Impact of Polymorphisms in Base Excision Repair Genes on Seminal Fluid Parameters

Impact of Polymorphisms in Base Excision Repair Genes on Seminal Fluid Parameters | 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 Impact of Polymorphisms in Base Excision Repair Genes on Seminal Fluid Parameters Fadel A. Sharif, Mahmoud A. Hassouna, Mazen M. Alzahrna, Mohammed J. Ashour, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6672168/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Abnormalities in sperm parameters can result from genetic variations in DNA repair genes. The base excision repair (BER) pathway maintains DNA integrity. Single nucleotide polymorphisms (SNPs) in BER genes may influence sperm DNA fragmentation (SDF) and other seminal fluid parameters. Therefore, we investigated the impact of SNPs in BER genes, specifically XRCC1 , OGG1 , MUTYH , and APEX1 , on SDF and seminal fluid parameters in a selected male population from the Gaza Strip. A case‒control study included 75 men with elevated SDF and 74 men with normal SDF. Semen samples were subjected to conventional semen analysis and the SDF test. DNA extracted from the samples was then genotyped via the allele-specific polymerase chain reaction (AS‒PCR) technique. Genotypes and allele frequencies were compared between the case and control groups via standard statistics. In terms of SDF, the XRCC1 "rs25487" polymorphism significantly differed between cases and controls, where the C allele and the CC genotype were more prevalent (P value = 0.004) in the control group. Additionally, the MUTYH "rs3219489" polymorphism was significantly different, with the GC genotype being more frequent (P value = 0.025) in the control group. OGG1 and APEX1 polymorphisms, however, were not significantly different between the two groups. The examined polymorphisms were not significantly related to conventional semen parameters. This study highlights the effects of genetic variations in DNA repair genes, specifically XRCC1 and MUTYH , on SDF. Further studies with a larger sample size are needed in order to confirm these findings and investigate the value of these SNPs on reproductive potential. Polymorphisms Base excision repair pathway Seminal parameters XRCC1 OGG1 MUTYH APEX1 Sperm DNA fragmentation AS‒PCR Figures Figure 1 Introduction 1.1 Overview The World Health Organization (WHO) has acknowledged infertility as a global health issue. It is characterized by the inability of a couple to achieve conception even after engaging in regular unprotected sexual intercourse for 12 months (World Health Organization 2021 ). Approximately 8–12% of couples worldwide are impacted by infertility, leading to emotional distress, psychological instability, and significant mental strain among those experiencing it (Shah et al. 2021 ). Approximately 40–50% of infertility cases are attributed to "male factor" infertility, indicating that these issues are related primarily to male reproductive health. Additionally, up to 2% of all men exhibit sperm parameters that fall below the standard quality, potentially contributing to fertility challenges (Kumar & Singh 2015 ). Male infertility is directly linked to the quality and quantity of sperm in the seminal fluid. Successful fertilization relies on the transmission of high-quality and sufficient numbers of sperm to the partner and the ability of the sperm to reach the fertilization site and successfully complete the fertilization process. Abnormalities in the quantity and quality of sperm can be caused by various factors, ranging from inherent birth defects and genetic disorders to lifestyle choices and environmental conditions (Shah et al. 2021 ). The analysis of semen plays a crucial role in evaluating male reproductive function, enabling appropriate treatment for male subfertility. A comprehensive assessment of seminal fluid can aid clinicians in making informed decisions about further examinations and managing couples facing fertility challenges. Sperm DNA fragmentation (SDF) has emerged as a new and valuable biomarker for identifying infertile men and providing valuable insights into the outcomes of assisted reproductive technology (ART). The sperm DNA carries half of the genomic material responsible for the offspring's structure. Therefore, maintaining the integrity of the genetic structure of sperm is essential for successful fertilization and the subsequent development of the embryo and fetus (Minhas et al. 2021 ). A lack of efficient DNA repair mechanisms can increase DNA damage in germ cells, resulting in abnormal spermatogenesis and infertility. The proper function of DNA repair is essential for maintaining the integrity and quality of the germ cell genome. Consequently, the DNA repair system plays a critical role in sperm production. Dysfunction of the genes involved in DNA damage repair within germ cells can decrease sperm quantity and promote abnormalities in sperm quality (Yang & Hui 2021 ). Therefore, this work was designed to explore the relationships of common single nucleotide polymorphisms (SNPs) of DNA damage repair genes in the base excision repair (BER) pathway, namely, XRCC1 (p.Arg399Gln), OGG1 (p.Ser326Cys), MUTYH (p.Gln324His), and APEX1 (p.Asp148Glu), with SDF results and other semen parameters. Until now, limited studies in our region have investigated the association between specific genetic variants and SDF. Therefore, this study aimed to examine the relationship between selected gene polymorphisms and SDF in the population of the Gaza Strip. Identifying genetic variants associated with SDF may provide insight into the underlying mechanisms and support the development of targeted interventions to improve sperm quality. Materials and Methods Study Subjects This case–control study consisted of 75 semen samples from males who have abnormal results of SDF test (cases) and 75 semen samples from males with normal SDF levels (controls). The study was ethically approved by the Ethical Research Committee at the Islamic University of Gaza and all subjects gave their consent to participate in the study. Data Collection The reports of semen analysis, the results of SDF test, and the general characteristics for all participants of this study were obtained from the records of Al-Basma IVF center in Gaza Strip. Semen analysis covered sperm count, liquefaction time, volume, morphology, motility, viscosity, and the percentage of abnormal sperm. The Spectrum Technologies (USA) HaloSperm kit was used to estimate the degree of DNA damage present in semen samples. Three to four hundred sperm were examined and a SDF of ≥ 30% was considered abnormal. DNA Extraction and Genotyping The genomic DNA was isolated from semen samples using Wizard Genomic DNA Purification Kit (Promega, USA) following the manufacturer’s instructions. The SNPs of the selected genes were genotyped using allele-specific PCR (AS-PCR) technique. The specific PCR primers were designed using a web-based allele-specific primer design application ( http://bioinfo.biotec.or.th/ WASP) and BatchPrimer3 web primer design program ( https://probes.pw.usda.gov/ cgi- bin/ batch prime r3). Primer sequences are available from the corresponding author upon request. The PCR products were separated by running the PCR products on ethidium bromide-stained 3% agarose gels and visualized on a gel documentation system. Statistical analysis Data were collected, summarized, categorized and analyzed using Statistical Package for Social Sciences (SPSS) software version 25. The frequencies of alleles and genotypes were compared between cases and controls by standard odds ratio (OR) at 95% confidence intervals (CI) using an online calculator ( https://www . medca lc.org/ calc/ odds_ ratio. php). Hardy–Weinberg equilibrium (HWE) was tested using the online: ( https://wpcalc.com/en/ equil ibrium- hardy- weinb erg/). All results were accepted as significant if p value ≤ 0.05. The multifactor dimensionality reduction (MDR) (v.3.0.2) software ( http://www . epist asisb log. org/) was used to evaluate SNP-SNP interactions (Küchler et al. 2021 ). The interaction entropy graphs were constructed based on MDR results to determine synergistic and non-synergistic interactions among the variables. Results Characteristics of the study population As previously reported in our earlier work (Sharif et al. 2023), the demographic characteristics and routine seminal parameters of the study population were as follows: No statistically significant differences between the two groups concerning mean age, BMI, smoking status, or IVF technique, as all P values were greater than 0.05. The liquefaction time of the cases was significantly longer than that of the controls (P value < 0.001), and the total motility of the sperm in the cases was significantly lower than that in the controls (P value < 0.001). The sperm normal form in patients was also significantly different from that in controls (P value = 0.049), with a lower proportion of patients having a normal form. However, there were no significant differences between cases and controls in terms of other seminal parameters, including viscosity, volume, and sperm count. Polymorphism genotyping Genotype and allele frequencies of the investigated polymorphisms Statistical analysis of genotypic frequencies for the investigated SNPs indicated no significant differences between SDF patients and controls concerning the OGG1 and APEX1 gene polymorphisms (P values > 0.05). However, for the XRCC1 "rs25487" polymorphism, a significant difference (P value < 0.05) was observed between SDF patients and controls. The CC genotype was significantly more common (P value = 0.004) in the control group, whereas the TC genotype was significantly less common (P value = 0.024) in the control group. Moreover, for the MUTYH "rs3219489" polymorphism, a significant difference (P value < 0.05) was found between SDF patients and controls, with the GC genotype being significantly more frequent (P value = 0.025) in the control group (Table 1). Statistical analysis of allelic frequencies of the tested SNPs revealed no significant difference in the tested gene polymorphisms of OGG1 , MUTYH , and APEX1 between SDF patients and controls (P values > 0.05). However, the allele frequency analysis for the XRCC1 "rs25487" polymorphism between SDF patients and controls revealed a significant difference between the two groups (P value = 0.004) (Table 2). Statistical analysis of XRCC1 rs25487 T > C SNP revealed a significant difference between the two groups under all inheritance models, except for the recessive model, with all (P values G and APEX1 rs1130409 G > T SNPs indicated no significant differences between the two groups according to the all models (P value > 0.05) (data not shown). The statistical analysis of MUTYH "rs3219489" G > C SNP revealed a significant difference between the two groups only under the overdominant model, with a P value