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Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements.

Hayes JE, Trynka G, Vijai J, Offit K, Raychaudhuri S, Klein RJ - PLoS ONE (2015)

Bottom Line: Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin's lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation.These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions.We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements.

View Article: PubMed Central - PubMed

Affiliation: Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Cell and Developmental Biology Graduate Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, New York, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Though numerous polymorphisms have been associated with risk of developing lymphoma, how these variants function to promote tumorigenesis is poorly understood. Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin's lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation. These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions. Furthermore, we identify putatively functional SNPs that are both in regulatory elements in lymphocytes and are associated with gene expression changes in blood. We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements. This multiscale approach integrating multiple datasets helps disentangle the underlying biology of lymphoma, and more broadly, is generally applicable to GWAS results from other diseases as well.

No MeSH data available.


Related in: MedlinePlus

UES algorithm visualization.This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.
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pone.0139360.g001: UES algorithm visualization.This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.

Mentions: We first asked if lymphoma risk SNPs are enriched in regions annotated as putatively regulatory in GM12878 using our novel method, UES (Fig 1). Using the NHGRI GWAS catalog [27], we identified 56 risk SNPs for lymphoma, including both the Hodgkin’s lymphoma (HD) and non-Hodgkin’s lymphoma (NHL) types. Once the list was pruned to ensure the SNPs were independent and the HLA region was excluded, the resultant list contained 36 risk SNPs (S1 Table) [4–16]. We confirmed that the minor allele distribution of our random SNPs were similar to the original input (input SNPs MAF mean = 0.277; random SNP sets mean = 0.259, median = 0.259, min = 0.176, 1st qu = 0.244, 3rd qu = 0.274, max = 0.347). We first looked at the Deoxyribonuclease I (DNase I) hypersensitivity sites (DHSs) for GM12878, since genomic regions open to DNase digestion have been shown to be accurate markers of regulatory DNA[28]. We queried the ENCODE “unified DNase” track for GM12878, which identifies regions of open chromatin regardless of the particular factors that bind. The lymphoma risk-SNPs were significantly enriched in GM12878 DNase hypersensitivity sites (p < 0.0001), with 16 distinct regions containing risk SNPs potentially explainable by a variant in a DNase hypersensitive site. The 10,000 control sets of randomly selected SNPs with similar characteristics only showed an average of 4.5 regions potentially explainable by variants overlapping a DNase hypersensitive site (Fig 2A and S2 Table). The lymphoma risk-SNPs showed equal enrichment (p<0.0001) in the Roadmap Epigenomics DNase data for GM12878 (S3 Table).


Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements.

Hayes JE, Trynka G, Vijai J, Offit K, Raychaudhuri S, Klein RJ - PLoS ONE (2015)

UES algorithm visualization.This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589387&req=5

pone.0139360.g001: UES algorithm visualization.This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.
Mentions: We first asked if lymphoma risk SNPs are enriched in regions annotated as putatively regulatory in GM12878 using our novel method, UES (Fig 1). Using the NHGRI GWAS catalog [27], we identified 56 risk SNPs for lymphoma, including both the Hodgkin’s lymphoma (HD) and non-Hodgkin’s lymphoma (NHL) types. Once the list was pruned to ensure the SNPs were independent and the HLA region was excluded, the resultant list contained 36 risk SNPs (S1 Table) [4–16]. We confirmed that the minor allele distribution of our random SNPs were similar to the original input (input SNPs MAF mean = 0.277; random SNP sets mean = 0.259, median = 0.259, min = 0.176, 1st qu = 0.244, 3rd qu = 0.274, max = 0.347). We first looked at the Deoxyribonuclease I (DNase I) hypersensitivity sites (DHSs) for GM12878, since genomic regions open to DNase digestion have been shown to be accurate markers of regulatory DNA[28]. We queried the ENCODE “unified DNase” track for GM12878, which identifies regions of open chromatin regardless of the particular factors that bind. The lymphoma risk-SNPs were significantly enriched in GM12878 DNase hypersensitivity sites (p < 0.0001), with 16 distinct regions containing risk SNPs potentially explainable by a variant in a DNase hypersensitive site. The 10,000 control sets of randomly selected SNPs with similar characteristics only showed an average of 4.5 regions potentially explainable by variants overlapping a DNase hypersensitive site (Fig 2A and S2 Table). The lymphoma risk-SNPs showed equal enrichment (p<0.0001) in the Roadmap Epigenomics DNase data for GM12878 (S3 Table).

Bottom Line: Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin's lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation.These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions.We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements.

View Article: PubMed Central - PubMed

Affiliation: Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Cell and Developmental Biology Graduate Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, New York, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Though numerous polymorphisms have been associated with risk of developing lymphoma, how these variants function to promote tumorigenesis is poorly understood. Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin's lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation. These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions. Furthermore, we identify putatively functional SNPs that are both in regulatory elements in lymphocytes and are associated with gene expression changes in blood. We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements. This multiscale approach integrating multiple datasets helps disentangle the underlying biology of lymphoma, and more broadly, is generally applicable to GWAS results from other diseases as well.

No MeSH data available.


Related in: MedlinePlus