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Objective: To date, genome-wide association studies (GWAS), which are primarily conducted in Europeans, have identified a large number of validated DNA methylation quantitative trait loci (meQTLs) acting in both cis and trans. However, it is recognized that there are racial differences in genetic architectures such as allele frequencies and linkage equilibrium patterns, which may differentially influence DNA methylation levels. This points to a critical need to conduct a methylome wide association study (MWAS) to identify meQTLs across multiethnic populations including understudied African Americans (AAs), Latinos, Japanese Americans (JAs), and Native Hawaiians (NHs). Methods: We are performing a GWAS to identify meQTLs in blood leukocytes in a multiethnic population using data from the Multiethnic Cohort Study (MEC). In MEC, “genome-wide” germline genetic variants and DNA methylation levels of blood leukocytes have been measured using the Illumina 1M and MethEPIC array, respectively, for 372 AAs, 408 Latinos, 531 JAs, 319 NHs, and 406 European Americans who were current smokers at time of blood draw. Genotyped data has been imputed with 1000 Genomes phase 3 dataset. For DNA methylation data, standardized quality control (QC) and normalization have been performed. We adjusted for the following potential confounders at time of blood draw: age, sex, body mass index, estimated cell type composition variables, genetic principal components, smoking pack-years, and urinary total nicotine equivalents (a biomarker for internal smoking dose). A stringent Bonferroni-corrected threshold (P<5×10-8/(850,355×6) = 9.80×10-15) was used to define statistical significance. An independent dataset of MEC participants including 114 AAs, 142 Latinos, 286 JAs, 108 NHs, and 270 European Americans will be used to replicate the identified meQTLs. Results: Our preliminary analyses for identifying cis-meQTL SNPs (residing within 1 Mb upstream or downstream of a CpG site of interest) found 169,823 CpG sites (20.0%) are associated with at least one nearby genetic variant in AAs. Similarly, 284,371 CpG sites (33.4%) in JAs, 168,580 CpG sites (19.8%) in Latinos, 123,032 CpG sites (14.5%) in NHs, and 234,822 CpG sites (27.6%) in European Americans are significantly associated with at least one nearby genetic variant. In the pan-ethnic analyses including all five ethnic/racial groups, 193,743 CpG sites (23.0%) are associated with at least one nearby genetic variant. Conclusion: Our preliminary findings identified ethnic-specific and pan-ethnic cis-meQTL SNPs across a multiethnic population that includes understudied AAs, Latinos, JAs, NHs, along with European Americans. If replicated in independent datasets, these findings will significantly improve our understanding of genetic regulation of DNA methylation levels in these populations. Citation Format: Lang Wu, Xiequn Xu, Dalia Ghoneim, Kayla Kim, Alexandra Binder, Yesha Patel, Daniel O. Stram, Loïc Le Marchand, Sungshim L. Park. Identifying DNA methylation quantitative trait loci across multi-ethnic populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5705.

Background: The value of polygenic risk scores (PRS) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves prediction of CAD beyond pooled cohort equations. Methods: An observation study of 291,305 unrelated White British UK Biobank participants en-rolled from 2006 to 2010 was conducted. A case-control sample of 9,499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygen-ic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to exam-ine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. Performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. Results: In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive ac-curacy, there were 7,036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and -0.023 (95% CI, -0.025 to -0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassi-fied to the lower-risk category. Conclusions and Relevance: Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.