BRCA1 rs799917 and rs1799966 Variants and Breast Cancer Risk in Nigerian Women: A Case–Control Study With Population-Based Allele Frequency Analysis

Abstract

Introduction

BRCA1 is a tumor suppressor gene involved in DNA repair, genomic stability, and cell cycle regulation. The BRCA1 single nucleotide polymorphisms (SNPs) rs799917 and rs1799966 have been widely investigated for their association with breast cancer (BCa) risk, with inconsistent and population-specific findings. However, their frequency and clinical relevance in Nigerian populations remain poorly characterized. This study evaluated their association with BCa risk in Nigerian women and interpreted these findings within a global context of allele frequency variation.

Methods

This prospective case-control study recruited 379 BCa cases and 196 age-matched controls. The samples were genotyped for BRCA1 rs799917 (A/G) and rs1799966 (C/T) using TaqMan real-time polymerase chain reaction (PCR). Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Allele frequencies from population-specific datasets in the database of single nucleotide polymorphism (dbSNP) were extracted and combined in a meta-analysis to compare variant distribution across global populations.

Results

No significant association was observed between BRCA1 variants (rs799917 and rs1799966) and BCa risk in this cohort. Genotype distributions were comparable between cases and controls, and none of the tested genetic models reached statistical significance. Meta-analysis revealed marked inter-population variation in allele frequencies. Africans showed the lowest frequency of the rs799917 G allele (13.8%), and the highest frequency of the A allele (86.36%), whereas Europeans showed the highest G allele frequency (65.92%). For rs1799966, Africans had the lowest C allele frequency (20.17%) and highest T allele frequency (79.8%), while Asians showed the highest C frequency (38.25%).

Conclusion

BRCA1 variants rs79917 and rs1799966 were not associated with BCa susceptibility in this Nigerian cohort. The observed inter-population differences in allele frequencies highlight the importance of genetic diversity in understanding BCa risk.

Keywords

breast cancer BRCA1 rs799917 rs1799966 Nigeria

Introduction

Breast cancer (BCa) is the most diagnosed malignancy among women worldwide,¹ with an estimated 2.3 million new cases and 666,103 deaths recorded in 2022.² In Nigeria, BCa remains the leading cause of cancer-related mortality among women, accounting for 32,278 (25.3%) new cases and 16,322 (20.5%) deaths in 2022.³ The high mortality rate among Nigerian women is often attributed to limited access to early diagnostic resources, late-stage disease presentation, and inadequate healthcare resources.^4,5 Furthermore, Nigerians tend to develop BCa at a younger median age compared to the Western population, with BCa accounting for 45.5% of cancers in women younger than 45 years.⁶ Although age is a major influencer of the etiology of BCa,⁷ the role of genetic susceptibility is becoming important in elucidating the mechanism driving the early onset and phenotype of BCa reported in this population.⁸ Perhaps, the most prominent of such genetic factors is germline variation in the breast cancer 1 gene (BRCA1), which has been reported to account for about 85% of inherited susceptibility to BCa, particularly the triple negative phenotype.⁹

Discovered as the first cancer-risk gene over 30 years ago,¹⁰ BRCA1 is a tumor suppressor gene, located on chromosome 17q21, and play a crucial role in DNA damage repair, genomic stability, and cell cycle regulation.¹¹ It consists of multiple functional domains that mediate interactions with various proteins for maintaining genomic integrity.¹¹

Among the identified BRCA1 polymorphisms, rs799917 and rs1799966 have been extensively studied for their potential association with BCa risk,^12,13 and we recently showed that they are the most predominant BRCA1 SNPs among Nigerian women.¹⁴ The rs799917 is an exonic variant characterized by three alleles (G>A, G>C, G>T).¹⁵ The G>A variant results in a proline to leucine substitution at position 871 of the BRCA1 protein, and has been shown to alter the interaction between miR-638 and BRCA1 mRNA, suggesting a reduced BRCA1 expression in individuals carrying the mutant allele.¹² The rs1799966 variant is located within the COOH-terminal domain of BRCA,¹² a critical region involved in protein-targeting and signaling within the DNA damage repair system.¹⁶ The rs1799966 also exhibits three alleles (T>A, T>C, T>G),¹⁵ with the T>C variant leading to a serine to glycine substitution at position 1613 of the BRCA1 protein.¹⁵ While these variants have been classified as benign/likely benign,¹⁷ conflicting reports exist regarding their frequency and association with BCa risk across different populations. For instance, Nicoloso, et al.¹⁸ reported an association between BCa and these SNPs, while Yang, et al.¹³ reported no significant association between these variants and BCa risk. It is, therefore, questionable whether the risk conferred by these SNPs and their influence on tumor biology is population specific.

Hence, despite the strong hypothesis supporting the involvement of these SNPs in BCa susceptibility, investigation on their association with BCa risk and the disease phenotype in indigenous Sub-Saharan African populations like Nigeria is limited. This study, therefore, aimed to determine the frequency of these polymorphisms in Nigerians, and to assess their potential association with BCa risk and phenotype. In addition, we also seek to examine the variation in the distribution of these SNPs across different populations globally. Identifying BRCA1 polymorphisms linked to BCa susceptibility could provide valuable insights into population-specific genetic predisposition.

Methods

Study Design and Population

This prospective case-control study recruited 379 histopathology confirmed BCa cases and 196 age-matched healthy controls from five tertiary hospitals across Nigeria: Federal Medical Centre Abuja (FMC), National Hospital Abuja (NHA), Lagos University Teaching Hospital (LUTH), Lagos State University Teaching Hospital (LASUTH), and University of Nigeria Teaching Hospital Enugu (UNTH). This study was designed as a targeted follow-up to prior sequencing of the BRCA1/2 genes in this population, which identified rs799917 and rs1799966 as relatively frequent variants.¹⁴

Participants were recruited between May 2021 to April 2022 and January to May 2024. The eligible cases were women aged 18-86 years with histologically confirmed BCa, while the healthy controls were age-matched women who self-reported no known history of cancer and had no clinical diagnosis of BCa at recruitment. Participants who were too frail or weak, diagnosed with other malignancies that could confound study outcomes, or were unable to provide informed consent were excluded from the study.

Demographic and clinical data were collected using structured questionnaires and supplemented with clinical records where available.

This study was reported in accordance with STREGA guidelines.¹⁹

Ethical Approval and Informed Consent Statements

Ethical approval for this study was obtained prior to commencement of the study from the National Health Research Ethics Committee (approval number: NHREC/01/01/2007-19/01/2024), and the Covenant Health Research Ethics Committee, Covenant University, Ota, Ogun State, Nigeria (approval number: CU/HREC/SOR/072/21). Additionally, written informed consent was obtained from all participants after the study details were fully explained and understood. To ensure confidentiality and protect the privacy of recruited participants, questionnaires and data were de-identified throughout the research process. This study was conducted in accordance with the Declaration of Helsinki (1975, as revised in 2024).²⁰

Blood Sample Collection

A total of 5 mL of venous blood was obtained from both BCa cases and healthy controls into EDTA Vacutainer tubes. The blood was centrifuged within 2 hours of collection at 4000 rpm for 15 minutes to obtain the buffy coat fraction for DNA extraction.

DNA Extraction

Genomic DNA (gDNA) was subsequently extracted from the buffy coat using the DNA extraction kit from Aidlab Biotechnologies (Haidian District, Beijing, China) according to the manufacturer’s protocol. Quantification of extracted gDNA was performed using the Denovix® broad-range assay kit on the DeNovix DS-11 FX Fluorometer (Wilmington, USA), and the samples were diluted to 5 ng/µL for the genotyping assay.

Determination of rs799917 and rs1799966 Polymorphisms by TaqMan Real-Time PCR

Genotyping of the rs799917 and rs1799966 SNPs was performed using TaqMan (Applied Biosystems, Thermo Fisher Scientific, Foster City, CA, USA) technology according to the manufacturer’s instructions. The TaqMan universal master mix and predesigned SNP genotyping assay mix, containing PCR primers and probes, rs799917(C___2287943_10)²¹ and rs1799966 (C___2615208_20)²² were purchased from Applied Biosystems (Thermo Fisher Scientific, Inc). All assays were performed in 96-well plates, and each plate included negative controls (no template controls). The plate was covered with a MicroAmp® Optical Adhesive Film and the real-time PCR amplification was carried out on the QuantStudio 5 using the following conditions: 60°C for 30 seconds, 95°C for 10 minutes, and 40 cycles of amplification (92°C denaturation for 15 seconds, 60°C annealing/extension for 1 minute). After real-time PCR amplification, the endpoint and pre-PCR fluorescence data were imported into the TaqMan Genotyper Software (Applied Biosystems/Thermo Fisher Scientific) for SNP genotyping analysis. Genotypes were assigned automatically using the software’s autocalling function, and allelic discrimination scatter plots were generated for each assay. Automatic calls were reviewed, and samples with undetermined genotypes were excluded from the analysis. Genotyping quality was further assessed using call rates, missingness, and Hardy–Weinberg equilibrium (HWE) in controls; detailed quality metrics are provided in Table S2.

Statistical Analysis

Descriptive statistics were computed for demographic, clinical, and genetic variables, and HWE among controls was assessed using the exact test, given the presence of small genotype counts. To evaluate associations between the BRCA1 SNPs (rs799917 and rs1799966) and BCa risk, binary logistic regression models were applied, with disease status (1 = BCa case, 0 = control) as the dependent variable and genotype category (e.g., AA, AG, GG) as the independent variable. Genetic association analyses were performed under dominant, recessive, additive, and codominant models. The use of these models is standard practice in genetic association studies, as it allows evaluation of different possible inheritance patterns of the variant allele.²³ The odds ratios (ORs) and 95% confidence intervals (CIs) were estimated after adjusting for age as a covariate. Model coefficients were exponentiated to obtain ORs with 95% CIs, and results were exported as summary tables.