of 0.02 (data not shown). Analysis of the observed and the calculated expected genotype frequencies in the control group showed that the distribution of genotypes is in Hardy–Weinberg equilibrium for all tested polymorphisms except for OGG1 "rs1052133" C > G SNP (data not shown). The findings indicated no statistically significant relationships between any SNPs and conventional semen parameters (sperm count, motility, and normal sperm form), as all P values were greater than 0.05 (data not shown). Table (1) Genotype frequencies of the investigated polymorphisms Genes/SNPs Genotypes Cases (N = 75) Controls (N = 74) Odds Ratio 95% CI P- value XRCC1 rs25487 T > C TT 6 (8%) 2 (3%) 3.130 (0.610 -16.04) 0.171 TC 37 (49%) 23 (31%) 2.159 (1.106–4.214) 0.024* CC 32 (43%) 49 (66%) 0.379 (0.195–0.738) 0.004* OGG1 rs1052133 C > G CC 33 (44%) 34 (46%) 0.924 (0.485–1.763) 0.811 CG 12 (16%) 13 (18%) 0.894 (0.378–2.112) 0.798 GG 30 (40%) 27 (36%) 1.160 (0.599–2.248) 0.659 MUTYH rs3219489 G > C GG 29 (38%) 25 (34%) 1.236 (0.632–2.413) 0.535 GC 23 (31%) 36 (49%) 0.467 (0.239–0.912) 0.025* CC 23 (31%) 13 (17%) 2.075 (0.957–4.501) 0.064 APEX1 rs1130409 G > T GG 15 (20%) 12 (16%) 1.292 (0.558–2.986) 0.549 GT 23 (31%) 34 (46%) 0.520 (0.266–1.017) 0.056 TT 37 (49%) 28 (38%) 1.599 (0.833–3.071) 0.158 * Statistically significant (P value C T 49 (33%) 27 (18%) 2.174 (1.268–3.727) 0.004* C 101 (67%) 121 (82%) OGG1 C > G C 78 (52%) 81 (55%) 0.896 (0.568–1.413) 0.637 G 72 (48%) 67 (45%) MUTYH G > C G 81 (54%) 86 (58%) 0.846 (0.535–1.338) 0.475 C 69 (46%) 62 (42%) APEX1 G > T G 53 (35%) 58 (39%) 0.848 (0.529–1.357) 0.491 T 97 (65%) 90 (61%) * Statistically significant (P value < 0.05). Multifactorial dimensionality reduction (MDR) analysis The highest risk combinations of the genotypes for the four SNPs, XRCC1 "rs25487", OGG1 "rs1052133", MUTYH "rs3219489" and APEX1 "rs1130409", are presented in Table 3. Table (3) The highest risk combinations of the genotypes for the investigated SNPs. SNPs MUTYH, XRCC1, APEX1, OGG1 Frequency (Case: Control) High/Low-Risk OR (95% CI) P value CC, CC, GT, CG 3:0 High 7.193 (0.365–141.7) 0.194 CC, TC, GT, GG 4:2 High 2.028 (0.360–11.42) 0.422 GG, CC, TT, CC 5:3 High 1.690 (0.389–7.344) 0.483 MUTYH (G > C), XRCC1 (T > C), APEX1 (G > T), OGG1 (C > G) The entropy values reflect how much a set of SNPs interact and, as a result, indicate how closely the analyzed SNPs are related to SDF (Fig. 1). The Fruchterman–Rheingold plot revealed the largest main effect, with higher entropy observed for XRCC1 (4.35%). The impact of polymorphisms can be graded in the following ascending order of entropy: OGG1 "rs1052133" (0.10%), APEX1 "rs1130409" (1.80%), MUTYH "rs3219489" (2.90%), and XRCC1 "rs25487" (4.35%). Discussion Accumulating evidence supports the crucial role of the BER pathway in the DNA repair system. Therefore, polymorphisms in BER pathway genes are potential risk factors for various diseases. In the present study, the main objective was to investigate whether specific genetic variations (SNPs) in XRCC1, OGG1, MUTYH , and APEX1 are linked to the quality of seminal fluid, particularly with increased risk of SDF in a selected male population from the Gaza Strip. The study focused on the analysis of the XRCCI (p.Arg399Gln) "rs25487", OGG1 (p.Ser326Cys) "rs1052133", MUTYH (p.Gln324His) "rs3219489" and APEX1 (p.Asp148Glu) "rs1130409" SNPs in a group of 75 men with positive SDF (cases) and compared the results to those of a group of 74 men with negative SDF (controls). Relationships between seminal parameters and SDF When various seminal parameters, which have been identified as potential indicators of sperm quality, were compared, the analysis revealed a statistically significant difference in the liquefaction time (P value < 0.001). The liquefaction time in the SDF patients was significantly longer (mean: 23.6 ± 5.76 minutes) than that in the controls (mean: 20.3 ± 1.51 minutes). This finding suggests that abnormal SDF may be associated with delayed or impaired semen liquefaction (which refers to the process by which semen changes from a gel-like state to a more liquid form) and, thus, the release and motility of sperm. This result is congruent with another study conducted in Iraq (Al-Fahham & Al-Nowainy 2015 ). The prolonged liquefaction time may reflect abnormalities in the seminal fluid composition or factors inhibiting the enzymatic processes involved in liquefaction. The mechanism linking abnormal SDF and prolonged liquefaction time could be due to oxidative stress (Agarwal & Bui 2017 ), inflammation, or other underlying factors associated with DNA damage that influence the composition and functionality of seminal fluid, leading to impaired liquefaction. One of the study's key findings revealed a decrease in total sperm motility among cases compared with controls, and this difference was statistically significant (P value < 0.001). Specifically, we found that the percentage of sperm motility (less than 42%) in the cases was 56%, whereas in the controls, it was only 9.3%. This substantial difference underscores the impact of abnormal SDF on the overall motility of spermatozoa. The observed decrease in total motility in cases compared with controls is consistent with previous research that reported similar findings. For example, a study by Lu et al. involving 1010 subfertile men in China also reported a significant decrease in sperm motility among patients with abnormal SDF (Lu et al. 2018 ). Furthermore, Campos et al. reported similar findings, with a significant reduction in sperm motility observed in individuals with abnormal SDF (Campos et al. 2021 ). These findings support the inverse relationship between SDF and semen parameters, particularly sperm motility. The precise mechanisms linking elevated SDF with decreased sperm motility remain somewhat unexplored. Nevertheless, it is plausible that the DNA damage observed in spermatozoa with abnormal fragmentation could interfere with the cellular processes essential for effective motility. Past studies have suggested that DNA damage in sperm might lead to oxidative stress and mitochondrial dysfunction, both of which have been linked to impaired sperm motility (R. John Aitken & De Iuliis 2007 ; Alahmar 2019 ). In addition, the present study revealed a notable dissimilarity in the normal form of sperm between the cases and controls. This disparity was statistically significant (P value = 0.049). Within the case group, the proportion of sperm with normal forms was lower than that in the control group. These findings suggest that abnormal SDF may contribute to variations in sperm morphology. Normal sperm morphology is crucial for successful fertilization and embryonic development. Previous research has established a link between abnormal sperm morphology and impaired sperm function, reducing fertility (Kruger 1988 ). The association between abnormal SDF and altered sperm morphology can be attributed to several underlying mechanisms. DNA fragmentation in sperm may arise from diverse factors, including oxidative stress, exposure to environmental toxins, or genetic abnormalities. These factors can disrupt the normal cellular processes of sperm development and maturation, leading to morphological abnormalities. Additionally, sperm DNA damage has been associated with compromised chromatin compaction, which can influence the overall structure and morphology of the sperm (R J Aitken & Iuliis 2010 ). Finally, there were no statistically significant differences between cases and controls regarding other seminal parameters, including viscosity, total semen volume, and sperm count. This finding suggests that abnormal SDF may not directly influence the viscosity of semen or the sperm count. This result aligns with previous studies that reported no significant correlation between the sperm count or semen viscosity and SDF (P value > 0.05). For example, a recent retrospective study by Chua et al. involving 2567 semen samples revealed similar findings (Chua et al. 2023 ). Similarly, Zhang et al. conducted a study on 5114 men and reported no significant difference in semen volume or total sperm count between SDF patients and controls (Zhang et al. 2021 ). These findings suggest that abnormal SDF may not strongly influence these specific seminal parameters. However, the associations between SDF and semen parameters remain uncertain and are subject to debate. Further investigations with larger participant numbers and a broader scope of seminal parameters are necessary for a more comprehensive understanding of the relationship between SDF and various seminal parameters. Relationships among XRCC1 , the rs25487 (T > C) SNP and SDF XRC1, a pivotal enzyme in the BER process, serves as a scaffolding protein that plays a crucial role in maintaining the stability of the BER pathway. Its primary function involves recruiting other relevant enzymes to the abasic site and coordinating their activities to increase the efficiency of the pathway. The XRCC1 gene is significantly expressed in the testes, especially in pachytene spermatocytes and round spermatids. This conserved expression pattern is vital for supporting spermatogenesis by facilitating DNA damage repair during meiosis and recombination in germ cells. Consequently, mutations or variations in XRCC1 can potentially disrupt the regular process of spermatogenesis, which is essential for normal sperm production (Gu et al. 2007 ). The p.Arg399Gln polymorphism, also known as rs25487, is a common genetic variation within the XRCC1 gene that has been extensively studied. It alters the amino acid from the basic and positively charged arginine to a polar but uncharged glutamine. The mutation occurs at a conserved residue within the poly (ADP‒ribose) polymerase binding domain of XRCC1 (Saadat & Ansari-Lari 2009 ). When the genotype frequencies in both the cases and controls were examined, interesting patterns emerged in the distribution of genotypes within the two groups. In the control group, the TT genotype was observed in 3% of the total, whereas in the case group (8%), the OR calculated for this genotype was 3.130. Despite the odds ratio (OR) indicating a greater risk of abnormal SDF in individuals with the TT genotype, the difference did not reach statistical significance (P value = 0.171). However, in contrast, the presence of the TC genotype was observed in 31% of the control group and 49% of the case group, with an odds ratio of 2.159. This comparison revealed a statistically significant association (P value = 0.024) between the TC genotype and SDF. Furthermore, the CC genotype was prevalent in 66% of the control group, whereas it was observed in 43% of the case group. The OR calculated for this genotype was 0.379, and the corresponding P value was 0.004, indicating a statistically significant association between the CC genotype and protection against SDF. Overall, these findings offer compelling