Genetic diversity of infertile males in India

Genetic diversity of infertile males in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic diversity of infertile males in India Harsh Sheth, Preeti Priya, Vineet Mishra, Shrutikaa Kale, Manali Ajagekar, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7849365/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Purpose To systematically investigate the genetic architecture of severe male infertility in Indian men, with a specific focus on chromosomal abnormalities and the contribution of de novo variants. Method We recruited 247 infertile males between 2021 and 2024 presenting with severe quantitative and qualitative sperm defects. All patients underwent karyotyping and Y chromosome microdeletion STS-PCR. A single molecule molecular inversion probe-based targeted sequencing assay covering 39 male infertility genes was performed in 120 patients, while whole exome sequencing (WES) was conducted in 48 patients using a duo/trio-based approach to enable segregation and de novo variant detection. Result Gonosomal aneuploidies were observed in 3/247 patients (1.2%), and causal AZF microdeletions in 8/247 (3.2%). Targeted sequencing identified pathogenic/ likely pathogenic (P/LP) variants in 4/120 patients (3.3%), with additional CFTR variants in 3 patients where parental DNA was unavailable for phasing. WES yielded P/LP variants in 4/48 patients (8.3%) affecting PMFBP1, DNAH1 , and AR genes, confirmed via segregation analysis. No de novo or copy number variants were confirmed as causative, though several candidate genes were prioritised. Sequencing-based approaches provided an additional ~ 6–8% diagnostic yield, with overall diagnostic rate reaching 7.7% (19/247). Conclusion Sequencing-based strategies, particularly family-based trio WES, significantly enhance diagnostic yield beyond current guideline-recommended tests and should be adopted as first-tier investigations for severe male infertility. This represents India’s largest cohort-based genomic study on male infertility to date. Larger family-based cohorts will be essential to delineate the contribution of de novo variants to male infertility genetics. Single molecule molecular inversion probes whole exome sequencing male infertility de novo variants Figures Figure 1 Introduction Male infertility (MI) is defined as the inability to conceive a child after at least two years of unprotected intercourse [ 1 ]. It is a heterogeneous condition that could be caused by infection, varicocele, immunological factors, genetic anomalies, endocrine factors, and idiopathic factors [ 2 ]. According to the World Health Organization (WHO), from 1990 to 2021, global infertility prevalence estimates indicate that approximately one in six people have experienced infertility at some stage in their lives ( https://www.who.int/ ). MI contributes to approximately 50% of all cases of infertility and 7% of the male population worldwide is affected by MI [ 3 ]. MI is classified clinically based on etiological factors such as hypothalamic-pituitary axis function, spermatogenic quantitative defects, spermatogenic qualitative defects, and ductal obstruction or dysfunction [ 4 ]. It is broadly classified into 7 common forms depending on the qualitative and quantitative sperm abnormalities, namely: azoospermia (no sperm), oligospermia (< 10 million/mL), asthenospermia ( 4% of morphological abnormality), acephalic sperm (sperm without head), globozoospermia (round-headed sperm), and cryptozoospermia (absence of sperm in a fresh sample). Among the various forms of MI, azoospermia is observed at a higher frequency, affecting nearly 1% of the overall male population and 10–15% of infertile males [ 5 ]. Genetic causes are estimated to contribute to a significant proportion of all MI cases, most commonly observed in severe forms of infertility such as non-obstructive azoospermia and other specific subtypes like multiple morphological abnormalities of sperm flagellum [ 6 ]. Latest WHO guidelines recommend karyotype and Y chromosome microdeletion test in all infertile males, and cystic fibrosis transmembrane conductance regulator ( CFTR ) mutation analysis in males suspected with congenital bilateral absence of vas deferens (CBAVD) or cystic fibrosis (CF) [ 7 ]. This approach has a combined diagnostic yield of 5⎼25%, depending on the spermatogenic phenotype [ 8 , 9 ]. The numerical and structural abnormalities in autosomal and sex chromosomes, as well as azoospermia factor (AZF) microdeletions, are comprehensively studied and well-recognized as causes of azoospermia. These genetic tests have prognostic implications on downstream artificial reproductive techniques (ARTs), clinical pregnancy rates, and live birth rates [ 10 , 11 ]. Despite this, a significant problem in andrology is that genetic testing strategies employed in clinics are not standardised, and in some countries, even the most basic tests recommended by the WHO are still not routinely used. Majority of all human protein-coding genes (84%, 16598) are expressed in testis [ 12 ] with more than 2000 genes being directly involved in spermatogenesis [ 13 , 14 ]. Advances in next-generation sequencing (NGS) have greatly facilitated unbiased exome-wide and genome-wide detection of genetic variants. The MI field is currently catching up with other medical disease types with a strong genetic component, which is aiding in the transition of findings into clinical practice [ 6 , 15 – 17 ]. To date, pathogenic variants in at least 104 genes are confidently linked to MI, and at least 138 other genes are classified as candidate genes [ 17 ]. A significant proportion of these studies however have occurred in European/ non-Hispanic white populations. There is a significant paucity of systematic genetic analysis of infertile males in the Indian subcontinent, likely due to limited adoption of NGS technologies in routine clinical care. Most studies from India are restricted to karyotyping and Y chromosome microdeletion testing in the infertile population or exome sequencing in selected conditions like azoospermia [ 10 , 11 , 18 ]. Here, we present results from our genomic study of 247 infertile Indian men which includes 48 duo/ trios affected by MI (affected male, mother, and father). Using combinatorial techniques like karyotyping, AZF microdeletion, targeted gene panel, and whole exome sequencing (WES), we present the genetic landscape of MI in India. We also provide an estimate of the diagnostic yields of respective techniques to enable rationalizing and prioritizing the diagnostic tests that must be offered in clinics for management of MI. Material and Methods Patient recruitment and sample collection We consecutively enrolled a total of 254 patients who presented with unexplained (idiopathic) MI at FRIGE Institute of Human Genetics between 2022 and 2024. These patients were referred by andrologists, urologists, and IVF specialists. The reference values and semen nomenclature and classification of MI were used according to the WHO guidelines [ 19 ]. Clinical evaluation included two consecutive semen analysis reports, at least two weeks apart, showing either quantitative (sperm concentration being < 10 million/mL) or qualitative defects (e.g. teratozoospermia, asthenozoospermia, etc.), and endocrine reports showing normal readings for follicle stimulating hormone (FSH; normal range 1.5–12.4 mIU/mL), luteinising hormone (LH; normal range 1.8–8.6 mIU/mL), and testosterone (normal range 270–1070 ng/dL). Detailed clinical as well as family history was collected for each patient, which included height, weight, current or previous intake of alcohol and/or tobacco, family members with clinical history of MI, and if available, ultra-sonography (USG) report of testis (Supplementary Table 1). Seven patients (2.8%) were detected with hypogonadotrophic hypogonadism based on their endocrine reports, and hence were not offered any genetic testing. The remaining 247 patients were subjected to karyotyping and AZF microdeletion testing on the Y chromosome. All patients negative for AZF microdeletions and chromosomal anomalies, along with their healthy parents, were invited for targeted gene panel and/or WES analyses (Fig. 1 ). The study protocol was approved by the ethics committee of FRIGE Institute of Human Genetics, Ahmedabad (REC ID: FRIGE/IEC/25/2022) and written informed consent from all patients and their parents was obtained prior to enrolment to the study, as per the Helsinki Declaration. High molecular weight genomic DNA extraction from peripheral whole blood samples was carried out using the desalting method [ 20 ] and stored at -20°C until molecular genetic testing was performed. Karyotyping and AZF microdeletion Karyotyping and AZF microdeletion testing were performed in 247 patients (Supplementary Table 2). Karyotyping was performed using GTG banding at 500 band resolution to assess for chromosomal abnormalities according to the International Standards for Human Cytogenetics Nomenclature [ 21 ]. Analysis for the AZF microdeletions was carried out using multiplex PCR for 16 sequence tagged site (STS) markers using a previously published method (AZF-STS PCR) [ 11 ]. The SRY-sex-determining region on the short arm of the Y chromosome (sY14) was used as an internal control. The following STS markers used were: sY84, sY82, sY86, sY746 (AZFa); sY143, sY134, sY127, sY128, sY121, sY173 (AZFb); sY254, sY160, sY145, sY255 (AZFc) and sY1291, sY1191 (gr/gr). The thermocycler protocol applied was as follows: 95°C for 5 min, 35 cycles of 95°C for 30 s, 56°C for 120 s, and 72°C for 30 s, preceding 72°C for 5 min. The qualitative analysis of the amplified PCR products was performed using agarose gel electrophoresis. smMIP based targeted sequencing assay Of 247 patients, 27 patients were diagnosed with either gonosomal aneuploidy or Yq microdeletion, and hence were not included in downstream analysis (Fig. 1 ). Of the remaining 220 patients, 120 patients gave consent to analyse their DNA using single-molecule molecular inversion probes (smMIP)-based targeted sequencing assay using previously described method (Supplementary Table 3) [ 8 ]. Although smMIPs targeting 106 genes were previously described [ 8 ], only 39 genes were targeted in the current study (Supplementary Tables 4 and 5) based on the recent systematic review of gene-disease relationship for MI [ 17 ]. Libraries from samples were pooled to equimolar concentration and purified using Agencourt AMPure XP beads according to manufacturer’s protocol (Beckman Coulter, USA). Each prepared library was diluted to a concentration of 4 pM and subsequently sequenced on the MiSeq platform (Illumina, USA) according to the manufacturer’s protocol resulting in 2x157 bp paired-end reads. The sequencing was performed at a mean depth of ~ 200x per loci. Data was analysed using an in-house pipeline, as previously described [ 22 ]. Briefly, reads were aligned against hg19/GRCh37 human reference genome using BWA-MEM (v.0.7.12) [ 23 ] followed by base quality score recalibration, and single nucleotide variants (SNV) and small indel calling using GATK HaplotypeCaller (v4.12) in accordance with the GATK’s best practice guidelines (McKenna et al. , 2010). Variants were annotated, filtered, and prioritised based on the patient’s phenotype (in human phenotype ontology [HPO] format) using Exomiser v13.1 [ 24 , 25 ] integrating data from SIFT ( https://sift.bii.a-star.edu.sg/www/SIFT_seq_submit2.html ) , Polyphen2 ( http://genetics.bwh.harward.edu/pph2 ) , MutationTaster ( http://www.mutationtaster.org ) , Combined Annotation Dependent Depletion (CADD) scores, REVEL scores, dbSNP ( www.ncbi.nlm.nih.gov/SNP/ ) , the Genome Aggregation Database (gnomAD; gnomad.broadinstitute.org ) and ClinVar ( www.ncbi.nlm.nih.gov/clinvar ). Copy number variant (CNV) calling was carried out using the CNVRobot (v4.1) tool ( https://github.com/AnetaMikulasova/CNVRobot ) [ 16 ] and a minimum of 5 “control” male samples where no prior CNVs in the targeted regions were observed. Details of validation sequencing run in a set of 20 samples with known genetic diagnosis is provided in Supplementary Table 6 and File 1. Whole exome Sequencing Sequencing and SNV calling Of the 220 patients, 48 patients and his parent(s) gave consent to carry out duo/ trio whole exome sequencing (see Supplementary File 2 for pedigree details). These patients were normal for karyotyping and AZF STS-PCR, but had not been assessed by smMIP-based targeted sequencing assay. Genomic DNA from duos and trios were prepared and enriched following the manufacturer’s protocol of Twist Bioscience’s Human Core Exome v2 kit (Twist Bioscience, USA). Sequencing was performed either on NovaSeq 6000 or NovaSeq X Plus platform (Illumina, USA) at an average depth of 80-100x. Sequenced reads were aligned against the human reference genome GRCh38/hg38 using BWA-MEM (v.0.7.12) [ 23 ] followed by base quality score recalibration, and single nucleotide variants (SNV) and small indel calling using GATK HaplotypeCaller (v4.12) in accordance with the GATK’s best practice guidelines [ 26 ]. The sex and relatedness of each sample was calculated using Peddy [ 27 ]. SNV variant filtration and prioritisation Variant annotation, filtration, and prioritisation was performed using Exomiser v13.1.0 [ 24 , 25 ]. Exomiser uses hiPHIVE prioritisation method that incorporates mode of inheritance (recessive, dominant, and maternally inherited), protein-protein interaction networks, and multi-species ontologies along with ranking candidate genes based on the predicted variant pathogenicity associated with the phenotype. The phenotype information was coded in uniform HPO terminologies [ 28 ]. Common variants were filtered based on minor allele frequency in the 1000Genomes Phase 3, TopMed, and gnomAD v2.1 databases. The minor allele frequency cut-off was set at 0.02 (2%). The cut-off was set assuming monogenic recessive as well as dominant de novo forms of infertility, especially commonly occurring variants in the CFTR gene. Only non-synonymous variants in the coding region and canonical splice site variants with a depth of > 20x were used for analysis and clinical correlation. Various in silico prediction tools such as PolyPhen-2 [ 29 ], SIFT [ 30 ], MutationTaster2 [ 31 ], and CADD [ 32 ] were used to predict pathogenicity of non-synonymous and indel variants. A