ABSTRACT
Objectives
Non‐syndromic orofacial cleft (NSOFC) is a complex congenital disease caused by genetic and environmental factors, and its aetiology remains unclear. This study aims to investigate the association between potentially functional single‐nucleotide polymorphisms (SNPs) in the RAD51 and E2F1 genes and the risk of developing NSOFC in the Han Chinese population.
Materials and Methods
A total of 200 NSOFC patients and 200 unrelated healthy controls of Han Chinese ancestry were recruited. Five candidate SNPs—rs1801320, rs45507396, rs7180135 and rs11855560 in the RAD51 gene, and rs3213180 in the E2F1 gene—were genotyped using the SNaPshot technique. Statistical and bioinformatics analyses were then performed to evaluate their associations with NSOFC.
Results
RAD51 variants were significantly associated with NSOFC. The G allele of rs45507396 was identified as a risk allele, showing significant associations under four genetic models, while rs1801320 was significantly associated with NSOFC under three genetic models. Bioinformatics analyses predicted that hsa‐miR‐299‐5p could bind to rs45507396, which is located within an ESE/ESS binding site and may induce exon skipping, potentially altering RAD51 coding. In contrast, rs1801320 was predicted to be a benign variant with no impact on splicing. Both SNPs are localised within ENCODE‐annotated cis‐regulatory elements (cCREs), with rs1801320 situated in a promoter‐like cCRE and rs45507396 in an enhancer‐like cCRE. Additionally, these variants may affect RNA‐binding protein interactions, with rs1801320 influencing HNRNPA2B1 binding and rs45507396 affecting SFPQ binding. No significant associations with NSOFC were observed for rs7180135, rs11855560 or rs3213180.
Conclusion
This study is the first to identify RAD51 variants rs45507396 and rs1801320 as susceptibility loci for NSOFC in the Han Chinese population, providing novel genetic insights and a theoretical basis for further investigation into the molecular mechanisms of NSOFC.
Keywords: bioinformatic analysis, E2F1, functional single nucleotide polymorphism, non‐syndromic orofacial cleft, RAD51
1. Introduction
Orofacial cleft (OFC) is the most common craniofacial birth defect in humans, with considerable variation in incidence across different countries, ethnic groups and socioeconomic strata. Overall, the highest prevalence is found among Asians and American Indians, reaching up to 2 per 1000 live births; intermediate prevalence occurs among Europeans, at approximately 1 per 1000 live births; while the lowest prevalence is reported among Africans, at about 0.4 per 1000 live births [1]. Beyond its impact on facial appearance, OFC causes a range of complications, including feeding difficulties, malnutrition, recurrent upper respiratory tract infections, otitis media, speech disorders, malocclusion and social integration challenges. Affected individuals often require multiple surgical procedures, speech therapy and psychosocial interventions from infancy through adulthood, which imposes a substantial economic and emotional burden on their families. Since prevention is preferable to treatment, a thorough understanding of the aetiology and pathogenesis of OFC is essential for developing effective preventive strategies.
OFC can be broadly divided into syndromic orofacial cleft (SOFC), which accounts for about 30% of cases and is accompanied by other congenital anomalies, and non‐syndromic orofacial cleft (NSOFC), which comprises about 70% of cases [2]. The aetiology of SOFC is relatively well understood, typically involving monogenic disorders and chromosomal abnormalities, and often presenting with clear familial inheritance patterns [3]. In contrast, NSOFC has a multifactorial aetiology, resulting from both genetic and environmental factors, as well as their interplay, with genetic predisposition playing the predominant role [4]. Research on NSOFC has primarily focused on identifying susceptibility genes and elucidating underlying pathogenic mechanisms. Although numerous single‐nucleotide polymorphisms (SNPs) have been associated with NSOFC through genome‐wide association studies and candidate gene studies [3, 5], most reside in intronic or intergenic regions. Consequently, translating these statistically significant associations into biological mechanisms remains a major challenge. Functional variants, which are more likely to influence gene expression or function, represent a more promising focus for elucidating disease mechanisms [6].
Craniofacial development is a complex and tightly regulated embryonic process, fundamentally dependent on the proper migration, differentiation and proliferation of neural crest cells, which are highly sensitive to genomic stability [7]. DNA damage repair (DDR) is a critical cellular mechanism that maintains genomic integrity by repairing damaged DNA [8]. Neural crest cell‐derived tissues are particularly vulnerable to the accumulation of DNA damage; defects in DDR pathways can impair neural crest cell function, ultimately leading to craniofacial developmental malformations [7]. Dysregulation of co‐expressed gene networks primarily associated with DNA double‐strand break (DSB) repair and cell cycle regulation has been observed in dental pulp stem cells from NSOFC patients, which also exhibit DSB accumulation upon exposure to hydrogen peroxide [9]. Furthermore, administration of poly(ADP‐ribose) polymerase inhibitors in mice induced DNA damage, impaired DDR function and resulted in cleft palate formation [10]. Consistently, BRCA1 and BRCA2, two essential DDR components, were shown to be indispensable for craniofacial development, as deficiency of Brca1 and/or Brca2 in mouse neural crest cells led to cleft palate [10]. The DDR protein Rad54B has also been implicated in cleft palate pathogenesis induced by all‐trans retinoic acid and 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin in mice [11]. Meanwhile, genetic association studies have further highlighted the role of DDR genes in NSOFC. For example, SNPs rs7922405 in MGMT, rs73039426 in RHPN2 and rs7552 in FAM49A are associated with NSOFC in the Chinese population [12]. Similarly, rs8075370 and rs9897121 in BRIP1 are found to confer NSOFC risk in the Polish population [13].
