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Next-Generation Sequencing Infertility Panel in Turkey: First Results

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06 mar 2025
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INTRODUCTION

The way that male infertility is treated has undergone a significant transformation as a result of our growing understanding of the physiology of male reproduction, fertilization, and the development of increasingly potent assisted reproductive procedures. A physical exam and medical history gathering are currently part of the diagnostic procedure provided to infertile male patients. This is followed by a mix of laboratory tests specifically chosen for each case, including a thorough genetic laboratory analysis. At least a year of infertility should precede the administration of diagnostic testing. Accordingly, a couple is considered infertile if they are unable to conceive following a year of regular, unprotected sex. 15% of male patients who are infertile have genetic issues. Chromosome abnormalities or single gene mutations are examples of them. The Online Mendelian Inheritance in Man (OMIM) database contains information on more than 200 genetic conditions associated with male infertility (14).

Many disorders, most notably Mendelian or uncommon diseases where having causal variants significantly reduces reproductive fitness, have had exceptional results using NGS (5).

The candidate gene approach in model animals and whole genome investigations using single-nucleotide polymorphism microarray and next-generation sequencing (NGS) technologies, such as exome or whole-genome sequencing, are the two main methods for identifying the genes responsible for infertility. The reason of male infertility is still unknown in up to 70% of instances, despite extensive diagnostic testing, because traditional genetic tests sometimes fall short of making a diagnosis. Recent studies appear to address how NGS technology is increasing the rate of male infertility diagnosis. Accordingly, it has already been established that several diagnostic genes have a role in the pathophysiology of male infertility. It may be possible to make a diagnosis with the use of prediagnostic genes, such as those that have been linked to male infertility but do not yet have solid proof of a causal relationship (6, 7, 8).

To do this, the current study was conducted to assess a number of pre-diagnostic genes by contrasting the outcomes with those obtained using our standard NGS custom-made gene panel for the diagnosis of male infertility, which consists of 132 genes. The genes included in the gene panel are composed of genes that have been associated with infertility to date.

METHODS
Patients and Samples

The research included 85 individuals with a clinical diagnosis of male infertility who had tested negative on diagnostic genetic testing. 84 individuals were thought to have primary spermatogenic failure, while one individual was thought to have central hypogonadism. Following the elimination of female factor infertility and acquired reasons of male infertility, main spermatogenic failure was suspected with a history of couple infertility longer than two years (e.g. male accessory gland infection, varicocele, testicular trauma, etc.). All patients are cases of infertility for two or more years. The patient group consists of patients who do not have any known additional disease or malignancy.

Additionally, individuals included in this study tested negative for early genetic anomalies such karyotype abnormalities, Y chromosome AZF microdeletions. MLPA technique was performed using the SALSA MLPA probemix P360 version B1 (MRC Holland, Amsterdam, The Netherlands) kit following the manufacturer’s instructions. The kit contained 55 probes, of which 12 were located in autosomal chromosomes (for internal control reaction), and 43 were located in Y-chromosome AZF regions (16 AZFa, 15 AZFb, and 12 AZFc regions).

Each patient provided written consent after being fully briefed. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, and the local Ethics Committee approved the study.

Conventional G-banded karyotype analysis from peripheral blood was performed as part of the initial screening tests. The study included peripheral lymphocyte culture by a standard method using the Leishman-banding technique, centromere-banding (C-banding) and nucleolar organizing region staining performed as needed according to the AGT Cytogenetics Laboratory Manual. The best metaphases were karyotyped, and the total chromosome count was usually determined in 25 cells. The International System for Human Cytogenetic Nomenclature (ISCN) was used for the nomenclature of human chromosomes. Patients with no anomalies as a result of karyotype analysis were included in the NGS analysis.