CADD_phred score of ≥ 15 and at least two damaging predictions from the remaining in silico tools were used for selection of candidate variants. In silico predictions along with available knowledge from various sources and databases as described below was used in prioritising variants. Post gross filtering, variants were prioritised based on the following: (a) known disease causing variant previously reported in the ClinVar database [ 33 ]; (b) novel variants in known genes based on the Z-score for missense and pLOEUF score for loss of function variants available in the gnomAD database; (c) variants in novel candidate genes wherein the respective gene was additionally evaluated for their function using UniProt (“UniProt,” 2019) and Human Protein Atlas ( www.proteinatlas.org ). Tissue expression using GTEx database ( www.gtexportal.org ) , association/ interaction with known male infertility genes using STRING database [ 34 ], and plausible phenotype outcome in murine models based on the MGI database ( https://www.informatics.jax.org/ ) were assessed. All candidate variants were assessed using Integrative Genomics Viewer (IGV) to evaluate their quality. Variants in genes with recessive or dominant mode of inheritance were analysed using the exact same method of filtration and interpretation as described above. De novo variant calling In the case of 28 patient-parent trio samples, de novo variants (DNMs) were identified using a deep convolutional neural network based DNM caller, DeNovoCNN [ 35 ]. To reduce probability of false positive DNMs, a cut-off for posterior probability score was set to > 0.5, a minimum of 30x coverage at the genomic co-ordinate across the trio, and variant allele frequency threshold of 30–70% in the proband and < 10% in the parents was used. Putative role of DNM in MI was assessed using 5 categories: RNA expression of the gene in testis, protein expression in testis, whether an infertile murine model exists for the given gene, protein function relative to spermatogenesis, and whether the gene interacts with known fertility genes. Expression data was assessed in GTEx database ( www.gtexportal.org ) and Human Protein Atlas ( www.proteinatlas.org ) , and interaction with known fertility genes was assessed using STRING database [ 34 ]. CNV analysis CNV calling was performed across the exome data using the CNVRobot (v4.1) tool ( https://github.com/AnetaMikulasova/CNVRobot ) which uses a custom GATK 4 based pipeline. This workflow exploits the GATK 4 sequence read depth normalisation and a custom R based segmentation and visualization. At least 5 male parental samples were used as controls for the normalisation step. CNVs were annotated using AnnotSV [ 36 ]. In the case of candidate CNVs, variants were primarily screened for population frequency and known disease associations using publicly available databases like gnomAD database, DGV [ 37 ], and OMIM [ 38 ]. CNVs present in more than 1% of the samples of the aforementioned databases and present in more than 10% of the patients were excluded from the analysis. The remaining rare deletions and duplications were individually inspected through the genomic profiles and detailed log2 ratio plots generated by CNVRobot. Variant validation All candidate SNVs, indels, and DNMs were validated in the probands and parent(s) using bi-directal Sanger sequencing approach on Applied Biosystems SeqStudio Genetic Analyzer (ThermoFisher, USA). The primers for SNV validation were designed using the Primer3 tool ( https://primer3.ut.ee/ ) [ 39 ] (Supplementary Tables 7 and 8) and PCR reactions were performed using Invitrogen’s Taq polymerase (ThermoFisher, USA) according to the manufacturer’s protocol. CNV validation was carried out using SYBR-Green based quantitative PCR on the Applied Biosystems StepOne Real-Time PCR System (ThermoFisher, USA). The identified SNVs and CNVs were classified according to the American College of Medical Genetics – American College of Pathologists (ACMG-AMP) guidelines [ 40 ] and the ClinGen framework [ 41 ]. Results Patient cohort The study cohort consisted of 247 infertile males, presenting with a spectrum of clinical phenotypes. The average age at recruitment was 34 ± 5 years and ranged from 24 to 51 years (Table 1 ; Supplementary Table 1). Forty-eight patients had either previously or were currently undergoing ART treatment, and male infertility was recorded in family members of 13 index patients (Table 1 ; Supplementary Table 1). Majority of the patients presented with mixed defects ⎼ oligoasthenoteratozoospermia (50.2%, n = 124/247) followed by quantitative sperm defects ⎼ azoospermia (31.9%, n = 79/247) and oligozoospermia (2%, n = 5/247) ⎼ with the remaining patients showing qualitative spermatogenic defects (Supplementary Table 1). Table 1 Demographic characteristics of the infertile male cohort Variables Total patients (N = 247) Age of proband, years (SD) 34 (5) Family member(s) diagnosed with male infertility, n (%) 13 (5.3) Semen phenotype, n (%) Quantitative defect Azoospermia 79 (31.9) Anejaculation 1 (0.1) Cryptozoospermia 6 (2.4) Oligozoospermia 5 (2.0) Qualitative defect Asthenoteratozoospermia 9 (3.6) Asthenoglobozoospermia 1 (0.1) Globozoospermia 1 (0.1) Teratozoospermia 4 (1.6) Asthenozoospermia 3 (1.2) Mixed defect Oligoasthenoteratozoospermia 124 (50.2) Oligoasthenozoospermia 7 (2.8) Oligoteratozoospermia 6 (2.4) Unknown phenotype 1 (0.1) Karyotyping and AZF microdeletion analysis Sequential genetic testing was performed in all 247 patients using karyotype and AZF STS-PCR. Of 247 patients, gross chromosomal aneuploidy was observed in 3 patients, where 2 patients had 47, XXY (Klinefelter syndrome) and 1 patient had 48, XXYY syndrome (Supplementary Table 2). With AZF STS-PCR, a microdeletion in the AZF region was observed in 24 patients. Causal microdeletions were detected in 8 cases (3.2%): b2/b3 sub-deletion in the AZFc locus in 1 patient (4.1%), b1/b3 sub-deletion in the AZFc locus in 1 patient (4.1%), complete AZFc microdeletion in 1 patient (4.1%), complete AZFb microdeletion + gr/gr sub-deletion in AZFc region in 1 patient (4.1%), complete AZFb microdeletion + b2/b3 sub-deletion in AZFc region in 1 patient (4.1%), complete AZFb microdeletion + b1/b3 sub-deletion in AZFc region in 2 patients (8.3%), and a partial AZFc deletion (sY255) in 1 patient (4.1%). Additionally, likely benign gr/gr sub-deletion was reported in 16 patients (6.5%). The combined diagnostic yield of karyotyping and Yq microdeletion testing in 247 patients was 4.5% (n = 11). Majority of the patients (n = 23/24) with AZF microdeletions presented with oligozoospermia or azoospermia, while 1 patient presented with asthenozoospermia (Supplementary Table 2). Targeted sequencing assay analysis We successfully designed a smMIP-based targeted enrichment approach using a grand total of 1,507 equimolarly pooled smMIPs capturing the exons and splice site regions of 39 genes on autosomes and sex chromosomes (Supplementary Table 5). After rebalancing our smMIP pool to optimise the capture efficiency, we sequenced 98.4% of the targeted regions covering a total region of approximately 165 kb within a mean read depth after duplicate removal of ~ 100x unique reads per sample. Validation of the method was carried out on a set of 20 previously orthogonally tested samples, including samples with Klinefelter syndrome, Y chromosome microdeletion, globozoospermia, and cystic fibrosis (Supplementary Table 6). 120 of 247 probands consented for the targeted sequencing (Supplementary Table 3). Of these, 55 had azoospermia, 50 had oligozoospermia, and remaining had qualitative defects. All patients were screened using the targeted assay for gonosomal aneuploidies, AZF deletions, and variants in 6 diagnostic infertility genes: AURKC, CFTR, DPY19L2, DD3X, SYCP3 , and TEX11. As patients had already been screened using karyotyping and AZF STS-PCR, no gonosomal aneuploidy or AZF deletions were detected. CNV analysis was followed by SNV analysis. Analysis of protein altering variants with minor allele frequency of < 1% in the healthy population showed 10 patients with predicted pathogenic/ likely pathogenic (P/LP): CFTR (n = 4), DNAH1 (n = 1), KHLH10 (n = 1), and NR5A1 (n = 1) (Tables 2 and 3 ). Of the 4 patients with at least 2 variants in the CFTR gene, 1 patient (MI-068) had 2 heterozygous variants in close proximity which allowed variant phasing and suggested the variants to be in trans (Supplementary Fig. 1). In silico analysis of variant co-occurrence estimation using the gnomAD v2.1.1 database suggests that the 2 heterozygous variants CFTR :c.926C > G and CFTR :c.473G > A in proband MI-102 also have a high probability of being in trans , whereas, for probands MI-023 and MI-227, co-occurrence analysis either could not be carried out or the probability estimates were uncertain. Nonetheless, for all 3 cases, due to the absence of parental samples and the use of short read sequencing, variant phasing could not be carried out. Critically, compound heterozygous variants in the CFTR gene can be present in patients without CBAVD diagnosis based on physical examination (Smits et al. , 2019), which requires expertise, despite which it is not always certain. In our clinical analysis of patients with 2 CFTR gene variants, only in 1 patient ultrasonography of scrotum was suggestive of bilateral calcification in epididymis (MI-068), the others were not clinically classified as CBAVD. However, in all the cases, semen analysis had indicated azoospermia/ oligozoospermia (Table 2 ). Table 2 Results of physical examination, semen analysis, and targeted gene analysis in patients with CFTR gene variants Patient ID Physical examination of vas deference Semen analysis phenotype CFTR variants* MI-023 Not available Azoospermia CFTR (NM_000492.4):c.1695T > A p.(Asp565Glu);het CFTR (NM_000492.4):c.1186A > T p.(Asn396Tyr);het MI-068 Not available Azoospermia with USG scrotum suggestive of bilateral calcification in epididymis CFTR (NM_000492.4):c.3197G > A p.(Arg1066His);het CFTR (NM_000492.4):c.3209G > A p.(Arg1070Gln);het MI-102 Not available Azoospermia CFTR (NM_000492.4):c.926C > G p.(Ala309Gly);het CFTR (NM_000492.4):c.473G > A p.(Ser158Asn);het MI-227 Not available Azoospermia CFTR (NM_000492.4):c.473G > A p.(Ser158Asn);het CFTR (NM_000492.4):c.3209G > A p.(Arg1070Gln);het * Only variants for MI-068 are in trans due to proximity of variants (Supplementary Fig. 1). For the remaining 3 patients, phase information is unknown; het = heterozygous. Table 3 Results of semen analysis and targeted gene analysis in patients with likely pathogenic/ pathogenic variants in male infertility genes, giving a monogenic cause of the fertility problem Patient ID Phenotype Result from targeted gene panel Associated phenotype (inheritance) (OMIM phenotype) MI-097 Asthenoteratozoopsermia DNAH1 (NM_015512.5):c.10468_10471del p.(Arg3490GlnfsTer4);hom Teratozoospermia (AR) (OMIM#617576; Spermatogenic failure 18) MI-125 Azoospermia with sertoli cell only syndrome KLHL10 (NM_152467.5):c.287A > G p.(Tyr96Cys);het Spermatogenic failure (AD) (OMIM#615081; Spermatogenic failure 11; terato/azthenozoospermia in some patients) MI-236 Azoospermia NR5A1 (NM_004959.5):c.1139A > G p.(Asp380Gly);het Oligo/azoospermia (AD) (OMIM #613957; spermatogenic failure 8; variable expression and incomplete penetrance described) het = heterozygous; hom = homozygous; AR = autosomal recessive; AD = autosomal dominant; OMIM = Online Mendelian Inheritance in Man Genotype-phenotype correlation in the case with a homozygous variant in both DNAH1 and NR5A1 genes showed complete overlap with the known set of phenotypes (Table 3 ) [ 42 , 43 ]. Interestingly for case MI-125, patient was clinically diagnosed with Sertoli cell-only syndrome (SCOS) (Supplementary Table 1). Genetic evaluation showed a heterozygous missense variant in the KLHL10 gene. Evaluation of KLHL10 haploinsufficiency in murine models has shown impact on spermatogenesis and spermiogenesis, with SCOS being observed in several cases [ 44 ]. Thus, targeted sequencing assay provided a conclusive diagnostic yield of 3.3% (n = 4/120) in addition to the cytogenetic and Y chromosome microdeletion analysis. Exome sequencing in patient-parent duos and trios A total of 100 patients, who had a normal karyotype and absence of Y chromosome microdeletion, did not participate in the targeted sequencing assay analysis. Of these, 48 patients and their parent(s) consented for whole exome sequencing (WES). Out of the 48 participants, 28 were patient-parent trios, 11 were categorised as duos (patient and a single parent or a fertile sibling in case of death of either or both parents before recruitment), 8 “partial” trios (patient, one parent, and a fertile sibling), and 1 singleton (parents passed away before their samples could be collected for the study). At the time of conception of the proband, the average age of the mother was 22 ± 5 years, and the average age of the father was 28 ± 6 years. We used Exomiser [ 24 , 25 ] based variant filtration and prioritisation engine which took into account patient’s clinical phenotypes and assessed genes and variants based on multiple modes of inheritance simultaneously in order to identify putative gene(s) of interest. Of 48 patients, 19 (39.6%) were detected with putative variants in genes that have enriched expression in testis or are known infertility genes (Supplementary Table 9). Of these, P/LP variants were identified in 4 patients (8.3%) (Table 4 ). Using segregation analysis in parental samples due to the duo/ trio approach used for WES, bi-allelic variants were detected in the PMFBP1 gene in 2 patients which is associated with spermatogenic failure 31 (OMIM#618112), and DNAH1 gene in 1 patient which is associated with spermatogenic failure 18 (OMIM#617576). One patient had a hemizygous variant in the AR gene, which is associated with androgen insensitivity (OMIM#300068). All 3 genes are known male infertility genes. Indeed, patients with PMFBP1 gene variants presented with oligozoospermia and/ or pin-head sperms which is observed in spermatogenic failure 31 (OMIM#618112), patient with DNAH1 gene variant presented with 96% of the sperms with an abnormal morphology which is observed in spermatogenic failure 18 (OMIM#617576), and lastly, patient with AR gene variant presented with azoospermia, varicocele, and bilateral epididymal cyst. However, clinical phenotypes such as female external genitalia in males, underdeveloped labia, labial testis, and blind vagina associated with androgen insensitivity (OMIM#300068), were not observed in the patient during trans-rectal and testis sonography (Supplementary Table 1). Furthermore, the patient had normal levels of plasma FSH, LH, and testosterone levels. Together with the presence of a missense variant, these clinical findings suggest that the patient was likely to be affected with partial/ mild androgen insensitivity syndrome (PAIS/ MAIS) whereby males can have normal sized penis and descended testis, but impaired spermatogenesis [ 45 ]. Table 4 Results of semen analysis and whole exome sequencing analysis in patients with likely pathogenic/ pathogenic variants in male infertility genes, giving a monogenic cause of the fertility problem Patient ID Phenotype Analysis type Result from whole exome sequencing Associated phenotype (inheritance) (OMIM phenotype) MI-022 Azoospermia Trio AR (NM_000044.6):c.2317G > A p.