e12600 (2017).
Huang, M. et al. Fine mapping the MHC region identified rs4997052 as a new variant associated with nonobstructive azoospermia in Han Chinese males. Fertil. Steril. 111, 61–68 (2019).
Carmona, F. D. et al. A large-scale genetic analysis reveals a strong contribution of the HLA class II region to giant cell arteritis susceptibility. Am. J. Hum. Genet. 96, 565–580 (2015).
Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS ONE 8, e64683 (2013).
Darlay, R. et al. Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis. PLoS Genet. 14, e1007833 (2018).
Dimitrov, I., Garnev, P., Flower, D. R. & Doytchinova, I. Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis. Eur. J. Med. Chem. 45, 236–243 (2010).
van Drongelen, V. et al. HLA-DRB1 allelic epitopes that associate with autoimmune disease risk or protection activate reciprocal macrophage polarization. Sci. Rep. 11, 2599 (2021).
Kim, K. et al. The HLA-DRbeta1 amino acid positions 11-13-26 explain the majority of SLE-MHC associations. Nat. Commun. 5, 5902 (2014).
Hu, X. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. Nat. Genet. 47, 898–905 (2015).
Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).
Hussein, M. R. et al. Phenotypic characterization of the immune and mast cell infiltrates in the human testis shows normal and abnormal spermatogenesis. Fertil. Steril. 83, 1447–1453 (2005).
Kurpisz, M. et al. Weak association of anti-sperm antibodies and strong association of familial cryptorchidism/infertility with HLA-DRB1 polymorphisms in prepubertal Ukrainian boys. Reprod. Biol. Endocrinol. 9, 129 (2011).
Spiess, A. N. et al. Cross-platform gene expression signature of human spermatogenic failure reveals inflammatory-like response. Hum. Reprod. 22, 2936–2946 (2007).
Punab, M. et al. Causes of male infertility: a 9-year prospective monocentre study on 1737 patients with reduced total sperm counts. Hum. Reprod. 32, 18–31 (2017).
Schuppe, H. C. et al. Urogenital infection as a risk factor for male infertility. Dtsch. Arztebl. Int. 114, 339–346 (2017).
Hedger, M. P. Immunophysiology and pathology of inflammation in the testis and epididymis. J. Androl. 32, 625–640 (2011).
Gong, J., Zeng, Q., Yu, D. & Duan, Y. G. T. Lymphocytes and testicular immunity: a new insight into immune regulation in testes. Int. J. Mol. Sci. 22, 57 (2020).
Huang, H. F., Pogach, L. M., Nathan, E., Giglio, W. & Seebode, J. J. Synergistic effects of follicle-stimulating hormone and testosterone on the maintenance of spermiogenesis in hypophysectomized rats: relationship with the androgen-binding protein status. Endocrinology 128, 3152–3161 (1991).
Ruwanpura, S. M., McLachlan, R. I., Matthiesson, K. L. & Meachem, S. J. Gonadotrophins regulate germ cell survival, not proliferation, in normal adult men. Hum. Reprod. 23, 403–411 (2008).
Camp, T. A., Rahal, J. O. & Mayo, K. E. Cellular localization and hormonal regulation of follicle-stimulating hormone and luteinizing hormone receptor messenger RNAs in the rat ovary. Mol. Endocrinol. 5, 1405–1417 (1991).
Wu, Q. et al. The susceptibility of FSHB-211G>T and FSHR G-29A, 919A>G, 2039A>G polymorphisms to men infertility: an association study and meta-analysis. BMC Med. Genet. 18, 81 (2017).
Haqiqi, H. et al. Association of FSHR missense mutations with female infertility, in silico investigation of their molecular significance and exploration of possible treatments using virtual screening and molecular dynamics. Anal. Biochem. 586, 113433 (2019).
Simoni, M. et al. Mutational analysis of the follicle-stimulating hormone (FSH) receptor in normal and infertile men: identification and characterization of two discrete FSH receptor isoforms. J. Clin. Endocrinol. Metab. 84, 751–755 (1999).
Ahda, Y. et al. Follicle-stimulating hormone receptor gene haplotype distribution in normozoospermic and azoospermic men. J. Androl. 26, 494–499 (2005).