Therefore, these findings suggest that genes related to DDR are closely associated with the pathogenesis of NSOFC, among which RAD51 and E2F1 represent key candidates. Rad51 and E2F1 are co‐expressed in the orofacial primordia of developing embryos and may act as molecular hubs in lip and palate morphogenesis [9]. In the Brazilian population, an interaction between the rs1801321 polymorphism of the RAD51 and maternal cigarette smoking is identified as a risk factor for NSOFC [14]. Furthermore, experimental evidence suggests E2f1 may participate in palatogenesis in mice [15]. Meanwhile, a negative feedback loop between E2F1 and miR‐17‐92 has also been implicated in palatal development through modulation of palatal mesenchymal cell proliferation and cell cycle progression [16]. However, the specific association and underlying mechanisms involving RAD51, E2F1 and NSOFC remain unclear in the Han Chinese population. Therefore, in this case–control study, we selected 5 potentially functional SNPs in the RAD51 and E2F1 genes and conducted preliminary bioinformatics analyses on the positive results.
2. Materials and Methods
2.1. Study Subjects
A total of 200 patients diagnosed with NSOFC were recruited as the case group, along with 200 healthy individuals without congenital malformations or a family history of hereditary disease as the control group. All participants were unrelated individuals of Han Chinese ethnicity. Cases were excluded if they had comorbid congenital malformations, chromosomal abnormalities, a history of known genetic syndromes, non‐Han Chinese ancestry or familial relatedness. Controls were excluded if they had a personal history of congenital malformations or hereditary diseases, a family history of OFC, non‐Han Chinese ancestry or familial relatedness. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved with the approval number CMUSH‐IRB‐KJ‐PJ‐2020‐01. Additionally, written informed consent was obtained from all participants or their legal guardians.
For patients with NSOFC, 2 mL of venous blood was collected, anticoagulated with EDTA and stored at −80°C. For the control group, 2 mL of saliva was collected using a DN‐30 saliva collector (Aidlab, Beijing, China) and stored at room temperature.
2.2. SNP Selection
Candidate functional SNPs in the RAD51 and E2F1 genes were screened in the Han Chinese population through the HapMap (http://hapmap.ncbi.nlm.nih.gov/) and NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/) databases. The following criteria were applied: (1) minor allele frequency (MAF) ≥ 5% in the Han Chinese population; (2) location in functionally important regions, including frameshift and missense mutations, 5′ untranslated regions (5′UTR), and 3′UTR. Additionally, functional SNPs reported in the literature for these candidate genes were also considered. Based on these criteria, five functional SNPs were selected for further evaluation (Table 1).
TABLE 1.
Basic Information and HWE about the candidate SNPs.
| Genes | SNP ID | Variant | MAF (case) | MAF (control) | HWE (p‐value) | Function |
|---|---|---|---|---|---|---|
| RAD51 | rs1801320 | G>C | 0.11 | 0.14 | 0.14 | 5′UTR |
| rs45507396 | A>G | 0.19 | 0.1 | 1 | 3′UTR | |
| rs7180135 | A>G | 0.04 | 0.07 | 0.24 | 3′UTR | |
| rs11855560 | C>T | 0.21 | 0.18 | 1 | 3′UTR | |
| E2F1 | rs3213180 | G>C | 0.23 | 0.22 | 0.68 | 3′UTR |
Abbreviations: HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism; UTR, untranslated region.
2.3. DNA Extraction and SNP Genotyping
DNA was extracted from venous blood using the TIANamp Blood DNA Kit (Tiangen, Beijing, China) or from saliva using the DN‐30 Saliva DNA Extraction Kit. The quantity and quality of the extracted DNA were assessed using spectrophotometry and agarose gel electrophoresis. Primers were designed based on gene sequence information and synthesised using Primer Premier 5 software (Premier Biosoft, Palo Alto, CA, USA). A total of 5 SNPs were genotyped using SNaPshot technology (Thermo Fisher Scientific, Waltham, MA, USA). Capillary electrophoresis was performed on a 3730XL Genetic Analyser (Applied Biosystems, Waltham, MA, USA), and data were analysed using GeneMapper v3.2 software (Thermo Fisher Scientific, Waltham, MA, USA).
