Polygenic risk score for breast cancer risk prediction in Asian BRCA1 and BRCA2 pathogenic variants carriers

Abstract

Polygenic risk scores (PRS) have been shown to be predictive of breast cancer (BC) risk in European BRCA1 and BRCA2 pathogenic variant (PV) carriers, but their utility in Asian populations has not been evaluated. In this study, we evaluated the association of two breast cancer PRS developed for the East Asian general population and three versions of a PRS developed for the European general population in 604 BRCA1 (390 affected by breast cancer) and 785 BRCA2 (552 affected by breast cancer) PV female carriers of Asian ancestry. Only the Asian-based PRS, constructed using approximately 1 million single-nucleotide variations (SNVs), showed a significant association with breast cancer risk (Hazard Ratio per standard deviation (95% Confidence Interval) is 1.47 (1.10–1.95) for BRCA1 and 1.43 (1.04–1.95) for BRCA2). Incorporating this PRS into risk prediction models may improve cancer risk assessment among PV carriers of Asian ancestry.

Introduction

Women with pathogenic or likely pathogenic variants (PV) in BRCA1 or BRCA2 are at increased risk of developing breast, ovarian and other cancers, and may benefit from risk-management strategies such as risk-reducing medication, risk-reducing surgery, or intensive surveillance¹. However, the breast cancer risk in BRCA1 or BRCA2 PV carriers varies depending on several factors, including genetics, lifestyle and reproductive risk factors, and also depends on the cancer incidence in the population^{2, 3, 4, 5–6}. There is a need for accurate risk stratification methods to empower women in making well-informed decisions⁷.

Polygenic risk scores (PRS), combining the effects of multiple disease-associated single nucleotide variations (SNVs), have emerged as powerful tools for breast cancer risk stratification in European populations, both in the general population and BRCA1 or BRCA2 PV carriers^3,4,8. Notably, a PRS developed in the European population using 313 SNVs (PRS₃₁₃) has shown predictive value for breast cancer risk in Asian populations, albeit with lower discrimination compared to women of European descent⁹. Subsequently, the predictive performance of this European-based PRS was further enhanced by incorporating additional SNVs associated in Asian studies, resulting in a PRS with 333 SNVs (PRS₃₃₃)¹⁰. Additionally, Ho et al.¹⁰ generated a PRS using a trans-ancestry Bayesian polygenic prediction approach implemented in PRS-CSx¹¹, resulting in a PRS that includes approximately one million SNVs (PRS_GW) and demonstrates the highest predictive accuracy compared to the other two PRS in predicting breast cancer risk in the general Asian populations.

In this study, we aimed to evaluate if these PRS can predict the risk of breast cancer in BRCA1 and BRCA2 PV carriers of Asian ancestry.

Results

The study cohort consists of 604 BRCA1 (390 affected with breast cancer, 96 with ovarian cancer, 118 unaffected with either cancer) and 785 BRCA2 (552 affected with breast cancer, 46 with ovarian cancer, 187 unaffected) PV carriers recruited from 4 different countries (Supplementary Table S1). The mean age of diagnosis for BRCA1 PV carriers was 40.9 (SD = 10.2 and for BRCA2 PV carriers was 44.3 (SD = 10.2), while the mean censoring age for unaffected individuals for BRCA1 PV carriers was 46.5 (SD = 13.9) and for BRCA2 carriers was 43.4 (SD = 13.8) (Table 1).