Trevisan, C. M., de Oliveira, R., Christofolini, D. M., Barbosa, C. P. & Bianco, B. Effects of a polymorphism in the promoter region of the follicle-stimulating hormone subunit beta (FSHB) gene on female reproductive outcomes. Genet. Test. Mol. Biomark. 23, 39–44 (2019).
Grigorova, M., Punab, M., Ausmees, K. & Laan, M. FSHB promoter polymorphism within evolutionary conserved element is associated with serum FSH level in men. Hum. Reprod. 23, 2160–2166 (2008).
Grigorova, M. et al. Increased prevalance of the -211 T allele of follicle stimulating hormone (FSH) beta subunit promoter polymorphism and lower serum FSH in infertile men. J. Clin. Endocrinol. Metab. 95, 100–108 (2010).
Grigorova, M. et al. Genetically determined dosage of follicle-stimulating hormone (FSH) affects male reproductive parameters. J. Clin. Endocrinol. Metab. 96, E1534–E1541 (2011).
Grigorova, M. et al. Reproductive physiology in young men is cumulatively affected by FSH-action modulating genetic variants: FSHR -29G/A and c.2039 A/G, FSHB -211G/T. PLoS ONE 9, e94244 (2014).
Ferlin, A. et al. Toward a pharmacogenetic approach to male infertility: polymorphism of follicle-stimulating hormone beta-subunit promoter. Fertil. Steril. 96, 1344–1349.e1342 (2011).
Selice, R. et al. The response to FSH treatment in oligozoospermic men depends on FSH receptor gene polymorphisms. Int. J. Androl. 34, 306–312 (2011).
Lindgren, I., Giwercman, A., Axelsson, J. & Lundberg Giwercman, Y. Association between follicle-stimulating hormone receptor polymorphisms and reproductive parameters in young men from the general population. Pharmacogenet. Genom. 22, 667–672 (2012).
Wunsch, A. et al. Single-nucleotide polymorphisms in the promoter region influence the expression of the human follicle-stimulating hormone receptor. Fertil. Steril. 84, 446–453 (2005).
Tuttelmann, F. et al. Combined effects of the variants FSHB -211G>T and FSHR 2039A>G on male reproductive parameters. J. Clin. Endocrinol. Metab. 97, 3639–3647 (2012).
Busch, A. S., Kliesch, S., Tuttelmann, F. & Gromoll, J. FSHB -211G>T stratification for follicle-stimulating hormone treatment of male infertility patients: making the case for a pharmacogenetic approach in genetic functional secondary hypogonadism. Andrology 3, 1050–1053 (2015).
Attia, A. M., Abou-Setta, A. M. & Al-Inany, H. G. Gonadotrophins for idiopathic male factor subfertility. Cochrane Database Syst. Rev. 8, CD005071 (2013).
Simoni, M., Brigante, G., Rochira, V., Santi, D. & Casarini, L. Prospects for FSH treatment of male infertility. J. Clin. Endocrinol. Metab. 105, 2105–2118 (2020).
Schlegel, P. N. et al. Diagnosis and treatment of infertility in men: AUA/ASRM guideline part l. Fertil. Steril. 115, 54–61 (2021).
Jarvi, K. et al. CUA Guideline: the workup of azoospermic males. Can. Urol. Assoc. J. = J. Assoc. Urol. Can. 4, 163–167 (2010).
Cooper, T. G. et al. World Health Organization reference values for human semen characteristics. Hum. Reprod. Update 16, 231–245 (2010).
Cervan-Martin, M. et al. Intronic variation of the SOHLH2 gene confers risk to male reproductive impairment. Fertil. Steril. 114, 398–406 (2020).
Koscinski, I. et al. Optimal management of extreme oligozoospermia by an appropriate cryopreservation programme. Hum. Reprod. 22, 2679–2684 (2007).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).
Brown, W. M. et al. Overview of the MHC fine mapping data. Diabetes Obes. Metab. 11(Suppl. 1), 2–7 (2009).
Skol, A. D., Scott, L. J., Abecasis, G. R. & Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006).
Boughton, A. P. et al. LocusZoom.js: interactive and embeddable visualization of genetic association study results. Bioinformatics 37, 3017–3018 (2021).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).
