Example of one configuration under different hypotheses.

<p>A configuration is represented by one binary vector for each trait of (0,1) values of length n = 8, the number of shared variants in a region. The value of 1 means that the variant is causally involved in disease, 0 that it is not. The first plot shows the case where only one dataset shows an association. The second plot shows that the causal SNP is different for the biomarker dataset compared to the expression dataset. The third plot shows the configuration where the single causal variant is the fourth one.</p

Novel loci not previously reported to colocalise with liver eQTL, but colocalising based on our analysis.

<p>Signals previously not reported as having a probable shared variant but supported by our method based on PP4 (posterior probability for a shared signal) >75% for colocalisation between the liver eQTL dataset and the Teslovich et al. meta-analysis of LDL, HDL, TG, TC, using the strict prior . For 11 genes with strong candidate status for lipid metabolism, we list a key reference that describes their function (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004383#pgen.1004383.s016" target="_blank">Text S2</a> for more details of gene functions).</p

LDL association and eQTL association plots at the <i>SYPL2</i> locus.

<p>The x-axis shows the physical position on the chromosome (Mb) <b>A</b>: -log10(p) association p-values for LDL. The p-values are from the Teslovich et al published meta-analysis of >100,000 individuals. <b>B</b>: −log10(p) association p-values for <i>SYPL2</i> expression in 966 liver samples. <b>C</b>: −log10(p) association p-values for <i>SYPL2</i> expression conditional on the top eQTL associated SNP at this locus (rs2359653).</p

Summary of proportional and Bayesian colocalisation analysis of simulated data.

<p>Each plot shows a different scenario, the total number of causal variants in a region is indicated by number of circles in the plot titles with causal variants affecting both traits, the eQTL trait only, or the biomarker trait only, indicated by full circles, top-shaded circles and bottom-shaded circles respectively. In the top row the causal variant is typed or imputed, whereas only tag variants are typed/imputed in the bottom row. For proportional testing (under the BMA approach), we show the proportion of simulations with posterior predictive p-value <0.05 (black horizontal line) while for our Bayesian analysis we plot the proportion of simulations with the posterior probability (PP3 or PP4) of the indicated hypothesis >0.9. Error bars show 95% confidence intervals (estimated based on an average of 1,000 simulations per scenario). In all cases, for the eQTL sample size is 1,000; genetic variants explain a total of 10% of eQTL variance; for the biomarker trait, the sample size is 10,000.</p

Simulation analysis with a shared causal variant between two studies.

<p>The two datasets used are one eQTL (sample size 966 samples, 10% of the variance explained by the variant) and one biomarker (such as LDL). The variance explained by the biomarker is colour coded and the x-axis shows the sample size of the biomarker study. The y axis shows the median, 10% and 90% quantile of the distribution of PP4 values (which supports a shared common variant).</p

Illustration of the colocalisation results.

<p>Negative [SPACE] (A–B, FRK gene and LDL, PP3 >90%) and positive (C–D, SDC1 gene and total cholesterol, PP4 >80%) colocalisation results. −log10(p) association p-values for biomarker (top, A and C) and −log10(p) association p-values for expression (bottom, B and D) at the <i>FRK</i> (A, B) and <i>SDC1</i> locus (C, D), 1Mb range.</p

SNPs associated with mucosal autoimmune diseases are enriched at T-bet binding sites.

<p>Scatter plots showing log-odds ratio against –log(10) p-value for the enrichment of disease-associated SNPs at different functional annotation datasets (DHS, histone modification, FAIRE-seq and transcription factor binding). Selected enriched functional annotation datasets are highlighted. GM12878 H3K4me1 indicates sites of H3K4me1 in the GM12878 lymphoblastoid cell line. Celiac disease, Crohn’s disease and UC-associated SNPs, but not RA, psoriasis or coronary artery disease-associated SNPs, are strongly enriched at T-bet binding sites (red dots with arrows).</p

Genetic variants alter T-bet binding <i>in vitro</i>.

<p><b>A.</b> Outline of the OligoFlow method. Double-stranded oligonucleotides are annealed to beads and incubated with cell lysate containing the transcription factor of interest. Fluorescently labelled antibody is added and MFI of the beads measured by flow cytometry. The histograms show the MFI of beads coated with oligonucleotides containing a T-bet binding motif (Motif +) or a mutated sequence (Motif -) after incubation with YT lysate, normalised for the number of beads acquired. <b>B.</b> Summary of OligoFlow results for the 11 SNPs tested. In each case, MFI for both alleles is normalised such that the negative control equals 1. Normalised MFI for the lowest binding allele was then subtracted from the value for the highest binding allele. Each cross represents one experiment, with the average difference between alleles represented by a horizontal line. * Significantly different binding between the two alleles (p < 0.05, paired t-test.) <b>C.</b> Representative experiment measuring the binding of T-bet to the A and G alleles of rs1465321. Data for the different oligonucleotide probes are separated according to the key on the right and the MFI is also shown. <b>D.</b> Bar chart showing all replicate experiments for rs1465321. The y-axis shows MFI for each allele normalised to the MFI of the negative control oligonucleotide (set to 1). Each pair of bars represents one experiment, performed with either YT cells (YT) or Th1-polarised primary CD4⁺ cells (Th1). <b>E.</b> As C but for rs1006353. <b>F.</b> As D but for rs1006353. <b>G.</b> As C but for rs11135484. <b>H.</b> As D but for rs11135484.</p

Genetic variants alter T-bet binding <i>in vivo</i>.

<p><b>A.</b> Genomic context of rs1465321 and rs2058622, which is in high LD (r² = 1.0) with rs1465321. <b>B.</b> T-bet ChIP and input sequencing reads that cross rs2058622 (chr2: 102985274–102985565; left) or rs1465321 (chr2: 102986477–102986768; right) in two donors heterozygous for rs1465321. In each case, the number of reads that match the reference allele are shown in black and the alternative allele in green. <b>C.</b> T-bet ChIP and input (Inp) sequencing reads at the set of 19 additional heterozygous SNPs that exhibited allelic imbalanced T-bet binding. For each SNP, the color shows fold-enrichment in the number of sequencing reads matching the Ref or Alt allele, relative to the average number of reads across all samples, as indicated by the scale on the right hand side. SNPs are divided into those exhibiting greater T-bet binding to the reference (Ref) allele (Ref > Alt, top) or the alternative (Alt) allele (Alt > Ref, bottom).</p

Motif analysis does not reliably predict impact on T-bet binding.

<p><b>A.</b> T-bet binding, IgG control and H3K27ac modification (ChIP-seq reads/million) at the genomic regions surrounding the SNPs rs1465321 (left), rs11135484 (center) and rs1006353 (right). The location of the SNPs are indicated by dashed vertical lines. The regions highlighted in grey are expanded in B. <b>B.</b> Expanded view of T-bet binding at the regions highlighted in grey in A. The locations of sequences matching the identified T-bet DNA binding motif (inset) are marked by red lines, together with their score (a negative value indicates a poor match). Only rs1006353 overlaps a T-bet DNA binding motif and the A allele is predicted to disrupt the motif and T-bet binding.</p