Table 1. Description of study cohort

Description	BRCA1 PV carriers			BRCA2 PV carriers
	Total	Aff (%)	Unaff (%)	Total	Aff (%)	Unaff (%)
Total, N	604	390 (100.0)	214 (100.0)	785	552 (100.0)	233 (100.0)
Country
Malaysia	255	126 (32.3)	129 (60.3)	220	143 (25.9)	77 (33.0)
Korea	218	159 (40.8)	59 (27.6)	359	229 (41.5)	130 (55.8)
Hong Kong	67	52 (13.3)	15 (7.0)	93	74 (13.4)	19 (8.2)
Singapore	64	53 (13.6)	11 (5.1)	113	106 (19.2)	7 (3.0)
Year of Birth
≤1940	15	6 (1.5)	9 (4.2)	16	15 (2.7)	1 (0.4)
1941-1950	40	22 (5.6)	18 (8.4)	71	58 (10.5)	13 (5.6)
1951-1960	127	86 (22.1)	41 (19.2)	193	140 (25.4)	53 (22.7)
1961-1970	172	118 (30.3)	54 (25.2)	256	189 (34.2)	67 (28.8)
>1970	250	158 (40.5)	92 (43.0	249	150 (27.2)	99 (42.5)
Censoring age
Mean (SD)	42.9 (12.0)	40.9 (10.2)	46.5 (13.9)	44.1 (11.4)	44.3 (10.2)	43.4 (13.8)
Range	19.13-80.91	19.73–73.63	19.13–80.91	19.19–80.94	22.74–77.77	19.19–80.94
Censoring age group
≤30	74	45 (11.5)	29 (13.6)	71	25 (4.5)	46 (19.7)
30-40	211	165 (42.3)	46 (21.5)	239	191 (34.6)	48 (20.6)
40-50	163	108 (27.7)	55 (25.7)	245	184 (33.3)	61 (26.2)
50-60	94	51 (13.1)	43 (20.1)	159	110 (19.9)	49 (21.0)
>60	62	21 (5.4)	41 (19.2)	71	42 (7.6)	29 (12.4)

Aff Affected, Unaff Unaffected, SD Standard deviation.

PRS association with breast cancer risk

Five PRS, which were previously demonstrated to be predictive for breast cancer risk in the general Asian population,n were selected for analysis: three based on PRS developed through analyses of studies in the Breast Cancer Association Consortium in European populations⁸, and two based on trans-ancestry analyses^9,10 (Supplementary Table S2). We evaluated the association of each PRS with breast cancer risk. The strongest association, in both BRCA1 and BRCA2 PV carriers, was with PRS_GW (HR = 1.47, 95% CI = 1.10–1.95, p = 0.0089 and HR = 1.43, 95% CI = 1.04–1.95, p = 0.0255, respectively) (Table 2). The estimated HRs for the other PRS were greater than 1 but smaller and not statistically significantly different from 1. Sensitivity analysis showed that the hazard ratios for PRS_GW were slightly lower and not significant for BRCA1 (HR = 1.31, 95% CI = 0.94–1.83, p = 0.1119) and similar for BRCA2 PV carriers (HR = 1.44, 95% CI = 1.01–2.07, p = 0.0457) when the weights were calculated based on respective country incidence rates rather than using average incidence rates. (Supplementary Table S3).

Table 2. Association of PRSs with BC risk in BRCA carriers

PRS	BRCA1 PV carriers (390 affected; 214 unaffected)		BRCA2 PV carriers (552 affected; 233 unaffected)
	HR (95% CI)	p-value	HR (95% CI)	p-value
Asian PRS
PRS333	1.15 (0.97–1.36)	0.1046	1.04 (0.88–1.22)	0.6755
PRSGWS	1.47 (1.10–1.95)	0.0089	1.43 (1.04–1.95)	0.0255
European PRS
PRSOVERALL	1.16 (0.98–1.37)	0.0761	1.09 (0.92–1.28)	0.3343
PRSER+	1.16 (0.98–1.38)	0.0862	1.10 (0.92–1.30)	0.3042
PRSER-	1.12 (0.95–1.32)	0.1905	1.07 (0.89–1.29)	0.4738

Analysis was conducted using weighted Cox regression, with the weights estimated based on the average breast cancer incidence among BRCA carriers in four countries. Aff Affected, Unaff Unaffected.

We further evaluated the association between PRS_GW and breast cancer risk when the PRS was treated as a categorical variable. Compared to women in the middle quintile (40–60%), the estimated HRs for developing breast cancer for women in quintiles 1 and 5 were 0.89 (95% CI 0.52–1.52) and 1.57 (0.99–2.49), respectively, for BRCA1, and 0.44 (0.24–0.80) and 1.09 (0.67–1.80) (Table 3) for BRCA2 PV carriers. The estimated HRs by PRS percentile did not differ from those predicted under a theoretical polygenic model in which the log HR depends linearly on the PRS: all predicted HRs fell within the confidence intervals of the observed HRs.

