# Estimating population structure using epigenome-wide methylation data

## TL;DR

This paper introduces a new method to estimate population structure using DNA methylation data, which can help improve the accuracy of epigenome-wide association studies.

## Contribution

The novel contribution is the development of methylation population scores (MPSs) to predict genetic principal components using supervised learning.

## Key findings

- MPSs showed strong correlation with genetic principal components (GPCs), with R² ranging from 0.27 to 0.98.
- MPSs outperformed alternative methylation-based methods in differentiating self-reported racial/ethnic groups.
- MPSs reduced inflation in EWAS similarly to GPCs and can be used when genetic data are unavailable.

## Abstract

Population stratification is one of the source of inflation in epigenome-wide association studies (EWAS) when not properly accounted for. To address this, we developed methylation population scores (MPSs) to predict genetic principal components (GPCs) using a feature selection approach. We used multi-ethnic DNA methylation data from Illumina EPIC arrays across five cohorts, including MESA (n = 929), CARDIA (n = 1123), JHS (n = 1365), ARIC (n = 2338), and HCHS/SOL (n = 1475), randomly splitting participants into training (85%) and test (15%) sets. Within each cohort, associations between GPCs and CpG sites were estimated using linear regression adjusting for age, sex, smoking and alcohol use, race/ethnicity, body mass index, and cell type proportions, followed by meta-analysis and selection of CpGs with FDR <0.05. We then applied a two-stage weighted least squares Lasso regression to construct MPSs, adjusting for the aforementioned covariates. In the test dataset, MPSs showed strong correlation with GPCs, with R² ranging from 0.27 (MPS7 vs. GPC7) to 0.98 (MPS1 vs. GPC1). Visualization demonstrated that MPSs recapitulated the pattern shown by GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups and outperformed methylation-based principal components constructed using alternative published methods. Additionally, MPSs showed comparable performance to GPCs in reducing inflation in EWAS. Overall, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations, and provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent.