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Title: Sensitive mapping of chromatin-altering polymorphisms reveals molecular drivers of autoimmune disease      
dateReleased:
03-01-2015
description:
Although most disease associations detected by GWAS are nongenic, very few have been mapped to causal regulatory variants. Here, we present a method for detecting regulatory QTLs that does not require genotyping or whole-genome sequencing. The method combines deep, long-read ChIP-seq with a new statistical test that simultaneously scores peak height correlation and allelic imbalance: the Genotype-independent Signal Correlation and Imbalance (G-SCI) test. We performed histone acetylation ChIP-seq on 57 human lymphoblastoid cell lines and used the resulting reads to call 500,066 SNPs de novo within regulatory elements. The G-SCI test annotated 8,764 of these as histone acetylation QTLs (haQTLs) - an order of magnitude larger than the set of candidates detected by expression QTL analysis. Lymphoblastoid haQTLs were highly predictive of autoimmune disease mechanisms. Thus, our method facilitates large-scale regulatory variant detection in any moderately-sized cohort for which functional profiling data can be generated, thus simplifying identification of causal variants within GWAS loci. We applied our method, named Regulatory Variant Ascertainment and chromatin Regression by sequencing (RegVAR-seq), to 57 cell lines from a single population group. We used the resulting sequence data for variant calling, and validated calls using an independent platform. We then identified histone acetylation QTLs (haQTLs) using a novel statistical test that requires no prior genotype information and combines peak height and allelic imbalance data across the 57 individuals. Transcription factor binding site analysis was used to independently support the functionality of haQTLs. Finally, we examined the association between haQTLs and SNPs associated with human phenotypes.
privacy:
not applicable
aggregation:
instance of dataset
ID:
E-GEOD-58852
refinement:
raw
alternateIdentifiers:
58852
keywords:
functional genomics
dateModified:
03-11-2015
availability:
available
types:
gene expression
name:
Homo sapiens
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-58852/E-GEOD-58852.raw.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-58852/E-GEOD-58852.processed.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58852
storedIn:
Gene Expression Omnibus
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
EBI
homePage: http://www.ebi.ac.uk/
ID:
SCR:004727
name:
European Bioinformatics Institute
homePage: https://www.ebi.ac.uk/arrayexpress/
ID:
SCR:002964
name:
ArrayExpress

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