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10.1016/j.cell.2016.10.026

http://scihub22266oqcxt.onion/10.1016/j.cell.2016.10.026
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C5119954!5119954!27863251
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pmid27863251      Cell 2016 ; 167 (5): 1398-1414.e24
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  • Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells #MMPMID27863251
  • Chen L; Ge B; Casale F; Vasquez L; Kwan T; Garrido-Martín D; Watt S; Yan Y; Kundu K; Ecker S; Datta A; Richardson D; Burden F; Mead D; Mann A; Fernandez J; Rowlston S; Wilder S; Farrow S; Shao X; Lambourne J; Redensek A; Albers C; Amstislavskiy V; Ashford S; Berentsen K; Bomba L; Bourque G; Bujold D; Busche S; Caron M; Chen SH; Cheung W; Delaneau O; Dermitzakis E; Elding H; Colgiu I; Bagger F; Flicek P; Habibi E; Iotchkova V; Janssen-Megens E; Kim B; Lehrach H; Lowy E; Mandoli A; Matarese F; Maurano M; Morris J; Pancaldi V; Pourfarzad F; Rehnstrom K; Rendon A; Risch T; Sharifi N; Simon MM; Sultan M; Valencia A; Walter K; Wang SY; Frontini M; Antonarakis S; Clarke L; Yaspo ML; Beck S; Guigo R; Rico D; Martens J; Ouwehand W; Kuijpers T; Paul D; Stunnenberg H; Stegle O; Downes K; Pastinen T; Soranzo N
  • Cell 2016[Nov]; 167 (5): 1398-1414.e24 PMID27863251show ga
  • 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.
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