Eighty five samples were sequenced using QIAseq Targeted DNA Custom Panel (Qiagen, Hilden, Germany). 2mL of peripheral blood were collected and then preserved in anticoagulation tubes. Genomic DNA was isolated from peripheral whole blood using the EZ1 DNA Investigator Kit (Qiagen, Hilden, Germany). After DNA extraction, target sequences were enriched by using customized capture probes chips (Illumina, San Diego, CA). This kit included 132 genes targeting disease. Libraries covering the target genes were prepared according to the QIAseq Targeted DNA Panel protocol (Qiagen, Hilden, Germany). Following the target enrichment process, libraries were sequenced on the MiSeq System (Illumina, San Diego, CA, USA). OCI analysis (Qiagen, Hilden, Germany) was used for Quality control and Variant Call Format file generation. In silico evaluation of the pathogenicity of nucleotide changes in exons was performed using Polymorphism Phenotyping v2 (PolyPhen-2, http://genetics.bwh.harvard.edu/pph2/), Sorting Intolerant from Tolerant (SIFT, https://sift.bii.a-star.edu.sg/), and MutationTaster (http://www.mutationtaster.org). Minor allele frequencies (MAF) were checked in the Genome Aggregation Database gnomAD (http://gnomad.broadinstitute.org/).Variant analysis was performed with Ingenuity software (Qiagen, Hilden, Germany). Variants were interpreted according to the American College of Medical Genetics and Genomics (ACMG) recommended standard. Sanger sequencing was performed for confirmation when the target region coverage was less than 15 reads. Nucleotide alterations were analyzed and validated by Sanger sequencing. After confirmation, each variant was classified as a pathogenic, likely pathogenic, variant of unknown significance (VUS), likely benign, or benign, according to the American College of Medical Genetics (ACMG) guidelines. Coding genomic regions (CDS) that were sequenced with coverage less than 15X were eventually re-sequenced using Sanger technology.

RESULTS

A total of 85 patients (85 males) between 21 years and 45 years old were included in the study group. NGS analysis had been applied in all the primary infertility cases. As a result of NGS analysis, 58 clinical variants in 28 genes were detected in 41 patients (%48,23- 41/85) (Table 1). Thirty-two of these variants are unknown clinical significance (VUS), 11 of them likely pathogenic, and 15 of these variants are classified as pathogenic in according to the Varsome, The Human Genomic Variant Search Engine, Franklin by Genoox, Clinvar and American College of Medical Genetics and Genomics (ACMG) databases.

NGS panel for the diagnosis of male infertility

1. NPHP4 23. DAZL 45. FKBPL 67. DDX25 89. ZMYND15 111. SYCP2
2. C1orf167 24. DNAH1 46. PLG 68. C1RL 90. GP1BA 112. CBS
3. MTHFR 25. PROS1 47. ZPBP 69. C1RL-AS1 91. KLHL10 113. DNMT3L
4. CLCA4 26. BOC 48. C7orf61 70. DPY19L2 92. ITGB3 114. POFUT2
5. BRDT 27. CFAP44 49. SERPINE1 71. CHPT1 93. TEX14 115. GP1BB
6. F3 28. CFAP44-AS1 50. POLR2J3 72. SYCP3 94. ACE 116. POLR2F
7. SPAG17 29. GP9 51. CFTR 73. CCDC62 95. PGS1 117. SOX10
8. F5 30. SPATA16 52. TEX15 74. PIWIL1 96. DNAH17 118. PICK1
9. SERPINC1 31. CCDC39 53. PLAT 75. CPB2 97. DNAH17-AS1 119. MEI1
10. F13B 32. GP5 54. CHD7 76. F7 98. TAF4B 120. ADGRG2
11. MTR 33. CEP135 55. TMEM70 77. F10 99. GGN 121. MAGEB4
12. LHCGR 34. SPINK2 56. CCIN 78. TDRD9 100. PLAUR 122. MAGEB1
13. FSHR 35. BMP3 57. NR5A1 79. CATSPER2 101. LHB 123. NR0B1
14. DNAH6 36. FGB 58. ASS1 80. TERB2 102. NLRP7 124. TBC1D25
15. NPAS2 37. FGA 59. UPF2 81. NME4 103. NLRP2 125. AR
16. LOC101927142 38. KLKB1 60. CFAP43 82. FAHD1 104. AURKC 126. TEX11
17. PROC 39. F11 61. NANOS1 83. MEIOB 105. SIRPG 127. USP26
18. TFPI 40. MTRR 62. SYCE1 84. SEPT12 106. SIRPA 128. F9
19. STRADB 41. PRDM9 63. FSHB 85. PRM1 107. THBD 129. F8
20. C2CD6 42. ITGA2 64. F2 86. TERB1 108. SUN5 130. FUNDC2
21. LOC100129175 43. F2R 65. MAJIN 87. SERPINF1 109. E2F1 131. SRY
22. CFAP65 44. F13A1 66. CATSPER1 88. CXCL16 110. PROCR 132. USP9Y

