STUDY QUESTION Do bi-allelic variants in the genes encoding the MSH4/MSH5 heterodimer cause male infertility? SUMMARY ANSWER We detected biallelic, (likely) pathogenic variants in MSH5 (4 men) and MSH4 (3 men) in six azoospermic men, demonstrating that genetic variants in these genes are a relevant cause of male infertility. WHAT IS KNOWN ALREADY MSH4 and MSH5 form a heterodimer, which is required for prophase of meiosis I. One variant in MSH5 and two variants in MSH4 have been described as causal for premature ovarian insufficiency (POI) in a total of five women, resulting in infertility. Recently, pathogenic variants in MSH4 have been reported in infertile men. So far, no pathogenic variants in MSH5 had been described in males. STUDY DESIGN, SIZE, DURATION We utilized exome data from 1305 men included in the Male Reproductive Genomics (MERGE) study, including 90 males with meiotic arrest (MeiA). Independently, exome sequencing was performed in a man with MeiA from a large consanguineous family. PARTICIPANTS/MATERIALS, SETTING, METHODS Assuming an autosomal-recessive mode of inheritance, we screened the exome data for rare, biallelic coding variants in MSH4 and MSH5. If possible, segregation analysis in the patients’ families was performed. The functional consequences of identified loss-of-function (LoF) variants in MSH5 were studied using heterologous expression of the MSH5 protein in HEK293T cells. The point of arrest during meiosis was determined by γH2AX staining. MAIN RESULTS AND THE ROLE OF CHANCE We report for the first time (likely) pathogenic, homozygous variants in MSH5 causing infertility in 2 out of 90 men with MeiA and overall in 4 out of 902 azoospermic men. Additionally, we detected biallelic variants in MSH4 in two men with MeiA and in the sister of one proband with POI. γH2AX staining revealed an arrest in early prophase of meiosis I in individuals with pathogenic MSH4 or MSH5 variants. Heterologous in vitro expression of the detected LoF variants in MSH5 showed that the variant p.(Ala620GlnTer9) resulted in MSH5 protein truncation and the variant p.(Ser26GlnfsTer42) resulted in a complete loss of MSH5. LARGE SCALE DATA All variants have been submitted to ClinVar (SCV001468891–SCV001468896 and SCV001591030) and can also be accessed in the Male Fertility Gene Atlas (MFGA). LIMITATIONS, REASONS FOR CAUTION By selecting for variants in MSH4 and MSH5, we were able to determine the cause of infertility in six men and one woman, leaving most of the examined individuals without a causal diagnosis. WIDER IMPLICATIONS OF THE FINDINGS Our findings have diagnostic value by increasing the number of genes associated with non-obstructive azoospermia with high clinical validity. The analysis of such genes has prognostic consequences for assessing whether men with azoospermia would benefit from a testicular biopsy. We also provide further evidence that MeiA in men and POI in women share the same genetic causes. STUDY FUNDING/COMPETING INTEREST(S) This study was carried out within the frame of the German Research Foundation sponsored Clinical Research Unit ‘Male Germ Cells: from Genes to Function’ (DFG, CRU326), and supported by institutional funding of the Research Institute Amsterdam Reproduction and Development and funds from the LucaBella Foundation. The authors declare no conflict of interest.

A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs.1–3 In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high density lipoprotein, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets. especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components.

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<p>A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. </p>

<p>A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. </p>

<p>A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. </p>

<p>A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. </p>

<p>A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein cholesterol, systolic blood pressure, triglycerides, and waist circumference was used; which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. </p>