Abstract
Genome-wide association studies have provided evidence for inherited genetic predisposition to chronic lymphocytic leukemia (CLL). To gain insight into the mechanisms underlying CLL risk we analyze chromatin accessibility, active regulatory elements marked by H3K27ac, and DNA methylation at 42 risk loci in up to 486 primary CLLs. We identify that risk loci are significantly enriched for active chromatin in CLL with evidence of being CLL-specific or differentially regulated in normal B-cell development. We then use in situ promoter capture Hi-C, in conjunction with gene expression data to reveal likely target genes of the risk loci. Candidate target genes are enriched for pathways related to B-cell development such as MYC and BCL2 signalling. At 14 loci the analysis highlights 63 variants as the probable functional basis of CLL risk. By integrating genetic and epigenetic information our analysis reveals novel insights into the relationship between inherited predisposition and the regulatory chromatin landscape of CLL.
Highlights
Genome-wide association studies have provided evidence for inherited genetic predisposition to chronic lymphocytic leukemia (CLL)
Characterization of the regulatory elements that are perturbed by CLL genome-wide association studies (GWAS) risk variants is central to efforts to define the mechanisms through which these variants operate and to reconstruct the biological networks that underlie CLL tumorigenesis
We combined the genetic and epigenetic data to determine in unprecedented detail genotype-dependent patterns of chromatin accessibility, activity, and DNA methylation in CLL
Summary
Genome-wide association studies have provided evidence for inherited genetic predisposition to chronic lymphocytic leukemia (CLL). By integrating genetic and epigenetic information our analysis reveals novel insights into the relationship between inherited predisposition and the regulatory chromatin landscape of CLL. Integration of chromatin immunoprecipitation–sequencing (ChIP-seq), chromatin accessibility, DNA methylation, and gene expression profiles with GWAS data has revealed novel biological insights for other diseases[11]. We used three-dimensional chromatin data to explore physical interactions between the risk loci and their candidate target genes. These analyses allowed for a detailed interpretation of GWAS signals for CLL and to infer the molecular mechanisms through which loci operate
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