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

Weang-Kee Ho,Swee Ho Lim,Daehee Kang,Sung-Won Kim,Mikael Hartman,Michiki Kubo,Kristan J Aronson,Hidemi Ito,Douglas F Easton,Tiara Hassan,Antonis C Antoniou,Taiki Yamaji,Rachel A Murphy,Manjeet K Bolla,Rajkumar Dorajoo,Kuang Lin,Xiang Shu,Boyoung Park,Iona Y Millwood,Keitaro Matsuo,Jirong Long,Alison M Dunning,Jacques Simard,Yukinori Okada,Ying Zheng,Jun Lv,Artitaya Lophatananon,Wonshik Han,Chiu-Chen Tseng,Qin Wang,Peh Joo Ho,Nur Aishah Mohd Taib,Veronique Kiak Mien Tan,Motoki Iwasaki,Su Ming Tan,Joe Dennis,Jingmei Li,Kartini Rahmat,Shinichi Namba,Kyriaki Michailidou,Ava Kwong,Yon-Ho Jee,Ji-Yeob Choi,Allison W Kurian,Ern Yu Tan,Benita Kiat Tee Tan,Nasim Mavaddat,Zhengming Chen,Pei-Ei Wu,Chen‐Yang Shen ,Kenneth Muir ,Chiea‐Chuen Khor ,Paul Pharoah ,Robin Walters ,Han‐Byoel Lee ,Zheng Wang ,Jian‐Min Yuan ,Woon‐Puay Koh ,Eldarina Wijaya ,Suhyun Lee ,Tsun Leung Chan ,Yeon Jung Kim ,Yu‐Tang Gao ,Sung Sup Park ,Cheng‐Har Yip ,Mei Chee Tai ,Soo‐Hwang Teo

doi:10.1016/j.gim.2021.11.008

Abstract

PurposeNon-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. MethodsThe 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). ResultsThe 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. ConclusionPRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.

Highlights

Genetic inheritance is an important risk factor for breast cancer.[1]
We considered 2 sets of weights for polygenic risk scores (PRSs) derivation using the 287 single-nucleotide variations (SNVs): (1) Asian weights estimated from the training data set 1, taking into account the correlation between SNVs using equation (1), and (2) weights based on a combination of the Asian and European weights using an Empirical Bayes (EB) approach, where the optimal weight is given by the following equation:
Our study provides essential information about the utility of PRSs for breast cancer risk prediction in women of Asian ancestry

Summary

Introduction

Genetic inheritance is an important risk factor for breast cancer.[1] Rare pathogenic variants in several susceptibility genes, including BRCA1, BRCA2 and PALB2, confer increased risks of breast cancer[2]; a majority of the genetic variations in risk is polygenic owing to the fact that a large number of genetic variants combine, in which each genetic variant confers a small increase in risk The effects of these variants can be summarized as polygenic risk scores (PRSs).[3,4] Mavaddat et al[3] developed and validated a 313 variant breast cancer PRS (PRS-313), using data from. This shows the potential of PRS to improve quantification of risk and optimize breast cancer screening and prevention strategies.[6]

Objectives

Methods

Results

Conclusion