evidence supporting the association between the XRCC1 (p.Arg399Gln) rs25487 (T > C) polymorphism and SDF. Specifically, individuals with the TC genotype were found to have a greater risk of abnormal SDF than those with the CC genotype. Conversely, individuals with the CC genotype presented a decreased risk of abnormal SDF compared with carriers of the TC genotype. In the same context, several other studies have reported a notable correlation between XRCC1 gene polymorphisms and susceptibility to idiopathic azoospermia (Gu et al. 2007 ; Zheng et al. 2012 ). Additionally, Garcia-Rodriguez et al. also reported significant differences in the genotypic frequencies of the XRCC1 Arg399Gln polymorphism between patients and controls when investigating its influence on seminal parameters and SDF. The heterozygous TC genotype was more prevalent in the control group (Garcia-Rodriguez et al. 2018 ). The discrepancy in the risk genotypes could arise from the diverse ethnic backgrounds of the population and the sample size examined. The genotypic distribution of XRCC1 rs25487 (T > C) in the control group conformed to HWE, indicating no significant deviation (P value = 0.718) between the observed and expected genotypes. This finding implies that the frequencies of genotypes and alleles for XRCC1 rs25487 (T > C) are randomly distributed in the population. The statistical analyses revealed a significant difference between the SDF patients and the control group under all inheritance models (except for the recessive model). This finding suggests that carrying the C allele appears to be protective against the risk of having elevated SDF. Relationships among OGG1 , the rs1052133 (C > G) SNP and SDF In human spermatozoa, 8-oxoguanine DNA glycosylase 1 (OGG1) is an essential BER pathway enzyme. As the primary enzyme in the BER DNA repair system, OGG1 plays a crucial role. Both the sperm nucleus and mitochondria rely on this glycosylase, which actively removes 8-hydroxy-2'-deoxyguanosine (8OHdG) and releases the adduct into the extracellular space (Smith et al. 2013 ). At least 20 confirmed sequence variations have been documented in online databases. Among these, the most extensively studied variant is a C > G substitution resulting in an amino acid alteration from serine to cysteine at codon 326 (Ser326Cys; rs1052133) (Hung et al. 2005 ). The Ser326Cys polymorphism has been linked to a decreased repair capacity (Smart et al. 2006 ). Our study did not observe any statistically significant differences in the genotypic or allelic frequencies between the control group and SDF patients (P values > 0.05). Although not statistically significant, the genotype (CG) and genotype (CC) have odds ratios of SDF risk under 1 (protective role), indicating a lower likelihood of experiencing abnormal SDF when those genotypes are present. These results are similar to those reported from Barcelona in 2018 (Garcia-Rodriguez et al. 2018 ). A study from China revealed a significant association between GG and SDF (Ji, Yan, Liu, Qu, et al. 2013). The variations observed could be attributed to variances in sample sizes or ethnic diversity among the participants. Furthermore, no significant associations were found between the two groups when genetic models were explored. As a result, it can be inferred that there is no correlation between OGG1 rs1052133 (C > G) and SDF in our population. The distribution of OGG1 rs1052133 (C > G) genotypes in the control group deviated significantly from HWE, as indicated by a significant P value of less than 0.001. This finding suggests that the distribution of alleles for OGG1 rs1052133 (C > G) is influenced by factors such as nonrandom mating, which can alter genotype frequencies within the population, or genetic drift, which refers to random fluctuations in allele frequencies, particularly in small populations. These factors could have contributed to the observed deviation from Hardy‒Weinberg equilibrium. Another reason may be gene flow, which can disrupt equilibrium by introducing new genetic variations or equalizing allele frequencies between populations (Gillespie 1998 ; Pirchner 2000 ). The most likely reason for the results of our study is the small (n = 74) sample size. Relationships between the MUTYH and rs3219489 (G > C) SNPs and SDF The p.Gln324His polymorphism, also known as rs3219489, is a common variant within the MUTYH gene. It results from an SNP leading to an amino acid change from glutamine (Gln) to histidine (His) at position 324 of the MUTYH protein. The difference in the nature of the two amino acids can affect the enzyme's function. The genotypic distribution of MUTYH rs3219489 (G > C) in the control group conforms to Hardy‒Weinberg equilibrium, as indicated by the nonsignificant deviation (P value = 0.995) between the observed and expected genotypes. This finding suggests that the alleles for MUTYH rs3219489 (G > C) are distributed randomly within the study population. The CC genotype was more prevalent in SDF patients (31%) than in control men (17%), although the difference did not reach significance (P value = 0.064). Similarly, the GG genotype did not significantly differ between the two groups, although it was less common in the control men (38% and 34%, respectively). The GC genotype, however, significantly varied between the SDF patients and the controls (31% and 49%, respectively), with a P value of 0.025. To date, no reports have investigated the potential impacts of the MUTYH Gln324His (G > C) SNP on seminal parameters or SDF. However, these results were similar to findings reported in colorectal cancer, cervical carcinoma, HR-HPV infection, endometrial cancer, lung cancer, urinary bladder cancer, end-stage renal disease (ESRD), and age-related macular degeneration (AMD) (Blasiak et al. 2012 ; Cai et al. 2012 ; H. Chen et al. 2019 ; Hogervorst et al. 2016 ; Kim et al. 2016 ; Osawa et al. 2012 ; Win 2017). These results suggest that genetic SNPs can influence the efficiency of DNA repair processes, leading to the accumulation of DNA damage, potentially contributing to the development of various complex diseases and affecting semen quality and SDF. In addition, a significantly increased risk of SDF with MUTYH , rs3219489 G > C, was observed under an overdominant genetic model (GG + CC versus GC) with a P value = 0.02. Overdominant genetic inheritance implies that the heterozygous genotype confers a different phenotype than its homozygous counterpart does. Relationships among APEX1 , the rs1130409 (G > T) SNP and SDF APEX1 plays a role in eliminating apurinic/apyrimidinic sites that arise from DNA cleavage by OGG1 and MUTYH while also facilitating the recruitment of additional BER players: DNA polymerase β and DNA ligase III (Bennett et al. 1997 ). Numerous genetic variations have been detected in this gene, including a G > T alteration in exon 5, resulting in the substitution of aspartic acid with glutamic acid (Asp148Glu; identified as rs1130409) (Hung et al. 2005 ). The distribution of APEX1 rs1130409 (G > T) genotypes in the control group was in Hardy‒Weinberg equilibrium, as there was no significant deviation (P value = 0.757) between the observed and expected genotypes. These findings suggest that the alleles for APEX1 rs1130409 (G > T) are randomly distributed in the study cohort. The distribution of genotypes was as follows: GG genotype in 15 cases (20%) and 12 controls (16%); GT genotype in 23 cases (31%) and 34 controls (46%); and TT genotype in 37 cases (49%) and 28 controls (38%), with no significant differences (all P values > 0.05). However, when the GG genotype was used as a reference, the OR for the GT genotype was 0.520 (P value = 0.056), indicating a trend toward a decreased risk of SDF. These findings suggest that the APEX1 rs1130409 SNP may be associated with the risk of SDF, although the observed associations did not reach statistical significance. The trend toward a decreased risk of SDF in individuals with the GT genotype suggests that the heterozygous genotype may confer some protective effect against SDF. To our knowledge, there are no published studies on this polymorphism in relation to infertility or SDF, but many studies have explored the associations between the rs1130409 SNP in APEX1 and various cancers and other diseases, such as Parkinson's disease, Paget's disease of bone, endometriosis, colorectal cancer, lung cancer, prostate cancer, hepatocellular carcinoma, and renal cell carcinoma (Ahmed 2021 ; Q. Cao et al. 2011 ; Hsu et al. 2014 ; Kasahara et al. 2008 ; Li et al. 2021 ; Saad et al. 2021 ; Usategui-Martín et al. 2018 ; Zhu et al. 2018 ). Relationships between the tested polymorphisms and sperm parameters The examined polymorphisms were not significantly related to conventional semen parameters, e.g., motility, sperm count, or sperm form. Importantly, the lack of statistical significance could be due to the relatively small sample size employed in this study. A larger sample size would provide more statistical power to detect significant associations. Additionally, other factors, such as environmental influences, lifestyle, and other genetic variations, may contribute to the overall risk of SDF and should be considered in future studies. Combined XRCC1 , OGG1 , MUTYH and APEX1 genotypes in the study population The highest risk genotypic combinations of the four SNPs, MUTYH (G > C), XRCC1 (T > C), APEX1 (G > T), and OGG1 (C > G), according to OR were [CC, CC, GT, CG] (OR = 7.193), followed by [CC, TC, GT, GG] (OR = 2.028), and finally [GG, CC, TT, CC] (OR = 1.690). However, those risk combinations were not significantly different between the study groups. The Fruchterman–Rheingold plot was used to evaluate the degree of interaction between the SNPs on the basis of entropy measurements (Rupasree et al. 2016 ). As shown in Fig. 1, there is a synergistic interaction between XRCC1 "rs25487" and APEX1 "rs1130409", which could affect an individual's susceptibility to DNA fragmentation in sperm. The figure also indicates a redundancy effect between the other SNPs. Conclusion This investigation involved Palestinian men residing in the Gaza Strip and experiencing increased SDF. The study focused on genetic variations of four SNPs: XRCCI (p.Arg399Gln) "rs25487", OGG1 (p.Ser326Cys) "rs1052133", MUTYH (p.Gln324His) "rs3219489", and APEX1 (p.Asp148Glu) "rs1130409". The major outcomes of the study can be summarized as follows: Elevated SDF is significantly correlated with low sperm motility, a low level of the normal form, and a high liquefaction time. On the basis of the frequencies of genotypes and alleles, the OGG1 C > G and APEX1 G > T polymorphisms are not linked to SDF in our study population. The MUTYH G > C polymorphism is associated with a lower risk for SDF, i.e., individuals carrying the MUTYH (G/C) genotype may have a protective advantage against SDF. The XRCC1 "rs25487" T > C polymorphism is associated