Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

By Lu Chen, B. Ge, Francesco Paolo Casale, Louella Vasquez, T. Kwan, D. Garrido-Martín, S. Watt, Ying Yan, Kousik Kundu, Simone Ecker and 66 more. Cell, 2016

619 citations recorded by the source corpus.

Summary Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bcc975b156e421a37d5e6e29c22971a2a658216f

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Citations in the corpus

Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, background): “8) with a given molecular event had a high probability (>99%) of colocalization (Chen et al., 2016).”
Impact of genetic polymorphisms on human immune cell gene expression (2018, neutral): “…(Grundberg et al., 2012; Wright et al., 2014; Zhernakova et al., 2017) or in a relatively small number of homogeneous cell types from the same study subjects, thus limiting cell-specific eQTL analyses (Chen et al., 2016; Fairfax et al., 2014; Lee et al., 2014; Quach et al., 2016; Raj et al., 2014).”
Genome-wide enhancer maps link risk variants to disease genes (2021, methodology): “We downloaded H3K27ac data in monocytes and T cells from the Blue-print Project and analysed allele-specific signals called by the WASP method as previously described 62 .”