Dong, S. & Boyle, A. P. Predicting functional variants in enhancer and promoter elements using RegulomeDB. Hum. Mutat. 40, 1292–1298 (2019).
Ward, L. D. & Kellis, M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).
Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–D1320 (2021).
Oscanoa, J. et al. SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update). Nucleic Acids Res. 48, W185–W192 (2020).
Luo, Y. et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 48, D882–D889 (2020).
Satterlee, J. S. et al. The NIH Common Fund/Roadmap Epigenomics Program: successes of a comprehensive consortium. Sci. Adv. 5, eaaw6507 (2019).
Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).
Acknowledgements
We thank the National DNA Bank Carlos III (University of Salamanca, Spain) for supplying part of the control DNA samples from Spain and all the participants for their essential collaboration. This work was supported by the Spanish Ministry of Science through the Spanish National Plan for Scientific and Technical Research and Innovation (refs. SAF2016-78722-R and PID2020-120157RB-I00), the Andalusian Plan for Research and Innovation (PAIDI 2020) (ref. PY20_00212), and the R+D+i Projects of the FEDER Operational Programme 2020 (ref. B-CTS-584-UGR20). F.D.C. was supported by the “Ramón y Cajal” programme (ref. RYC-2014-16458), and L.B.C. was supported by the Spanish Ministry of Economy and Competitiveness through the “Juan de la Cierva Incorporación” programme (ref. IJC2018-038026-I, funded by MCIN/AEI /10.13039/501100011033), all of them including FEDER funds. A.G.J. was funded by MCIN/AEI /10.13039/501100011033 and FSE “El FSE invierte en tu futuro” (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). A.M.L. is funded by the Portuguese Government through FCT (IF/01262/2014). P.I.M. is supported by the FCT post-doctoral fellowship (SFRH/BPD/120777/2016), financed from the Portuguese State Budget of the Ministry for Science, Technology and High Education and from the European Social Fund, available through the Programa Operacional do Capital Humano. ToxOmics—Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, Nova Medical School, Lisbon, is also partially supported by FCT (Projects: UID/BIM/00009/2013 and UIDB/UIDP/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 2017SGR191). SLarriba is sponsored by the “Researchers Consolidation Programme” from the SNS-Departament de Salut Generalitat de Catalunya (Exp. CES09/020). The German cohort was recruited within the Male Reproductive Genomics (MERGE) study and supported by the German Research Foundation Clinical Research Unit ‘Male Germ Cells’ (DFG CRU326, grants to F.T. and J.G.). This article is related to the Ph.D. Doctoral Thesis of Miriam Cerván-Martín (grant ref. BES-2017-081222 funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro”).
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F.D.C. and R.J.P.-M. were involved in the conception, design, and supervision of the study. M.C.-M., F.T., A.M.L., L.B.-C., S.G.-M., A.G.-J., M.B., and R.J. participated in the methodology. M.C.-M., L.B.-C., S.G.-M., and A.G.-J. performed the formal analysis. M.C.-M., F.T., A.M.L., L.B.-C., F.D.C., and R.J.P.-M. were involved in the interpretation of the data. A.M.L., F.T., R.R.-E., N.G., S.Lu., G.R., S.S.-R., J.A.C., M.C.G., A.C., V.M., F.J.V., A.P., C.G., S.G., D.A., J.A., F.Q., C.C.-J., A.A., J.N., S.So., I.P., M.G.P., S.C., J.S.-C., O.L.-R., J.M., I.P.-C., P.I.M., F.C., A.B., J.Gr., L.B., S.Se., J.Go., S.La. and S.K. were responsible for study subject and data recruitment. M.C.-M., A.M.L., F.T., R.J.P.-M., and F.D.C. were involved in the original draft preparation. All authors revised critically and approved the final manuscript.
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Cerván-Martín, M., Tüttelmann, F., Lopes, A.M. et al. Immune and spermatogenesis-related loci are involved in the development of extreme patterns of male infertility. Commun Biol 5, 1220 (2022). https://doi.org/10.1038/s42003-022-04192-0
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DOI: https://doi.org/10.1038/s42003-022-04192-0
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