The binary logistic regression model formula used was:

L o g i t (P) = \ln \ln (\frac{P}{1 - P}) = β_{0} + β_{1} X_{1} + β_{2} X_{2} \dots β_{n} X_{n}

For rs799917, the A allele was used as the reference allele in statistical analyses due to its predominance in the study population, and the effect of the G allele was estimated relative to this reference. For rs1799966, the T allele was used as the reference allele. Exploratory analyses were performed to examine genotype–phenotype relationships. Associations between BRCA1 genotypes and immunohistochemistry (IHC) subtypes were evaluated using 2×2 contingency tables and Fisher’s exact test, comparing each non-reference genotype with the reference genotype. Odds ratios and 95% CIs were calculated using the Table 2x2 function from the statsmodels package in Python.

Additionally, age-stratified analyses were conducted to determine whether genotype–disease associations differed between younger (≤50 years) and older (>50 years) women. Within each age stratum, Fisher’s exact test was used to compare genotype distributions between cases and controls, and ORs with 95% CIs were calculated.

Post hoc statistical power analysis was performed using the observed allele frequencies and effect sizes for each SNP to evaluate the study’s ability to detect genetic associations. Effect sizes were estimated using Cohen’s w, derived from genotype distributions between cases and controls. Power calculations were conducted within a Pearson’s chi-square test framework for case–control genetic association studies.

To account for multiple comparisons arising from the evaluation of multiple genetic models and subgroup analyses, p-values were adjusted using the false discovery rate (FDR) method. Statistical significance was determined based on FDR-adjusted p-values, with a threshold of FDR < 0.05. All tests were two-tailed and performed using R (v4.4.3) and Python (v3.10).

Meta-Analysis

Population-based allele frequency data for the BRCA1 SNPs rs799917 (A/G) and rs1799966 (C/T) were obtained from the dbSNP database.¹⁵ Datasets reporting allele frequencies were screened for relevance and completeness; those with incomplete data or aggregated global frequencies were excluded to avoid duplication and population overlap (see Figure S1).

Meta-analysis of allele frequencies across global populations was performed in R (version 4.4.3) using the meta and metafor packages. Pooled allele frequencies were estimated using a random-effects model with inverse-variance weighting and the Freeman–Tukey double arcsine transformation to stabilize variance in proportions. Exact binomial 95% confidence intervals were calculated using the Clopper–Pearson method. Between-study heterogeneity was evaluated using Cochran’s Q statistic, I², and H statistics as described previously by Onyia, et al.⁸

To account for population genetic structure, subgroup meta-analyses were conducted by continental ancestry group. Sensitivity analyses were performed by excluding datasets with extremely large sample sizes to evaluate the influence of large genomic reference panels on pooled estimates. In addition, leave-one-out influence analysis was conducted to determine whether any individual dataset disproportionately affected the pooled allele frequency estimates.

Results

Clinical and Pathological Characteristics of BCa and Control Subjects

The study recruited a total of 379 BCa cases and 196 controls. The mean age of the BCa cases was 49.8 ± 10.8 years, and 47.69 ± 8.8 years in controls. A large proportion (79.68%) of BCa cases reported no family history of BCa, while 16.89% reported a family history of BCa, and 3.43% were unknown. Tumors were located in the left breast (LB) in 50.13% of the cases, the right breast (RB) in 41.43%, bilateral (BB) in 4.22%, while 4.22% of the tumor location was not reported. Histopathological analysis showed that invasive ductal carcinoma (IDC) was predominant (91.29%), followed by invasive carcinoma of no special type (NST) (5.54%) and other less frequent subtypes, including invasive lobular carcinoma (1.32%), mucinous carcinoma (0.53%), metaplastic carcinoma (0.79%), and papillary carcinoma (0.53%).

Based on IHC profiling, triple-negative breast cancer (TNBC) was the most common subtype (93 cases, 24.54%), followed by luminal A (74 cases, 19.54%), human epidermal growth factor receptor 2 (HER2) enriched (23 cases, 6.07%), and luminal B (15 cases, 3.96%). IHC data were unavailable for 174 cases (45.91%). Detailed clinical and pathological characteristics of the study participants are presented in Table 1.

Table 1.