with a greater risk for SDF in the study population with the (T/C) genotype. On the other hand, the genotype (C/C) has been associated with a lower risk for SDF. The associations between XRCC1 , rs25487, and SDF are significant under codominant, dominant, overdominant, and log-additive models, whereas the MUTYH rs3219489 polymorphism is significant under the overdominant model. There was no significant relationship between XRCC1 (T > C), OGG1 (C > G), MUTYH (G > C) or APEX1 (G > T) and the semen parameters (sperm count, motility, and sperm form) in our study sample. The highest SDF risk combination of the genotypes MUTYH (G > C), XRCC1 (T > C), APEX1 (G > T), and OGG1 (C > G) is CC, CC, GT, and CG, respectively. Declarations Acknowledgements: This work was performed at the genetic diagnosis laboratory of the Islamic Universityof Gaza. Author contributions: Prof. Dr. Fadel A. Sharif and Dr. Mazen M. Alzahrna are the principal investigators and supervisors of this work. Collecting data and supervising the practical side were performed by Mr. Mohammed J. Ashour. Statistical analysis of results was carried out by Ms. Hadeer N. Abuwarda. The first draft of the manuscript was written by Dr. Fadel A. Sharif, Mr. Mahmoud A. Hassouna and Ms. Hadeer N. Abuwarda. Experimental part was done by Mr. Mahmoud A. Hassouna. All authors approved the final manuscript . Funding: Funding was not received for the study. Availability of Data and Materials : The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Eur Urol 80:603–620. https://doi.org/10.1016/j.eururo.2021.08.014 Osawa K, Nakarai C, Uchino K, Yoshimura M, Tsubota N, Takahashi J, Kido Y (2012) XRCC3 gene polymorphism is associated with survival in Japanese lung cancer patients. Int J Mol Sci 13:16658–16667. https://doi.org/10.3390/ijms131216658 Pirchner F (2000) Principles of population genetics. J Anim Breed Genet 117:143–144. https://doi.org/10.1111/j.1439-0388.2000.201-2.x Rupasree Y, Naushad SM, Varshaa R, Mahalakshmi GS, Kumaraswami K, Rajasekhar L, Kutala VK (2016) Application of various statistical models to explore gene–gene interactions in folate, xenobiotic, toll-like receptor and STAT4 pathways that modulate susceptibility to systemic lupus erythematosus. Mol Diagn Ther 20:83–95. https://doi.org/10.1007/s40291-015-0181-0 Saad AM, Abdel-Megied AES, Elbaz RA, Hassab El-Nabi SE, Elshazli RM (2021) Genetic variants of APEX1 p.Asp148Glu and XRCC1 p.Gln399Arg with the susceptibility of hepatocellular carcinoma. 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J Cell Sci 126:1488–1497. https://doi.org/10.1242/jcs.121657 Usategui-Martín R, Gutiérrez-Cerrajero C, Jiménez-Vázquez S, Calero-Paniagua I, García-Aparicio J, Corral-Gudino L, del Pino-Montes J, González-Sarmiento R (2018) Polymorphisms in genes implicated in base excision repair (BER) pathway are associated with susceptibility to Paget’s disease of bone. Bone 112:19–23. https://doi.org/10.1016/j.bone.2018.04.003 Win AK et al (2017) Risk of extracolonic cancers for people with biallelic and monoallelic mutations in MUTYH. Physiol Behav 176:139–148. https://doi.org/10.1002/ijc.30197 World Health Organization (2021) WHO laboratory manual for the examination and processing of human semen, 6th edn. World Health Organization, Geneva. http://whqlibdoc.who.int/publications/2010/9789241547789_eng.pdf Yang RQ, Hui L (2021) Polymorphisms of DNA damage repair genes and male infertility: advances in studies. Zhonghua Nan Ke Xue 27:456–460. http://europepmc.org/abstract/MED/34914323 Zhang F, Li J, Liang Z, Wu J, Li L, Chen C, Jin F, Tian Y (2021) Sperm DNA fragmentation and male fertility: a retrospective study of 5114 men attending a reproductive center. J Assist Reprod Genet 38:1133–1141. https://doi.org/10.1007/s10815-021-02120-5 Zheng LR, Wang XF, Zhou DX, Zhang J, Huo YW, Tian H (2012) Association between XRCC1 single-nucleotide polymorphisms and infertility with idiopathic azoospermia in northern Chinese Han males. Reprod Biomed Online 25:402–407. https://doi.org/10.1016/j.rbmo.2012.06.014 Zhu J, Jia W, Wu C, Fu W, Xia H, Liu G, He J (2018) Base excision repair gene polymorphisms and Wilms tumor susceptibility. EBioMedicine 33:88–93. https://doi.org/10.1016/j.ebiom.2018.06.018 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6672168","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458208935,"identity":"6722772c-63f0-4c4b-9302-c52802a35f0a","order_by":0,"name":"Fadel A. Sharif","email":"","orcid":"","institution":"Islamic University of Gaza","correspondingAuthor":false,"prefix":"","firstName":"Fadel","middleName":"A.","lastName":"Sharif","suffix":""},{"id":458208938,"identity":"78adecba-0346-4b36-a817-6913aeb8f446","order_by":1,"name":"Mahmoud A. Hassouna","email":"","orcid":"","institution":"Islamic University of Gaza","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"A.","lastName":"Hassouna","suffix":""},{"id":458208939,"identity":"e61181ea-0a7c-48ac-8ead-5649a25a3dc7","order_by":2,"name":"Mazen M. Alzahrna","email":"","orcid":"","institution":"Islamic University of Gaza","correspondingAuthor":false,"prefix":"","firstName":"Mazen","middleName":"M.","lastName":"Alzahrna","suffix":""},{"id":458208940,"identity":"8a1f09b2-6beb-422e-b02c-edf300aed2cc","order_by":3,"name":"Mohammed J. Ashour","email":"","orcid":"","institution":"Islamic University of Gaza","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"J.","lastName":"Ashour","suffix":""},{"id":458208941,"identity":"98b08286-5c3a-4611-8631-106093b05f82","order_by":4,"name":"Hadeer N. Abuwarda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACAzBiYEgA83gYbIAkY+MBUrSkgbQ0kKTlMJjGq8Wc/fDGBx/32OUZ3Eh+9uFNzXm7te2HgbbU2ETj0mLZk1ZsOONZcrHBjTTjmXOO3U7ediYRqOVYWm4DLocdyDGT5jnAnLjhdoIxMw/b7WSzA0AtjA2HcWs5/8b8958D9UAt6Z+Zef6dSzY7/5CAlhs5ZswMBw4DteQYM/O2HbAzu0HIlhvPiiV7Dhwvlrz/pphxbl9ygtkNoC0J+PxyPnnjhx8HqvP4zhzfzPDmm5292fn0hw8+1Njg1IIBEsEqE4hVDgL2pCgeBaNgFIyCkQEAQ9lsMAhqziwAAAAASUVORK5CYII=","orcid":"","institution":"Islamic University of Gaza","correspondingAuthor":true,"prefix":"","firstName":"Hadeer","middleName":"N.","lastName":"Abuwarda","suffix":""}],"badges":[],"createdAt":"2025-05-15 11:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6672168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6672168/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83047832,"identity":"bc409e19-af6a-4999-9128-cd7681ca803e","added_by":"auto","created_at":"2025-05-19 12:08:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56532,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction entropy diagram for gene‒gene interactions and SDF risk. The red line represents the synergistic interaction between \u003cem\u003eXRCC1 \u003c/em\u003e\"rs25487\" and \u003cem\u003eAPEX1 \u003c/em\u003e\"rs1130409\", which could affect an individual's susceptibility to DNA fragmentation in sperm. The gold-colored line represents the redundant effect between the remaining gene‒gene interactions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6672168/v1/89fbde43080d9bf4507839f5.png"},{"id":83050334,"identity":"0e3938d4-5d06-40d2-8c7b-89d4d6fce826","added_by":"auto","created_at":"2025-05-19 12:32:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1146313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6672168/v1/20236fdb-e2e1-4312-96b2-06ee6fd0d89d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Polymorphisms in Base Excision Repair Genes on Seminal Fluid Parameters","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Overview\u003c/h2\u003e \u003cp\u003eThe World Health Organization (WHO) has acknowledged infertility as a global health issue. It is characterized by the inability of a couple to achieve conception even after engaging in regular unprotected sexual intercourse for 12 months (World Health Organization \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Approximately 8\u0026ndash;12% of couples worldwide are impacted by infertility, leading to emotional distress, psychological instability, and significant mental strain among those experiencing it (Shah et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Approximately 40\u0026ndash;50% of infertility cases are attributed to \"male factor\" infertility, indicating that these issues are related primarily to male reproductive health. Additionally, up to 2% of all men exhibit sperm parameters that fall below the standard quality, potentially contributing to fertility challenges (Kumar \u0026amp; Singh \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMale infertility is directly linked to the quality and quantity of sperm in the seminal fluid. Successful fertilization relies on the transmission of high-quality and sufficient numbers of sperm to the partner and the ability of the sperm to reach the fertilization site and successfully complete the fertilization process. Abnormalities in the quantity and quality of sperm can be caused by various factors, ranging from inherent birth defects and genetic disorders to lifestyle choices and environmental conditions (Shah et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis of semen plays a crucial role in evaluating male reproductive function, enabling appropriate treatment for male subfertility. A comprehensive assessment of seminal fluid can aid clinicians in making informed decisions about further examinations and managing couples facing fertility challenges.\u003c/p\u003e \u003cp\u003eSperm DNA fragmentation (SDF) has emerged as a new and valuable biomarker for identifying infertile men and providing valuable insights into the outcomes of assisted reproductive technology (ART). The sperm DNA carries half of the genomic material responsible for the offspring's structure. Therefore, maintaining the integrity of the genetic structure of sperm is essential for successful fertilization and the subsequent development of the embryo and fetus (Minhas et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA lack of efficient DNA repair mechanisms can increase DNA damage in germ cells, resulting in abnormal spermatogenesis and infertility. The proper function of DNA repair is essential for maintaining the integrity and quality of the germ cell genome. Consequently, the DNA repair system plays a critical role in sperm production. Dysfunction of the genes involved in DNA damage repair within germ cells can decrease sperm quantity and promote abnormalities in sperm quality (Yang \u0026amp; Hui \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, this work was designed to explore the relationships of common single nucleotide polymorphisms (SNPs) of DNA damage repair genes in the base excision repair (BER) pathway, namely, \u003cem\u003eXRCC1\u003c/em\u003e (p.Arg399Gln), \u003cem\u003eOGG1\u003c/em\u003e (p.Ser326Cys), \u003cem\u003eMUTYH\u003c/em\u003e (p.Gln324His), and \u003cem\u003eAPEX1\u003c/em\u003e (p.Asp148Glu), with SDF results and other semen parameters.