Table 3. Proportion of samples in percentile categories of PRS_GW and their associations with breast cancer risk

Percentile	BRCA1 PV carrier				BRCA2 PV carriers
	Unaff.	Aff.	Estimated HR (95% CI)	Predicted HR	Unaff.	Aff.	Estimated HR (95% CI)	Predicted HR
0-20	44	57	0.89 (0.52–1.52)	0.59	48	62	0.44 (0.24–0.80)	0.61
20-40	43	42	0.61 (0.34–1.07)	0.82	47	91	0.91 (0.53–1.59)	0.83
40-60	43	72	1	1	47	109	1	1
60-80	43	102	1.57 (0.96–2.55)	1.23	47	136	1.22 (0.73–2.04)	1.21
80-100	44	121	1.57 (0.99–2.49)	1.23	47	155	1.09 (0.67–1.80)	1.67

Aff Affected, Unaff Unaffected.

Predicted absolute risk by PRS percentile

We used the hazard ratio estimates and the average breast cancer incidence across the four countries included in this study to compute age-specific absolute cumulative breast cancer risks for PV carriers by PRS percentiles according to PRS_GW (Fig. 1). BRCA1 PV carriers at the 5^th percentile of the PRS distribution had an estimated risk of 17% of developing breast cancer by age 50 years and a 38% risk by age 80 years. In contrast, the BRCA1 PV carriers at the 95^th percentile of the PRS distribution had a 45% breast cancer risk by age 50 years and 81% by age 80 years. BRCA2 carriers at the 5^th percentile of the PRS distribution had a risk of 10% of developing breast cancer by age 50 years and a 28% risk by age 80 years. In contrast, BRCA2 carriers at the 95^th percentile of the PRS distribution had a 30% breast cancer risk by age 50 years and 65% by age 80 years.

Fig. 1 Age-specific absolute cumulative breast cancer risks for PV carriers by PRS percentiles according to PRS_GW. [Images not available. See PDF.]

aBRCA1 PV carriers and (b)BRCA2 PV carriers.

Discussion

In this study, we evaluated the association of PRS, previously validated in the Asian general population, with the risk of breast cancer among carriers of BRCA1 and BRCA2 PVs of Asian ancestry. While all estimated HRs were above one 1 for all PRS, the association was statistically significant only for the PRS_GW, constructed using approximately 1 million variants. The magnitude of the association was markedly higher than for the other PRS in both BRCA1 (1.47 versus 1.12–1.16) and BRCA2 (1.43 versus 1.04–1.10) PV carriers.

This is qualitatively consistent with our previous observations in the general population, which showed that PRS_GW (HR = 1.62, 95%CI:1.46–1.80) outperformed PRS₃₃₃ (HR=1.53, 95%CI:1.37–1.71) and PRS_OVERALL (HR = 1.46, 95%CI:1.34–1.60) in prospective cohorts¹⁰, although the difference was much less marked. PRS_GW was developed through integrating GWAS summary statistics from multiple populations and leveraging linkage disequilibrium diversity across discovery samples¹¹. This enables more accurate effect size estimation and hence improves the predictive accuracy of PRS in the target population.

We found that the estimated effect sizes for all of the PRS on cancer risks in BRCA1 and BRCA2 PV carriers were lower than those previously observed in the general population: for PRS_GW, the corresponding HR in Asian prospective cohorts was 1.62 (95%CI:1.46-1.80). Again, attenuation is qualitatively consistent with what was observed in European populations (e.g., for the 313SNV PRS, the HR was estimated to be 1.61 in the general population compared to 1.20 and 1.31 in BRCA1 and BRCA2 PV carriers^4,8).