Clinical and Pathological Characteristics of BCa and Control Subjects

Variable	Cases (n=379), n(%)	Control (n=196), n (%)
Age (years)
0-50	205 (54.09%)	129 (65.82%)
>50	174 (45.91%)	67 (34.18%)
BMI	26.69 ± 4.63	27.2 ± 4.18
Clincal characteristics (Cases only)
Variable	n (%)
Tumor Location	Number
LB	190 (50.13%)
RB	157 (41.43%)
BB	16 (4.22%)
Unknown	16 (4.22%)
Histopathology
IDC	346 (91.29%)
ILC	5 (1.32%)
Mucinous	2 (0.53%)
Metaplastic	3 (0.79%)
Papillary	2 (0.53%)
IBC-NST	21 (5.54%)
IHC
Luminal A	74 (19.54%)
Luminal B	15 (3.96%)
HER2-enriched	23 (6.07%)
TNBC	93 (24.54%)
Unknown	174 (45.91%)
Risk
Smoking
Never smoker	238 (62.80%)
Former smoker	3 (0.79%)
Current smoker	1 (0.26%)
Choose not to say	137 (36.15%)
Alcohol Intake
Never	294 (77.57%)
Current	15 (3.96%)
Stopped drinking	48 (12.66%)
Choose not to say	22 (5.81%)
Menopausal Status
Yes	171 (45.12%)
No	131 (34.56%)
Choose not to say	77 (20.32%)
Family history
Yes	64 (16.89%)
No	302 (79.68%)
Unknown	13 (3.43%)
Oral contraceptive use
Yes	85 (22.43%)
No	269 (70.97%)
Choose not to say	25 (6.60%)

Values are presented as number (percentage). Continuous variables are presented as mean ± standard deviation.

Frequency of rs799917 and rs179966 SNPs in BCa and Controls

The genotype and allele distributions of the BRCA1 polymorphisms rs799917 (Pro871Leu, G>A) and rs1799966 (Ser1613Gly, T>C) were evaluated in BCa cases and controls.

For rs799917, genotype data from 359 cases and 189 controls were included in the analysis after quality control filtering. The genotypes comprised the homozygous wild-type (GG), heterozygous (AG), and homozygous mutant (AA). The frequencies of the AA, AG, and GG genotypes were 82.5% vs. 88.9%, 16.4% vs. 10.1%, and 1.1% vs. 1.0% in BCa cases and controls respectively. Genotype distributions in controls were consistent with HWE based on the exact test (p = 0.53) (Table 2).

Table 2.

Logistic Regression Analysis of the Association Between rs799917 and rs1799966 Polymorphisms and BCa Risk

Genotypes	Cases n (%)	Controls n (%)	OR (95% CI, age-adjusted)	p-value	FDR	HWE (controls, p-value)
rs799917	(n=359)	(n=189)				0.53
AA	296 (82.5)	168 (88.9)	1.0 (reference)	(reference)	(reference)
AG	59 (16.4)	19 (10.1)	1.76 (1.02-3.06)	0.04	0.21
GG	4 (1.1)	2 (1.0)	1.14 (0.21-6.26)	0.92	0.96
A allele	651 (90.66)	355 (93.91)	1.0 (reference)	(reference)	(reference)
G allele	67 (9.33)	23 (6.08)	1.58 (0.97-2.58)	0.07	0.21
rs1799966	(n=318)	(n=167)				1.00
TT	203 (63.8)	103 (61.70)	1.0 (reference)	(reference)	(reference)
CC	19 (5.90)	7 (4.20)	1.38 (0.56-3.39)	0.48	0.73
CT	96 (30.20)	57 (34.10)	0.86 (0.57-1.29)	0.47	0.73
T allele	503 (78.93)	263 (78.74)	1.0 (reference)	(reference)	(reference)
C allele	135 (21.06)	71 (21.25)	0.99 (0.72-1.37)	0.96	0.96

n = number of subjects; OR = odds ratio; CI = confidence interval; HWE = Hardy–Weinberg equilibrium. Values are presented as number (percentage). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using age-adjusted logistic regression.

Logistic regression analysis showed a nominal association between the rs799917 AG genotype and BCa risk (OR = 1.76, 95% CI: 1.02–3.06; p = 0.04); however, this association did not remain significant after FDR correction (OR = 1.76, 95% CI: 1.02–3.06; FDR = 0.21). The GG genotype was not associated with BCa risk (OR=1.14, 95% CI: 0.21–6.26, FDR = 0.96). Although the A allele had a higher frequency than the G allele in both cases and controls, no significant association with BCa risk was observed (OR=1.58, 95% CI:0.97-2.58; FDR=0.21) (Table 2).

For rs1799966, genotype data from 318 cases and 167 controls were included in the analysis after quality control filtering. The frequencies of the TT, CC, and CT genotypes were 63.8% vs. 61.7%, 5.9% vs. 4.2%, and 30% vs. 34.1%, respectively, among BCa cases and controls. Genotype distributions in controls were consistent with HWE based on the exact test (p = 1.00) (Table 2).

Similarly, no statistically significant association was observed between rs1799966 genotypes and BCa risk after FDR correction. Compared with the TT genotype, the CC genotype had an OR of 1.38 (95% CI: 0.56–3.39, FDR = 0.73), while the CT genotype had an OR of 0.86 (95% CI: 0.57–1.29, FDR = 0.73). The T allele frequency was more frequent than the C allele in both groups, however, no significant association with BCa risk was observed (OR = 0.99, 95% CI: 0.72–1.37; FDR = 0.96) (Table 2).

