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.

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DEAR EDITOR, Atopic dermatitis (AD) is a common, chronic inflammatory skin disease with a complex aetiology including genetic and environmental risk factors. 1 Obesity has been associated with increased AD risk and severity, 2 but a causal relationship has not been established. In a recent article in the Journal of Allergy and Clinical Immunology, Budu-Aggrey et al. 3 assessed the causality between elevated body mass index (BMI) and AD using a Mendelian randomization (MR) approach. 4 The authors demonstrated a small but significant association between genetically predicted BMI and AD, inferring a causal effect of higher BMI in increasing AD risk (~2% for each 1 kg m -2 increase in BMI), suggesting that obesity may be a modifiable risk factor for AD development.The authors explored the strength of the association between BMI and AD by performing a meta-analysis of 33 observational studies and found that the odds ratio of AD was 1Á02 for each 1 kg m -2 increase in BMI (P = 0Á07 for patients with BMI 25-30, and P = 3Á3 9 10 -4 for patients with BMI > 30). However, as observational studies demonstrate correlations only and are limited by confounders and reverse causation, the authors used MR for further inference on causality.MR is a powerful analytic tool that can infer causal relationships between variables via genetic variants as proxies for exposures and outcomes. However, these inferences depend on three assumptions: (i) that genetic variants associate with the risk factor of interest; (ii) that they share no common cause with the outcome; and (iii) that they do not affect the outcome except through the risk factor of interest (i.e. no horizontal pleiotropy). Horizontal pleiotropy occurs when genetic variants affect the outcome through factors other than the one examined. While these assumptions cannot be validated empirically, the authors' design choices, multiple sensitivity analyses and cautious reasoning provide some confidence that causal inference is supported. 5 The authors used one-and two-sample MR including individual and summative data from the UK Biobank and HUNT databases. Genetic variants associated with BMI and AD were derived from large genome-wide association studies and combined into genetic risk scores (GRS). The relationship between