Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, polygenic risk scores (PRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring PRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses and 4) to predict individual risks of disease or treatment success. One of the biggest challenges for PRS is its limited applicability across ancestries. We adopted the popular PRS pruning and thresholding approach to methylation data and evaluated its applicability across ancestries by using publicly available data from 1199 samples with African, European and Indian ancestry. First, we used the Co-Methylation with genomic CpG Background (CoMeBack) method to define Co-methylated Regions (CMRs), spanning sets of array probes constructed based on all genomic CpG sites (similar to “LD clusters” in genotype data). Next, we selected one CpG site from each CMR based on its association with the trait of interest in an independent reference dataset (p-value below specific threshold, “pruning and thresholding”). Finally, we calculated a methylation risk score for each sample as a sum of the remaining beta values weighted by the corresponding beta estimates from the independent summary statistics. In contrast to PRS, our preliminary findings suggest that the derived MRS are comparable across different ancestries, which indicates their broad applicability even for ethnically heterogeneous study populations. Finally, we will apply this approach in the multi-ethnic Drakenstein Child Health Study to better understand the biological mechanisms behind the association between prenatal air pollution exposure and infant lung function.