2.4. Data Analysis
Statistical analyses were performed using PLINK v1.07 (Broad Institute, Cambridge, MA, USA). The Hardy–Weinberg Equilibrium (HWE) test was used to evaluate the representativeness of the study population; a p value > 0.05 indicated that the population was representative. Allele frequencies between case and control groups were compared using the chi‐square (χ 2) test. The SNPStats online program was applied to analyse the associations between five genetic models (co‐dominant, dominant, recessive, overdominant and additive) and the risk of NSOFC using logistic regression models [17]. The Akaike Information Criterion (AIC) was used to determine the optimal genetic model, with the smallest AIC value indicating the best fit for each SNP. Odds ratios (ORs), 95% confidence intervals (95% CIs) and p values were calculated. Bonferroni correction was applied for multiple testing, and the corrected p value < 0.05 was considered statistically significant. Statistical power was estimated using Quanto v1.2.4 (http://biostats.usc.edu/Quanto.html), assuming a log‐additive inheritance mode, a population disease prevalence of 1.82‰, an expected OR of 1.8, a two‐sided type I error, and a study design comprising 200 unmatched case–control pairs.
To investigate the potential functional roles and regulatory mechanisms of the identified SNPs in RAD51 and E2F1, a comprehensive bioinformatics analysis was conducted. SNPinfo (https://snpinfo.niehs.nih.gov/) [18] was applied to predict miRNA binding sites and splicing regulatory elements. Pathogenicity and potential effects on gene splicing were assessed using Fathmm (http://fathmm.biocompute.org.uk) [19], MutationTaster2025 (https://www.genecascade.org/MutationTaster2025/) [20] and RDDC v2.0 (https://rddc.tsinghua‐gd.org/zh). At the transcriptional level, regulatory elements in the genomic regions harbouring the positive SNPs were annotated using the SCREEN tool from the ENCODE project (http://screen.encodeproject.org) [21] to determine the type and activity of corresponding candidate cis‐regulatory elements (cCREs). At the post‐transcriptional level, RBPsuite v2.0 (http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/) [22] and RBPmap v1.3 (http://rbpmap.technion.ac.il/) [23] were employed to analyse RNA‐binding protein (RBP) interactions in the SNP‐containing regions of RAD51 and E2F1 mRNAs. For SNPs predicted to affect protein structure, three‐dimensional models were constructed using the Swiss‐Model web server (https://swissmodel.expasy.org/) [24] and visualised with PyMOL v2.5 software (Schrödinger LLC, New York, NY, USA).
3. Results
A total of 200 NSOFC cases and 200 healthy controls were recruited to investigate the association between functional SNPs in the RAD51 and E2F1 genes and NSOFC in the Han Chinese population. The case group consisted of two subgroups: 141 cases of non‐syndromic cleft lip with cleft palate (NSCLP) and 59 cases of non‐syndromic cleft lip only (NSCLO). No statistical difference in gender distribution was observed between the NSOFC cases and the controls (p > 0.05) (Table 2). All SNPs in this study were successfully genotyped in both groups, and all SNPs in the control group were in HWE (Table 1).
TABLE 2.
Clinical characteristics of participants.
| Control N (%) | NSOFC N (%) | NSCLP N (%) | NSCLO N (%) | |
|---|---|---|---|---|
| Gender | ||||
| Male | 80 (40%) | 71 (35.5%) | 49 (34.8%) | 22 (37.3%) |
| Female | 120 (60%) | 129 (64.5%) | 92 (65.2%) | 37 (62.7%) |
| Total | 200 | 200 | 141 | 59 |
Note: p = 0.353 (Control vs. NSOFC, gender distribution; chi‐squared test).
Abbreviations: NSCLO, non‐syndromic cleft lip onlyNSCLP, non‐syndromic cleft lip with cleft palate; NSOFC, non‐syndromic orofacial cleft.
The χ 2 test showed that the frequency of the G allele at rs45507396 of the RAD51 gene was significantly higher in the case group than in the control group (OR = 2.17, 95% CI = 1.43–3.3, corrected p = 0.001). Since the OR value was greater than 1, the G allele was considered a risk allele (Table 3). Rs45507396 was associated with NSOFC under the codominant, dominant, overdominant and log‐additive genetic models (codominant model: OR [G/A vs. A/A] = 0.45, 95% CI [G/A vs. A/A] = 0.28–0.72, OR [G/G vs. A/A] = 0.16, 95% CI [G/G vs. A/A] = 0.02–1.37, corrected p = 0.004; dominant model: G/A‐G/G vs. A/A, OR = 0.43, 95% CI = 0.27–0.68, corrected p = 0.002; overdominant model: G/A vs. A/A‐G/G, OR = 0.47, 95% CI = 0.29–0.74, corrected p = 0.006 and log‐additive model: OR = 0.44, 95% CI = 0.29–0.68, corrected p = 0.001). Among these, the log‐additive model was determined to be the optimal genetic model for rs45507396 (AIC = 534.5), indicating a significant association with the onset of NSOFC (Table 4).