In the European studies, the PRS adapted for ER-negative breast cancer (PRS_ER-) was shown to be more predictive than PRS_OVERALL for breast cancer risk in BRCA1 PV carriers, consistent with the known strong association of BRCA1 PVs with ER-negative (specifically triple negative) breast cancer, while PRS_OVERALL was more predictive for BRCA2 PVs. PRS_OVERALL was clearly weaker in BRCA1 than BRCA2 PVs. This difference was not apparent in this study, possibly due to chance, given the wide confidence limits.

Previous research by Kuchenbäcker et al.³, indicating improved performance of an 88-SNV PRS in the European population after incorporating BRCA-specific SNVs identified from BRCA1 and BRCA2 specific genome-wide association studies (GWAS) compared to 77-SNV PRS derived from the general population. This suggests including variants that are associated with cancer risk in BRCA1 and BRCA2 PV carriers might potentially further improve the predictive accuracy of PRS. However, SNVs for Asian BRCA PV carriers remain elusive as current GWAS in BRCA PV carriers lack representation from Asian populations.

We showed that PRS_GW can achieve a useful level of risk stratification in Asian BRCA1 and BRCA2 PV carriers, where the cumulative risk was substantially lower for carriers in the lowest PRS percentile compared to the highest PRS percentile (Fig. 1). A previous study has shown that the absolute breast cancer risk of Asian PV carriers varies depending on the underlying population-specific cancer incidence⁶. Asian carriers residing in countries with significantly lower population cancer incidences are expected to have markedly lower absolute cancer risks compared to European ancestry carriers. While risk stratification by PRS may not alter screening recommendations, it can refine risk assessment. For instance, carriers identified as having lower risk may consider delaying prophylactic surgeries, such as mastectomy or oophorectomy, thus balancing the benefits and potential harms of such interventions. Implementation of PRS comprising such an expansive SNV set can be difficult in practice. However, given the significant findings, PRS_GW can be an alternative until larger GWAS of Asian ancestry BRCA1 and BRCA2 PV carriers become available for the development of BRCA-specific PRS.

One limitation of our study is that although this is the largest available dataset for Asian BRCA1 and BRCA2 PV carriers, our study may lack the power to detect associations with PRS of marginal magnitude. Moreover, the confidence limits associated with the HR estimates were wide. Sensitivity analyses utilizing weights derived from country-specific cancer incidence rates yielded generally comparable HRs to those derived from average cancer incidence rates. However, the statistical significance was reduced in the sensitivity analysis, particularly among BRCA1 PV carriers. This attenuation may be due to additional heterogeneity introduced when applying country-specific incidence rates to stratified age groups, particularly where sample sizes within certain age-country strata were small (Supplementary Table S4). These findings highlight the challenges in achieving robust statistical power in subgroup analyses and underscore the need for larger, well-powered studies in diverse populations.

In summary, the results demonstrate the potential utility of PRS_GW in predicting the risk of breast cancer for Asian carriers of both BRCA1 and BRCA2 PVs. Incorporating this polygenic risk score into risk prediction models for PV carriers, alongside other risk modifiers, may be crucial for refining population-specific cancer risk assessments, especially for Asian carriers with a lower risk of breast cancer.

Methods

Study population

Eligible study subjects included in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) were self-reported Asian female carriers of a pathogenic or likely pathogenic variant (PV) in either BRCA1 or BRCA2 who were age 18 years or older. The germline mutations were classified as pathogenic or likely pathogenic if they resulted in a truncated protein or have been previously reported as disease-associated by ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) or ENIGMA BRCA1/2 expert panel guidelines (https://enigmaconsortium.org/) based on ACMG/AMP Guidelines. Carriers with variants of uncertain significance were excluded. Carriers were recruited from seven study centres in four countries, as part of either population or hospital-based case-control studies, or through genetics clinics (Supplementary Table S1). All research reported here was performed in accordance with the Declaration of Helsinki and each of the study centres recruited carriers under protocols approved by local ethics review boards. The Malaysian Breast Cancer Genetic Study (MyBrCa) was approved by the Independent Ethics Committee, Ramsay Sime Darby Health Care (reference no: 201109.4 and 201208.1), and the Medical Ethics Committee, University Malaya Medical Centre (reference no: 842.9). SGBCC was approved by the National Healthcare Group Institutional Review Board (NHG DSRB Ref: 2009/00501, approval date 16 December 2009) and the SingHealth Duke-NUS Institutional Review Board (CIRB Ref: 2019/2246, approval date 29 October 2010). Each study listed was approved by the local institutional ethics committees and review boards. Written informed consent was obtained from all subjects. Subjects without genotype data were excluded from the analyses, leaving 604 BRCA1 (390 affected with breast cancer, 96 with ovarian cancer, 118 unaffected with either cancer) and 785 BRCA2 (552 affected with breast cancer, 46 with ovarian cancer, 187 unaffected) PV carriers in this study. Blood samples were genotyped with the iCOGS array or Oncoarray, which provides genome-wide genotyping^12,13. Standard quality control processes applied to the genotype data have been described in detail elsewhere^14,15: these, which included assessment of the SNV call rate, allele frequency, genotyping intensity clustering metrics, Hardy-Weinberg equilibrium and SNV concordance in duplicate samples. Genotypes for variants not on the arrays were estimated using two-stage imputation, using SHAPEIT and IMPUTE2, with the 1000 Genomes Project (Phase 3) samples as the reference panel.

Polygenic Risk Score

PRS were computed using the standard formula:

1

$$\mathit{PRS}={\beta }_1{x}_1+{\beta }_2{x}_2+...+{\beta }_k{x}_k+\dots +{\beta }_n{x}_n$$ where x_k is the dosage of risk allele (0-2) for SNV k and β_k is the corresponding log odds ratio for SNV k.

The list of SNVs and their corresponding log odds ratios is in accordance with those reported in previous publications. PRS_OVERALL was based on the 313 SNV PRS developed by Mavaddat et al. For these analyses, the PRS was restricted to the 287 SNVs with imputation accuracy >0.9 in the Asian studies (that is, the original weights were used for the 287 SNVs, but weights for the remaining 26 SNVs were set to zero)⁹. PRS_ER+ and PRS_ER- are modified versions of PRS_OVERALL in which the weights from Mavaddat et al optimised for the prediction of ER+ and ER- negative, were used. The trans-ancestry PRS, PRS₃₃₃, was a weighted average of the European-based PRS_OVERALL and 46 SNV PRS derived from GWAS in Asian populations, as given in Ho et al.¹⁰. Thus, PRS₃₃₃ was derived using:

2

$${\mathrm{PRS}}_{333}={\mathrm{\alpha }}_1{\mathrm{PRS}}_{\mathrm{ASN}}+{\mathrm{\alpha }}_2{\mathrm{PRS}}_{\mathrm{OVERALL}}+{\mathrm{\alpha }}_0,$$ where α₁ = 0.14893, α₂ = 0.35354, and α₀ = -0.05224, and PRS_ASN was a 46 SNV PRS. The final PRS, PRS_GW, was derived as a weighted average of European and Asian-specific PRS, generated using a Bayesian polygenic prediction model in PRS-CSx, thus:

3

$${\mathrm{PRS}}_{\mathrm{GW}}={\mathrm{\alpha }}_1{\mathrm{PRS}}_{\mathrm{GW\_ASN}}+{\mathrm{\alpha }}_2{\mathrm{PRS}}_{\mathrm{GW\_EUR}}+{\mathrm{\alpha }}_0,$$ where α₁ = 0.16856, α₂ = 0.38484, and α₀ = 0.54881. Lists of SNVs and the corresponding weights as describe in the original articles.

To facilitate a direct comparison of the performance of each PRS, we standardized the PRS to the standard deviation (SD) of the PRS in the validation set of control subjects previously reported^9,10.

Statistical analysis

The association between each PRS and the incidence of breast cancer was evaluated in a survival analysis framework. Individuals were considered at risk from birth and censored at the age of the first breast or ovarian cancer diagnosis, age at bilateral prophylactic mastectomy, or the age at last follow-up. There were two women in the study with censoring age > 80 (both with age of last follow-up at age 81). PV carriers censored at ovarian cancer diagnosis were considered unaffected for the breast cancer analysis. To account for the oversampling of affected BRCA1 and BRCA2 PV carriers, the association of each PRS with breast cancer risk was analysed using a weighted cohort Cox regression with time to breast cancer diagnosis as the outcome¹⁶. This method involves assigning different weights to affected and unaffected individuals, which are age- and gene-specific, so that the weighted observed incidence rate aligned with externally derived incidence rates for carriers. The country-specific breast cancer incidence rates for BRCA1 and BRCA2 PV carriers were estimated using country-specific population breast cancer incidence, the reported log relative risks and the method described in Ho et al.⁶, where the log relative risks were assumed to be the same across all countries. The weights for non-random sampling adjustment were calculated based on the average breast cancer incidence rates in BRCA1 or BRCA2 PV carriers across all countries. The estimated BRCA1 and BRCA2 PV carrier breast cancer incidence rates and the corresponding weights are provided in Supplementary Table S5 and Supplementary Table S6, respectively.

PRS was treated as either a continuous or a categorical variable in the model. The first 4 ancestry principal components (PCs) and birth cohort (in decades) were included as covariates. The robust variance approach was used to account for related individuals in the study by clustering on family membership. All models were fitted separately in BRCA1 and BRCA2 PV carriers. When used as a categorical predictor, the PRS was grouped into quintiles based on the PRS distribution in unaffected PV carriers. The middle group (40–60%) was used as the reference category. The observed HRs by PRS percentiles were compared with the theoretical HR predictions under a multiplicative polygenic model of inheritance¹⁷. The weighted cohort analysis was carried out in R “survival” library command coxph(model,robust = TRUE,weights = w) where w represents the age-specific weights.

The age-specific absolute risks of developing breast cancer in each PRS percentile were calculated using the following formula described in Barnes et al.⁴:

4

$${\mathrm{AR}}_{\mathrm{g}}\left(t\right)=\sum _{u=0}^t{\lambda }_{\mathrm{g}}\left(u\right)·{\mathrm{S}}_{\mathrm{g}}\left(u\right)$$ where ${\lambda }_{\mathrm{g}}$ (u) = ${\lambda }_0\left(u\right)\exp {(\beta }_g)$ is the estimated breast cancer incidence associated with PRS at age u, with ${\lambda }_0\left(u\right)$ representing the baseline incidence and ${\beta }_g$ the corresponding log hazard ratio of association with breast cancer risk for PV carriers in PRS category g relative to the reference category. Here, S_g(u) is the probability of being breast cancer free at age u. The PRS-specific breast cancer incidences, ${\lambda }_{\mathrm{g}}$ (u), were calculated iteratively by assuming that the average age-specific breast cancer incidence over all PRS percentiles agreed with the estimated average BRCA1 or BRCA2 PV carrier breast cancer incidence.

All statistical analyses were conducted using R v.3.6.3.