\u003c/p\u003e \u003cp\u003eUntil now, limited studies in our region have investigated the association between specific genetic variants and SDF. Therefore, this study aimed to examine the relationship between selected gene polymorphisms and SDF in the population of the Gaza Strip. Identifying genetic variants associated with SDF may provide insight into the underlying mechanisms and support the development of targeted interventions to improve sperm quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eThis case\u0026ndash;control study consisted of 75 semen samples from males who have abnormal results of SDF test (cases) and 75 semen samples from males with normal SDF levels (controls). The study was ethically approved by the Ethical Research Committee at the Islamic University of Gaza and all subjects gave their consent to participate in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThe reports of semen analysis, the results of SDF test, and the general characteristics for all participants of this study were obtained from the records of Al-Basma IVF center in Gaza Strip. Semen analysis covered sperm count, liquefaction time, volume, morphology, motility, viscosity, and the percentage of abnormal sperm. The Spectrum Technologies (USA) HaloSperm kit was used to estimate the degree of DNA damage present in semen samples. Three to four hundred sperm were examined and a SDF of \u0026ge;\u0026thinsp;30% was considered abnormal.\u003c/p\u003e\n\u003ch3\u003eDNA Extraction and Genotyping\u003c/h3\u003e\n \u003cp\u003eThe genomic DNA was isolated from semen samples using Wizard Genomic DNA Purification Kit (Promega, USA) following the manufacturer\u0026rsquo;s instructions. The SNPs of the selected genes were genotyped using allele-specific PCR (AS-PCR) technique. The specific PCR primers were designed using a web-based allele-specific primer design application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.biotec.or.th/\u003c/span\u003e\u003cspan address=\"http://bioinfo.biotec.or.th/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e WASP) and BatchPrimer3 web primer design program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://probes.pw.usda.gov/\u003c/span\u003e\u003cspan address=\"https://probes.pw.usda.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e cgi- bin/ batch prime r3). Primer sequences are available from the corresponding author upon request. The PCR products were separated by running the PCR products on ethidium bromide-stained 3% agarose gels and visualized on a gel documentation system.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were collected, summarized, categorized and analyzed using Statistical Package for Social Sciences (SPSS) software version 25. The frequencies of alleles and genotypes were compared between cases and controls by standard odds ratio (OR) at 95% confidence intervals (CI) using an online calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www\u003c/span\u003e\u003cspan address=\"https://www\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. medca lc.org/ calc/ odds_ ratio. php). Hardy\u0026ndash;Weinberg equilibrium (HWE) was tested using the online: (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wpcalc.com/en/\u003c/span\u003e\u003cspan address=\"https://wpcalc.com/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e equil ibrium- hardy- weinb erg/). All results were accepted as significant if p value\u0026thinsp;\u0026le;\u0026thinsp;0.05. The multifactor dimensionality reduction (MDR) (v.3.0.2) software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www\u003c/span\u003e\u003cspan address=\"http://www\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. epist asisb log. org/) was used to evaluate SNP-SNP interactions (K\u0026uuml;chler et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The interaction entropy graphs were constructed based on MDR results to determine synergistic and non-synergistic interactions among the variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eAs previously reported in our earlier work (Sharif et al. 2023), the demographic characteristics and routine seminal parameters of the study population were as follows: No statistically significant differences between the two groups concerning mean age, BMI, smoking status, or IVF technique, as all P values were greater than 0.05. The liquefaction time of the cases was significantly longer than that of the controls (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the total motility of the sperm in the cases was significantly lower than that in the controls (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The sperm normal form in patients was also significantly different from that in controls (P value\u0026thinsp;=\u0026thinsp;0.049), with a lower proportion of patients having a normal form. However, there were no significant differences between cases and controls in terms of other seminal parameters, including viscosity, volume, and sperm count.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePolymorphism genotyping\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotype and allele frequencies of the investigated polymorphisms\u003c/h2\u003e \u003cp\u003eStatistical analysis of genotypic frequencies for the investigated SNPs indicated no significant differences between SDF patients and controls concerning the \u003cem\u003eOGG1\u003c/em\u003e and \u003cem\u003eAPEX1\u003c/em\u003e gene polymorphisms (P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, for the \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" polymorphism, a significant difference (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed between SDF patients and controls. The CC genotype was significantly more common (P value\u0026thinsp;=\u0026thinsp;0.004) in the control group, whereas the TC genotype was significantly less common (P value\u0026thinsp;=\u0026thinsp;0.024) in the control group. Moreover, for the \u003cem\u003eMUTYH\u003c/em\u003e \"rs3219489\" polymorphism, a significant difference (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was found between SDF patients and controls, with the GC genotype being significantly more frequent (P value\u0026thinsp;=\u0026thinsp;0.025) in the control group (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eStatistical analysis of allelic frequencies of the tested SNPs revealed no significant difference in the tested gene polymorphisms of \u003cem\u003eOGG1\u003c/em\u003e, \u003cem\u003eMUTYH\u003c/em\u003e, and \u003cem\u003eAPEX1\u003c/em\u003e between SDF patients and controls (P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the allele frequency analysis for the \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" polymorphism between SDF patients and controls revealed a significant difference between the two groups (P value\u0026thinsp;=\u0026thinsp;0.004) (Table\u0026nbsp;2). Statistical analysis of \u003cem\u003eXRCC1\u003c/em\u003e rs25487 T\u0026thinsp;\u0026gt;\u0026thinsp;C SNP revealed a significant difference between the two groups under all inheritance models, except for the recessive model, with all\u003c/p\u003e \u003cp\u003e(P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (data not shown). The statistical analysis of \u003cem\u003eOGG1\u003c/em\u003e \"rs1052133\" C\u0026thinsp;\u0026gt;\u0026thinsp;G and \u003cem\u003eAPEX1\u003c/em\u003e rs1130409 G\u0026thinsp;\u0026gt;\u0026thinsp;T SNPs indicated no significant differences between the two groups according to the all models (P value\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (data not shown). The statistical analysis of \u003cem\u003eMUTYH\u003c/em\u003e \"rs3219489\" G\u0026thinsp;\u0026gt;\u0026thinsp;C SNP revealed a significant difference between the two groups only under the overdominant model, with a P value of 0.02 (data not shown). Analysis of the observed and the calculated expected genotype frequencies in the control group showed that the distribution of genotypes is in Hardy\u0026ndash;Weinberg equilibrium for all tested polymorphisms except for \u003cem\u003eOGG1\u003c/em\u003e \"rs1052133\" C\u0026thinsp;\u0026gt;\u0026thinsp;G SNP (data not shown). The findings indicated no statistically significant relationships between any SNPs and conventional semen parameters (sperm count, motility, and normal sperm form), as all P values were greater than 0.05 (data not shown).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;(1)\u003c/strong\u003e \u003cp\u003eGenotype frequencies of the investigated polymorphisms\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes/SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP- value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eXRCC1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ers25487\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.610\u0026nbsp;-16.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.106\u0026ndash;4.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.195\u0026ndash;0.738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOGG1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ers1052133\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.485\u0026ndash;1.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.378\u0026ndash;2.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.599\u0026ndash;2.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMUTYH\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ers3219489\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.632\u0026ndash;2.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.239\u0026ndash;0.912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.025*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.957\u0026ndash;4.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAPEX1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ers1130409\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.558\u0026ndash;2.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.266\u0026ndash;1.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.833\u0026ndash;3.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Statistically significant (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;(2)\u003c/strong\u003e \u003cp\u003eAllele frequencies of the investigated gene polymorphisms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes/SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (N\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (N\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP- value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eXRCC1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(1.268\u0026ndash;3.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.004*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOGG1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(0.568\u0026ndash;1.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMUTYH\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(0.535\u0026ndash;1.