Overall, although a nominal association was observed for rs799917, this did not remain significant after FDR correction. No statistically significant associations were identified between rs799917 or rs1799966 polymorphisms and BCa risk after multiple testing correction (Table 2).

Genetic Model Analysis of BRCA1 rs799917 and rs1799966 Variants and BCa Risk

The association between BRCA1 rs799917 and rs179996 variants and BCa risk was further evaluated under dominant, recessive, additive and co-dominant genetic models using logistic regression. For rs799917, although borderline associations were observed with the co-dominant model (AG vs AA: OR = 1.7, 95% CI: 0.86–3.34; p = 0.05), none of the models showed a statistically significant association with BCa risk after FDR correction. Similarly, no statistically significant associations were identified for rs1799966 under any of the tested genetic models (Table 3).

Table 3.

Genetic Model Analyses on the Association Between rs799917 and rs1799966

Genetic models	Genotypes	Or (95% CI)	p-value	FDR
rs799917
Dominant	AG +GG vs AA	1.6 (0.97-2.8)	0.06	0.29
Recessive	GG vs AG + AA	0.9 (0.17-5.48)	0.99	0.99
Additive	Per allele (G)	1.5 (0.9-2.4)	0.09	0.31
Co-Dominant	AG vs AA	1.7 (0.86-3.34)	0.05	0.29
Co-Dominant	GG vs AA	1.1 (0.19-5.9)	0.94	0.99
rs1799966
Dominant	CC+CT vs TT	0.8 (0.6-1.31)	0.55	0.85
Recessive	CC vs CT + TT	1.4 (0.58-3.5)	0.43	0.85
Additive	Per allele (C)	0.9 (0.7-1.33)	0.85	0.99
Co-Dominant	CT vs TT	0.8 (0.55-1.25)	0.38	0.86
Co-Dominant	CC vs TT	1.3 (0.5-3.29)	0.52	0.86

ORs and 95% CIs were estimated using age-adjusted logistic regression.

Association of BRCA1 Variants With BCa Risk Stratified by Age

Participants were stratified into two age groups (≤50 years and >50 years) to assess the association between the BRCA1 rs799917 and rs1799966 variants and BCa risk across age groups.

For rs799917, no significant association between genotype and BCa risk was observed in either age group. In individuals aged ≤50 years, the AG genotype showed a non-significant increase in risk compared with the AA genotype (OR = 1.85, 95% CI: 0.89–3.85; p = 0.12), while the GG genotype showed no association (OR = 0.34, 95% CI: 0.03–3.79; p = 0.57). Similarly, in individuals aged >50 years, neither the AG genotype (OR = 1.51, 95% CI: 0.65–3.50; p = 0.42) nor the GG genotype (OR = 2.92, 95% CI: 0.15–57.56; p = 0.56) was significantly associated with BCa risk.

For rs1799966, no significant associations were observed across age groups. In participants aged ≤50 years, the CT genotype (OR = 0.80, 95% CI: 0.49–1.29; p = 0.38) and CC genotype (OR = 1.14, 95% CI: 0.37–3.48; p = 1.00) were not associated with BCa risk. Similarly, in participants aged >50 years, neither the CT genotype (OR = 0.49, 95% CI: 0.49–1.79; p = 0.86) nor the CC genotype (OR = 1.98, 95% CI: 0.42–9.29; p = 0.51) showed significant associations.

Overall, no statistically significant associations were identified between rs799917 or rs1799966 polymorphisms and BCa risk across age groups (Table 4).

Table 4.

Association of BRCA1 rs799917 and rs1799966 Genotypes With Age in BCa Cases and Controls

SNP	Genotypes	Age group	Cases n (%)	Control n (%)	Or (95% CI)	p value
rs799917			(n=359)	(n=189)
	GG vs AA	<=50	163 (59.27)	112 (40.73)	0.34 (0.03-3.79)	0.57
	AG vs AA	<=50	192 (61.34)	121 (38.66)	1.85 (0.89-3.85)	0.12
	GG vs AA	>50	133 (71.12)	54 (28.88)	2.92 (0.15-57.56)	0.56
	AG vs AA	>50	159 (71.95)	62 (28.05)	1.51 (0.65-3.5)	0.42
rs1799966			(n=318)	(n=167)
	CC vs TT	<=50	9 (64.30)	5 (35.70)	1.14 (0.37-3.48)	1.0
	CT vs TT	<=50	54 (57.40)	40 (42.60)	0.80 (0.49-1.29)	0.38
	CC vs TT	>50	10 (83.30)	2 (16.70)	1.98 (0.42-9.29)	0.51
	CT vs TT	>50	42 (71.20)	17 (28.80)	0.49 (0.49-1.79)	0.86

Values are presented as number (percentage). OR = odds ratio; CI = confidence interval.