The most frequently observed variants are those observed in the CFTR gene. 18 CFTR gene variants were detected in 16 different patients. Among these, 7 variants are pathogenic, 4 variants are likely pathogenic and 7 variants are VUS. The remaining 40 variants are distributed among the other 27 genes in the panel. Among these, 8 variants were evaluated as pathogenic, 7 variants as likely pathogenic, and 25 variants as VUS. Segregation analyses could not be performed in patients with VUS. Pathogenic and likely pathogenic variants were detected de novo. Among the variants considered as VUS, the most frequently observed variants clustered in the DNAH1 gene. Among the pathogenic variants, the most common variants after CFTR were detected in the CBS and F11 genes (Table 2). We also detected a compound heterozygous CFTR variant in one of our patients. This condition of the patient was evaluated clinically.

Variants detected in the patient group as a result of NGS

Patient n. VUS - inheritance Likely pathogenic-inheritance Pathogenic-inheritance
1. NM_000130.5(F5):c.1128G>T p.R376S (PP3) (AD,AR)NM_001370.2(DNAH6):c.8422G>A (p.V2808I) (PP3) (n/a)
2. NM_000313.4(PROS1): c.1021G>T (p.A341S) (PM1,PM2,PP3) (AD) NM_000492.4(CFTR):c.1516A>G (p.Ile506Val) (PM1,PM2,PM3,PP3,BP6) (AR)
3. NM_015512.5(DNAH1):c.8885A>C (p.Lys2962Thr) (PP3) (AR)
4. NM_000492.4(CFTR):c.443T>C (p.Ile148Thr) (PS3,M1,PM2,PM3,PP3,BS2,BS3,BP2,BP6) (AR)
5. NM_000492.4(CFTR):c.2981T>G (p.Phe994Cys) (PM1,PM2,PP3) (AR) NM_000128.4(F11):c.1556G>A (p.Trp519Ter) (PVS1,PS4,PM2,PM3,PP5)NM_000128.4(F11):c.403G>T (p.Glu135Ter) (PA1,PVS1,PS3,PS4,PM3,PP5,BS1,BS2) (AD,AR)
6. NM_001242805.2 (BRDT):c.163C>T (p.Pro55Ser) (PP3) (AR)
7. NM_001350162.2 (TEX15):c.2580_2583del (p.Asp860GlufsTer15) (PS3,PP3) (AR) NM_000173.7(GP1BA): c.1235_1298delAGCCCAC… (p.E412fs*39) (PVS1,PM2) (AD,AR)
8. NM_000789.4(ACE):c.2299G>A (p.Glu767Lys) (PM2,PP3) (AR)
9. NM_000312.4(PROC):c.982C>T (p.Arg328Cys) (PM1,PM2,PP3,PP5) (AD,AR) NM_002203.4(ITGA2):c.981_985del (p.Lys327AsnfsTer6) (PVS1,PM2) (n/a)
10. NM_000492.4 (CFTR):c.2491G>T (p.Glu831Ter) (PVS1,PS3,PS4,PM2) (AR)
11. NM_000492.3(CFTR):c.1521_1523del (p.Phe508del) (PA2,PS3,PM1,PM4,PP3,BS3, BS4,BP2,BP5) (AR)
12. NM_000492.4(CFTR):c.1210-11T>G (PA2,PS3,PM3,PP5,BS1,BS2,BP2) (AR)
13. NM_000492.4(CFTR):c.2991G>C (p.Leu997Phe) (PS1,PM1,PM2,PM3,PP3,BS2,BP2,BP6) (AR)
14. NM_054012.4(ASS1):c.535T>C (p.Trp179Arg) (PS3,PS4,PM1,PM2,PM3,PP3,PP5) (AR)