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAPEX1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(0.529\u0026ndash;1.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Statistically significant (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultifactorial dimensionality reduction (MDR) analysis\u003c/h2\u003e \u003cp\u003eThe highest risk combinations of the genotypes for the four SNPs, \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\", \u003cem\u003eOGG1\u003c/em\u003e \"rs1052133\", \u003cem\u003eMUTYH\u003c/em\u003e \"rs3219489\" and \u003cem\u003eAPEX1\u003c/em\u003e \"rs1130409\", are presented in Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;(3)\u003c/strong\u003e \u003cp\u003eThe highest risk combinations of the genotypes for the investigated SNPs.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003cp\u003e\u003cem\u003eMUTYH, XRCC1, APEX1, OGG1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(Case: Control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh/Low-Risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(95% CI) \u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\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\u003eCC, CC, GT, CG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.365\u0026ndash;141.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCC, TC, GT, GG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.360\u0026ndash;11.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGG, CC, TT, CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5:3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.389\u0026ndash;7.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMUTYH\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eXRCC1\u003c/em\u003e (T\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eAPEX1\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;T), \u003cem\u003eOGG1\u003c/em\u003e (C\u0026thinsp;\u0026gt;\u0026thinsp;G)\u003c/p\u003e \u003cp\u003eThe entropy values reflect how much a set of SNPs interact and, as a result, indicate how closely the analyzed SNPs are related to SDF (Fig.\u0026nbsp;1). The Fruchterman\u0026ndash;Rheingold plot revealed the largest main effect, with higher entropy observed for \u003cem\u003eXRCC1\u003c/em\u003e (4.35%). The impact of polymorphisms can be graded in the following ascending order of entropy: \u003cem\u003eOGG1\u003c/em\u003e \"rs1052133\" (0.10%), \u003cem\u003eAPEX1\u003c/em\u003e \"rs1130409\" (1.80%), \u003cem\u003eMUTYH\u003c/em\u003e \"rs3219489\" (2.90%), and \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" (4.35%).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccumulating evidence supports the crucial role of the BER pathway in the DNA repair system. Therefore, polymorphisms in BER pathway genes are potential risk factors for various diseases. In the present study, the main objective was to investigate whether specific genetic variations (SNPs) in \u003cem\u003eXRCC1, OGG1, MUTYH\u003c/em\u003e, and \u003cem\u003eAPEX1\u003c/em\u003e are linked to the quality of seminal fluid, particularly with increased risk of SDF in a selected male population from the Gaza Strip. The study focused on the analysis of the \u003cem\u003eXRCCI\u003c/em\u003e (p.Arg399Gln) \"rs25487\", \u003cem\u003eOGG1\u003c/em\u003e (p.Ser326Cys) \"rs1052133\", \u003cem\u003eMUTYH\u003c/em\u003e (p.Gln324His) \"rs3219489\" and \u003cem\u003eAPEX1\u003c/em\u003e (p.Asp148Glu) \"rs1130409\" SNPs in a group of 75 men with positive SDF (cases) and compared the results to those of a group of 74 men with negative SDF (controls).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between seminal parameters and SDF\u003c/h2\u003e \u003cp\u003eWhen various seminal parameters, which have been identified as potential indicators of sperm quality, were compared, the analysis revealed a statistically significant difference in the liquefaction time (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The liquefaction time in the SDF patients was significantly longer (mean: 23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76 minutes) than that in the controls (mean: 20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 minutes). This finding suggests that abnormal SDF may be associated with delayed or impaired semen liquefaction (which refers to the process by which semen changes from a gel-like state to a more liquid form) and, thus, the release and motility of sperm. This result is congruent with another study conducted in Iraq (Al-Fahham \u0026amp; Al-Nowainy \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prolonged liquefaction time may reflect abnormalities in the seminal fluid composition or factors inhibiting the enzymatic processes involved in liquefaction. The mechanism linking abnormal SDF and prolonged liquefaction time could be due to oxidative stress (Agarwal \u0026amp; Bui \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), inflammation, or other underlying factors associated with DNA damage that influence the composition and functionality of seminal fluid, leading to impaired liquefaction.\u003c/p\u003e \u003cp\u003eOne of the study's key findings revealed a decrease in total sperm motility among cases compared with controls, and this difference was statistically significant (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, we found that the percentage of sperm motility (less than 42%) in the cases was 56%, whereas in the controls, it was only 9.3%. This substantial difference underscores the impact of abnormal SDF on the overall motility of spermatozoa. The observed decrease in total motility in cases compared with controls is consistent with previous research that reported similar findings. For example, a study by Lu et al. involving 1010 subfertile men in China also reported a significant decrease in sperm motility among patients with abnormal SDF (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Campos et al. reported similar findings, with a significant reduction in sperm motility observed in individuals with abnormal SDF (Campos et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings support the inverse relationship between SDF and semen parameters, particularly sperm motility. The precise mechanisms linking elevated SDF with decreased sperm motility remain somewhat unexplored. Nevertheless, it is plausible that the DNA damage observed in spermatozoa with abnormal fragmentation could interfere with the cellular processes essential for effective motility. Past studies have suggested that DNA damage in sperm might lead to oxidative stress and mitochondrial dysfunction, both of which have been linked to impaired sperm motility (R. John Aitken \u0026amp; De Iuliis \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Alahmar \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the present study revealed a notable dissimilarity in the normal form of sperm between the cases and controls. This disparity was statistically significant (P value\u0026thinsp;=\u0026thinsp;0.049). Within the case group, the proportion of sperm with normal forms was lower than that in the control group. These findings suggest that abnormal SDF may contribute to variations in sperm morphology. Normal sperm morphology is crucial for successful fertilization and embryonic development. Previous research has established a link between abnormal sperm morphology and impaired sperm function, reducing fertility (Kruger \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The association between abnormal SDF and altered sperm morphology can be attributed to several underlying mechanisms. DNA fragmentation in sperm may arise from diverse factors, including oxidative stress, exposure to environmental toxins, or genetic abnormalities. These factors can disrupt the normal cellular processes of sperm development and maturation, leading to morphological abnormalities. Additionally, sperm DNA damage has been associated with compromised chromatin compaction, which can influence the overall structure and morphology of the sperm (R J Aitken \u0026amp; Iuliis \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, there were no statistically significant differences between cases and controls regarding other seminal parameters, including viscosity, total semen volume, and sperm count. This finding suggests that abnormal SDF may not directly influence the viscosity of semen or the sperm count. This result aligns with previous studies that reported no significant correlation between the sperm count or semen viscosity and SDF (P value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For example, a recent retrospective study by Chua et al. involving 2567 semen samples revealed similar findings (Chua et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, Zhang et al. conducted a study on 5114 men and reported no significant difference in semen volume or total sperm count between SDF patients and controls (Zhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that abnormal SDF may not strongly influence these specific seminal parameters. However, the associations between SDF and semen parameters remain uncertain and are subject to debate. Further investigations with larger participant numbers and a broader scope of seminal parameters are necessary for a more comprehensive understanding of the relationship between SDF and various seminal parameters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships among\u003c/b\u003e \u003cb\u003eXRCC1\u003c/b\u003e, \u003cb\u003ethe rs25487 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) SNP and SDF\u003c/b\u003e\u003c/p\u003e \u003cp\u003eXRC1, a pivotal enzyme in the BER process, serves as a scaffolding protein that plays a crucial role in maintaining the stability of the BER pathway. Its primary function involves recruiting other relevant enzymes to the abasic site and coordinating their activities to increase the efficiency of the pathway. The \u003cem\u003eXRCC1\u003c/em\u003e gene is significantly expressed in the testes, especially in pachytene spermatocytes and round spermatids. This conserved expression pattern is vital for supporting spermatogenesis by facilitating DNA damage repair during meiosis and recombination in germ cells. Consequently, mutations or variations in \u003cem\u003eXRCC1\u003c/em\u003e can potentially disrupt the regular process of spermatogenesis, which is essential for normal sperm production (Gu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The p.Arg399Gln polymorphism, also known as rs25487, is a common genetic variation within the \u003cem\u003eXRCC1\u003c/em\u003e gene that has been extensively studied. It alters the amino acid from the basic and positively charged arginine to a polar but uncharged glutamine. The mutation occurs at a conserved residue within the poly (ADP‒ribose) polymerase binding domain of \u003cem\u003eXRCC1\u003c/em\u003e (Saadat \u0026amp; Ansari-Lari \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the genotype frequencies in both the cases and controls were examined, interesting patterns emerged in the distribution of genotypes within the two groups. In the control group, the TT genotype was observed in 3% of the total, whereas in the case group (8%), the OR calculated for this genotype was 3.130. Despite the odds ratio (OR) indicating a greater risk of abnormal SDF in individuals with the TT genotype, the difference did not reach statistical significance (P value\u0026thinsp;=\u0026thinsp;0.171). However, in contrast, the presence of the TC genotype was observed in 31% of the control group and 49% of the case group, with an odds ratio of 2.159. This comparison revealed a statistically significant association (P value\u0026thinsp;=\u0026thinsp;0.024) between the TC genotype and SDF. Furthermore, the CC genotype was prevalent in 66% of the control group, whereas it was observed in 43% of the case group. The OR calculated for this genotype was 0.379, and the corresponding P value was 0.004, indicating a statistically significant association between the CC genotype and protection against SDF. Overall, these findings offer compelling evidence supporting the association between the \u003cem\u003eXRCC1\u003c/em\u003e (p.Arg399Gln) rs25487 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) polymorphism and SDF. Specifically, individuals with the TC genotype were found to have a greater risk of abnormal SDF than those with the CC genotype. Conversely, individuals with the CC genotype presented a decreased risk of abnormal SDF compared with carriers of the TC genotype. In the same context, several other studies have reported a notable correlation between \u003cem\u003eXRCC1\u003c/em\u003e gene polymorphisms and susceptibility to idiopathic azoospermia (Gu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, Garcia-Rodriguez et al. also reported significant differences in the genotypic frequencies of the \u003cem\u003eXRCC1\u003c/em\u003e Arg399Gln polymorphism between patients and controls when investigating its influence on seminal parameters and SDF. The heterozygous TC genotype was more prevalent in the control group (Garcia-Rodriguez et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The discrepancy in the risk genotypes could arise from the diverse ethnic backgrounds of the population and the sample size examined.\u003c/p\u003e \u003cp\u003eThe genotypic distribution of \u003cem\u003eXRCC1\u003c/em\u003e rs25487 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) in the control group conformed to HWE, indicating no significant deviation (P value\u0026thinsp;=\u0026thinsp;0.718) between the observed and expected genotypes. This finding implies that the frequencies of genotypes and alleles for \u003cem\u003eXRCC1\u003c/em\u003e rs25487 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) are randomly distributed in the population.\u003c/p\u003e \u003cp\u003eThe statistical analyses revealed a significant difference between the SDF patients and the control group under all inheritance models (except for the recessive model). This finding suggests that carrying the C allele appears to be protective against the risk of having elevated SDF.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships among\u003c/b\u003e \u003cb\u003eOGG1\u003c/b\u003e, \u003cb\u003ethe rs1052133 (C\u0026thinsp;\u0026gt;\u0026thinsp;G) SNP and SDF\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn human spermatozoa, 8-oxoguanine DNA glycosylase 1 (OGG1) is an essential BER pathway enzyme. As the primary enzyme in the BER DNA repair system, \u003cem\u003eOGG1\u003c/em\u003e plays a crucial role. Both the sperm nucleus and mitochondria rely on this glycosylase, which actively removes 8-hydroxy-2'-deoxyguanosine (8OHdG) and releases the adduct into the extracellular space (Smith et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). At least 20 confirmed sequence variations have been documented in online databases. Among these, the most extensively studied variant is a C\u0026thinsp;\u0026gt;\u0026thinsp;G substitution resulting in an amino acid alteration from serine to cysteine at codon 326 (Ser326Cys; rs1052133) (Hung et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The Ser326Cys polymorphism has been linked to a decreased repair capacity (Smart et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study did not observe any statistically significant differences in the genotypic or allelic frequencies between the control group and SDF patients (P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Although not statistically significant, the genotype (CG) and genotype (CC) have odds ratios of SDF risk under 1 (protective role), indicating a lower likelihood of experiencing abnormal SDF when those genotypes are present. These results are similar to those reported from Barcelona in 2018 (Garcia-Rodriguez et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A study from China revealed a significant association between GG and SDF (Ji, Yan, Liu, Qu, et al. 2013). The variations observed could be attributed to variances in sample sizes or ethnic diversity among the participants.\u003c/p\u003e \u003cp\u003eFurthermore, no significant associations were found between the two groups when genetic models were explored. As a result, it can be inferred that there is no correlation between \u003cem\u003eOGG1\u003c/em\u003e rs1052133 (C\u0026thinsp;\u0026gt;\u0026thinsp;G) and SDF in our population.\u003c/p\u003e \u003cp\u003eThe distribution of \u003cem\u003eOGG1\u003c/em\u003e rs1052133 (C\u0026thinsp;\u0026gt;\u0026thinsp;G) genotypes in the control group deviated significantly from HWE, as indicated by a significant P value of less than 0.001. This finding suggests that the distribution of alleles for \u003cem\u003eOGG1\u003c/em\u003e rs1052133 (C\u0026thinsp;\u0026gt;\u0026thinsp;G) is influenced by factors such as nonrandom mating, which can alter genotype frequencies within the population, or genetic drift, which refers to random fluctuations in allele frequencies, particularly in small populations. These factors could have contributed to the observed deviation from Hardy‒Weinberg equilibrium. Another reason may be gene flow, which can disrupt equilibrium by introducing new genetic variations or equalizing allele frequencies between populations (Gillespie \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Pirchner \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The most likely reason for the results of our study is the small (n\u0026thinsp;=\u0026thinsp;74) sample size.\u003c/p\u003e \u003cp\u003e \u003cp\u003e \u003cb\u003eRelationships between the\u003c/b\u003e \u003cb\u003eMUTYH\u003c/b\u003e \u003cb\u003eand rs3219489 (G\u0026thinsp;\u0026gt;\u0026thinsp;C) SNPs and SDF\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe p.Gln324His polymorphism, also known as rs3219489, is a common variant within the \u003cem\u003eMUTYH\u003c/em\u003e gene. It results from an SNP leading to an amino acid change from glutamine (Gln) to histidine (His) at position 324 of the MUTYH protein. The difference in the nature of the two amino acids can affect the enzyme's function.\u003c/p\u003e \u003cp\u003eThe genotypic distribution of \u003cem\u003eMUTYH\u003c/em\u003e rs3219489 (G\u0026thinsp;\u0026gt;\u0026thinsp;C) in the control group conforms to Hardy‒Weinberg equilibrium, as indicated by the nonsignificant deviation (P value\u0026thinsp;=\u0026thinsp;0.995) between the observed and expected genotypes. This finding suggests that the alleles for \u003cem\u003eMUTYH\u003c/em\u003e rs3219489 (G\u0026thinsp;\u0026gt;\u0026thinsp;C) are distributed randomly within the study population.\u003c/p\u003e \u003cp\u003eThe CC genotype was more prevalent in SDF patients (31%) than in control men (17%), although the difference did not reach significance (P value\u0026thinsp;=\u0026thinsp;0.064). Similarly, the GG genotype did not significantly differ between the two groups, although it was less common in the control men (38% and 34%, respectively). The GC genotype, however, significantly varied between the SDF patients and the controls (31% and 49%, respectively), with a P value of 0.025. To date, no reports have investigated the potential impacts of the \u003cem\u003eMUTYH\u003c/em\u003e Gln324His (G\u0026thinsp;\u0026gt;\u0026thinsp;C) SNP on seminal parameters or SDF. However, these results were similar to findings reported in colorectal cancer, cervical carcinoma, HR-HPV infection, endometrial cancer, lung cancer, urinary bladder cancer, end-stage renal disease (ESRD), and age-related macular degeneration (AMD) (Blasiak et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; H. Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hogervorst et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Osawa et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Win 2017). These results suggest that genetic SNPs can influence the efficiency of DNA repair processes, leading to the accumulation of DNA damage, potentially contributing to the development of various complex diseases and affecting semen quality and SDF.\u003c/p\u003e \u003cp\u003eIn addition, a significantly increased risk of SDF with \u003cem\u003eMUTYH\u003c/em\u003e, rs3219489 G\u0026thinsp;\u0026gt;\u0026thinsp;C, was observed under an overdominant genetic model (GG\u0026thinsp;+\u0026thinsp;CC versus GC) with a P value\u0026thinsp;=\u0026thinsp;0.02. Overdominant genetic inheritance implies that the heterozygous genotype confers a different phenotype than its homozygous counterpart does.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships among\u003c/b\u003e \u003cb\u003eAPEX1\u003c/b\u003e, \u003cb\u003ethe rs1130409 (G\u0026thinsp;\u0026gt;\u0026thinsp;T) SNP and SDF\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eAPEX1\u003c/em\u003e plays a role in eliminating apurinic/apyrimidinic sites that arise from DNA cleavage by \u003cem\u003eOGG1\u003c/em\u003e and \u003cem\u003eMUTYH\u003c/em\u003e while also facilitating the recruitment of additional BER players: DNA polymerase β and DNA ligase III (Bennett et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Numerous genetic variations have been detected in this gene, including a G\u0026thinsp;\u0026gt;\u0026thinsp;T alteration in exon 5, resulting in the substitution of aspartic acid with glutamic acid (Asp148Glu; identified as rs1130409) (Hung et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distribution of \u003cem\u003eAPEX1\u003c/em\u003e rs1130409 (G\u0026thinsp;\u0026gt;\u0026thinsp;T) genotypes in the control group was in Hardy‒Weinberg equilibrium, as there was no significant deviation (P value\u0026thinsp;=\u0026thinsp;0.757) between the observed and expected genotypes. These findings suggest that the alleles for \u003cem\u003eAPEX1\u003c/em\u003e rs1130409 (G\u0026thinsp;\u0026gt;\u0026thinsp;T) are randomly distributed in the study cohort.