Accounting for cellular heterogeneity is critical in epigenome-wide association studies

By Andrew E. Jaffe, Rafael A. Irizarry. Genome Biology, 2014

1,011 citations recorded by the source corpus.

Epigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common. A series of papers reporting age-related changes in DNA methylation profiles in peripheral blood have already been published. However, blood is a heterogeneous collection of different cell types, each with a very different DNA methylation profile. Using a statistical method that permits estimating the relative proportion of cell types from DNA methylation profiles, we examine data from five previously published studies, and find strong evidence of cell composition change across age in blood. We also demonstrate that, in these studies, cellular composition explains much of the observed variability in DNA methylation. Furthermore, we find high levels of confounding between age-related variability and cellular composition at the CpG level. Our findings underscore the importance of considering cell composition variability in epigenetic studies based on whole blood and other heterogeneous tissue sources. We also provide software for estimating and exploring this composition confounding for the Illumina 450k microarray.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/59419d6c818f97000efc1c68eadc4f38b26007d6

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Citations in the corpus

Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, neutral): “PMID: 24874100 [8] Birnbaum R, Jaffe AE, Qiang Chen, Hyde TM, Kleinman JE, Weinberger DR.”
DNA methylation and healthy human aging (2015, background): “For example, many studies have shown that brain regions follow this pattern, showing rapid changes in DNA methylation in the early life period and then slowing gradually over the lifespan (Horvath et al., 2012; Numata et al., 2012 Lister et al., 2013).”
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, methodology): “The cell composition of blood in RA patients is a known source of confounding (Jaffe and Irizarry 2014), and we accounted for cell-type heterogeneity by using K ¼ 5 factors in PCA, CATE, and LFMM 2.0.”
DNA methylation age of blood predicts all-cause mortality in later life (2015, neutral): “It is well known that blood cell types have distinct methylation profiles [21,22].”
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “It has been shown that in epigenome-wide association studies (EWAS), celullar heterogeneity, that is different cell-type proportions across samples, can lead to spurious associations when confounded with the outcome of interest (Houseman et al., 2012; Jaffe and Irizarry, 2014).”
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “This is particularly relevant for Epigenome-Wide Association Studies (EWAS) where the effect sizes of interest could be small compared to changes in tissue composition [3, 4].”
SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions (2018, methodology): “Most importantly, in the case of EWAS or mQTL/meQTL studies, where (i) mixed tissues are used (implication for this was discussed by Jaffe and Irizarry (53)) and (ii) there often lacks a clonal expansion as in the case of cancer, real ‘signal’s are often of a lower scale than the effect size of thes”
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data (2016, methodology): “…from known tissue-specific differentially methylated positions (tDMPs) and cell type-specific DMPs (cDMPs): the heatmap is a composite of results for the top 1,000 tDMPs for blood, kidney, and lung (Lowe et al., 2015), and top cDMPs for CD14+, T cells, and NK cells (Jaffe and Irizarry, 2014).”