15. NM_144605.4(SEPT12): c.208T>C (p.Phe70Leu) (PM2,PP3) NM_000492.4(CFTR): c.1397C>T (p.S466L) (PM1,PM2,PM3,PP3) (AR)
16. NM_000492.4(CFTR):c.2973A>G (p.Ile991Met) (PM1,PM2) (AR)
17. NM_015512.5(DNAH1):c.10164G>T (p.K3388N)(PP3) (AR)
18. NM_015102.5(NPHP4):c.224G>A (p.Trp75Ter) (AR)
19. NM_000492.4(CFTR): c.1043T>A (p.M348K) (PM1,PM2,PM3,PP3,BP2,BP6) (AR)NM_012128.4 (CLCA4): c.760dupA (p.T254fs*3) (PVS1) (n/a) NM_000492.4 (CFTR) : c.3038C>T (p.P1013L) (PM1,PM2,PM3,PP3)
20. NM_000071.3(CBS):c.833T>C (p.Ile278Thr) (PS3,PS4,PM1,PM2,PM3,PM5,PP3) (AR)
21. NM_000128.4(F11):c.325G>A (p.Ala109Thr) (AD,AR) (PS3,PS4,PM1,PM2,PM3,PP3,PP5)NM_000492.4(CFTR):c.1516A>G (p.Ile506Val) (PM1,PM2,PM3,PP3,BP6) (AR)
22. NM_173812.5(DPY19L2): c.247C>T (p.Q83*) (PVS1,PM2) (AR) NM_000492.4(CFTR):c.1521_1523delCTT (p.F508del) (PA2,PS3,PS4,PM1,PM3,PM4, PP3,PP5,BS1,BS2,BS4,BP2) (AR)
23. NM_000492.3(CFTR):c.3872A>G (p.Q1291R) (PM1,PM2,PM3,PM5,PP3,PP5) (AR)
24. NM_000071.3(CBS):c.833T>C (p.I278T) (PS3,PS4,PM1,PM2,PM3,PM5,PP3) (AR)
25. NM_000492.4(CFTR):c.3256A>G (p.Thr1086Ala)(PP3) (AR)
26. NM_000894.2 (LHB):c.169T>C NP_000885.1:p.Tyr57His (PM2,PP3) (AR)
27. NM_000301.5(PLG):c.2384G>A (p.Arg795His) (PS4,PM2,PP3) (AD,AR)
28. NNM_000789.4(ACE): c.3490G>A (p.G1164R) (PM2,PP3) (AR)
29. NM_000492.4(CFTR): c.350G>A (p.R117H) (PA2,PS3,PM1,PM2,PM3,PM5,PP1,PP3, PP5,BS2,BS4,BP2) (AR)
30. NM_015512.5 (DNAH1): c.8976C>G p.F2992L(PP3) (AR)
31. NM_000131.4 (F7): c.805+3_805+6delGGGT (-) (PVS1,PM2) (AR)
32. NM_001994.3(F13B): c.209A>C (p.Q70P) (PM2,PP3) (AR) NM_000492.4 (CFTR) : c.1521_1523delCTT (p.F508del) (PA2,PS3,PM1,PM3,PM4,PP3,PP5,BS1,BS2,BS3,BS4,BP2) (AR)
33. NM_017780.4 (CHD7) :c.5995G>A (p.A1999T)(PS4,PM2,PP2,PP3) (AD)
34. NM_012128.3 (CLCA4):c.575C>A NP_036260.2:p.Ser192Cys (PP3) (n/a)
35. NM_015512.5(DNAH1): c.9495G>A (p.Thr3165) (PM2,PP3) (AR)NM_152467.5 (KLHL10): c.1226A>G (p.E409G) (PM2,PP3) (AD)NM_144605.4 (SEPTIN12) :c.611G>T (p.Arg204Leu) (PP3) (AD)
36. NM_001330438.2 (DDX25): c.110C>T (p.Ala37Val) (PP3) (n/a)
37. NM_000071.3(CBS):c.833T>C (p.Ile278Thr) (PS3,PM1,PM5,PP3)
38. NM_001312675.1 (F10):c.202C>T NP_001299604.1:p.Arg68Cys (PM2,PP3) (AR)NM_000212.2 (ITGB3):c.1576G>CNP_000203.2:p.Glu526Lys (PP3) (AD,AR)NM_015512.5 (DNAH1): c.4642C>G (p.L1548V) (PM2,PP3) (AR)
39. NM_000301.5(PLG):c.2134G>A (p.Gly712Arg) (PP3) (AD,AR)
40. NM_000492.4(CFTR):c.890G>A (p.Arg297Gln) (PS3,PM1,PP3,BS3,BS6) (AR)NM_173812.5(DPY19L2): c.2221C>A (p.P741T) (PM2,PP3) (AR)
41. NM_173628.3(DNAH17):c.7752+2T>A (PVS1) (AR)
Total 32 11 15