TABLE 3.
Comparison of allele frequencies of 5 SNPs between the case group and the control group.
| Genes | SNPs | Allele | Case (Freq) | Control (Freq) | OR (95% CI) | χ 2 | p a | p b |
|---|---|---|---|---|---|---|---|---|
| RAD51 | rs1801320 | G | 326 (88.6%) | 343 (86.2%) | 0.8 (0.52–1.23) | 1.001 | 0.317 | 1 |
| C | 42 (11.4%) | 55 (13.8%) | ||||||
| rs45507396 | A | 317 (81.3%) | 358 (90.4%) | 2.17 (1.43–3.3) | 13.48 | < 0.001 | 0.001 | |
| G | 73 (18.7%) | 38 (9.6%) | ||||||
| rs7180135 | A | 369 (95.6%) | 370 (0.93) | 0.61 (0.33–1.13) | 2.507 | 0.113 | 0.567 | |
| G | 17 (4.4%) | 28 (0.07) | ||||||
| rs11855560 | C | 303 (78.9%) | 325 (81.7%) | 1.19 (0.84–1.69) | 0.936 | 0.333 | 1 | |
| T | 81 (21.1%) | 73 (18.3%) | ||||||
| E2F1 | rs3213180 | G | 286 (77.3%) | 311 (78.1%) | 1.05 (0.75–1.48) | 0.079 | 0.779 | 1 |
| C | 84 (22.7%) | 87 (21.9%) |
Abbreviations: 95% CI, 95% confidence interval; Freq, allele frequency; OR, Odds ratio; SNP, single nucleotide polymorphism.
p value was calculated by the chi‐square test.
p value was calculated by the chi‐square test after Bonferroni correction.
TABLE 4.
Association between 5 SNPs and NSOFC under different genetic models.
| SNPs | Model | Genotype | Case (Freq) | Control (Freq) | OR (95% CI) | p a | p b | AIC |
|---|---|---|---|---|---|---|---|---|
| rs1801320 | Codominant | G/G | 150 (81.5%) | 145 (72.9%) | 1 | < 0.001 | 0.003 | 521.3 |
| G/C | 26 (14.1%) | 53 (26.6%) | 2.11 (1.25–3.55) | |||||
| C/C | 8 (4.3%) | 1 (0.5%) | 0.13 (0.02–1.05) | |||||
| Dominant | G/G | 150 (81.5%) | 145 (72.9%) | 1 | 0.043 | 0.215 | 530.3 | |
| G/C‐C/C | 34 (18.5%) | 54 (27.1%) | 1.64 (1.01–2.67) | |||||
| Recessive | G/G‐G/C | 176 (95.7%) | 198 (99.5%) | 1 | 0.009 | 0.043 | 527.5 | |
| C/C | 8 (4.3%) | 1 (0.5%) | 0.11 (0.01–0.90) | |||||
| Overdominant | G/G‐C/C | 158 (85.9%) | 146 (73.4%) | 1 | 0.002 | 0.012 | 525.1 | |
| G/C | 26 (14.1%) | 53 (26.6%) | 2.21 (1.31–3.71) | |||||
| Log additive | — | — | — | 1.23 (0.81–1.86) | 0.33 | 1 | 533.4 | |
| rs45507396 | Codominant | A/A | 127 (65.1%) | 161 (81.3%) | 1 | < 0.001 | 0.004 | 536.4 |
| G/A | 63 (32.3%) | 36 (18.2%) | 0.45 (0.28–0.72) | |||||
| G/G | 5 (2.6%) | 1 (0.5%) | 0.16 (0.02–1.37) | |||||
| Dominant | A/A | 127 (65.1%) | 161 (81.3%) | 1 | < 0.001 | 0.002 | 535.5 | |
| G/A‐G/G | 68 (34.9%) | 37 (18.7%) | 0.43 (0.27–0.68) | |||||
| Recessive | A/A‐G/A | 190 (97.4%) | 197 (99.5%) | 1 | 0.082 | 0.41 | 545.8 | |
| G/G | 5 (2.6%) | 1 (0.5%) | 0.19 (0.02–1.67) | |||||
| Overdominant | A/A‐G/G | 132 (67.7%) | 162 (81.8%) | 1 | 0.001 | 0.006 | 538.3 | |
| G/A | 63 (32.3%) | 36 (18.2%) | 0.47 (0.29–0.74) | |||||
| Log additive | — | — | — | 0.44 (0.29–0.68) | < 0.001 | 0.001 | 534.5 | |
| rs7180135 | Codominant | A/A | 177 (91.7%) | 173 (86.9%) | 1 | 0.3 | 1 | 546.9 |
| G/A | 15 (7.8%) | 24 (12.1%) | 1.64 (0.83–3.23) | |||||
| G/G | 1 (0.5%) | 2 (1%) | 2.05 (0.18–22.77) | |||||
| Dominant | A/A | 177 (91.7%) | 173 (86.9%) | 1 | 0.12 | 0.6 | 545 | |
| G/A‐G/G | 16 (8.3%) | 26 (13.1%) | 1.66 (0.86–3.21) | |||||
| Recessive | A/A‐G/A | 192 (99.5%) | 197 (99%) | 1 | 0.58 | 1 | 547 | |
| G/G | 1 (0.5%) | 2 (1%) | 1.95 (0.18–21.65) | |||||
| Overdominant | A/A‐G/G | 178 (92.2%) | 175 (87.9%) | 1 | 0.15 | 0.75 | 545.3 | |
