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  • 1
    Publication Date: 2012-02-07
    Description: The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation. However, it remains difficult to identify the causal variants underlying eQTLs, and little is known about the regulatory mechanisms by which they act. Here we show that genetic variants that modify chromatin accessibility and transcription factor binding are a major mechanism through which genetic variation leads to gene expression differences among humans. We used DNase I sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines, for which genome-wide genotypes and estimates of gene expression levels are also available. We obtained a total of 2.7 billion uniquely mapped DNase I-sequencing (DNase-seq) reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 8,902 locations at which the DNase-seq read depth correlated significantly with genotype at a nearby single nucleotide polymorphism or insertion/deletion (false discovery rate = 10%). We call such variants 'DNase I sensitivity quantitative trait loci' (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16%) of dsQTLs are also associated with variation in the expression levels of nearby genes (that is, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55% of eQTL single nucleotide polymorphisms are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome and are likely to be important contributors to phenotypic variation.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3501342/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3501342/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Degner, Jacob F -- Pai, Athma A -- Pique-Regi, Roger -- Veyrieras, Jean-Baptiste -- Gaffney, Daniel J -- Pickrell, Joseph K -- De Leon, Sherryl -- Michelini, Katelyn -- Lewellen, Noah -- Crawford, Gregory E -- Stephens, Matthew -- Gilad, Yoav -- Pritchard, Jonathan K -- HG006123/HG/NHGRI NIH HHS/ -- MH084703/MH/NIMH NIH HHS/ -- MH090951/MH/NIMH NIH HHS/ -- R01 HG006123/HG/NHGRI NIH HHS/ -- R01 HG006123-01/HG/NHGRI NIH HHS/ -- R01 HG006123-02/HG/NHGRI NIH HHS/ -- R01 MH090951/MH/NIMH NIH HHS/ -- R01 MH090951-01/MH/NIMH NIH HHS/ -- R01 MH090951-02/MH/NIMH NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2012 Feb 5;482(7385):390-4. doi: 10.1038/nature10808.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22307276" target="_blank"〉PubMed〈/a〉
    Keywords: Chromatin/genetics/metabolism ; *DNA Footprinting ; Deoxyribonuclease I/*metabolism ; Gene Expression Profiling ; Gene Expression Regulation/*genetics ; Genetic Variation/*genetics ; Genome, Human/genetics ; Humans ; Phenotype ; Polymorphism, Single Nucleotide/genetics ; Quantitative Trait Loci/*genetics ; Sequence Analysis, DNA ; Transcription Factors/metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2015-04-12
    Description: Motivation: Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele-specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available, it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. Results: We present QuASAR, quantitative allele-specific analysis of reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls, while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high-quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available. Availability and implementation: http://github.com/piquelab/QuASAR . Contact: fluca@wayne.edu or rpique@wayne.edu Supplementary information: Supplementary Material is available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2015-10-29
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Springer Nature
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