(Glu773Lys); hem. mat. Azoospermia (XLR) (OMIM#300068; Androgen insensitivity syndrome) MI-053 Severe oligoteratozoospermia Trio PMFBP1 (NM_031293.3):c.1888G > T p.(Glu630Ter); het. mat. PMFBP1 (NM_031293.3):c.1462C > T p.(Gln488Ter); het. pat. Oligozoospermia and acephalic sperms (AR) (OMIM#618112; Spermatogenic failure 31; oligozoospermia with acephalic spermatozoa in most cases, some cases have tailless sperms) MI-099 Severe oligoasthenoteratozoospermia Trio DNAH1 (NM_015512.5):c.8668del p.(Thr2890GlnfsTer46); het. pat. DNAH1 (NM_015512.5):c.5054C > G p.(Pro1685Arg); het. mat. Teratozoospermia (AR) (OMIM#617576; Spermatogenic failure 18) MI-244 Teratozoospermia with pin head sperms Trio PMFBP1 (NM_031293.3):c.841C > T p.(Gln281Ter); het. pat. PMFBP1 (NM_031293.3):c.841C > T p.(Gln281Ter); het. mat. Oligozoospermia and acephalic sperms (AR) (OMIM#618112; Spermatogenic failure 31; oligozoospermia with acephalic spermatozoa in most cases, some cases have tailless sperms) het = heterozygous; hom = homozygous; hem = hemizygous; mat = Maternal; pat = Paternal, AR = autosomal recessive, AD = autosomal dominant, XLR = X linked recessive, OMIM = Online Mendelian Inheritance in Man Across the patients, an average of 365 CNV calls per exome were observed prior to filtering and prioritisation (Supplementary Table 10). Following filtration steps, there were an average of 48 CNV calls per sample. Manual inspection of the CNVs involved retaining CNVs spanning at least 2 consecutive exons to minimize false positives and detected in ≤ 2 probands but absent in their parents. No causative de novo , homozygous, or hemizygous CNVs were however detected in our cohort. Role of de novo variants in male infertility To assess the role of DNMs in male infertility, we restricted our analysis to 28 patient-parent trios. Thirty-three DNMs were detected and validated by Sanger sequencing with an average of 1.2 DNMs per patient (minimum = 0 and maximum = 5; Supplementary Table 11). Except for a single DNM located on the X chromosome, all of the variants were identified in autosomes. None of the 33 DNMs occurred in a gene already known for its involvement in autosomal dominant MI, which is as anticipated since only 4 autosomal dominant genes have so far been linked with isolated MI in humans [ 9 , 17 ]. Across genetic disorders, genes with dominant phenotypes are usually intolerant to loss of function (LoF) or missense variants, as suggested by high pLI and Z-score or a low LOEUF score. Indeed, our cohort of infertile males showed enrichment of LoF-intolerant genes with LoF or missense DNMs (18.2%, n = 6/33). We systematically evaluated the likelihood of missense DNMs causing MI by assessing the predicted pathogenicity using 3 in silico tools and Z-score from the gnomAD database, as previously carried out by Oud et al. [ 16 ]. Overall, 2 likely pathogenic DNMs were detected in 2 genes. However, unlike Oud et al. , no missense or LoF DNM in these genes were found to be a candidate for explaining MI in the present cohort. Interestingly, we identified a de novo heterozygous nonsense variant c.886C > T (p.Gln296Ter) in CCDC183 gene in a patient with severe oligoasthenoteratozoospermia (MI-138; Supplementary Tables 9 and 11). The gene has testis enriched expression and knockout studies in murine models have shown its role in axoneme microtubules elongation in the centrioles during spermiogenesis [ 46 ]. In the absence of CCDC183 protein activity, loss of cytoplasmic invagination around the sperm flagellum is observed due to which, the flagellar compartment does not form properly and axonemal microtubules collapse during spermiogenesis. This suggests that CCDC183 gene plays a crucial role in spermatozoa formation and is a putative novel MI gene. However, based on the Z, pLI, and LOEUF scores, the gene is likely to cause recessive forms of infertility, and no second pathogenic variant in trans was detected by exome sequencing approach. Overall, conclusive diagnosis obtained from each technique is: 1.2% (n = 3/247) for karyotyping, 3.2% (n = 8/247) for Y chromosome microdeletion testing, 3.3% (n = 4/120) for targeted sequencing panel, and 8.3% (n = 4/48) for duo-/trio-based WES. Despite over twice the diagnostic yield observed from duo/trio WES compared to targeted sequencing panel, the difference was not statistically significant (χ2 = 1.89, p = 0.17). Discussion Almost a decade ago, the WHO produced consensus guidelines recommending karyotyping and Y chromosome microdeletion testing in all males with severe oligozoospermia or non-obstructive azoospermia prior to any therapeutic procedure, and CFTR gene mutation analysis in all males suspected with CBAVD [ 7 ]. Since then however, several monogenic causes of quantitative and qualitative spermatogenic defects have been detected with high confidence ([ 16 , 17 , 47 ]. Sequencing based approaches, including both targeted and exome, have led to the discovery of these diseases, with results suggesting improvement in diagnostic yield over the traditional paradigm [ 48 , 49 ]. Majority of this data has been generated using infertile males of non-Hispanic European ethnicity, thereby limiting their generalizability towards other ethnic populations. Prior approaches to understanding the genetic architecture of MI in India have been limited to Klinefelter syndrome and Y chromosome microdeletions, with only 1 study utilising exome sequencing to detect novel candidate genes, albeit in a small cohort of 47 patients [ 11 , 18 ]. To the best of our knowledge, we here report the first description of the genetic architecture of MI and simultaneously carry out diagnostic yield comparisons of karyotype, Y chromosome microdeletion testing, targeted sequencing, and WES in a cohort of 247 infertile males of Indian origin with conclusive yields of 1.2% (n = 3/247), 3.2% (n = 8/247), 3.3% (n = 4/120), and 8.3% (n = 4/48), respectively. Our data is in congruence with prior reports and studies suggesting Klinefelter syndrome to be observed at a higher frequency in patients with non-obstructive azoospermia in comparison with the general population [ 47 ]. In the present study, two men with 47, XXY chromosome complement were observed in a cohort of 247 infertile males of whom 85 men had azoospermia, thereby having a frequency of 0.8% (~ 1 in 123 men), which is significantly higher than the estimated frequency of 1 in 600 in the general population. When screening for Y chromosome microdeletions, we observed causal AZF deletion frequency of 3.2% (n = 8/247) in a cohort of 247 infertile males. These men presented with either severe oligozoospermia or azoospermia (Supplementary Tables 1 and 2). Additionally, 6.5% of infertile men presented with gr/gr deletions (n = 16/247; Supplementary Table 2) in the AZFc locus. gr/gr deletions are known to be the most frequently occurring genetic anomaly in the AZF region [ 47 , 50 ]. In the present study, 66.6% of the patients with gr/gr deletion presented with oligozoospermia and 27.7% of patients with azoospermia. Unlike in the Caucasian population, men with gr/gr deletions have been suggested to be at an increased risk of quantitative sperm defects including oligozoospermia and azoospermia in the Indian population, with further analysis suggesting Y chromosome haplogroup as poor marker for risk assessment among Indian infertile men [ 50 ]. Sequencing based approaches to detect monogenic causes of infertility have yielded a growing list of high confidence and putative genes over the past decade [ 9 , 17 , 49 ]. In a recent set of systematic gene-disease relationships (GDR) for monogenic causes of infertility, a total of 120 genes were reported to be at least moderately linked with 104 MI cases [ 17 ]. Of these, 48, 27, and 45 genes were classified as definitive, strong, and moderate, respectively. In the targeted gene sequencing assay used in the current study, genes classified as definitive (n = 10), strong (n = 4), and moderate (n = 3) have been included (Supplementary Table 4). Furthermore, 22 genes encompassing the Y chromosome were included in the sequencing assay to ensure the ability to detect Y chromosome microdeletions (Supplementary Tables 4 and 6). The targeted smMIP-based sequencing approach used in the present study has been used previously, whereby the panel covering 107 genes was used in a cohort of 1,112 men with idiopathic azoospermia or severe oligozoospermia [ 8 ]. Interestingly, only ~ 1.5% diagnostic yield was observed in their cohort compared to 3.3% (n = 4/120) yield in our cohort. One reason for this could be the exclusion of men with retrograde ejaculation and/or proven CBAVD, whereby, bi-allelic variants in the CFTR gene are observed in most cases. In contrast to this, men with azoospermia, with the exception of 1 patient, were not clinically classified as CBAVD prior to enrollment on the study, thereby, enriching the diagnostic yield of the targeted assay by 25% (n = 1/4) with bi-allelic variants in the CFTR gene. In addition to this, the assay was able to detect pathogenic variants in 3 genes which are classed as definitively linked with MI [ 17 ]. We demonstrate the ability of the assay to detect Klinefelter syndrome, Yq microdeletion, and CNV calling across genes in the autosomes (Supplementary Table 6). Of note, had the targeted sequencing assay been consented to be utilised in the trio/duo cohort of 48 patients, the assay would have provided two additional diagnoses as two genes, DNAH1 and AR , were covered by the targeted assay. The final part of the study focused on assessing the utility of WES for diagnosis and management for men with infertility. Of the 48 patients, 4 patients received a confirmed molecular diagnosis (8.3%, n = 4/48) in 3 genes which are classified as definitive or strongly associated with MI. The overall diagnostic yield of WES in our cohort is in congruence with those reported in the ESTAND cohort, Netherlands, and a cohort from North Africa for monogenic forms of MI [ 6 , 48 , 51 ]. This suggests that the overall genetic architecture of MI is similar across population groups. Furthermore, as 28 patients were assessed as a patient-parent trio, we could assess the role of autosomal dominant traits that contribute to infertility by screening for de novo variants. In 2010, a pilot study gave the first evidence of a de novo paradigm for intellectual disability and neurodevelopmental disorders [ 52 ], and in 2022, we gave the first evidence of benefit for similar gene-disease relationship discoveries using patient-parent trio approach in MI [ 16 ]. Through the approach, we identified enrichment of de novo variants in RBM5 gene, and provided evidence for its impact on male germ cell pre-mRNA splicing and MI. The data presented here is the first pilot patient-parent exome trio approach in MI in India and only the second one in the world. Whilst the authors are aware that the sample size for trio dataset is limited, prior modelling studies suggests more than 350,000 trios are likely to be required to achieve 80% statistical power for detecting haploinsufficient genes causing this disorder [ 16 , 53 ]. The diagnostic community has an enormous task of implementing newer genomic technologies in the clinic whilst simultaneously sharing anonymised data with the international research community, and simultaneously, the research community has to functionally validate the impact of new DNMs and genes on spermatogenesis [ 54 ]. Interestingly, we observe over twice the diagnostic yield from duo/ trio WES compared to targeted sequencing panel, but the difference is not statistically different (8.3% versus 3.3%; χ2 = 1.89, p = 0.17) and is largely explained by the fact that duo/trios were used for WES, allowing for phasing of bi-allelic mutations. One of the reasons could be that ~ 95% of the genes most robustly associated with MI phenotype have an autosomal recessive mode of inheritance [ 49 ]. This is reflected in the genes included in the targeted sequencing assay in the current study and a higher a priori probability of detecting a recessive phenotype, since several communities in India practice endogamy or consanguinity [ 55 ]. Critically though, the diagnostic yield of the sequencing assays did not differ by infertility subtype (azoospermic versus oligozoospermic patients) in both targeted sequencing assay and WES (5.1% versus 2.4%; χ2 = 1.013, p = 0.31). Both sequencing technologies offer significant advantages over conventional technologies such as: flexibility of sample type and target region enrichment, automatable data analysis and variant interpretation, and ability to deliver submicroscopic deletions in the Yq region. In addition, the smMIP platform though require a high initial investment, the per-library preparation and sequencing costs are substantially low compared to WES [ 8 ]. Since both sequencing approaches will miss balanced translocations and inversions, which account for up to 0.9% of infertile men [ 8 ], application of sequencing approaches together with karyotyping is suggested, as is also recommended by the WHO guideline [ 7 ]. In the future, long read whole genome sequencing (lrWGS) may likely replace existing conventional cytogenetic and short read sequencing technologies due to its capabilities of de novo genome assembly, detection of multiple variant types including