DNase I sensitivity QTLs are a major determinant of human expression variation

Abstract

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.

Figure 1. Genome-wide identification of dsQTLs and a typical example

(A) QQ-plots for all tests of association between DNaseI cut rates in 100bp windows, and variants within 2kb (green) and 40kb (black) regions centered on the target DHS windows. (B) Allele-specific analysis of dsQTLs in heterozygotes. Plotted are the predicted (x-axis) and observed (y-axis) fractions of reads carrying the major allele based on the genotype means. (C) Example of a dsQTL (rs4953223). The black line indicates the position of the associated SNP. (D) Boxplot showing that rs4953223 is strongly associated with local chromatin accessibility (P=3×10⁻¹³). (E) The T allele, which is associated with low DNaseI sensitivity, disrupts the binding motif of a previously identified NF-κB binding site at this location (F). NF-κB ChIP-seq data from 10 individuals indicates a strong effect of this SNP on NF-κB binding.

Figure 2. Properties of dsQTLs

(A) Aggregated plot of DNaseI-sensitivity for high-confidence dsQTLs that lie within the target DHS. Individuals were assigned into the high-sensitivity (blue), heterozygote (green), and low-sensitivity (red) classes. The shading indicates the bootstrap 95% confidence intervals. (B) The peak density of dsQTLs is very tightly focused around the target DHS window. (C) Total fraction of cis-dsQTLs that fall into different categories of distance from the target window (x-axis) and different annotations (y-axis). The total area of each rectangle is proportional to the estimated number of dsQTLs in that category. (D) Boxplot showing distribution of PWM score differences between high sensitivity and low sensitivity dsQTL alleles, respectively. Notches indicate 95% CI for median. (E) The x-axis shows the fraction of sequence reads predicted to carry the major allele based on the DNaseI genotype means; the y-axis shows the observed fraction in ChIP-seq data. The lines show the regression fits for each factor separately; the numbers in the legend show the fraction of sites that are in a concordant direction for each factor.

Figure 3. Relationship between dsQTLs and eQTLs

(A) Example of a dsQTL SNP that is also an eQTL for the gene SLFN5. The SNP disrupts an interferon-sensitive response element, thereby changing local chromatin accessibility within the first intron of SLFN5. Expression of SLFN5 has been shown to be inducible by interferon-α in melanoma cell-lines. DNase-seq (left column) and RNA-seq (right column) measurements from DNase-seq and RNA-seq are plotted, stratified by genotype at the putative causal SNP. (B) QQ-plot of the t-statistic for association with gene expression changes (eQTL) of dsQTL SNPs. The sign of the eQTL t-statistic is with respect to the genotype that increases DNase sensitivity.

Figure 4. Relationship between dsQTLs and eQTLs

(A) Most joint dsQTL-eQTLs lie close to the gene TSS. (B) Effect of various factors on the log odds that a given dsQTL is also an eQTL, while controlling for the strong distance relationship observed in panel A. In annotations (1) and (2) we do not consider the direction of transcription. In annotations (6-8), ChIP-seq is measured on the dsQTL window. One of the most significant annotations in delineating the regulatory regions is defined by the presence of the CTCF insulator element, which reduces the probability that a dsQTL is an eQTL by 2.4-fold. Error bars represent 95% confidence intervals