| G/A | 15 (7.8%) | 24 (12.1%) | 1.63 (0.83–3.21) | |||||
| Log additive | — | — | — | 1.59 (0.87–2.90) | 0.13 | 0.65 | 545 | |
| rs11855560 | Codominant | C/C | 122 (63.5%) | 132 (66.3%) | 1 | 0.41 | 1 | 546.1 |
| C/T | 59 (30.7%) | 61 (30.6%) | 0.96 (0.62–1.48) | |||||
| T/T | 11 (5.7%) | 6 (3%) | 0.50 (0.18–1.40) | |||||
| Dominant | C/C | 122 (63.5%) | 132 (66.3%) | 1 | 0.56 | 1 | 545.6 | |
| C/T–T/T | 70 (36.5%) | 67 (33.7%) | 0.88 (0.58–1.34) | |||||
| Recessive | C/C‐C/T | 181 (94.3%) | 193 (97%) | 1 | 0.19 | 0.95 | 544.2 | |
| T/T | 11 (5.7%) | 6 (3%) | 0.51 (0.19–1.41) | |||||
| Overdominant | C/C‐T/T | 133 (69.3%) | 138 (69.3%) | 1 | 0.99 | 1 | 545.9 | |
| C/T | 59 (30.7%) | 61 (30.6%) | 1.00 (0.65–1.53) | |||||
| Log additive | — | — | — | 0.84 (0.60–1.20) | 0.34 | 1 | 545 | |
| rs3213180 | Codominant | G/G | 114 (61.6%) | 120 (60.3%) | 1 | 0.34 | 1 | 535.7 |
| G/C | 58 (31.4%) | 71 (35.7%) | 1.16 (0.76–1.79) | |||||
| C/C | 13 (7%) | 8 (4%) | 0.58 (0.23–1.46) | |||||
| Dominant | G/G | 114 (61.6%) | 120 (60.3%) | 1 | 0.79 | 1 | 535.8 | |
| G/C‐C/C | 71 (38.4%) | 79 (39.7%) | 1.06 (0.70–1.59) | |||||
| Recessive | G/G‐G/C | 172 (93%) | 191 (96%) | 1 | 0.19 | 0.95 | 534.1 | |
| C/C | 13 (7%) | 8 (4%) | 0.55 (0.22–1.37) | |||||
| Overdominant | G/G‐C/C | 127 (68.7%) | 128 (64.3%) | 1 | 0.37 | 1 | 535 | |
| G/C | 58 (31.4%) | 71 (35.7%) | 1.21 (0.79–1.86) | |||||
| Log additive | — | — | — | 0.95 (0.68–1.33) | 0.78 | 1 | 535.7 |
Abbreviations: 95% CI, 95% confidence interval; AIC, Akaike information criterion; Freq, Allele frequency; OR, Odds Ratio; SNP, single nucleotide polymorphism.
p value was calculated by the non‐conditional logistic regression.
p value was calculated by the non‐conditional logistic regression after Bonferroni correction.
Unconditional logistic regression analysis showed that rs1801320 of the RAD51 gene was significantly associated with the onset of NSOFC under the codominant, recessive and overdominant genetic models (codominant model: OR [G/C vs. G/G] = 2.11, 95% CI [G/C vs. G/G] = 1.25–3.55, OR [C/C vs. G/G] = 0.13, 95% CI [C/C vs. G/G] = 0.02–1.05, corrected p = 0.003; recessive model: C/C vs. G/G‐G/C, OR = 0.11, 95% CI = 0.01–0.90, corrected p = 0.043 and overdominant model: G/C vs. G/G‐C/C, OR = 2.21, 95% CI = 1.31–3.71, corrected p = 0.012). However, after Bonferroni correction, the association between rs1801320 and NSOFC under the dominant model was not statistically significant (dominant model: G/C‐C/C vs. G/G, OR = 1.64, 95% CI = 1.01–2.67, p = 0.043, corrected p = 0.215). The codominant model was identified as the optimal genetic model for rs1801320 (AIC = 521.3), under which carriers of the G/C genotype exhibited a 2.11‐fold increased risk compared with G/G carriers, whereas the C/C genotype conferred a protective effect relative to G/G carriers (Table 4).
No statistically significant associations with NSOFC were observed for the remaining two RAD51 variants, rs7180135 and rs11855560, or E2F1 rs3213180 (Tables 3 and 4). Statistical power analysis using Quanto software revealed that all 5 SNPs had > 80% power, with the positive loci rs45507396 and rs1801320 achieving 85% and 90% power, respectively.
