Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

Abstract

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. Current approaches to the association detection claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not at aggregate level and can suffer from low statistical power. Here, we propose a statistical method, HIgh REsolution (HIRE), which not only improves the power of association detection at aggregate level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

Fig. 1

A simple cartoon illustration of the HIRE model with three cell types (K = 3) and two phenotypes (disease status and age; q = 2). a Data generation procedure for the observed methylation vector O_i for sample i (i = 1, …, n). In the top panel, O_i is the convolution of cell-type-specific methylation profiles u_i with cellular compositions p_i. Both u_i and p_i depend on the attributes of sample i. The bottom panel describes how sample i’s phenotypes affect u_i via two phenotype-effect matrices B₁ and B₂. In B₁ and B₂, the white square represents zero, which indicates that the phenotype exerts no influence on the corresponding methylation level in u_i. b Inputs and outputs of HIRE. We input the observed methylation matrix O, the phenotype data matrix X, and a predetermined cell type number K into HIRE, and HIRE outputs the estimates for the cellular compositions $\widehat{\mathbf{p}}$, the baseline methylation profiles $\widehat{\mathit{\mu }}$, the phenotype effects ${\widehat{\mathbf{B}}}_{\ell }$, and the penalized BIC value. In addition, HIRE tests whether there is any association between CpG site j and phenotype $\ell$ in cell type k—$H_0:{\beta }_{jk\ell }=0$ vs $H_1:{\beta }_{jk\ell }\ne 0$—and provides the p-values

Fig. 2

Association detection performance of HIRE and commonly used methods in the true alternative setting with K = 3 and n = 180. Source data are provided as a Source Data file. In all figures, red corresponds to HIRE; yellow indicates the unadjusted analysis; brown represents SVA; purple refers to RefFreeEWAS; dark blue indicates EWASher; and light blue corresponds to ReFACTor. a ROC curves of HIRE and commonly used methods. HIRE has the largest area under the curve among all of the methods. b True cell-type-specific association pattern with disease status for 10,000 simulated CpG sites; columns correspond to cell types, and the rows represent the CpG sites. Dark cells correspond to risk-CpG sites, and grey cells are CpG sites not associated with the disease status. c Detected cell-type-specific association pattern with disease status by HIRE. Darkness represents $-{\log }_{10}\left(p-\mathrm{value}\right)$ d–i The p-value density plots for association with disease status in the simulation dataset for d HIRE, e unadjusted analysis, f SVA, g RefFreeEWAS, h EWASHer, and i ReFACTor. j–o The Q-Q plots for association with disease status for j HIRE, k unadjusted analysis, l SVA, m RefFreeEWAS, n EWASHer, and o ReFACTor

Fig. 3

Application of HIRE and commonly used methods to two real methylation datasets: RA and GALA II. Source data are provided as a Source Data file. a Cell-type-specic association pattern with RA status detected by HIRE in the RA dataset. Darkness represents the −log10(p−value). b Cell-type-specic association pattern with gender detected by HIRE in the GALA II dataset. The darkness represents the −log10(p−value). c–h The p-value density plots for association with RA status in the RA dataset for c HIRE, d unadjusted analysis, e SVA, f RefFreeEWAS, g EWASHer, and h ReFACTor. i-n Q-Q plots for association with RA status in the RA dataset for i HIRE, j unadjusted analysis, k SVA, l RefFreeEWAS, m EWASHer, and n ReFACTor