Acknowledgements

This study was supported by Wellcome Trust grant [grant no: v203477/Z/16/Z] and Basser Centre grant. W.-K.H. is funded by Wellcome Trust Career Development Award [grant no. 227824/Z/23/Z (UNS146816)]. J. D. was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312). CIMBA is supported by Cancer Research U grant: PPRPGM-Nov20\100002, and the Gray Foundation. This work was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312) [*]. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for iCOGS came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. KOHBRA is supported by a grant from the National R&D Program for Cancer Control, Ministry for Health, Welfare and Family Affairs, Republic of Korea (1020350). MyBrCa was funded by the Malaysian Ministry of Science, the Malaysian Ministry of Higher Education High Impact Research Grant (Grant No.: UM.C/HIR MOHE/06). MaGiC was funded in part through an AstraZeneca External Investigator Grant. MyBrCa, MyOvCa and MaGiC were funded by charitable funds from Yayasan Sime Darby, Yayasan PETRONAS, Estee Lauder Group of Companies, Khind Starfish Foundation, Vistage Group of Companies and other donors of Cancer Research Malaysia. SGBCC is funded by the National Research Foundation Singapore, NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant, Breast Cancer Prevention Programme, Asian Breast Cancer Research Fund, the NMRC Clinician Scientist Award (SI Category) and the Breast Cancer Screening and Prevention Programme. NCCS is supported in part by the National Research Foundation, Singapore, through the Singapore Ministry of Health’s National Medical Research Council and the Precision Health Research, Singapore (PRECISE), under PRECISE’s Clinical Implementation Pilot grant scheme. *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. We also thank all the participants and their families for taking part in the research studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. KOHBRA thanks the KOHBRA study group. MyBrCa, MyOvCa, and MaGiC thank all research staff at Cancer Research Malaysia, University Malaya, participating Ministry of Health Malaysia hospitals, Subang Jaya Medical Centre, Beacon Hospital, Gleneagles Penang, Hospital Universiti Sains Malaysia, KPJ Ampang Puteri Specialist Hospital, KPJ Johor Specialist Hospital, KPJ Sabah Specialist Hospital, Loh Guan Lye Specialist Centre, Mount Miriam Cancer Hospital, Pantai Hospital Kuala Lumpur, Penang Adventist Hospital, Universiti Kebangsaan Malaysia Medical Centre and Sunway Medical Centre who assisted in recruitment and interviews for their contributions and commitment to this study. We want to thank Siti Norhidayu Hasan, Lau Shao Yan and Habibatul Saadiah Isa for assistance with DNA preparation for MaGiC; Lee Sheau Yee, Daphne SC Lee, Wong Siu Wan and Lee Yong Quan for their assistance in curating family history data for MyBrCa, MyOvCa and MaGiC. SGBCC thanks Dr Jingmei Li, Dr Peh Joo Ho and Alexis Jiaying Khng for their assistance with DNA preparation for SGBCC; Dr Ern Yu Tan, A/Prof Benita Kiat-Tee Tan, A/Prof Veronique Kiak Mien Tan, Dr Geok Hoon Lim, and Dr Su-Ming Tan for their support and supervision of recruitment at the individual participating sites. For the NCCS study, we thank all our clinical partners and genetic counsellors Shao Tzu Li, Jeanette Yuen, Hui Xuan Goh and laboratory staff Sock Hoai Chan, Ee Ling Chew and Siao Ting Chong for their efforts in patient recruitment and database management. We thank all NCCS patients and families for their support of our research efforts. HRBCP acknowledges Edmond SK Ma, CH Au, Cecilia YS Ho for research coordination; WP Luk and LH Fung for HRBCP data and statistical support; doctors, nurses and genetic counsellors from the University of Hong Kong-affiliated Hospitals from Hong Kong for recruitment.

Author contributions

Conceptualization: D.F.E, G.C.T., A.C.A., S.H.T., W.K.H.; Project administration: Data curation: M.C.T., J.D., Z.L.W., G.L., M.K.B., D.R.B, M.T.P, P.S; Formal analysis: M.C.T., W.K.H; Resources: S.K.P, S.W.K., J.W.L., T.H., A.K., M.H., S.Y.Y., J.N.Y.Y, Y.L.W., B.P., J.S., N.A.M.T, C.H.Y.; Writing – original draft: M.C.T., D.F.E, G.C.T., A.C.A., S.H.T., W.K.H.; Writing – review & editing: all authors.

Data availability

CIMBA data is available on request. To receive access to the data, a concept form must be submitted, which will then be reviewed by the CIMBA Data Access Coordination Committee (DACC). Concept forms and the process of submitting data access requests can be found at: https://www.ccge.medschl.cam.ac.uk/consortium-investigators-modifiers-brca12-cimba/data-data-access.

Code availability

The code for the statistical analysis performed in R v.3.6.3, using the R package survival and rms, and PRS was compute using PLINK 2.0, can be shared with interested readers upon request via email to the corresponding author.

Competing interests

A. Kwong received educational grants and funding support for genetic testing from Astrazeneca and Pfizer. S. Y. Yoon is a recipient of an Investigator-Initiated Grant from AstraZeneca for the MaGIC Study. J. Ngeow received research support from AstraZeneca. All other authors declare no conflicts of interest.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41523-025-00820-0.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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