structural variants, haplotype construction, and variant phasing in the absence of parental samples [ 56 ]. However, its current high cost may be prohibitive for clinical applications in the short term. Conclusion Data from large scale genomic and transcriptomic studies have helped to delineate the genetic architecture of male infertility in European/ non-Hispanic white populations. To the best of our knowledge, this is the first study to delineate the genetic architecture of MI in the Indian population, with autosomal recessive traits based on genes previously identified with a significant gene-disease relationship being the common cause. We demonstrate a significant proportion of infertile males with CFTR gene variants in India, thus advocating for an increased uptake in sequencing-based approaches for genetic diagnosis. We present results of a low-cost, automatable, and high-throughput targeted gene sequencing assay based on smMIP technology, having the ability to detect a wide variety of genomic rearrangements thus obviating the need for STS-PCR based Y chromosome microdeletion detection and WES based detection of MI genes. Our results clearly demonstrate the importance of a family-based approach to reach the highest diagnostic yield, independent of genomic technology used. To the best of our knowledge, ours is the first study from India and only second in the world to use a novel patient-parent trio exome approach for discovery of the role of DNMs in MI. Taken together, the current study estimates 12.8%-19.3% of the infertile males with an underlying genetic aetiology, and advocates adoption of targeted or whole sequencing based genetic testing in andrology to contribute towards diagnosis and management of MI in India. Declarations Competing Interests Harsh Sheth, Srutikaa Kale, and Joris Andre Veltman are named as inventors on the patent describing the use of smMIP based target capture and associated computational analyses for simultaneous detection of single nucleotide variants, copy number variants, and gonosomal aneuploidies in the germline DNA. The patent is held by Decipher DNA Pvt. Ltd. (Patent ID: TEMP/E-1/25374/2025-MUM, submitted in March 2025). All other authors declare that they have no conflict of interest. Author Contribution HS, JAV, FS, JS, and ASP were involved in study design. HS, PP, VM, DM, MB, AP, NB, TS, FS, and JS were involved in patient recruitment. HS, SK, MA, TD, MD, SC and BA were involved in study execution. HS, SK, MA, TD, SC, ASP, and JAV were involved in data analysis. HS, MA, TD, SK, SC, PJ, and JAV were involved in manuscript drafting and critical discussion. All authors approved the final manuscript. Acknowledgement We are grateful to the patients and their families for participation in the study. Data Availability Exome sequencing data has been deposited in the European Genome-phenome Archive (EGA) under the accession code EGAS00001008171 and will be made available upon reasonable request for academic use and within the limitations of the provided informed consent by the corresponding author upon acceptance. Every request will be reviewed by the Data Access Committee at the FRIGE Institute of Human Genetics; the researcher will need to sign a data access agreement after approval. 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Supplementary Files SupplementaryFigure1.pptx SupplementaryFile1MappingofYchromosomemicrodeletiondetectionstrategies.docx SupplementaryFile2PedigreeofWESsamples.docx SupplementaryTable4Listofgenessequencedbythetargetedsequencingassay.docx SupplementaryTable7PrimersequenceforcandidategenevariantsinWES.xlsx SupplementaryTable6DatafromvalidationrunofsmMIPbasedtargetedsequencingassayin20samples.xlsx SupplementaryTable3Listof120infertilemalestestedusingtargetedsequencingassay.xlsx SupplementaryTable1Patientcohortandclinicaldetails.xlsx SupplementaryTable8Primersequencesfordenovovariantvalidation.xlsx Supplementarytable2KaryotypeandYqmicrodeletion.xlsx SupplementaryTable11ListofDNMvariantsinWESdata.xlsx SupplementaryTable10CNVcallsfromWESdataset.xlsx SupplementaryTable9ListofcandidategenevariantsinWES.xlsx SupplementaryTable5Listofsinglemoleculemolecularinversionprobestargeting39genes.xlsx SupplementaryData.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 13 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:01:24","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11581,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7PrimersequenceforcandidategenevariantsinWES.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/d23a6c46a15f1708a5a9cd34.xlsx"},{"id":94874790,"identity":"aee6bb9b-1f09-45c4-9737-b674db7cb3e4","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13505,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6DatafromvalidationrunofsmMIPbasedtargetedsequencingassayin20samples.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/bcb3241ff1d7acd574e6a971.xlsx"},{"id":94986427,"identity":"f3caf45a-4015-4d9e-82df-61c99ee7b078","added_by":"auto","created_at":"2025-11-03 07:00:19","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":31818,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3Listof120infertilemalestestedusingtargetedsequencingassay.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/1ef279991a559260d580adf1.xlsx"},{"id":94874792,"identity":"36e946ab-888b-4b96-a5d3-9eaa9ca4f4d4","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":116167,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1Patientcohortandclinicaldetails.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/3f20d0c03fe6920e882b55c4.xlsx"},{"id":94874807,"identity":"ba179cbe-17c7-45d7-9c9d-66a0f6b340b4","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":69700,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable8Primersequencesfordenovovariantvalidation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/ae2c5d6965bd0c6f9472dfc0.xlsx"},{"id":94874785,"identity":"77d1442a-b4dd-4eb5-86ee-c1e9b7751494","added_by":"auto","created_at":"2025-10-31 15:32:47","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":66496,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2KaryotypeandYqmicrodeletion.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/2d1b40b9ada64322bc907f0a.xlsx"},{"id":94986686,"identity":"a74007c7-1f48-4ce3-ad27-653211474610","added_by":"auto","created_at":"2025-11-03 07:00:37","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":70875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable11ListofDNMvariantsinWESdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/c851cdf47875fad882140da2.xlsx"},{"id":94874797,"identity":"c20893c0-0f17-4c1f-a845-883ea8030a3c","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":78601,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable10CNVcallsfromWESdataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/1af3cc1e7de9d647b176e55d.xlsx"},{"id":94874804,"identity":"25481367-d32a-4b38-9b58-94e0c1e186a0","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":271839,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable9ListofcandidategenevariantsinWES.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/dfa02d14c86ed571d6c2c15e.xlsx"},{"id":94874809,"identity":"1690dbc5-76d9-4c41-80e8-5948221c0ffb","added_by":"auto","created_at":"2025-10-31 15:32:48","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":336589,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5Listofsinglemoleculemolecularinversionprobestargeting39genes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/09809d3c01a8c37d399ac052.xlsx"},{"id":94986612,"identity":"41fa3a55-3b28-49af-baed-2f2356fbe557","added_by":"auto","created_at":"2025-11-03 07:00:30","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":14634,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7849365/v1/8082c3869bb94f189711fcb0.docx"}],"financialInterests":"Competing interest reported. Harsh Sheth, Srutikaa Kale, and Joris Andre Veltman are named as inventors on the patent describing the use of smMIP based target capture and associated computational analyses for simultaneous detection of single nucleotide variants, copy number variants, and gonosomal aneuploidies in the germline DNA. The patent is held by Decipher DNA Pvt. Ltd. (Patent ID: TEMP/E-1/25374/2025-MUM, submitted in March 2025). All other authors declare that they have no conflict of interest.","formattedTitle":"Genetic diversity of infertile males in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMale infertility (MI) is defined as the inability to conceive a child after at least two years of unprotected intercourse [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is a heterogeneous condition that could be caused by infection, varicocele, immunological factors, genetic anomalies, endocrine factors, and idiopathic factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization (WHO), from 1990 to 2021, global infertility prevalence estimates indicate that approximately one in six people have experienced infertility at some stage in their lives (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/\u003c/span\u003e\u003cspan address=\"https://www.who.int/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e MI contributes to approximately 50% of all cases of infertility and 7% of the male population worldwide is affected by MI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMI is classified clinically based on etiological factors such as hypothalamic-pituitary axis function, spermatogenic quantitative defects, spermatogenic qualitative defects, and ductal obstruction or dysfunction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is broadly classified into 7 common forms depending on the qualitative and quantitative sperm abnormalities, namely: azoospermia (no sperm), oligospermia (\u0026lt;\u0026thinsp;10\u0026nbsp;million/mL), asthenospermia (\u0026lt;\u0026thinsp;32% progressive sperm motility), teratozoospermia (\u0026gt;\u0026thinsp;4% of morphological abnormality), acephalic sperm (sperm without head), globozoospermia (round-headed sperm), and cryptozoospermia (absence of sperm in a fresh sample). Among the various forms of MI, azoospermia is observed at a higher frequency, affecting nearly 1% of the overall male population and 10\u0026ndash;15% of infertile males [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenetic causes are estimated to contribute to a significant proportion of all MI cases, most commonly observed in severe forms of infertility such as non-obstructive azoospermia and other specific subtypes like multiple morphological abnormalities of sperm flagellum [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Latest WHO guidelines recommend karyotype and Y chromosome microdeletion test in all infertile males, and cystic fibrosis transmembrane conductance regulator (\u003cem\u003eCFTR\u003c/em\u003e) mutation analysis in males suspected with congenital bilateral absence of vas deferens (CBAVD) or cystic fibrosis (CF) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This approach has a combined diagnostic yield of 5⎼25%, depending on the spermatogenic phenotype [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The numerical and structural abnormalities in autosomal and sex chromosomes, as well as azoospermia factor (AZF) microdeletions, are comprehensively studied and well-recognized as causes of azoospermia. These genetic tests have prognostic implications on downstream artificial reproductive techniques (ARTs), clinical pregnancy rates, and live birth rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite this, a significant problem in andrology is that genetic testing strategies employed in clinics are not standardised, and in some countries, even the most basic tests recommended by the WHO are still not routinely used.\u003c/p\u003e\u003cp\u003eMajority of all human protein-coding genes (84%, 16598) are expressed in testis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] with more than 2000 genes being directly involved in spermatogenesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Advances in next-generation sequencing (NGS) have greatly facilitated unbiased exome-wide and genome-wide detection of genetic variants. The MI field is currently catching up with other medical disease types with a strong genetic component, which is aiding in the transition of findings into clinical practice [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To date, pathogenic variants in at least 104 genes are confidently linked to MI, and at least 138 other genes are classified as candidate genes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A significant proportion of these studies however have occurred in European/ non-Hispanic white populations.\u003c/p\u003e\u003cp\u003eThere is a significant paucity of systematic genetic analysis of infertile males in the Indian subcontinent, likely due to limited adoption of NGS technologies in routine clinical care. Most studies from India are restricted to karyotyping and Y chromosome microdeletion testing in the infertile population or exome sequencing in selected conditions like azoospermia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Here, we present results from our genomic study of 247 infertile Indian men which includes 48 duo/ trios affected by MI (affected male, mother, and father). Using combinatorial techniques like karyotyping, AZF microdeletion, targeted gene panel, and whole exome sequencing (WES), we present the genetic landscape of MI in India. We also provide an estimate of the diagnostic yields of respective techniques to enable rationalizing and prioritizing the diagnostic tests that must be offered in clinics for management of MI.