AD: autosomal dominant; AR: autosomal recessive

DISCUSSION

Highly diverse phenotypic representation and a complicated multifactorial etiology, including environmental and genetic factors, characterize the condition of male infertility. In most cases, it is challenging to identify a genetic cause of infertility due to the large number of candidate genes (9, 10). In any case, a multi-disease gene panel can help identify the cause of male infertility. In order to categorize genetic variants, a multifactorial likelihood model can be used to assess the likelihood that a variant is pathogenic based on a previous likelihood of pathogenicity based on in silico research and the genetic and epidemiological data that are currently available (1113). Genetic variants can be categorized into five categories according to the American College of Medical Genetics and Genomics’ references: pathogenic, likely pathogenic, variant of unknown consequence, likely benign, or benign (14). A genetic alteration known as a VUS has ambiguous effects on gene function. The interpretation of VUS is a challenging task for the clinical management of infertile male patients and genetic counseling. Since VUS are not clearly related with a phenotype currently, but could be categorized as pathogenic in the future, it is crucial to detect and assess them. An example of this situation is the NM_000071.3(CBS):c.833T>C (p.Ile278Thr) variant detected in our patient group. This variant, which was evaluated as VUS in the databases at the beginning of our study, was later classified as pathogenic.

Variants in PROS1 and CFTR were detected in our patient number 2. PROS1 variants show autosomal dominant inheritance. Even though the detected variant was evaluated as VUS, the classification of this variant should be followed in the future.

Similarly, in our patient number 5, 2 pathogenic variants belonging to the F11 gene and a variant evaluated as VUS in CFTR were detected. F11 variants show autosomal dominant inheritance. The detected variants were evaluated as pathogenic. OMIM has been associated with Factor XI deficiency.

In another patient, case number 7, variants were detected in 2 separate genes. VUS evaluation was performed for TEX15. TEX15 is associated with the Spermatogenic failure 25 phenotype in the OMIM database, and exhibits an autosomal recessive pattern of inheritance. The other variant detected in the patient is a possible pathogenic variant belonging to the GP1BA gene. This gene, which is associated with different types of diseases in the OMIM database, can show autosomal dominant and recessive inheritance. Particularly notable among these diseases are Bernard-Soulier syndrome, type A2 (dominant) and von Willebrand disease, platelet-type (dominant).

