The X chromosome and male infertility

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Machine learning based prediction models in male reproductive health: Development of a proof-of-concept model for Klinefelter Syndrome in azoospermic patients

Background Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Objective Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Materials and methods Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Results Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on . Discussion Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Conclusions Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians

Copy Number Variants in Patients with Severe Oligozoospermia and Sertoli-Cell-Only Syndrome

A genetic origin is estimated in 30% of infertile men with the common phenotypes of oligo- or azoospermia, but the pathogenesis of spermatogenic failure remains frequently obscure. To determine the involvement of Copy Number Variants (CNVs) in the origin of male infertility, patients with idiopathic severe oligozoospermia (N = 89), Sertoli-cell-only syndrome (SCOS, N = 37)) and controls with normozoospermia (N = 100) were analysed by array-CGH using the 244A/400K array sets (Agilent Technologies). The mean number of CNVs and the amount of DNA gain/loss were comparable between all groups. Ten recurring CNVs were only found in patients with severe oligozoospermia, three only in SCOS and one CNV in both groups with spermatogenic failure but not in normozoospermic men. Sex-chromosomal, mostly private CNVs were significantly overrepresented in patients with SCOS. CNVs found several times in all groups were analysed in a case-control design and four additional candidate genes and two regions without known genes were associated with SCOS (P<1×10−3). In conclusion, by applying array-CGH to study male infertility for the first time, we provide a number of candidate genes possibly causing or being risk factors for the men's spermatogenic failure. The recurring, patient-specific and private, sex-chromosomal CNVs as well as those associated with SCOS are candidates for further, larger case-control and re-sequencing studies

Can Unlikely Neanderthal Chloride Channel CLC-2 Gene Variants Provide Insights in Modern Human Infertility?

Background/Aims: Neanderthals, although well adapted to local environments, were rapidly replaced by anatomically modern humans (AMH) for unknown reasons. Genetic information on Neanderthals is limited restricting applicability of standard population genetics. Methods: Here, we apply a novel combination of restricted genetic analyses on preselected physiological key players (ion channels), electrophysiological analyses of gene variants of unclear significance expressed in Xenopus laevis oocytes using two electrode voltage clamp and transfer of results to AMH genetics. Using genetic screening in infertile men identified a loss of CLC-2 associated with sperm deficiency. Results: Increased genetic variation caused functionally impaired Neanderthals CLC-2 channels. Conclusion: Increased genetic variation could reflect an adaptation to different local salt supplies at the cost of reduced sperm density. Interestingly and consistent with this hypothesis, lack of CLC-2 protein in a patient associates with high blood K+ concentration and azoospermia

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach

To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single-nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics, and a machine learning (ML) classifier to describe cases and controls in 3 different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P<0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both the datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a 7-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals and ML allows a fair prediction of case or control status based solely on the individual genetic background

A systematic review of the validated monogenic causes of human male infertility : 2020 update and a discussion of emerging gene-disease relationships

Altres ajuts: National Health and Medical Research Council (APP1120356); Netherlands Organisation for Scientific Research (918-15-667); Wellcome Trust (209451); German Research Foundation (DFG, CRU326); National Institutes of Health: Genomics of Spermatogenic Impairment (R01HD078641); Ministerio de Sanidad.Background: Human male infertility has a notable genetic component, including well-established diagnoses such as Klinefelter syndrome, Y-chromosome microdeletions and monogenic causes. Approximately 4% of all infertile men are now diagnosed with a genetic cause, but a majority (60-70%) remain without a clear diagnosis and are classified as unexplained. This is likely in large part due to a delay in the field adopting next-generation sequencing (NGS) technologies, and the absence of clear statements from field leaders as to what constitutes a validated cause of human male infertility (the current paper aims to address this). Fortunately, there has been a significant increase in the number of male infertility NGS studies. These have revealed a considerable number of novel gene-disease relationships (GDRs), which each require stringent assessment to validate the strength of genotype-phenotype associations. To definitively assess which of these GDRs are clinically relevant, the International Male Infertility Genomics Consortium (IMIGC) has identified the need for a systematic review and a comprehensive overview of known male infertility genes and an assessment of the evidence for reported GDRs. Objective and Rationale: In 2019, the first standardised clinical validity assessment of monogenic causes of male infertility was published. Here, we provide a comprehensive update of the subsequent 1.5 years, employing the joint expertise of the IMIGC to systematically evaluate all available evidence (as of 1 July 2020) for monogenic causes of isolated or syndromic male infertility, endocrine disorders or reproductive system abnormalities affecting the male sex organs. In addition, we systematically assessed the evidence for all previously reported possible monogenic causes of male infertility, using a framework designed for a more appropriate clinical interpretation of disease genes. Search Methods: We performed a literature search according to the PRISMA guidelines up until 1 July 2020 for publications in English, using search terms related to 'male infertility' in combination with the word 'genetics' in PubMed. Next, the quality and the extent of all evidence supporting selected genes were assessed using an established and standardised scoring method. We assessed the experimental quality, patient phenotype assessment and functional evidence based on gene expression, mutant in-vitro cell and in-vivo animal model phenotypes. A final score was used to determine the clinical validity of each GDR, across the following five categories: no evidence, limited, moderate, strong or definitive. Variants were also reclassified according to the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines and were recorded in spreadsheets for each GDR, which are available at imigc.org. Outcomes: The primary outcome of this review was an overview of all known GDRs for monogenic causes of human male infertility and their clinical validity. We identified a total of 120 genes that were moderately, strongly or definitively linked to 104 infertility phenotypes. Wider Implications: Our systematic review curates all currently available evidence to reveal the strength of GDRs in male infertility. The existing guidelines for genetic testing in male infertility cases are based on studies published 25 years ago, and an update is far overdue. The identification of 104 high-probability 'human male infertility genes' is a 33% increase from the number identified in 2019. The insights generated in the current review will provide the impetus for an update of existing guidelines, will inform novel evidence-based genetic testing strategies used in clinics, and will identify gaps in our knowledge of male infertility genetics. We discuss the relevant international guidelines regarding research related to gene discovery and provide specific recommendations to the field of male infertility. Based on our findings, the IMIGC consortium recommend several updates to the genetic testing standards currently employed in the field of human male infertility, most important being the adoption of exome sequencing, or at least sequencing of the genes validated in this study, and expanding the patient groups for which genetic testing is recommended