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient recruitment and sample collection\u003c/h2\u003e\u003cp\u003eWe consecutively enrolled a total of 254 patients who presented with unexplained (idiopathic) MI at FRIGE Institute of Human Genetics between 2022 and 2024. These patients were referred by andrologists, urologists, and IVF specialists. The reference values and semen nomenclature and classification of MI were used according to the WHO guidelines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Clinical evaluation included two consecutive semen analysis reports, at least two weeks apart, showing either quantitative (sperm concentration being \u0026lt;\u0026thinsp;10\u0026nbsp;million/mL) or qualitative defects (e.g. teratozoospermia, asthenozoospermia, etc.), and endocrine reports showing normal readings for follicle stimulating hormone (FSH; normal range 1.5\u0026ndash;12.4 mIU/mL), luteinising hormone (LH; normal range 1.8\u0026ndash;8.6 mIU/mL), and testosterone (normal range 270\u0026ndash;1070 ng/dL). Detailed clinical as well as family history was collected for each patient, which included height, weight, current or previous intake of alcohol and/or tobacco, family members with clinical history of MI, and if available, ultra-sonography (USG) report of testis (Supplementary Table\u0026nbsp;1). Seven patients (2.8%) were detected with hypogonadotrophic hypogonadism based on their endocrine reports, and hence were not offered any genetic testing. The remaining 247 patients were subjected to karyotyping and AZF microdeletion testing on the Y chromosome. All patients negative for AZF microdeletions and chromosomal anomalies, along with their healthy parents, were invited for targeted gene panel and/or WES analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study protocol was approved by the ethics committee of FRIGE Institute of Human Genetics, Ahmedabad (REC ID: FRIGE/IEC/25/2022) and written informed consent from all patients and their parents was obtained prior to enrolment to the study, as per the Helsinki Declaration. High molecular weight genomic DNA extraction from peripheral whole blood samples was carried out using the desalting method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and stored at -20\u0026deg;C until molecular genetic testing was performed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eKaryotyping and AZF microdeletion\u003c/h3\u003e\n\u003cp\u003eKaryotyping and AZF microdeletion testing were performed in 247 patients (Supplementary Table\u0026nbsp;2). Karyotyping was performed using GTG banding at 500 band resolution to assess for chromosomal abnormalities according to the International Standards for Human Cytogenetics Nomenclature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Analysis for the AZF microdeletions was carried out using multiplex PCR for 16 sequence tagged site (STS) markers using a previously published method (AZF-STS PCR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The SRY-sex-determining region on the short arm of the Y chromosome (sY14) was used as an internal control. The following STS markers used were: sY84, sY82, sY86, sY746 (AZFa); sY143, sY134, sY127, sY128, sY121, sY173 (AZFb); sY254, sY160, sY145, sY255 (AZFc) and sY1291, sY1191 (gr/gr). The thermocycler protocol applied was as follows: 95\u0026deg;C for 5 min, 35 cycles of 95\u0026deg;C for 30 s, 56\u0026deg;C for 120 s, and 72\u0026deg;C for 30 s, preceding 72\u0026deg;C for 5 min. The qualitative analysis of the amplified PCR products was performed using agarose gel electrophoresis.\u003c/p\u003e\n\u003ch3\u003esmMIP based targeted sequencing assay\u003c/h3\u003e\n\u003cp\u003eOf 247 patients, 27 patients were diagnosed with either gonosomal aneuploidy or Yq microdeletion, and hence were not included in downstream analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the remaining 220 patients, 120 patients gave consent to analyse their DNA using single-molecule molecular inversion probes (smMIP)-based targeted sequencing assay using previously described method (Supplementary Table\u0026nbsp;3) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although smMIPs targeting 106 genes were previously described [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], only 39 genes were targeted in the current study (Supplementary Tables\u0026nbsp;4 and 5) based on the recent systematic review of gene-disease relationship for MI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Libraries from samples were pooled to equimolar concentration and purified using Agencourt AMPure XP beads according to manufacturer\u0026rsquo;s protocol (Beckman Coulter, USA). Each prepared library was diluted to a concentration of 4 pM and subsequently sequenced on the MiSeq platform (Illumina, USA) according to the manufacturer\u0026rsquo;s protocol resulting in 2x157 bp paired-end reads. The sequencing was performed at a mean depth of ~\u0026thinsp;200x per loci.\u003c/p\u003e\u003cp\u003eData was analysed using an in-house pipeline, as previously described [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Briefly, reads were aligned against hg19/GRCh37 human reference genome using BWA-MEM (v.0.7.12) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] followed by base quality score recalibration, and single nucleotide variants (SNV) and small indel calling using GATK HaplotypeCaller (v4.12) in accordance with the GATK\u0026rsquo;s best practice guidelines (McKenna \u003cem\u003eet al.\u003c/em\u003e, 2010). Variants were annotated, filtered, and prioritised based on the patient\u0026rsquo;s phenotype (in human phenotype ontology [HPO] format) using Exomiser v13.1 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] integrating data from SIFT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sift.bii.a-star.edu.sg/www/SIFT_seq_submit2.html\u003c/span\u003e\u003cspan address=\"https://sift.bii.a-star.edu.sg/www/SIFT_seq_submit2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, Polyphen2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harward.edu/pph2\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harward.edu/pph2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, MutationTaster (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mutationtaster.org\u003c/span\u003e\u003cspan address=\"http://www.mutationtaster.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, Combined Annotation Dependent Depletion (CADD) scores, REVEL scores, dbSNP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/SNP/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/SNP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the Genome Aggregation Database (gnomAD; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003egnomad.broadinstitute.org\u003c/span\u003e) and ClinVar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/clinvar\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/clinvar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Copy number variant (CNV) calling was carried out using the CNVRobot (v4.1) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AnetaMikulasova/CNVRobot\u003c/span\u003e\u003cspan address=\"https://github.com/AnetaMikulasova/CNVRobot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and a minimum of 5 \u0026ldquo;control\u0026rdquo; male samples where no prior CNVs in the targeted regions were observed. Details of validation sequencing run in a set of 20 samples with known genetic diagnosis is provided in Supplementary Table\u0026nbsp;6 and File 1.\u003c/p\u003e\n\u003ch3\u003eWhole exome Sequencing\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSequencing and SNV calling\u003c/h2\u003e\u003cp\u003eOf the 220 patients, 48 patients and his parent(s) gave consent to carry out duo/ trio whole exome sequencing (see Supplementary File 2 for pedigree details). These patients were normal for karyotyping and AZF STS-PCR, but had not been assessed by smMIP-based targeted sequencing assay. Genomic DNA from duos and trios were prepared and enriched following the manufacturer\u0026rsquo;s protocol of Twist Bioscience\u0026rsquo;s Human Core Exome v2 kit (Twist Bioscience, USA). Sequencing was performed either on NovaSeq 6000 or NovaSeq X Plus platform (Illumina, USA) at an average depth of 80-100x. Sequenced reads were aligned against the human reference genome GRCh38/hg38 using BWA-MEM (v.0.7.12) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] followed by base quality score recalibration, and single nucleotide variants (SNV) and small indel calling using GATK HaplotypeCaller (v4.12) in accordance with the GATK\u0026rsquo;s best practice guidelines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The sex and relatedness of each sample was calculated using Peddy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSNV variant filtration and prioritisation\u003c/h2\u003e\u003cp\u003eVariant annotation, filtration, and prioritisation was performed using Exomiser v13.1.0 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Exomiser uses hiPHIVE prioritisation method that incorporates mode of inheritance (recessive, dominant, and maternally inherited), protein-protein interaction networks, and multi-species ontologies along with ranking candidate genes based on the predicted variant pathogenicity associated with the phenotype. The phenotype information was coded in uniform HPO terminologies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Common variants were filtered based on minor allele frequency in the 1000Genomes Phase 3, TopMed, and gnomAD v2.1 databases. The minor allele frequency cut-off was set at 0.02 (2%). The cut-off was set assuming monogenic recessive as well as dominant \u003cem\u003ede novo\u003c/em\u003e forms of infertility, especially commonly occurring variants in the \u003cem\u003eCFTR\u003c/em\u003e gene. Only non-synonymous variants in the coding region and canonical splice site variants with a depth of \u0026gt;\u0026thinsp;20x were used for analysis and clinical correlation. Various \u003cem\u003ein silico\u003c/em\u003e prediction tools such as PolyPhen-2 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], SIFT [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], MutationTaster2 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and CADD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] were used to predict pathogenicity of non-synonymous and indel variants. A CADD_phred score of \u0026ge;\u0026thinsp;15 and at least two damaging predictions from the remaining \u003cem\u003ein silico\u003c/em\u003e tools were used for selection of candidate variants. \u003cem\u003eIn silico\u003c/em\u003e predictions along with available knowledge from various sources and databases as described below was used in prioritising variants.\u003c/p\u003e\u003cp\u003ePost gross filtering, variants were prioritised based on the following: (a) known disease causing variant previously reported in the ClinVar database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; (b) novel variants in known genes based on the Z-score for missense and pLOEUF score for loss of function variants available in the gnomAD database; (c) variants in novel candidate genes wherein the respective gene was additionally evaluated for their function using UniProt (\u0026ldquo;UniProt,\u0026rdquo; 2019) and Human Protein Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.proteinatlas.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Tissue expression using GTEx database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gtexportal.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.gtexportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, association/ interaction with known male infertility genes using STRING database [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and plausible phenotype outcome in murine models based on the MGI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.informatics.jax.org/\u003c/span\u003e\u003cspan address=\"https://www.informatics.jax.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were assessed. All candidate variants were assessed using Integrative Genomics Viewer (IGV) to evaluate their quality. Variants in genes with recessive or dominant mode of inheritance were analysed using the exact same method of filtration and interpretation as described above.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDe novo variant calling\u003c/h3\u003e\n\u003cp\u003eIn the case of 28 patient-parent trio samples, \u003cem\u003ede novo\u003c/em\u003e variants (DNMs) were identified using a deep convolutional neural network based DNM caller, DeNovoCNN [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To reduce probability of false positive DNMs, a cut-off for posterior probability score was set to \u0026gt;\u0026thinsp;0.5, a minimum of 30x coverage at the genomic co-ordinate across the trio, and variant allele frequency threshold of 30\u0026ndash;70% in the proband and \u0026lt;\u0026thinsp;10% in the parents was used. Putative role of DNM in MI was assessed using 5 categories: RNA expression of the gene in testis, protein expression in testis, whether an infertile murine model exists for the given gene, protein function relative to spermatogenesis, and whether the gene interacts with known fertility genes. Expression data was assessed in GTEx database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gtexportal.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.gtexportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and Human Protein Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.proteinatlas.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and interaction with known fertility genes was assessed using STRING database [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCNV analysis\u003c/h3\u003e\n\u003cp\u003eCNV calling was performed across the exome data using the CNVRobot (v4.1) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AnetaMikulasova/CNVRobot\u003c/span\u003e\u003cspan address=\"https://github.com/AnetaMikulasova/CNVRobot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e which uses a custom GATK 4 based pipeline. This workflow exploits the GATK 4 sequence read depth normalisation and a custom R based segmentation and visualization. At least 5 male parental samples were used as controls for the normalisation step. CNVs were annotated using AnnotSV [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the case of candidate CNVs, variants were primarily screened for population frequency and known disease associations using publicly available databases like gnomAD database, DGV [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and OMIM [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CNVs present in more than 1% of the samples of the aforementioned databases and present in more than 10% of the patients were excluded from the analysis. The remaining rare deletions and duplications were individually inspected through the genomic profiles and detailed log2 ratio plots generated by CNVRobot.