PROC variant was detected in patient number 9 and was reported as possibly pathogenic. PROC variants have been associated with autosomal recessive and dominant forms of Thrombophilia 3 due to protein C deficiency in OMIM. Another variant in the patient is the pathogenic variant in the ITGA2 gene. The inheritance pattern and phenotype of variant of this gene have not yet been elucidated.

We detected variants in SEPT 12 and CFTR genes in patient number 15. SEPT 12 variants cause autosomal dominant Spermatogenic failure 10.

In patient number 19, we detected compound heterozygous variants of the CFTR gene and an additional variant of the CLCA4 gene. While CFTR gene variants exhibit autosomal recessive inheritance, there are no entries in the databases yet for the CLCA4 gene.

We detected F11 and CFTR pathogenic variants in patient number 21. F11 was found to be associated with autosomal dominant and recessive forms of Factor XI deficiency in the OMIM database.

We detected DPY19L2 and CFTR pathogenic variants in patient number 22. DPY19L2 was found to be associated with autosomal recessive forms of spermatogenic failure 9 in the OMIM database. Similarly, patient number 40 has variants in these two genes that were evaluated as VUS.

F13B and CFTR variants were detected in another of our patients, case number 32. F13B has been associated with Factor XIIIB deficiency in OMIM. F13B variants show autosomal recessive inheritance.

Interestingly, variants in 3 different genes were detected in our last 2 patients. Variants considered to be VUS were detected in the DNAH1, KLHL10 and SEPT12 genes in the first patient. KLHL10 and SEPT12 variants have been associated with autosomal dominant spermato-genic failure. DNAH1 has been found to be associated with autosomal recessive Ciliary dyskinesia, primary and Spermatogenic failure. These findings were associated with the patient’s phenotype. In the second patient, variants considered to be VUS were detected in the DNAH1, F10 and ITGB3 genes. The F10 gene has been associated with autosomal recessive Factor X deficiency. ITGB3 has been associated with autosomal recessive Bleeding disorder, platelet-type and autosomal dominant Glanzmann thrombasthenia.

Although it is difficult to reconcile those with recessive inheritance in the detected variants with the patient clinics, those with dominant inheritance were compatible with the patient clinics.

DNAH1 gene is the most frequently detected VUS variant in our patient group. A diverse range of patients with aberrant flagellar structures have been shown to have mutant DNAH1 in the majority of current research. Male infertility has been linked to numerous morphologic defects of the sperm flagella caused by mutations in DNAH1. After intracytoplasmic sperm injection, patients with multiple morphologic abnormalities of the flagella (MMAF) caused by mutations in the DNAH1 gene have a favorable prognosis. These investigations have demonstrated that dysplasia of the sperm fibrous sheath (DFS) and infertility are directly related to abnormalities in the DNAH1 gene (1518).

The most frequently detected CFTR variants in our patient group were seen in all 3 groups (pathogenic, likely pathogenic, VUS). One of the most researched genes for male infertility, the CFTR gene, has 27 exons and more than 180,000 base pairs of DNA. A membrane ion channel protein called CFTR, which is encoded on chromosome 7p, controls the vas deferens in the male genital tract. Considered a moderate type of cystic fibrosis (CF), CBAVD is a major contributing factor to obstructive azoospermia (OA) and is one of the primary factors leading to male reproductive abnormalities. Previous research has shown a positive correlation between CFTR mutations and CBAVD. There are about 1,500 variants listed in the CFTR database. Mutations in the F508 and IVS8-5T genes may be important in nonobstructive male infertility disorders such oligozoospermia and nonobstructive azoospermia (NOA). According to our findings, there is a chance that the genetic variant IVS8-5T could serve as a biomarker for nonobstructive male infertility. Three of the pathogenic variants we detected in the patient group are IVS8-5T. We also detected a compound heterozygous CFTR variant in one of our patients. The condition of the patient was evaluated clinically. CFTR variant rates are also significantly higher than the carrier rate reported in our patient group (1922).