\u003c/p\u003e \u003cp\u003eThe distribution of genotypes was as follows: GG genotype in 15 cases (20%) and 12 controls (16%); GT genotype in 23 cases (31%) and 34 controls (46%); and TT genotype in 37 cases (49%) and 28 controls (38%), with no significant differences (all P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, when the GG genotype was used as a reference, the OR for the GT genotype was 0.520 (P value\u0026thinsp;=\u0026thinsp;0.056), indicating a trend toward a decreased risk of SDF. These findings suggest that the \u003cem\u003eAPEX1\u003c/em\u003e rs1130409 SNP may be associated with the risk of SDF, although the observed associations did not reach statistical significance. The trend toward a decreased risk of SDF in individuals with the GT genotype suggests that the heterozygous genotype may confer some protective effect against SDF.\u003c/p\u003e \u003cp\u003eTo our knowledge, there are no published studies on this polymorphism in relation to infertility or SDF, but many studies have explored the associations between the rs1130409 SNP in \u003cem\u003eAPEX1\u003c/em\u003e and various cancers and other diseases, such as Parkinson's disease, Paget's disease of bone, endometriosis, colorectal cancer, lung cancer, prostate cancer, hepatocellular carcinoma, and renal cell carcinoma (Ahmed \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Q. Cao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hsu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kasahara et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saad et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Usategui-Mart\u0026iacute;n et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between the tested polymorphisms and sperm parameters\u003c/h2\u003e \u003cp\u003eThe examined polymorphisms were not significantly related to conventional semen parameters, e.g., motility, sperm count, or sperm form.\u003c/p\u003e \u003cp\u003eImportantly, the lack of statistical significance could be due to the relatively small sample size employed in this study. A larger sample size would provide more statistical power to detect significant associations. Additionally, other factors, such as environmental influences, lifestyle, and other genetic variations, may contribute to the overall risk of SDF and should be considered in future studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCombined\u003c/b\u003e \u003cb\u003eXRCC1\u003c/b\u003e, \u003cb\u003eOGG1\u003c/b\u003e, \u003cb\u003eMUTYH\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eAPEX1\u003c/b\u003e \u003cb\u003egenotypes in the study population\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe highest risk genotypic combinations of the four SNPs, \u003cem\u003eMUTYH\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eXRCC1\u003c/em\u003e (T\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eAPEX1\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;T), and \u003cem\u003eOGG1\u003c/em\u003e (C\u0026thinsp;\u0026gt;\u0026thinsp;G), according to OR were [CC, CC, GT, CG] (OR\u0026thinsp;=\u0026thinsp;7.193), followed by [CC, TC, GT, GG] (OR\u0026thinsp;=\u0026thinsp;2.028), and finally [GG, CC, TT, CC] (OR\u0026thinsp;=\u0026thinsp;1.690). However, those risk combinations were not significantly different between the study groups.\u003c/p\u003e \u003cp\u003eThe Fruchterman\u0026ndash;Rheingold plot was used to evaluate the degree of interaction between the SNPs on the basis of entropy measurements (Rupasree et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As shown in Fig.\u0026nbsp;1, there is a synergistic interaction between \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" and \u003cem\u003eAPEX1\u003c/em\u003e \"rs1130409\", which could affect an individual's susceptibility to DNA fragmentation in sperm. The figure also indicates a redundancy effect between the other SNPs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis investigation involved Palestinian men residing in the Gaza Strip and experiencing increased SDF. The study focused on genetic variations of four SNPs: \u003cem\u003eXRCCI\u003c/em\u003e (p.Arg399Gln) \"rs25487\", \u003cem\u003eOGG1\u003c/em\u003e (p.Ser326Cys) \"rs1052133\", \u003cem\u003eMUTYH\u003c/em\u003e (p.Gln324His) \"rs3219489\", and \u003cem\u003eAPEX1\u003c/em\u003e (p.Asp148Glu) \"rs1130409\". The major outcomes of the study can be summarized as follows: Elevated SDF is significantly correlated with low sperm motility, a low level of the normal form, and a high liquefaction time. On the basis of the frequencies of genotypes and alleles, the \u003cem\u003eOGG1\u003c/em\u003e C\u0026thinsp;\u0026gt;\u0026thinsp;G and \u003cem\u003eAPEX1\u003c/em\u003e G\u0026thinsp;\u0026gt;\u0026thinsp;T polymorphisms are not linked to SDF in our study population. The \u003cem\u003eMUTYH\u003c/em\u003e G\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphism is associated with a lower risk for SDF, i.e., individuals carrying the \u003cem\u003eMUTYH\u003c/em\u003e (G/C) genotype may have a protective advantage against SDF. The \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" T\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphism is associated with a greater risk for SDF in the study population with the (T/C) genotype. On the other hand, the genotype (C/C) has been associated with a lower risk for SDF. The associations between \u003cem\u003eXRCC1\u003c/em\u003e, rs25487, and SDF are significant under codominant, dominant, overdominant, and log-additive models, whereas the \u003cem\u003eMUTYH\u003c/em\u003e rs3219489 polymorphism is significant under the overdominant model. There was no significant relationship between \u003cem\u003eXRCC1\u003c/em\u003e (T\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eOGG1\u003c/em\u003e (C\u0026thinsp;\u0026gt;\u0026thinsp;G), \u003cem\u003eMUTYH\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;C) or \u003cem\u003eAPEX1\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;T) and the semen parameters (sperm count, motility, and sperm form) in our study sample. The highest SDF risk combination of the genotypes \u003cem\u003eMUTYH\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eXRCC1\u003c/em\u003e (T\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eAPEX1\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;T), and \u003cem\u003eOGG1\u003c/em\u003e (C\u0026thinsp;\u0026gt;\u0026thinsp;G) is CC, CC, GT, and CG, respectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e This work was performed at the genetic diagnosis laboratory of the Islamic Universityof Gaza.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e Prof. Dr. Fadel A. Sharif and Dr. Mazen M. Alzahrna are the principal investigators and supervisors of this work. Collecting data and supervising the practical side were performed by Mr. Mohammed J. Ashour. Statistical analysis of results was carried out by Ms. Hadeer N. Abuwarda. The first draft of the manuscript was written by Dr. Fadel A. Sharif, Mr. Mahmoud A. Hassouna and Ms. Hadeer N. Abuwarda. Experimental part was done by Mr. Mahmoud A. Hassouna. All authors approved the final manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Funding was not received for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e: The study was ethically approved by the Ethical Research Committee at the Islamic University of Gaza and all subjects gave their consent to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwal A, Bui AD (2017) Oxidation-reduction potential as a new marker for oxidative stress: Correlation to male infertility. 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EBioMedicine 33:88\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ebiom.2018.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2018.06.018\" 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":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polymorphisms, Base excision repair pathway, Seminal parameters, XRCC1, OGG1, MUTYH, APEX1, Sperm DNA fragmentation, AS‒PCR","lastPublishedDoi":"10.21203/rs.3.rs-6672168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6672168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAbnormalities in sperm parameters can result from genetic variations in DNA repair genes. The base excision repair (BER) pathway maintains DNA integrity. Single nucleotide polymorphisms (SNPs) in BER genes may influence sperm DNA fragmentation (SDF) and other seminal fluid parameters. Therefore, we investigated the impact of SNPs in BER genes, specifically \u003cem\u003eXRCC1\u003c/em\u003e, \u003cem\u003eOGG1\u003c/em\u003e, \u003cem\u003eMUTYH\u003c/em\u003e, and \u003cem\u003eAPEX1\u003c/em\u003e, on SDF and seminal fluid parameters in a selected male population from the Gaza Strip. A case‒control study included 75 men with elevated SDF and 74 men with normal SDF. Semen samples were subjected to conventional semen analysis and the SDF test. DNA extracted from the samples was then genotyped via the allele-specific polymerase chain reaction (AS‒PCR) technique. Genotypes and allele frequencies were compared between the case and control groups via standard statistics. In terms of SDF, the \u003cem\u003eXRCC1\u003c/em\u003e \"rs25487\" polymorphism significantly differed between cases and controls, where the C allele and the CC genotype were more prevalent (P value\u0026thinsp;=\u0026thinsp;0.004) in the control group. Additionally, \u003cem\u003ethe MUTYH\u003c/em\u003e \"rs3219489\" polymorphism was significantly different, with the GC genotype being more frequent (P value\u0026thinsp;=\u0026thinsp;0.025) in the control group. \u003cem\u003eOGG1\u003c/em\u003e and \u003cem\u003eAPEX1\u003c/em\u003e polymorphisms, however, were not significantly different between the two groups. The examined polymorphisms were not significantly related to conventional semen parameters. This study highlights the effects of genetic variations in DNA repair genes, specifically \u003cem\u003eXRCC1\u003c/em\u003e and \u003cem\u003eMUTYH\u003c/em\u003e, on SDF. Further studies with a larger sample size are needed in order to confirm these findings and investigate the value of these SNPs on reproductive potential.\u003c/p\u003e","manuscriptTitle":"Impact of Polymorphisms in Base Excision Repair Genes on Seminal Fluid Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 12:07:56","doi":"10.21203/rs.3.rs-6672168/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12811186-8fcc-4742-b545-a754dc29d470","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T12:07:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 12:07:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6672168","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6672168","identity":"rs-6672168","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}