Association of BRCA1 rs799917 and rs1799966 With IHC Subtypes in BCa Cases

The association between BRCA1 rs799917 and rs1799966 variants and tumor IHC subtypes was evaluated in the BCa cases.

For rs799917, no statistical associations were observed across the IHC subtypes after FDR correction; however, weak, non-significant trends were noted in some comparisons (e.g., AG vs AA in luminal A tumors: OR = 0.84, 95% CI: 0.43–1.65).

For rs1799966, no statistically significant associations were observed across IHC subtypes following FDR correction. However, nominal associations were identified for the CT genotype in TNBC (OR = 0.54, 95% CI: 0.31–0.93; p = 0.041), although this did not remain significant after FDR correction (FDR = 0.30). No significant associations were observed for luminal B, or HER2, subtypes (Table 5).

Table 5.

Association of BRCA1 Polymorphisms With Tumor Immunohistochemical (IHC) Subtypes Among Breast Cancer Cases

Variant	Subtype	Comparison	Genotype distribution n (%)	Or (95% CI)	p value	FDR
rs799917	Luminal A	GG vs AA	0 (0.0) vs 58 (100.0)	2.21 (0.12-41.58)	1.0	1.0
	Luminal A	AG vs AA	13 (100) vs 58 (81.7)	0.84 (0.43-1.65)	0.72	1.0
	Luminal B	GG vs AA	0 (0.0) vs 13 (100.0)	0.43 (0.02-8.37)	1.0	1.0
	Luminal B	AG vs AA	1 (7.1) vs 13 (92.9)	1.86 (0.34-10.27)	0.48	0.99
	TNBC	GG vs AA	2 (2.8) vs 70 (97.2)	0.31 (0.05-1.83)	0.24	0.87
	TNBC	AG vs AA	13 (15.7) vs 70 (84.3)	1.07 (0.55-2.08)	0.86	1.0
	HER2	GG vs AA	0 (0.0) vs 16 (100.0)	0.53 (0.03-10.25)	1.0	1.0
	HER2	AG vs AA	6 (27.3) vs 16 (73.7)	0.48 (0.19-1.26)	0.23	0.87
	Unknown	GG vs AA	2 (1.4) vs 139 (98.6)	0.89 (0.15-5.19)	1.0	1.0
	Unknown	AG vs AA	26 (15.8) vs 139 (84.2)	1.12 (0.64-1.96)	0.77	1.0
rs1799966	Luminal A	CC vs TT	5 (12.5) vs 35 (87.5)	0.58 (0.20-1.72)	0.34	0.85
	Luminal A	CT vs TT	22 (38.6) vs 35 (61.4)	0.70 (0.38-1.28)	0.27	0.85
	Luminal B	CC vs TT	1 (11.1) vs 8 (88.9)	0.74 (0.09-6.24)	0.55	1.0
	Luminal B	CT vs TT	5 (38.5) vs 8 (61.5)	0.75 (0.24-2.35)	0.76	1.0
	TNBC	CC vs TT	5 (10.9) vs 41 (89.1)	0.71 (0.24-2.08)	0.55	1.0
	TNBC	CT vs TT	30 (42.3) vs 41 (57.7)	0.54 (0.31-0.93)	0.041	0.30
	HER2	CC vs TT	2 (14.3) vs 12 (85.7)	0.53 (0.11-2.58)	0.34	0.87
	HER2	CT vs TT	4 (25.0) vs 12 (75.0)	1.36 (0.45-4.10)	0.59s	1.0
	Unknown	CC vs TT	6 (5.3) vs 107 (94.7)	2.41 (0.88-6.60)	0.095	0.63
	Unknown	CT vs TT	35 (24.6) vs107 (75.4)	1.94 (1.18-3.20)	0.009	0.18

Values are presented as number (percentage) of genotypes within each breast cancer subtype. Percentages are calculated relative to the total number of cases within each subtype.

Combined Evidence From Meta-Analysis of rs799917 and rs1799966

The allele frequency distributions of BRCA1 rs799917 and rs1799966 SNPs from this study and other populations reported in the dbSNP database¹⁷ were combined in a meta-analysis. A total of 28 genomic projects reporting allele frequencies for these SNPs were initially identified. After screening for relevance and data completeness, two projects were excluded. In addition, datasets reporting aggregated “global” allele frequencies were removed to avoid duplication and population overlap, leaving 26 population-specific datasets for analysis (Figure S1).

Sensitivity analyses, including leave-one-out analysis and exclusion of larger studies, did not alter the pooled estimates, indicating that the observed allele frequencies were not disproportionately influenced by any single study or subset of studies (Figures S2–S3). Cumulative meta-analysis further demonstrated stabilization of allele frequency estimates increasing sample size (Figure S4).

For rs799917, the results showed inter-population variation in the distribution of the G allele frequency which was lowest in. Africans (14.00% CI: 11.00 - 14.00, I²=98.2%), and highest in, Europeans (66.00% CI:66.00 - 66.00, I²=59.1%), with similar patterns observed in Asian, North and South American populations (Figure 1). The A allele showed the inverse pattern, with the highest frequency observed in African populations (Figure 2).