A de novo paradigm for male infertility

De novo mutations are known to play a prominent role in sporadic disorders with reduced fitness. We hypothesize that de novo mutations play an important role in severe male infertility and explain a portion of the genetic causes of this understudied disorder. To test this hypothesis, we utilize trio-based exome sequencing in a cohort of 185 infertile males and their unaffected parents. Following a systematic analysis, 29 of 145 rare (MAF < 0.1%) protein-altering de novo mutations are classified as possibly causative of the male infertility phenotype. We observed a significant enrichment of loss-of-function de novo mutations in loss-of-function-intolerant genes (p -value = 1.00 × 10 −5) in infertile men compared to controls. Additionally, we detected a significant increase in predicted pathogenic de novo missense mutations affecting missense-intolerant genes (p -value = 5.01 × 10 −4) in contrast to predicted benign de novo mutations. One gene we identify, RBM5, is an essential regulator of male germ cell pre-mRNA splicing and has been previously implicated in male infertility in mice. In a follow-up study, 6 rare pathogenic missense mutations affecting this gene are observed in a cohort of 2,506 infertile patients, whilst we find no such mutations in a cohort of 5,784 fertile men (p -value = 0.03). Our results provide evidence for the role of de novo mutations in severe male infertility and point to new candidate genes affecting fertility. Germline de novo mutations can impact individual fitness, but their role in human male infertility is understudied. Trio-based exome sequencing identifies many new candidate genes affecting male fertility, including an essential regulator of male germ cell pre-mRNA splicing

Immune and spermatogenesis-related loci are involved in the development of extreme patterns of male infertility

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-DR beta 1 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. A GWAS in a large case-control cohort of European ancestry identifies two genomic regions, the MHC class II gene HLA-DRB1 and an upstream locus of VRK1, that are associated with the most severe phenotype of spermatogenic failure

Lower Incidence of M2/ANXA5 Carriage in Recurrent Pregnancy Loss Patients With Elevated Lipoprotein(a) Levels

This study compared the incidence of M2/ANXA5 haplotype carriage, a documented repeated miscarriage risk factor, in patient groups with normal and elevated lipoprotein(a) (Lp(a)) levels. A total of 138 women with ≥2 consecutive, idiopathic recurrent miscarriages, categorized in patients with elevated (≥30 mg/dL, n = 44) and normal Lp(a) level (<30 mg/dL, n = 94) were recruited at the recurrent pregnancy loss (RPL) clinic of Munich Großhadern University Hospital. A total of 500 fertile women served as controls. All patients were genotyped for ANXA5 promoter haplotypes, genetic frequencies were compared, and odds ratios (ORs) and relative risks of M2 carriers were calculated. Women with M2 haplotype had an almost 2 times higher relative risk of RPL (OR 2.6, 95% confidence interval 1.5-4.6, P = .001) than fertile controls. Furthermore, risk rises to 2.47 in patients having normal Lp(a) levels (OR 3.2, 95% confidence interval 1.7-5.9, P = .001), whereas women with high Lp(a) levels exhibit notably lower apparent RPL risk of 1.39 (OR 1.4, 95% confidence interval 0.5-4.1, P = .659)