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eVariant validation\u003c/h2\u003e\u003cp\u003eAll candidate SNVs, indels, and DNMs were validated in the probands and parent(s) using bi-directal Sanger sequencing approach on Applied Biosystems SeqStudio Genetic Analyzer (ThermoFisher, USA). The primers for SNV validation were designed using the Primer3 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://primer3.ut.ee/\u003c/span\u003e\u003cspan address=\"https://primer3.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (Supplementary Tables\u0026nbsp;7 and 8) and PCR reactions were performed using Invitrogen\u0026rsquo;s \u003cem\u003eTaq\u003c/em\u003e polymerase (ThermoFisher, USA) according to the manufacturer\u0026rsquo;s protocol. CNV validation was carried out using SYBR-Green based quantitative PCR on the Applied Biosystems StepOne Real-Time PCR System (ThermoFisher, USA). The identified SNVs and CNVs were classified according to the American College of Medical Genetics \u0026ndash; American College of Pathologists (ACMG-AMP) guidelines [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and the ClinGen framework [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePatient cohort\u003c/h2\u003e\u003cp\u003eThe study cohort consisted of 247 infertile males, presenting with a spectrum of clinical phenotypes. The average age at recruitment was 34\u0026thinsp;\u0026plusmn;\u0026thinsp;5 years and ranged from 24 to 51 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;1). Forty-eight patients had either previously or were currently undergoing ART treatment, and male infertility was recorded in family members of 13 index patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;1). Majority of the patients presented with mixed defects ⎼ oligoasthenoteratozoospermia (50.2%, n\u0026thinsp;=\u0026thinsp;124/247) followed by quantitative sperm defects ⎼ azoospermia (31.9%, n\u0026thinsp;=\u0026thinsp;79/247) and oligozoospermia (2%, n\u0026thinsp;=\u0026thinsp;5/247) ⎼ with the remaining patients showing qualitative spermatogenic defects (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of the infertile male cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal patients (N\u0026thinsp;=\u0026thinsp;247)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge of proband, years (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily member(s) diagnosed with male infertility, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (5.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSemen phenotype, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuantitative defect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (31.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnejaculation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCryptozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOligozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQualitative defect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthenoteratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthenoglobozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthenozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMixed defect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOligoasthenoteratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (50.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOligoasthenozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOligoteratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown phenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eKaryotyping and AZF microdeletion analysis\u003c/h2\u003e\u003cp\u003eSequential genetic testing was performed in all 247 patients using karyotype and AZF STS-PCR. Of 247 patients, gross chromosomal aneuploidy was observed in 3 patients, where 2 patients had 47, XXY (Klinefelter syndrome) and 1 patient had 48, XXYY syndrome (Supplementary Table\u0026nbsp;2). With AZF STS-PCR, a microdeletion in the AZF region was observed in 24 patients. Causal microdeletions were detected in 8 cases (3.2%): b2/b3 sub-deletion in the AZFc locus in 1 patient (4.1%), b1/b3 sub-deletion in the AZFc locus in 1 patient (4.1%), complete AZFc microdeletion in 1 patient (4.1%), complete AZFb microdeletion\u0026thinsp;+\u0026thinsp;gr/gr sub-deletion in AZFc region in 1 patient (4.1%), complete AZFb microdeletion\u0026thinsp;+\u0026thinsp;b2/b3 sub-deletion in AZFc region in 1 patient (4.1%), complete AZFb microdeletion\u0026thinsp;+\u0026thinsp;b1/b3 sub-deletion in AZFc region in 2 patients (8.3%), and a partial AZFc deletion (sY255) in 1 patient (4.1%). Additionally, likely benign gr/gr sub-deletion was reported in 16 patients (6.5%). The combined diagnostic yield of karyotyping and Yq microdeletion testing in 247 patients was 4.5% (n\u0026thinsp;=\u0026thinsp;11). Majority of the patients (n\u0026thinsp;=\u0026thinsp;23/24) with AZF microdeletions presented with oligozoospermia or azoospermia, while 1 patient presented with asthenozoospermia (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTargeted sequencing assay analysis\u003c/h2\u003e\u003cp\u003eWe successfully designed a smMIP-based targeted enrichment approach using a grand total of 1,507 equimolarly pooled smMIPs capturing the exons and splice site regions of 39 genes on autosomes and sex chromosomes (Supplementary Table\u0026nbsp;5). After rebalancing our smMIP pool to optimise the capture efficiency, we sequenced 98.4% of the targeted regions covering a total region of approximately 165 kb within a mean read depth after duplicate removal of ~\u0026thinsp;100x unique reads per sample. Validation of the method was carried out on a set of 20 previously orthogonally tested samples, including samples with Klinefelter syndrome, Y chromosome microdeletion, globozoospermia, and cystic fibrosis (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e120 of 247 probands consented for the targeted sequencing (Supplementary Table\u0026nbsp;3). Of these, 55 had azoospermia, 50 had oligozoospermia, and remaining had qualitative defects. All patients were screened using the targeted assay for gonosomal aneuploidies, AZF deletions, and variants in 6 diagnostic infertility genes: \u003cem\u003eAURKC, CFTR, DPY19L2, DD3X, SYCP3\u003c/em\u003e, and \u003cem\u003eTEX11.\u003c/em\u003e As patients had already been screened using karyotyping and AZF STS-PCR, no gonosomal aneuploidy or AZF deletions were detected. CNV analysis was followed by SNV analysis. Analysis of protein altering variants with minor allele frequency of \u0026lt;\u0026thinsp;1% in the healthy population showed 10 patients with predicted pathogenic/ likely pathogenic (P/LP): \u003cem\u003eCFTR\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eDNAH1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eKHLH10\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), and \u003cem\u003eNR5A1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Of the 4 patients with at least 2 variants in the \u003cem\u003eCFTR\u003c/em\u003e gene, 1 patient (MI-068) had 2 heterozygous variants in close proximity which allowed variant phasing and suggested the variants to be in \u003cem\u003etrans\u003c/em\u003e (Supplementary Fig.\u0026nbsp;1). \u003cem\u003eIn silico\u003c/em\u003e analysis of variant co-occurrence estimation using the gnomAD v2.1.1 database suggests that the 2 heterozygous variants \u003cem\u003eCFTR\u003c/em\u003e:c.926C\u0026thinsp;\u0026gt;\u0026thinsp;G and \u003cem\u003eCFTR\u003c/em\u003e:c.473G\u0026thinsp;\u0026gt;\u0026thinsp;A in proband MI-102 also have a high probability of being in \u003cem\u003etrans\u003c/em\u003e, whereas, for probands MI-023 and MI-227, co-occurrence analysis either could not be carried out or the probability estimates were uncertain. Nonetheless, for all 3 cases, due to the absence of parental samples and the use of short read sequencing, variant phasing could not be carried out. Critically, compound heterozygous variants in the \u003cem\u003eCFTR\u003c/em\u003e gene can be present in patients without CBAVD diagnosis based on physical examination (Smits \u003cem\u003eet al.\u003c/em\u003e, 2019), which requires expertise, despite which it is not always certain. In our clinical analysis of patients with 2 \u003cem\u003eCFTR\u003c/em\u003e gene variants, only in 1 patient ultrasonography of scrotum was suggestive of bilateral calcification in epididymis (MI-068), the others were not clinically classified as CBAVD. However, in all the cases, semen analysis had indicated azoospermia/ oligozoospermia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of physical examination, semen analysis, and targeted gene analysis in patients with \u003cem\u003eCFTR\u003c/em\u003e gene variants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical examination of vas deference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemen analysis phenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e variants*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.1695T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Asp565Glu);het\u003c/p\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.1186A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\u003cp\u003ep.(Asn396Tyr);het\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAzoospermia with USG scrotum suggestive of bilateral calcification in epididymis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.3197G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Arg1066His);het\u003c/p\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.3209G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Arg1070Gln);het\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.926C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003cp\u003ep.(Ala309Gly);het\u003c/p\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.473G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Ser158Asn);het\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.473G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Ser158Asn);het\u003c/p\u003e\u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e(NM_000492.4):c.3209G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003cp\u003ep.(Arg1070Gln);het\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e*\u003c/b\u003eOnly variants for MI-068 are in \u003cem\u003etrans\u003c/em\u003e due to proximity of variants (Supplementary Fig.\u0026nbsp;1). For the remaining 3 patients, phase information is unknown; het\u0026thinsp;=\u0026thinsp;heterozygous.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of semen analysis and targeted gene analysis in patients with likely pathogenic/ pathogenic variants in male infertility genes, giving a monogenic cause of the fertility problem\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResult from targeted gene panel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssociated phenotype (inheritance) (OMIM phenotype)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsthenoteratozoopsermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eDNAH1\u003c/em\u003e(NM_015512.5):c.10468_10471del p.(Arg3490GlnfsTer4);hom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTeratozoospermia (AR)\u003c/p\u003e\u003cp\u003e(OMIM#617576; Spermatogenic failure 18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAzoospermia with sertoli cell only syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eKLHL10\u003c/em\u003e(NM_152467.5):c.287A\u0026thinsp;\u0026gt;\u0026thinsp;G p.(Tyr96Cys);het\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpermatogenic failure (AD)\u003c/p\u003e\u003cp\u003e(OMIM#615081; Spermatogenic failure 11; terato/azthenozoospermia in some patients)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNR5A1\u003c/em\u003e(NM_004959.5):c.1139A\u0026thinsp;\u0026gt;\u0026thinsp;G p.(Asp380Gly);het\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOligo/azoospermia (AD)\u003c/p\u003e\u003cp\u003e(OMIM #613957; spermatogenic failure 8; variable expression and incomplete penetrance described)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ehet\u0026thinsp;=\u0026thinsp;heterozygous; hom\u0026thinsp;=\u0026thinsp;homozygous; AR\u0026thinsp;=\u0026thinsp;autosomal recessive; AD\u0026thinsp;=\u0026thinsp;autosomal dominant; OMIM\u0026thinsp;=\u0026thinsp;Online Mendelian Inheritance in Man\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGenotype-phenotype correlation in the case with a homozygous variant in both \u003cem\u003eDNAH1\u003c/em\u003e and \u003cem\u003eNR5A1\u003c/em\u003e genes showed complete overlap with the known set of phenotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Interestingly for case MI-125, patient was clinically diagnosed with Sertoli cell-only syndrome (SCOS) (Supplementary Table\u0026nbsp;1). Genetic evaluation showed a heterozygous missense variant in the \u003cem\u003eKLHL10\u003c/em\u003e gene. Evaluation of \u003cem\u003eKLHL10\u003c/em\u003e haploinsufficiency in murine models has shown impact on spermatogenesis and spermiogenesis, with SCOS being observed in several cases [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thus, targeted sequencing assay provided a conclusive diagnostic yield of 3.3% (n\u0026thinsp;=\u0026thinsp;4/120) in addition to the cytogenetic and Y chromosome microdeletion analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eExome sequencing in patient-parent duos and trios\u003c/h2\u003e\u003cp\u003eA total of 100 patients, who had a normal karyotype and absence of Y chromosome microdeletion, did not participate in the targeted sequencing assay analysis. Of these, 48 patients and their parent(s) consented for whole exome sequencing (WES). Out of the 48 participants, 28 were patient-parent trios, 11 were categorised as duos (patient and a single parent or a fertile sibling in case of death of either or both parents before recruitment), 8 \u0026ldquo;partial\u0026rdquo; trios (patient, one parent, and a fertile sibling), and 1 singleton (parents passed away before their samples could be collected for the study). At the time of conception of the proband, the average age of the mother was 22\u0026thinsp;\u0026plusmn;\u0026thinsp;5 years, and the average age of the father was 28\u0026thinsp;\u0026plusmn;\u0026thinsp;6 years.