Dataset Information

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

ABSTRACT:

Purpose

Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.

Methods

The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).

Results

The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.

Conclusion

PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.

SUBMITTER: Ho WK

PROVIDER: S-EPMC7612481 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

Ho Weang-Kee WK Tai Mei-Chee MC Dennis Joe J Shu Xiang X Li Jingmei J Ho Peh Joo PJ Millwood Iona Y IY Lin Kuang K Jee Yon-Ho YH Lee Su-Hyun SH Mavaddat Nasim N Bolla Manjeet K MK Wang Qin Q Michailidou Kyriaki K Long Jirong J Wijaya Eldarina Azfar EA Hassan Tiara T Rahmat Kartini K Tan Veronique Kiak Mien VKM Tan Benita Kiat Tee BKT Tan Su Ming SM Tan Ern Yu EY Lim Swee Ho SH Gao Yu-Tang YT Zheng Ying Y Kang Daehee D Choi Ji-Yeob JY Han Wonshik W Lee Han-Byoel HB Kubo Michiki M Okada Yukinori Y Namba Shinichi S Park Sue K SK Kim Sung-Won SW Shen Chen-Yang CY Wu Pei-Ei PE Park Boyoung B Muir Kenneth R KR Lophatananon Artitaya A Wu Anna H AH Tseng Chiu-Chen CC Matsuo Keitaro K Ito Hidemi H Kwong Ava A Chan Tsun L TL John Esther M EM Kurian Allison W AW Iwasaki Motoki M Yamaji Taiki T Kweon Sun-Seog SS Aronson Kristan J KJ Murphy Rachel A RA Koh Woon-Puay WP Khor Chiea-Chuen CC Yuan Jian-Min JM Dorajoo Rajkumar R Walters Robin G RG Chen Zhengming Z Li Liming L Lv Jun J Jung Keum-Ji KJ Kraft Peter P Pharoah Paul D B PDB Dunning Alison M AM Simard Jacques J Shu Xiao-Ou XO Yip Cheng-Har CH Taib Nur Aishah Mohd NAM Antoniou Antonis C AC Zheng Wei W Hartman Mikael M Easton Douglas F DF Teo Soo-Hwang SH

Genetics in medicine : official journal of the American College of Medical Genetics 20211215 3

<h4>Purpose</h4>Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.<h4>Methods</h4>The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Emp ...[more]

PMID: 34906514

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Project description:ImportancePolygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women.ObjectiveTo develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors.Design, setting, and participantsThis diagnostic study included women of Asian ancestry from the Asia Breast Cancer Consortium. PRSs were developed using data from genomewide association studies (GWASs) of breast cancer conducted among 123 041 women with Asian ancestry (including 18 650 women with breast cancer) using 3 approaches: (1) reported PRS for women with European ancestry; (2) breast cancer-associated single-nucleotide variations (SNVs) identified by fine-mapping of GWAS-identified risk loci; and (3) genomewide risk prediction algorithms. A nongenetic risk score (NGRS) was built, including 7 well-established nongenetic risk factors, using data of 416 case participants and 1558 control participants from a prospective cohort study. PRSs were initially validated in an independent data set including 1426 case participants and 1323 control participants and further evaluated, along with the NGRS, in the second data set including 368 case participants and 736 control participants nested within a prospective cohort study.Main outcomes and measuresLogistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% CIs and area under the receiver operating characteristic curve (AUC).ResultsA total of 126 894 women of Asian ancestry were included; 20 444 (16.1%) had breast cancer. The mean (SD) age ranged from 49.1 (10.8) to 54.4 (10.4) years for case participants and 50.6 (9.5) to 54.0 (7.4) years for control participants among studies that provided demographic characteristics. In the prospective cohort, a PRS with 111 SNVs developed using the fine-mapping approach (PRS111) showed a prediction performance comparable with a genomewide PRS that included more than 855 000 SNVs. The OR per SD increase of PRS111 score was 1.67 (95% CI, 1.46-1.92), with an AUC of 0.639 (95% CI, 0.604-0.674). The NGRS had a limited predictive ability (AUC, 0.565; 95% CI, 0.529-0.601). Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and NGRS were at a 3.84-fold (95% CI, 2.30-6.46) and 2.10-fold (95% CI, 1.22-3.62) higher risk of breast cancer, respectively. The prediction model including both PRS111 and NGRS achieved the highest prediction accuracy (AUC, 0.648; 95% CI, 0.613-0.682).Conclusions and relevanceIn this study, PRSs derived using breast cancer risk-associated SNVs had similar predictive performance in Asian and European women. Including nongenetic risk factors in models further improved prediction accuracy. These findings support the utility of these models in developing personalized screening and prevention strategies.

Project description:BackgroundGenome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates.MethodsWe constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS.ResultsThe PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2×10 -53 ). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2×10 -20 ). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS.ConclusionsBC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management.

Dataset Information

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

Purpose

Methods

Results

Conclusion

Publications

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

Similar Datasets

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