DNaseI sensitivity QTLs are a major determinant of human expression variation

By J. Degner, A. Pai, R. Pique-Regi, Jean-Baptiste Veyrieras, Daniel J. Gaffney, Joseph K. Pickrell, Sherryl De Leon, K. Michelini, Noah Lewellen, Gregory E. Crawford and 3 more. Nature, 2012

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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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/230f57422b47cbeb95af7ce383a2aa414fa2b8e1

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Citations in the corpus

Data-driven hypothesis weighting increases detection power in genome-scale multiple testing (2016, methodology): “[38] split the eQTL hypotheses into 10 bins based on DNAseI sensitivity measurements and then applied Storey’s q–value procedure within each bin [3].”
Enhancer Evolution across 20 Mammalian Species (2015, background): “, 2005), as has now been demonstrated across many organisms (Arnold et al., 2014; Cotney et al., 2013; Degner et al., 2012; McLean et al., 2011; Shibata et al., 2012).”
Joint analysis of functional genomic data and genome-wide association studies of 18 human traits (2013, neutral): “Degner et al. [2012]; Giambartolomei et al.”
A method to predict the impact of regulatory variants from DNA sequence (2015, methodology): “To systematically assess the ability of deltaSVM to predict the impact of SNPs on DNase I sensitivity, we examined the relationship between deltaSVM and the effect sizes β for the set of dsQTLs 13 .”
Systems genetics approaches to understand complex traits (2013, background): “Recent studies show that a substantial proportion of eQTLs is in open chromatin regions 30 , methylated regions 31 or transcription factor-binding sites 32 .”
Joint profiling of chromatin accessibility, DNA methylation and transcription in single cells (2017, methodology): “From the RNA-seq data we discarded cells that had (1) less than 300,000 reads mapped (2) more than 15% of total reads mapped to mitochondrial genes, (3) less than 2,000 genes expressed.”