Another gene we detected among pathogenic gene variants is the CBS gene. The CBS gene is the most common locus for mutations associated with homocystinuria. Cystathionine-β-synthase, also known as CBS, is an enzyme that is encoded by the CBS gene in humans. The trans-sulfuration pathway enzymes cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE) are recognized for their non-specific substrate recognition. These enzymes provide their substrates an alternate CBS and CSE pathway, allowing them to function somewhat in reverse. In addition, these enzymes are involved in the synthesis of hydrogen sulphide (H2S). This is a gaseous transmitter with antioxidant and anti-inflammatory properties. Although both CSE and CBS are frequently present in the testes—CSE is mostly found in immature germ cells and Sertoli cells, while CBS is extensively distributed in Leydig cells, germ cells, and Sertoli cells—it is unclear how much each kind of cell contributes to the production of H2S in the testes. Numerous research studies have demonstrated that male infertility is associated with failure of one carbon metabolism, namely the imbalance of CBS and CSE enzymes in the trans-sulfuration pathway, and a specific deficit in H2 S output is documented (23, 24).

Another gene we detected as a pathogenic variant in our patient group was FXI. Compound heterozygous FXI pathogenic variant was detected in one of our patients. In another patient, we detected a heterozygous FXI variant of CFTR accompanied by pathogenic variant. FXI, or coagulation factor XI, inhibits fibrinolysis and encourages the production of fibrin. One measurement only elevated plasma FXI levels are linked to an increased risk of thrombosis. Hemophilia C, another name for factor XI deficiency, is an autosomal recessive condition mostly affecting Ashkenazi Jews. It is typically linked to variants in bleeding characteristics. The majority of transmission is autosomal recessive. Couples who are considered at-risk (both individuals carry a mutation that causes the disease) should be informed through genetic counseling that there is a 25% chance that each pregnancy will result in a homozygous child who is affected. There have also been reports of heterozygous patients with bleeding symptoms, pointing to an autosomal dominant mode of transmission with varying penetrance (25, 26).

Genes that code for hormones and hormone receptors which are involved in the functioning of the human reproductive system are included in the fertility panel design. Numerous investigations have demonstrated the association between specific polymorphisms in genes encoding receptors, including those that bind to FSH and LH, and the results of an ovarian hyperstimulation cycle under control and in vitro fertilization treatment. The intended genetic panel’s results will yield the data required to ascertain the frequency of these variants in our community and assess the panel’s usefulness in clinical settings.

The involvement of the clinicians who seek this genetic investigation needs to be emphasized. If the gene panel is able to pinpoint the underlying reason of infertility, clinicians will need to have a comprehensive picture of the patient’s phenotype. “Idiopathic infertility” affects a large number of individuals, and while a genetic component may be identified in certain cases, the absence of a distinct phenotype may make it more difficult to interpret the data, particularly variants with unclear significance. Clinicians should also be aware that three factors play a major role in how these investigations are interpreted: the patient’s phenotypic characteristics, their medical history, and any pertinent family history. For the diagnostic laboratory to properly interpret variants found through testing, it is imperative that they have information about all observable traits as well as the family’s medical history (2730).

The first unique gene sequencing panel intended for the diagnosis of hereditary infertility in males is presented here, for the first time in Turkey. The use of this panel will advance knowledge of the genetic causes of infertility, enhance genetic and reproductive counseling, and eventually lead to more accurate assisted reproductive techniques.

CONCLUSION

Consequently, pre-diagnostic genes included in a custom-made NGS panel test can enhance genetic diagnostic testing and have an impact on the clinical management of male infertility. There are currently no comprehensive systematic studies or meta-analyses on the epidemiology of male infertility, and it is unknown how common male infertility is. The need of diagnosing hereditary infertility is further supported by the epidemiological data that show infertile patients have greater morbidity and a shorter life expectancy. Finally, we demonstrated the effectiveness of NGS-based methods that additionally use pre-diagnostic genes. This gene panel may aid in determining the disorder’s underlying etiology and directing clinical treatment.

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Inglese
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2 volte all'anno
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Medicina, Scienze medicali di base, Scienze medicali di base, altro