In this study, two SNPs of RAD51, rs45507396 (located in the 3′UTR) and rs1801320 (located in the 5′UTR), were significantly associated with NSOFC. SNPinfo prediction of potential miRNA binding sites using the online tool SNPinfo indicated that hsa‐miR‐299‐5p may target rs45507396 (Table S1). Meanwhile, rs45507396 was predicted to reside within a binding region of either an exonic splicing enhancer (ESE) or an exonic splicing silencer (ESS) (Table S2). Functional prediction using Fathmm, MutationTaster2025 and RDDC software indicated that rs45507396 is generally benign; however, RDDC further predicted that this variant may affect splicing and potentially lead to exon skipping. Specifically, disruption of the coding sequence of the last exon (exon 10) might result in a 41‐amino‐acid deletion in the encoded protein (Figure 1A,B). In contrast, rs1801320 was predicted by all three tools to be benign and unlikely to affect splicing. At the transcriptional regulation level, regulatory elements in the genomic regions harbouring these two SNPs were further annotated using the SCREEN tool from the ENCODE project. rs1801320 was located within a high‐confidence active promoter‐like cCRE, whereas rs45507396 resided within a moderate‐activity enhancer‐like cCRE (Figure 1C,D). At the post‐transcriptional level, further predictive analysis indicated that both SNPs may serve as potential RBP binding sites. Genetic variations at these loci may disrupt specific RBP interactions, with rs1801320 located within the binding region of HNRNPA2B1 and rs45507396 within that of SFPQ (Figure 1E).
FIGURE 1.
Bioinformatic analyses of rs45507396 and rs1801320 in the RAD51 gene. (A) Predicted three‐dimensional structure of wild‐type RAD51 protein. (B) Predicted three‐dimensional structure of mutant RAD51 protein encoded by the rs45507396 variant. (C) ENCODE SCREEN‐based regulatory annotation of the rs1801320 locus. Tracks from top to bottom: Promoter‐like cCRE (red); aggregated DNase‐seq signal (green); aggregated H3K4me3 ChIP‐seq signal (red); aggregated H3K27ac ChIP‐seq signal (yellow); aggregated CTCF ChIP‐seq signal (blue). The vertical dashed line indicates the position of rs1801320. (D) ENCODE SCREEN‐based regulatory annotation of the rs45507396 locus. Tracks from top to bottom: Enhancer‐like cCRE (yellow); aggregated DNase‐seq signal (green); aggregated H3K4me3 ChIP‐seq signal (red); aggregated H3K27ac ChIP‐seq signal (yellow); aggregated CTCF ChIP‐seq signal (blue). The vertical dashed line indicates the position of rs45507396. (E) Prediction of RBP binding sites: Rs1801320 for HNRNPA2B1 and rs45507396 for SFPQ.
4. Discussion
Our study is the first to identify a significant association between the rs45507396 and rs1801320 loci in the RAD51 gene and NSOFC in the Han Chinese population. Specifically, the G allele at rs45507396 acts as the risk allele for NSOFC in this population. Notably, rs45507396 demonstrated a significant correlation with NSOFC occurrence across multiple genetic models, including the codominant, dominant, overdominant and log‐additive genetic models, with the log‐additive model identified as the best fit. In contrast, rs1801320 was correlated with NSOFC development under the codominant, recessive and overdominant genetic models, with the codominant model emerging as the optimal genetic model. Although rs1801320 also showed nominal significance under the dominant model, this association did not remain statistically significant after Bonferroni correction. Bioinformatics analyses further revealed that miR‐299‐5p may bind to rs45507396, which is located within an ESE/ESS binding region, potentially inducing exon skipping and subsequent abnormalities in the RAD51 coding sequence and protein structure. In contrast, rs1801320 was predicted to be a benign variant with no predicted impact on splicing. Moreover, annotation using the ENCODE SCREEN tool showed that rs1801320 is located within a high‐confidence active promoter‐like cCRE, whereas rs45507396 resides within a moderate‐activity enhancer‐like cCRE, suggesting potential roles in regulating RAD51 transcription. Additionally, both SNPs were predicted to overlap with RBP binding sites, with rs1801320 corresponding to HNRNPA2B1 and rs45507396 to SFPQ, suggesting that genetic variation may disrupt specific RBP–mRNA interactions and impair post‐transcriptional regulation of RAD51.