Figure 1.

Comparative distribution of BRCA1 rs799917 G allele frequencies across global populations, illustrating population-specific variation

Figure 2.

Comparative distribution of BRCA1 rs799917 A allele frequencies across global populations

For rs1799966, a contrasting pattern of inter-population variation was observed. The C allele frequency was lowest in African populations (20.00%, 95% CI: 18.00–22.00; I² = 88.5%) and highest in Asian populations (38.00%, 95% CI: 35.00–41.00; I² = 98.3%) (Figure 3). In contrast, the T allele demonstrated the inverse distribution, with higher frequencies observed in African populations (Figure 4).

Figure 3.

Comparative distribution of BRCA1 rs1799966 T allele frequencies across global populations

Figure 4.

Comparative distribution of BRCA1 rs1799966 C allele frequencies across global populations

Discussion

This study found no significant association between BRCA1 rs799917 (c.2612C>T; p.Pro871Leu) and rs1799966 (c.4837A>G; p.Ser1613Gly) variants and BCa risk among Nigerian women, although population-specific differences in allele frequencies were observed in the accompanying meta-analysis of allele frequency distributions across global populations.

The rs799917 variant is commonly reported as a C/T change in the literature; however, in this study, alleles are reported as G/A based on strand orientation, where the T allele corresponds to the A allele. The A allele was used as the reference allele in statistical analyses due to its predominance in the study population, and all interpretations are made relative to this framework. The high and comparable frequency of the A allele observed in both cases and controls is consistent with reports from other African populations, including Ghana,²⁴ and Burkina Faso.²⁵ This variant has been widely studied across different populations and cancer types; however, findings remain inconsistent, with some studies reporting associations and others reporting none, suggesting a population-specific effect.^12,13,26 For example, studies in Caucasian breast and ovarian cancer cohorts have reported no association between rs799917 and disease risk.^21,22 Similarly, large studies such as the Multiethnic Cohort Study, and the WECARE study found no evidence supporting a role for this variant in BCa susceptibility.^24,27 In contrast, a case–control study in Chinese women with cervical cancer reported a reduced risk associated with the rs799917 TT genotype under a recessive model, indicating a potential protective effect.^25,28

Beyond cancer susceptibility, rs799917 has also been implicated in disease prognosis. It has been associated with chemotherapy response and overall survival in cancer cases, suggesting a role in tumor progression rather than disease initiation.²⁹ In TNBC, the T allele (corresponding to the A allele in this study) has been linked to increased risk of disease progression and shorter progression-free survival, whereas the CC genotype has been associated with improved outcomes, particularly following radiotherapy.³⁰ Furthermore, gene-environment interactions have been reported, as demonstrated in German postmenopausal women, where rs799917 modified BCa risk in the context of estrogen monotherapy, with increasing risk observed among carriers of the T allele.³¹

Haplotype-based analyses may further explain the role of rs799917. A cohort of BCa cases and controls from the Nurses’ Health Study reported that a BRCA1 haplotype containing rs799917 (C A G G) was associated with increased BCa risk, particularly among homozygous carriers and individuals with a positive family history.³² The allele contributing to this risk haplotype corresponds to the less frequent allele in our study population; however, no association was observed when analyzed independently. This suggests that rs799917 may not act as a standalone risk variant but may contribute to disease susceptibility through combined effects with other SNPs within a haplotype.

A higher frequency of the variant allele was observed in both BCa cases and controls for rs1799966 (p.Ser1613Gly), with no significant association identified across allelic, genotypic, or additive models. These findings are consistent with reports from other populations including African American and Latina cohorts.³³ In contrast to rs799917, functional evidence indicates that rs1799966 is a neutral variant with no measurable impact on BRCA1 activity, even when combined with other variants.³⁴ This supports the lack of association observed in the present study and suggests that rs1799966 is unlikely to play a significant role in BCa susceptibility.

Despite its classification as a functionally neutral variant, some studies suggest that rs1799966 may influence disease progression and prognosis. For instance, the GG genotype has been associated with an increased risk of radiation-induced esophagitis.³⁵ Additionally, this variant has been linked to poorer prognosis in pancreatic cancer, particularly in cases with locally advanced disease.³⁶ It has also been associated with increased risk of aggressive prostate cancer, where the CC genotype has been linked with moderately to poorly differentiated tumors.³⁷

The lack of association observed in this study for both rs799917 and rs17999666 is consistent with previous meta-analyses, which have reported no significant relationship between these variants and BCa risk across multiple genetic models and ethnic groups. For instance, a meta-analysis in Chinese populations found no evidence supporting an association between these variants and BCa susceptibility.¹³ Collectively, these findings suggest that BRCA1 rs799917 and rs17999666 do not independently contribute to BCa risk.