\u003c/p\u003e\u003cp\u003eWe used Exomiser [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] based variant filtration and prioritisation engine which took into account patient\u0026rsquo;s clinical phenotypes and assessed genes and variants based on multiple modes of inheritance simultaneously in order to identify putative gene(s) of interest. Of 48 patients, 19 (39.6%) were detected with putative variants in genes that have enriched expression in testis or are known infertility genes (Supplementary Table\u0026nbsp;9). Of these, P/LP variants were identified in 4 patients (8.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Using segregation analysis in parental samples due to the duo/ trio approach used for WES, bi-allelic variants were detected in the \u003cem\u003ePMFBP1\u003c/em\u003e gene in 2 patients which is associated with spermatogenic failure 31 (OMIM#618112), and \u003cem\u003eDNAH1\u003c/em\u003e gene in 1 patient which is associated with spermatogenic failure 18 (OMIM#617576). One patient had a hemizygous variant in the \u003cem\u003eAR\u003c/em\u003e gene, which is associated with androgen insensitivity (OMIM#300068). All 3 genes are known male infertility genes. Indeed, patients with \u003cem\u003ePMFBP1\u003c/em\u003e gene variants presented with oligozoospermia and/ or pin-head sperms which is observed in spermatogenic failure 31 (OMIM#618112), patient with \u003cem\u003eDNAH1\u003c/em\u003e gene variant presented with 96% of the sperms with an abnormal morphology which is observed in spermatogenic failure 18 (OMIM#617576), and lastly, patient with \u003cem\u003eAR\u003c/em\u003e gene variant presented with azoospermia, varicocele, and bilateral epididymal cyst. However, clinical phenotypes such as female external genitalia in males, underdeveloped labia, labial testis, and blind vagina associated with androgen insensitivity (OMIM#300068), were not observed in the patient during trans-rectal and testis sonography (Supplementary Table\u0026nbsp;1). Furthermore, the patient had normal levels of plasma FSH, LH, and testosterone levels. Together with the presence of a missense variant, these clinical findings suggest that the patient was likely to be affected with partial/ mild androgen insensitivity syndrome (PAIS/ MAIS) whereby males can have normal sized penis and descended testis, but impaired spermatogenesis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of semen analysis and whole exome sequencing analysis in patients with likely pathogenic/ pathogenic variants in male infertility genes, giving a monogenic cause of the fertility problem\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnalysis type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResult from whole exome sequencing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated phenotype (inheritance) (OMIM phenotype)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAzoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAR\u003c/em\u003e(NM_000044.6):c.2317G\u0026thinsp;\u0026gt;\u0026thinsp;A p.(Glu773Lys); hem. mat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAzoospermia (XLR)\u003c/p\u003e\u003cp\u003e(OMIM#300068; Androgen insensitivity syndrome)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere oligoteratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePMFBP1\u003c/em\u003e(NM_031293.3):c.1888G\u0026thinsp;\u0026gt;\u0026thinsp;T p.(Glu630Ter); het. mat.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMFBP1\u003c/em\u003e(NM_031293.3):c.1462C\u0026thinsp;\u0026gt;\u0026thinsp;T p.(Gln488Ter); het. pat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOligozoospermia and acephalic sperms (AR)\u003c/p\u003e\u003cp\u003e(OMIM#618112; Spermatogenic failure 31; oligozoospermia with acephalic spermatozoa in most cases, some cases have tailless sperms)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere oligoasthenoteratozoospermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eDNAH1\u003c/em\u003e(NM_015512.5):c.8668del p.(Thr2890GlnfsTer46); het. pat.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDNAH1\u003c/em\u003e(NM_015512.5):c.5054C\u0026thinsp;\u0026gt;\u0026thinsp;G p.(Pro1685Arg); het. mat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTeratozoospermia (AR)\u003c/p\u003e\u003cp\u003e(OMIM#617576; Spermatogenic failure 18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI-244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeratozoospermia with pin head sperms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePMFBP1\u003c/em\u003e(NM_031293.3):c.841C\u0026thinsp;\u0026gt;\u0026thinsp;T p.(Gln281Ter); het. pat.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMFBP1\u003c/em\u003e(NM_031293.3):c.841C\u0026thinsp;\u0026gt;\u0026thinsp;T p.(Gln281Ter); het. mat.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOligozoospermia and acephalic sperms (AR)\u003c/p\u003e\u003cp\u003e(OMIM#618112; Spermatogenic failure 31; oligozoospermia with acephalic spermatozoa in most cases, some cases have tailless sperms)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ehet\u0026thinsp;=\u0026thinsp;heterozygous; hom\u0026thinsp;=\u0026thinsp;homozygous; hem\u0026thinsp;=\u0026thinsp;hemizygous; mat\u0026thinsp;=\u0026thinsp;Maternal; pat\u0026thinsp;=\u0026thinsp;Paternal, AR\u0026thinsp;=\u0026thinsp;autosomal recessive, AD\u0026thinsp;=\u0026thinsp;autosomal dominant, XLR\u0026thinsp;=\u0026thinsp;X linked recessive, OMIM\u0026thinsp;=\u0026thinsp;Online Mendelian Inheritance in Man\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross the patients, an average of 365 CNV calls per exome were observed prior to filtering and prioritisation (Supplementary Table\u0026nbsp;10). Following filtration steps, there were an average of 48 CNV calls per sample. Manual inspection of the CNVs involved retaining CNVs spanning at least 2 consecutive exons to minimize false positives and detected in \u0026le;\u0026thinsp;2 probands but absent in their parents. No causative \u003cem\u003ede novo\u003c/em\u003e, homozygous, or hemizygous CNVs were however detected in our cohort.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eRole of de novo variants in male infertility\u003c/h2\u003e\u003cp\u003eTo assess the role of DNMs in male infertility, we restricted our analysis to 28 patient-parent trios. Thirty-three DNMs were detected and validated by Sanger sequencing with an average of 1.2 DNMs per patient (minimum\u0026thinsp;=\u0026thinsp;0 and maximum\u0026thinsp;=\u0026thinsp;5; Supplementary Table\u0026nbsp;11). Except for a single DNM located on the X chromosome, all of the variants were identified in autosomes. None of the 33 DNMs occurred in a gene already known for its involvement in autosomal dominant MI, which is as anticipated since only 4 autosomal dominant genes have so far been linked with isolated MI in humans [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcross genetic disorders, genes with dominant phenotypes are usually intolerant to loss of function (LoF) or missense variants, as suggested by high pLI and Z-score or a low LOEUF score. Indeed, our cohort of infertile males showed enrichment of LoF-intolerant genes with LoF or missense DNMs (18.2%, n\u0026thinsp;=\u0026thinsp;6/33). We systematically evaluated the likelihood of missense DNMs causing MI by assessing the predicted pathogenicity using 3 \u003cem\u003ein silico\u003c/em\u003e tools and Z-score from the gnomAD database, as previously carried out by Oud \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Overall, 2 likely pathogenic DNMs were detected in 2 genes. However, unlike \u003cem\u003eOud et al.\u003c/em\u003e, no missense or LoF DNM in these genes were found to be a candidate for explaining MI in the present cohort. Interestingly, we identified a \u003cem\u003ede novo\u003c/em\u003e heterozygous nonsense variant c.886C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Gln296Ter) in \u003cem\u003eCCDC183\u003c/em\u003e gene in a patient with severe oligoasthenoteratozoospermia (MI-138; Supplementary Tables\u0026nbsp;9 and 11). The gene has testis enriched expression and knockout studies in murine models have shown its role in axoneme microtubules elongation in the centrioles during spermiogenesis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the absence of CCDC183 protein activity, loss of cytoplasmic invagination around the sperm flagellum is observed due to which, the flagellar compartment does not form properly and axonemal microtubules collapse during spermiogenesis. This suggests that \u003cem\u003eCCDC183\u003c/em\u003e gene plays a crucial role in spermatozoa formation and is a putative novel MI gene. However, based on the Z, pLI, and LOEUF scores, the gene is likely to cause recessive forms of infertility, and no second pathogenic variant in \u003cem\u003etrans\u003c/em\u003e was detected by exome sequencing approach.\u003c/p\u003e\u003cp\u003eOverall, conclusive diagnosis obtained from each technique is: 1.2% (n\u0026thinsp;=\u0026thinsp;3/247) for karyotyping, 3.2% (n\u0026thinsp;=\u0026thinsp;8/247) for Y chromosome microdeletion testing, 3.3% (n\u0026thinsp;=\u0026thinsp;4/120) for targeted sequencing panel, and 8.3% (n\u0026thinsp;=\u0026thinsp;4/48) for duo-/trio-based WES. Despite over twice the diagnostic yield observed from duo/trio WES compared to targeted sequencing panel, the difference was not statistically significant (χ2\u0026thinsp;=\u0026thinsp;1.89, p\u0026thinsp;=\u0026thinsp;0.17).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlmost a decade ago, the WHO produced consensus guidelines recommending karyotyping and Y chromosome microdeletion testing in all males with severe oligozoospermia or non-obstructive azoospermia prior to any therapeutic procedure, and \u003cem\u003eCFTR\u003c/em\u003e gene mutation analysis in all males suspected with CBAVD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Since then however, several monogenic causes of quantitative and qualitative spermatogenic defects have been detected with high confidence ([\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Sequencing based approaches, including both targeted and exome, have led to the discovery of these diseases, with results suggesting improvement in diagnostic yield over the traditional paradigm [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Majority of this data has been generated using infertile males of non-Hispanic European ethnicity, thereby limiting their generalizability towards other ethnic populations. Prior approaches to understanding the genetic architecture of MI in India have been limited to Klinefelter syndrome and Y chromosome microdeletions, with only 1 study utilising exome sequencing to detect novel candidate genes, albeit in a small cohort of 47 patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To the best of our knowledge, we here report the first description of the genetic architecture of MI and simultaneously carry out diagnostic yield comparisons of karyotype, Y chromosome microdeletion testing, targeted sequencing, and WES in a cohort of 247 infertile males of Indian origin with conclusive yields of 1.2% (n\u0026thinsp;=\u0026thinsp;3/247), 3.2% (n\u0026thinsp;=\u0026thinsp;8/247), 3.3% (n\u0026thinsp;=\u0026thinsp;4/120), and 8.3% (n\u0026thinsp;=\u0026thinsp;4/48), respectively.\u003c/p\u003e\u003cp\u003eOur data is in congruence with prior reports and studies suggesting Klinefelter syndrome to be observed at a higher frequency in patients with non-obstructive azoospermia in comparison with the general population [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the present study, two men with 47, XXY chromosome complement were observed in a cohort of 247 infertile males of whom 85 men had azoospermia, thereby having a frequency of 0.8% (~\u0026thinsp;1 in 123 men), which is significantly higher than the estimated frequency of 1 in 600 in the general population. When screening for Y chromosome microdeletions, we observed causal AZF deletion frequency of 3.2% (n\u0026thinsp;=\u0026thinsp;8/247) in a cohort of 247 infertile males. These men presented with either severe oligozoospermia or azoospermia (Supplementary Tables\u0026nbsp;1 and 2). Additionally, 6.5% of infertile men presented with \u003cem\u003egr/gr\u003c/em\u003e deletions (n\u0026thinsp;=\u0026thinsp;16/247; Supplementary Table\u0026nbsp;2) in the AZFc locus. \u003cem\u003egr/gr\u003c/em\u003e deletions are known to be the most frequently occurring genetic anomaly in the AZF region [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In the present study, 66.6% of the patients with \u003cem\u003egr/gr\u003c/em\u003e deletion presented with oligozoospermia and 27.7% of patients with azoospermia. Unlike in the Caucasian population, men with \u003cem\u003egr/gr\u003c/em\u003e deletions have been suggested to be at an increased risk of quantitative sperm defects including oligozoospermia and azoospermia in the Indian population, with further analysis suggesting Y chromosome haplogroup as poor marker for risk assessment among Indian infertile men [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSequencing based approaches to detect monogenic causes of infertility have yielded a growing list of high confidence and putative genes over the past decade [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In a recent set of systematic gene-disease relationships (GDR) for monogenic causes of infertility, a total of 120 genes were reported to be at least moderately linked with 104 MI cases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Of these, 48, 27, and 45 genes were classified as definitive, strong, and moderate, respectively. In the targeted gene sequencing assay used in the current study, genes classified as definitive (n\u0026thinsp;=\u0026thinsp;10), strong (n\u0026thinsp;=\u0026thinsp;4), and moderate (n\u0026thinsp;=\u0026thinsp;3) have been included (Supplementary Table\u0026nbsp;4). Furthermore, 22 genes encompassing the Y chromosome were included in the sequencing assay to ensure the ability to detect Y chromosome microdeletions (Supplementary Tables\u0026nbsp;4 and 6). The targeted smMIP-based sequencing approach used in the present study has been used previously, whereby the panel covering 107 genes was used in a cohort of 1,112 men with idiopathic azoospermia or severe oligozoospermia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Interestingly, only\u0026thinsp;~\u0026thinsp;1.5% diagnostic yield was observed in their cohort compared to 3.3% (n\u0026thinsp;=\u0026thinsp;4/120) yield in our cohort. One reason for this could be the exclusion of men with retrograde ejaculation and/or proven CBAVD, whereby, bi-allelic variants in the \u003cem\u003eCFTR\u003c/em\u003e gene are observed in most cases. In contrast to this, men with azoospermia, with the exception of 1 patient, were not clinically classified as CBAVD prior to enrollment on the study, thereby, enriching the diagnostic yield of the targeted assay by 25% (n\u0026thinsp;=\u0026thinsp;1/4) with bi-allelic variants in the \u003cem\u003eCFTR\u003c/em\u003e gene. In addition to this, the assay was able to detect pathogenic variants in 3 genes which are classed as definitively linked with MI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We demonstrate the ability of the assay to detect Klinefelter syndrome, Yq microdeletion, and CNV calling across genes in the autosomes (Supplementary Table\u0026nbsp;6). Of note, had the targeted sequencing assay been consented to be utilised in the trio/duo cohort of 48 patients, the assay would have provided two additional diagnoses as two genes, \u003cem\u003eDNAH1\u003c/em\u003e and \u003cem\u003eAR\u003c/em\u003e, were covered by the targeted assay.\u003c/p\u003e\u003cp\u003eThe final part of the study focused on assessing the utility of WES for diagnosis and management for men with infertility. Of the 48 patients, 4 patients received a confirmed molecular diagnosis (8.3%, n\u0026thinsp;=\u0026thinsp;4/48) in 3 genes which are classified as definitive or strongly associated with MI. The overall diagnostic yield of WES in our cohort is in congruence with those reported in the ESTAND cohort, Netherlands, and a cohort from North Africa for monogenic forms of MI [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This suggests that the overall genetic architecture of MI is similar across population groups. Furthermore, as 28 patients were assessed as a patient-parent trio, we could assess the role of autosomal dominant traits that contribute to infertility by screening for \u003cem\u003ede novo\u003c/em\u003e variants. In 2010, a pilot study gave the first evidence of a \u003cem\u003ede novo\u003c/em\u003e paradigm for intellectual disability and neurodevelopmental disorders [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and in 2022, we gave the first evidence of benefit for similar gene-disease relationship discoveries using patient-parent trio approach in MI [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Through the approach, we identified enrichment of \u003cem\u003ede novo\u003c/em\u003e variants in \u003cem\u003eRBM5\u003c/em\u003e gene, and provided evidence for its impact on male germ cell pre-mRNA splicing and MI. The data presented here is the first pilot patient-parent exome trio approach in MI in India and only the second one in the world. Whilst the authors are aware that the sample size for trio dataset is limited, prior modelling studies suggests more than 350,000 trios are likely to be required to achieve 80% statistical power for detecting haploinsufficient genes causing this disorder [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The diagnostic community has an enormous task of implementing newer genomic technologies in the clinic whilst simultaneously sharing anonymised data with the international research community, and simultaneously, the research community has to functionally validate the impact of new DNMs and genes on spermatogenesis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterestingly, we observe over twice the diagnostic yield from duo/ trio WES compared to targeted sequencing panel, but the difference is not statistically different (8.3% versus 3.3%; χ2\u0026thinsp;=\u0026thinsp;1.89, p\u0026thinsp;=\u0026thinsp;0.17) and is largely explained by the fact that duo/trios were used for WES, allowing for phasing of bi-allelic mutations. One of the reasons could be that ~\u0026thinsp;95% of the genes most robustly associated with MI phenotype have an autosomal recessive mode of inheritance [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This is reflected in the genes included in the targeted sequencing assay in the current study and a higher \u003cem\u003ea priori\u003c/em\u003e probability of detecting a recessive phenotype, since several communities in India practice endogamy or consanguinity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Critically though, the diagnostic yield of the sequencing assays did not differ by infertility subtype (azoospermic versus oligozoospermic patients) in both targeted sequencing assay and WES (5.1% versus 2.4%; χ2\u0026thinsp;=\u0026thinsp;1.013, p\u0026thinsp;=\u0026thinsp;0.31). Both sequencing technologies offer significant advantages over conventional technologies such as: flexibility of sample type and target region enrichment, automatable data analysis and variant interpretation, and ability to deliver submicroscopic deletions in the Yq region. In addition, the smMIP platform though require a high initial investment, the per-library preparation and sequencing costs are substantially low compared to WES [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Since both sequencing approaches will miss balanced translocations and inversions, which account for up to 0.9% of infertile men [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], application of sequencing approaches together with karyotyping is suggested, as is also recommended by the WHO guideline [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the future, long read whole genome sequencing (lrWGS) may likely replace existing conventional cytogenetic and short read sequencing technologies due to its capabilities of \u003cem\u003ede novo\u003c/em\u003e genome assembly, detection of multiple variant types including structural variants, haplotype construction, and variant phasing in the absence of parental samples [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, its current high cost may be prohibitive for clinical applications in the short term.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eData from large scale genomic and transcriptomic studies have helped to delineate the genetic architecture of male infertility in European/ non-Hispanic white populations. To the best of our knowledge, this is the first study to delineate the genetic architecture of MI in the Indian population, with autosomal recessive traits based on genes previously identified with a significant gene-disease relationship being the common cause. We demonstrate a significant proportion of infertile males with \u003cem\u003eCFTR\u003c/em\u003e gene variants in India, thus advocating for an increased uptake in sequencing-based approaches for genetic diagnosis. We present results of a low-cost, automatable, and high-throughput targeted gene sequencing assay based on smMIP technology, having the ability to detect a wide variety of genomic rearrangements thus obviating the need for STS-PCR based Y chromosome microdeletion detection and WES based detection of MI genes. Our results clearly demonstrate the importance of a family-based approach to reach the highest diagnostic yield, independent of genomic technology used. To the best of our knowledge, ours is the first study from India and only second in the world to use a novel patient-parent trio exome approach for discovery of the role of DNMs in MI. Taken together, the current study estimates 12.8%-19.3% of the infertile males with an underlying genetic aetiology, and advocates adoption of targeted or whole sequencing based genetic testing in andrology to contribute towards diagnosis and management of MI in India.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarsh Sheth, Srutikaa Kale, and Joris Andre Veltman are named as inventors on the patent describing the use of smMIP based target capture and associated computational analyses for simultaneous detection of single nucleotide variants, copy number variants, and gonosomal aneuploidies in the germline DNA. The patent is held by Decipher DNA Pvt. Ltd. (Patent ID: TEMP/E-1/25374/2025-MUM, submitted in March 2025). All other authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHS, JAV, FS, JS, and ASP were involved in study design. HS, PP, VM, DM, MB, AP, NB, TS, FS, and JS were involved in patient recruitment. HS, SK, MA, TD, MD, SC and BA were involved in study execution. HS, SK, MA, TD, SC, ASP, and JAV were involved in data analysis. HS, MA, TD, SK, SC, PJ, and JAV were involved in manuscript drafting and critical discussion. All authors approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are grateful to the patients and their families for participation in the study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eExome sequencing data has been deposited in the European Genome-phenome Archive (EGA) under the accession code EGAS00001008171 and will be made available upon reasonable request for academic use and within the limitations of the provided informed consent by the corresponding author upon acceptance. Every request will be reviewed by the Data Access Committee at the FRIGE Institute of Human Genetics; the researcher will need to sign a data access agreement after approval. Targeted single molecule molecular inversion probe-based assay data (FASTQ files) has been deposited in the EMBL-EBI European Nucleotide Archive, accession number is PRJEB88168 (ERP171304).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCaldamone AA, Valvo JR, Cockett AT. Evaluation of the infertile or subfertile male. Urol Clin North Am United States. 1981;8:17\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZargar AH, Wani AI, Masoodi SR, Laway BA, Salahuddin M. Epidemiologic and etiologic aspects of primary infertility in the Kashmir region of India. 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Nucleic Acids Res. 2023;51:8020\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkad532\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-assisted-reproduction-and-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Assisted Reproduction and Genetics","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Single molecule molecular inversion probes, whole exome sequencing, male infertility, de novo variants","lastPublishedDoi":"10.21203/rs.3.rs-7849365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7849365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo systematically investigate the genetic architecture of severe male infertility in Indian men, with a specific focus on chromosomal abnormalities and the contribution of \u003cem\u003ede novo\u003c/em\u003e variants.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eWe recruited 247 infertile males between 2021 and 2024 presenting with severe quantitative and qualitative sperm defects. All patients underwent karyotyping and Y chromosome microdeletion STS-PCR. A single molecule molecular inversion probe-based targeted sequencing assay covering 39 male infertility genes was performed in 120 patients, while whole exome sequencing (WES) was conducted in 48 patients using a duo/trio-based approach to enable segregation and \u003cem\u003ede novo\u003c/em\u003e variant detection.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eGonosomal aneuploidies were observed in 3/247 patients (1.2%), and causal AZF microdeletions in 8/247 (3.2%). Targeted sequencing identified pathogenic/ likely pathogenic (P/LP) variants in 4/120 patients (3.3%), with additional \u003cem\u003eCFTR\u003c/em\u003e variants in 3 patients where parental DNA was unavailable for phasing. WES yielded P/LP variants in 4/48 patients (8.3%) affecting \u003cem\u003ePMFBP1, DNAH1\u003c/em\u003e, and \u003cem\u003eAR\u003c/em\u003e genes, confirmed via segregation analysis. No \u003cem\u003ede novo\u003c/em\u003e or copy number variants were confirmed as causative, though several candidate genes were prioritised. Sequencing-based approaches provided an additional\u0026thinsp;~\u0026thinsp;6\u0026ndash;8% diagnostic yield, with overall diagnostic rate reaching 7.7% (19/247).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003e Sequencing-based strategies, particularly family-based trio WES, significantly enhance diagnostic yield beyond current guideline-recommended tests and should be adopted as first-tier investigations for severe male infertility. This represents India\u0026rsquo;s largest cohort-based genomic study on male infertility to date. Larger family-based cohorts will be essential to delineate the contribution of \u003cem\u003ede novo\u003c/em\u003e variants to male infertility genetics.\u003c/p\u003e","manuscriptTitle":"Genetic diversity of infertile males in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 15:32:42","doi":"10.21203/rs.3.rs-7849365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T14:17:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T10:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21435256445872836713568992161919104073","date":"2025-10-23T21:23:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75777829706076887409836542712841482473","date":"2025-10-22T13:18:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159300464256389150142912774715229520776","date":"2025-10-22T12:32:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T08:02:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150208069100497115088710287387029799348","date":"2025-10-22T00:10:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185967071159478815491296588488706470453","date":"2025-10-21T15:01:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T14:57:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T08:31:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T08:29:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Assisted Reproduction and Genetics","date":"2025-10-13T12:56:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-assisted-reproduction-and-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Assisted Reproduction and Genetics","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b9bdfbba-0ce8-4cc6-ae55-d5d5b69a2efa","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T17:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 15:32:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7849365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7849365","identity":"rs-7849365","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}