RAD51 (15q15.1) is highly evolutionarily conserved and shares homology with bacterial RecA and Saccharomyces cerevisiae Rad51. This eukaryotic ATP‐dependent core recombinase mediates homologous recombination repair and plays a critical role in maintaining genomic stability, DSB repair, cell cycle regulation and gametogenesis [25]. Previous research on RAD51 has mainly focused on fields such as tumours, hereditary syndromes [25] and reproductive system disorders, whereas studies related to NSOFC remain limited. Mutations in RAD51 have been linked to Fanconi anaemia, a hereditary disease associated with submucous cleft palate [26]. In mice, Rad51 is expressed in the mesenchyme of craniofacial primordia that form the lips and developing palatal shelves. More importantly, the transcriptional network involving BRCA1 and its co‐functional partner RAD51 is dysregulated in NSOFC cells, potentially contributing to the accumulation of DSBs in NSOFC cells [9]. In the Brazilian population, fetuses carrying the risk allele of RAD51 whose mothers were exposed to smoking during the first trimester of pregnancy had a 4.25‐fold increased risk of developing NSOFC compared with those carrying the same risk allele but whose mothers were not exposed to smoking [14]. In the Polish population, no associations were found between the rs2619681 or rs7177265 loci of the RAD51 gene and NSOFC [13]. In our study of the Han Chinese population, rs45507396 and rs1801320 in RAD51 were significantly associated with the onset of NSOFC. To our knowledge, no prior reports have described this association.
Although the genetic architecture of complex human diseases remains difficult to elucidate at present, association studies provide a promising strategy to uncover underlying mechanisms. Two main approaches are functional SNP‐based studies and linkage disequilibrium (LD)‐based studies. Among them, LD‐based association studies efficiently capture common pathogenic variants but are less suitable for rare alleles. In contrast, functional SNP‐based studies are more efficient because they target SNPs with the highest prior probability of being disease‐associated, yet they lack comprehensiveness [6, 27, 28]. This study investigated the association between functional SNPs in the RAD51 and E2F1 genes and NSOFC. We identified two positive variants: rs45507396 (located in the 3′UTR) and rs1801320 (located in the 5′UTR). As mRNA sequences flanking the start and end of coding sequences, UTRs harbour 3.7% of the detected genetic variants in genome‐wide association studies. Although these variants do not directly alter predicted protein sequences, they may play a key role in the regulation of gene expression. Disruption of UTR‐mediated regulatory mechanisms may perturb molecular pathways and cellular processes, thereby affecting phenotypes and disease onset, and may even influence clinical outcomes [29, 30, 31].
Rs45507396 is located in the 3′UTR of the RAD51 gene. The 3′UTR, a non‐coding region at the terminal end of mRNA, links the open reading frame that encodes proteins to the polyadenylate (Poly (A)) tail. Serving as a regulatory hub, it regulates mRNA stability, translation efficiency, subcellular localisation and protein function [32]. Most miRNA binding sites are localised within the 3′UTR. When a miRNA pairs with a complementary target sequence in this region, it facilitates the formation of the RNA‐induced silencing complex, which ultimately downregulates target gene expression by suppressing translation or promoting mRNA degradation [33]. In this study, a potential binding interaction between miR‐299‐5p and the 3′UTR of RAD51 was predicted. Accordingly, rs45507396 in the 3′UTR region of the RAD51 gene may alter the binding efficiency between miR‐299‐5p and RAD51 mRNA, thereby modulating post‐transcriptional regulation and contributing to NSOFC pathogenesis. Alternative splicing is another mechanism that could be influenced by this variant. In eukaryotic gene expression, a single precursor mRNA (pre‐mRNA) can generate multiple mature transcripts with distinct structures and functions through different splicing patterns, such as exon skipping, mutually exclusive exons, intron retention and alternative splicing site selection at the 5′ or 3′ ends [34]. This process is co‐regulated by the spliceosome, cCREs, trans‐acting factors and cellular signals, and its dysregulation often produces aberrant splicing products that underline diseases [35]. Although rs45507396 was predicted to be benign in terms of pathogenicity, it was mapped to a putative ESE/ESS binding region and localised within a moderate‐activity enhancer‐like cCRE, suggesting potential dual effects on both splicing regulation and transcriptional control of RAD51. This variant may disrupt mRNA splicing, alter splicing patterns and generate abnormal RAD51 isoforms, while also interfering with normal transcriptional activity by affecting the function of its enhancer‐like cis‐regulatory element. Additionally, this locus acts as a potential binding site for the RNA‐binding protein SFPQ, and genetic variation may disrupt its specific interaction with RAD51 mRNA, thereby impairing the post‐transcriptional regulatory network governing RAD51 expression. Collectively, these regulatory disturbances may contribute to the pathogenesis of NSOFC.
Rs1801320 is located in the 5′UTR of the RAD51 gene. In eukaryotic mRNAs, the 5′UTR spans from the transcription initiation site to the nucleotide immediately upstream of the coding sequence and does not encode amino acids [36]. This region contains multiple functional elements, such as upstream open reading frames, internal ribosomal entry sites, miRNA binding sites, and structural motifs that regulate mRNA stability, pre‐mRNA splicing and translation initiation. The 5′UTR plays a critical role in controlling translation efficiency, influencing mRNA stability, localisation and overall protein synthesis. Variations in its length or sequence can markedly affect protein yield [37]. In this study, rs1801320, located in the 5′UTR region, was found to be significantly associated with the risk of NSOFC. However, predictive analyses did not indicate pathogenicity or splicing effects. Bioinformatic predictions further revealed that this locus is located within a high‐confidence active promoter‐like cCRE and may serve as a potential binding site for HNRNPA2B1. These findings suggest that rs1801320 may regulate RAD51 expression at both transcriptional and post‐transcriptional levels by modulating promoter‐like cCRE activity and disrupting HNRNPA2B1‐RAD51 mRNA interactions. Such alterations may impair post‐transcriptional processes, including translation initiation and mRNA stability. Collectively, these potential regulatory perturbations may contribute to the association between rs1801320 and susceptibility to NSOFC, although experimental validation is required to confirm these predicted effects.