The observed differences in allele frequencies across populations could be attributed to multiple factors, including genetic drift, admixture and founder effects, which shape the distribution of genetic variants across populations.³⁸ These differences may have significant implications for interpretation of disease risk, as variants that are highly prevalent within a population may show limited ability to distinguish cases from controls, as their effect sizes are typically small.³⁹ Although these variants showed no significant association with BCa risk in our cohort, they may contribute to disease susceptibilty on the broader polygenic background, where multiple variants collectively influence risk.⁴⁰

A key limitation of this study is the relatively small sample size, as post hoc power analysis indicated insufficient power to detect modest effect sizes (Table S1) thereby increasing the likelihood of Type II error. In addition, multiple genetic models and subgroup analyses were evaluated, which may increase the risk of Type I error despite the application of FDR correction. Another limitation is the lack of complete IHC data, which is a common challenge in Nigerian cohorts.^14,41 This limited our ability to perform detailed subtype-specific analyses to fully evaluate potential associations across BCa subgroups. Furthermore, despite evidence suggesting that these variants may influence disease progression and treatment response, their potential prognostic or therapeutic relevance could not be evaluated in this study.

Nevertheless, this study remains important as, to the best of our knowledge, it provides one of the first targeted evaluations of the rs799917 and rs1799966 variants in relation to BCa risk in a Nigerian population.

Conclusion

In conclusion, this study found no significant association between BRCA1 rs799917 and rs1799966 variants and BCa risk among Nigerian women. The observed population-specific differences in allele frequencies further highlights the need to consider genetic diversity in cancer risk studies. Future studies with larger cohorts, haplotypes-based analyses are required to better define the role of the rs799917 variants in BCa biology, particularly in underrepresented populations like Nigeria.

Supplemental Material

Supplemental Material - BRCA1 rs799917 and rs1799966 Variants and Breast Cancer Risk in Nigerian Women: A Case–Control Study With Population-Based Allele Frequency Analysis

Supplemental Material for BRCA1 rs799917 and rs1799966 Variants and Breast Cancer Risk in Nigerian Women: A Case–Control Study With Population-Based Allele Frequency Analysis by Ogunniyi B. Oluwabusayo, Abimbola F. Onyia, Olutola E. Olasehinde, Oluwatomiwa K. Paimo, Divine C. Sylvester, Nwamaka N. Lasebikan, AbdulRazzaq Lawal, Adewumi Alabi, Anthonia Sowunmi, Eben A. Aje, Uchechukwu Shagaya, Emmanuella Nwachukwu, Ademola Oyekan, Temitope Olatunji, Omolara Fatiregun, Chidiebere Ogo, Ebenezer S. Nkom, Abidemi E. Omonisi, Olayinka B. Popoola, Oiza T. Ahmadu, Opeyemi C. De Campos, Oluwakemi A. Rotimi, Toluwanimi O. Ajibola, Timothy A. Anake, Usman M. Aliyu and Solomon O. Rotimi in Cancer Control.

Footnotes

ORCID iDs

Ogunniyi B. Oluwabusayo

Oluwatomiwa K. Paimo

Divine C. Sylvester

Ademola Oyekan

Omolara Fatiregun

Solomon O. Rotimi

Ethical Considerations

Before conducting the study, ethical approval was obtained from the Covenant University Human Research Ethics Committee, Covenant University, Ota, Ogun State, Nigeria (approval number: CU/HREC/SOR/072/21) and the Nigerian National Ethics Committee (approval number: NHREC/01/01/2007-19/01/2024).

Consent to Participate

Additionally, all participants recruited for the study provided written informed consent after the details of the study were fully explained and understood. To ensure confidentiality and protect participants’ privacy, their identities were de-identified on the questionnaires and throughout the research process.

Authors Contributions

SOR, OBO, AFO, OAR, UMA and OEO were in conceptualization and study design. NNL, AL, AA, AS, EAA, US, EN, AO, TO, OF, CO, ESN, AEO, OBP, and OTA were involved in data collection. SOR, OBO, AFO, OEO, OKP, DCS, OCD, TOA and TAA were involved in writing the original draft, and data analysis. SOR, OBO, OAR, and UMA were involved in reviewing and editing the manuscript. All authors read and approved the final version for publication.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SOR is supported by funding from the Office of the Assistant Secretary of Defense for Health Affairs through the Congressionally Directed Medical Research Programs (CDMRP), Prostate Cancer Research Program, Health Equity Research, and Outcomes Improvement Consortium, under Award Number (W81XWH2210972) and by the National Cancer Institute of the National Institutes of Health under Award Number P50CA116201 through the sub-award COV-182363-02, a part of the Mayo Clinic Breast Cancer SPORE – Project 1. OAR is supported by FIC and NCI of the NIH under award number K43TW011942.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All relevant data supporting the findings of this study are presented in the result section. Additional data can be obtained from the corresponding author upon request.*

Supplemental Material

Supplemental material for this article is available online.

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