Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese
Metrics: PDF 186 views | HTML 265 views | ?
Claire Hian Tzer Chan1,*, Prabhakaran Munusamy1,*, Sau Yeen Loke1, Geok Ling Koh1, Audrey Zhi Yi Yang1, Hai Yang Law2, Chui Sheun Yoon2, Chow Yin Wong3, Wei Sean Yong4, Nan Soon Wong5,6, Raymond Chee Hui Ng5, Kong Wee Ong4, Preetha Madhukumar4, Chung Lie Oey4, Gay Hui Ho4,7, Puay Hoon Tan8, Min Han Tan5,9,10, Peter Ang5,6, Yoon Sim Yap5 and Ann Siew Gek Lee1,11,12
1Division of Medical Sciences, Humphrey Oei Institute of Cancer Research, National Cancer Centre, Singapore
2DNA Diagnostic and Research Laboratory, KK Women’s and Children’s Hospital, Singapore
3Department of General Surgery, Singapore General Hospital, Singapore
4Department of Surgical Oncology, National Cancer Centre, Singapore
5Department of Medical Oncology, National Cancer Centre, Singapore
6Oncocare Cancer Centre, Gleneagles Medical Centre, Singapore
7Koong and Ho Surgery Centre, Singapore
8Department of Pathology, Singapore General Hospital, Singapore
9Institute of Bioengineering and Nanotechnology, Singapore
10Lucence Diagnostics Pte Ltd, Singapore
11Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
12Office of Clinical and Academic Faculty Affairs, Duke-NUS Graduate Medical School, Singapore
*These authors contributed equally to the work
Ann Siew Gek Lee, email: firstname.lastname@example.org
Keywords: breast cancer; single-nucleotide polymorphism; risk loci; genotyping; polygenic risk score
Received: July 27, 2017 Accepted: January 25, 2018 Published: January 31, 2018
Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.