The E2F1 gene is localised at chromosome 22q12.1 and encodes a transcription factor belonging to the E2F family. As a key transcriptional regulator, E2F1 plays important roles in multiple biological processes, including cell cycle regulation, DNA replication and repair, cell apoptosis, checkpoint control as well as cell development and differentiation [38]. Dysregulation in E2F1 function has been linked to various diseases such as cancer, metabolic disorders and developmental defects [39]. Animal experiments have revealed the expression of E2F family transcription factors in the palatal tissues of embryonic mice, and disruptions in palatal proliferation contribute to cleft palate formation [40]. E2f1 is also expressed in the mesenchyme of the facial primordia that form the lips and developing palatal shelves [9, 16]. More importantly, in the Polish population, two polymorphic loci of the E2F1 gene (rs3213180 and rs2071054) were reported to be associated with NSOFC risk, although the associations did not withstand correction for multiple testing. Additionally, the haplotype comprising these two variants exerts a protective effect against NSOFC [13]. In this study, we also investigated the association between rs3213180 and NSOFC; however, no significant relationship was observed. This discrepancy may be attributable to differences in the ethnic backgrounds of the study populations.
This study has several limitations. First, although the overall statistical power was adequate, the relatively small sample size limited the power of subtype‐stratified analyses, precluding subtype‐specific association testing. Future studies with larger subtype‐specific sample sizes are needed to enable well‐powered genetic analyses and to further elucidate the genetic heterogeneity and distinct pathogenic mechanisms among NSOFC subtypes. Second, functional interpretations of the associated loci were based solely on bioinformatic predictions without experimental validation. Future studies should include systematic in vitro and in vivo functional validations. For example, reporter constructs carrying different alleles of rs45507396 and rs1801320 could be transfected into palatal mesenchymal or cranial neural crest cells to assess their effects on gene expression, mRNA splicing and transcriptional activity. In addition, in vitro assays are needed to validate the predicted interaction between rs45507396 and miR‐299‐5p and its regulatory effect on RAD51, while animal models could be used to evaluate their impact on craniofacial development. These approaches would help clarify the precise molecular mechanisms underlying the involvement of these variants in NSOFC pathogenesis and further strengthen the biological significance of the study.
5. Conclusions
This study is the first to reveal that variants rs45507396 and rs1801320 in the RAD51 gene are significantly associated with NSOFC susceptibility in the Han Chinese population, providing new candidate loci for genetic studies of NSOFC. Our findings suggest that these variants may regulate RAD51 expression through both transcriptional and post‐transcriptional mechanisms, as indicated by preliminary bioinformatic analyses. Notably, no significant associations were observed for RAD51 rs7180135, rs11855560 or E2F1 rs3213180. By affecting RAD51‐mediated DDR and craniofacial developmental processes, these functional variants could contribute to the pathogenesis of NSOFC. Overall, this study provides preliminary evidence supporting the involvement of RAD51 variants in NSOFC susceptibility. Future studies incorporating larger subtype‐specific sample sizes and systematic in vitro and in vivo functional analyses are needed to further elucidate the underlying molecular mechanisms.
Author Contributions
Siyuan Guo: conceptualisation, funding acquisition, methodology, resources, software, validation, visualisation, writing – original draft and writing – review and editing. Tingting Guo: data curation, investigation, methodology, resources and validation. Yi Xu: conceptualisation, investigation, methodology and software. Renji Chen: conceptualisation, data curation and resources. Xuejiu Wang: conceptualisation, data curation, project administration, resources, supervision and writing – review and editing.
Funding
This work was supported by the Discipline Construction Fund of Beijing Stomatological Hospital Affiliated to Capital Medical University, 19‐09‐09.
Ethics Statement
This study was approved by the Ethics Committee of Beijing Stomatological Hospital, Capital Medical University (Approval No.: CMUSH‐IRB‐KJ‐PJ‐2020‐01).
Consent
Written informed consent was obtained from all participants or their legal guardians.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: shows the potential matching miRNA for rs45507396.
Table S2: presents the prediction of ESE or ESS sites for rs45507396.
Acknowledgements
We sincerely acknowledge the study participants for their dedication and support.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: shows the potential matching miRNA for rs45507396.
Table S2: presents the prediction of ESE or ESS sites for rs45507396.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.