Smoking is associated with an increased risk of developing ACPA-positive but not ACPA-negative rheumatoid arthritis in Asian populations: evidence from the Malaysian MyEIRA case-control study.

We investigated the association between cigarette smoking and the risk of developing rheumatoid arthritis (RA) in the Malaysian population. A total of 1,056 RA patients and 1,416 matched controls aged 18-70 years within a defined area of Peninsular Malaysia were evaluated in a case-control study between August 2005 and December 2009. A case was defined as a person with early diagnosed RA using the 1987 American College of Rheumatology criteria for RA. Controls were randomly selected matched for sex, age, and residential area. Cases and controls answered a questionnaire on a broad range of issues, including lifestyle factors and smoking habits wherein current and former smoking was classified as ever-smoking. The presence of anti-citrullinated peptide antibodies (ACPA) was determined for cases and controls. We found that ever-smokers had an increased risk of developing ACPA-positive RA [odds ratio (OR) = 4.1, 95% confidence interval (CI) 1.9-9.2] but not ACPA-negative RA (OR = 0.7, 95% CI 0.3-2.0), compared with never-smokers. A significant dose-response relationship between cumulative dose of smoking and risk of ACPA-positive RA was observed (<20 pack-years OR = 3.3, 95% CI 1.1-9.8; at least 20 pack-years OR = 5.2, 95% CI 1.6-17.6). Hence, smoking is associated with an increased risk of ACPA-positive RA in the Malaysian population, in which the genetic context is similar to several other Asian countries.

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.