1. Volume 165, Issue 3, Pages 730-741 (April 2016)
Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk 
Ashley K. Tehranchi, Marsha Myrthil, Trevor Martin, Brian L. Hie, David Golan, Hunter B. Fraser 
Cell 
Volume 165, Issue 3, Pages (April 2016)
DOI: /j.cell
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3. Figure 1 Outline and Results of Pooled ChIP-Seq
(A) Performing ChIP in a pool of individuals selects only the DNA molecules bound by a TF (or with a particular histone modification), thus enriching for high-affinity alleles. In this example, the G allele has a low pre-ChIP frequency but a high post-ChIP frequency, due to its higher affinity. The allele frequencies and read counts are for the SNP highlighted in (B).
(B) For NF-κB, plotting pre-ChIP versus post-ChIP allele frequencies shows that the vast majority of allele frequencies change very little, as expected. One SNP (rs ) significantly off the diagonal is highlighted. This SNP occurs in the boxed position of an NF-κB binding site motif, where the G allele has higher predicted affinity, consistent with the allele frequency shift.
(C) The number of independent bQTLs (after removing those in LD) (left) and the percent of all bound SNPs called as bQTLs (right).
(D) Directionality agreement between predicted effects of SNPs in TF binding motifs and their observed associations. SNPs that are bound but have no allele frequency shift (p > 0.5) show ∼50% agreement, as expected by chance; bQTL (p < 0.005) agreement is significantly higher. H3K4me3 has no motif, so is not included. All comparisons are significant at Fisher’s exact test p < except for Stat1, due to its small number of bQTLs.
See also Figure S1 and Table S1.
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4. Figure 2 Comparing Pooled bQTLs with ChIP-Seq in Individual Samples
(A) Examining allele-specific binding of PU.1 (measured in individual heterozygous samples) at 592 PU.1 bQTLs (Waszak et al., 2015), we found 88% directionality agreement.
(B) Comparing our PU.1 bQTLs to allele-specific binding at 89 SNPs previously reported as PU.1 bQTLs (Waszak et al., 2015), we found 99% directionality agreement.
(C) Effect sizes of our bQTLs compared to the strength of allelic bias summed across individual heterozygous samples.
(D) Number of PU.1 bQTLs per sequence read and per ChIP, at equivalent significance cutoffs for each dataset.
See also Figure S2.
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5. Figure 3 Comparisons with Other Molecular-Level QTLs
For each type of previously published QTL, we calculated the degree of allelic concordance with our bQTLs.
(A) H3K4me3 QTLs (Grubert et al., 2015).
(B) dsQTLs (Degner et al., 2012).
(C) eQTLs (Lappalainen et al., 2013).
(D) Causal SNPs underlying eQTLs (Tewhey et al., 2016).
See also Figure S3.
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6. Figure 4 Causal Relationships between TFs
(A) For every pairwise combination of bQTLs, we calculated the enrichment of their overlaps (upper right, relative to bound SNPs that are not bQTLs) and the degree of allelic concordance among the overlaps (lower left).
(B) We tested whether SNPs in the binding motifs for one TF were predictive of binding of another TF. Scatter plots show allele frequencies before and after ChIP (as in Figure 1B), with those SNPs whose effect is in the direction predicted by the motif’s PWM colored to match the ChIP-ed TF and those not matching colored black.
(C) Allelic bias of CTCF binding at heterozygous sites (Ding et al., 2014) generally agrees in direction with our bQTLs. See also Figure S4.
(D) Our model for CTCF as a major pioneer factor.
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7. Figure 5 Long-Range Effects of bQTLs
(A) An example of a bQTL for all five TFs that affects the expression of five genes, including one (BPGM) 525 kb away.
(B) The pattern of chromosomal contacts around the bQTL shown above indicates high levels of interactions with the promoter regions of the distal genes that it affects. No enrichment is observed with CALD1, a gene within this region not affected by the bQTL. Color bar indicates the ratio of observed/expected contact, given the distance between loci (even though the deletion is not enriched more than expected for contacts with the two closest genes, C7ORF49 and TMEM140, there is still a high level of contact with these and other proximal loci). Hi-C data were normalized with the “balanced” method (Rao et al., 2014). See also Figure S5.
(C) Enrichment of bQTLs for local and distal hQTLs affecting H3K4me3 show a consistent bias for local effects.
(D) Enrichment of bQTLs for local and distal hQTLs affecting H3K4me1 show a consistent bias for distal effects.
(E) bQTLs affect the extent of long-range contacts. For each factor, the number of bQTLs where the higher-bound allele has more distal (>30 kb) interactions is plotted in dark blue, and the number where the lower-bound allele has more is plotted in light blue. The difference is significant in every case.
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8. Figure 6 Effects of bQTLs on Disease Risk
Each example involves a SNP in a TF binding motif that is also a bQTL and is associated with disease risk.
(A) bQTL for NF-κB (highlighted in Figure 1B) in an NF-κB binding motif that is also an eQTL for EIF2AK1 and associated with myocardial infarction.
(B) bQTL for NF-κB in an NF-κB binding motif that is also an eQTL for CCND1 and is associated with prostate cancer. See also Figure S5.
(C) bQTL for PU.1, NF-κB, and JunD in a PU.1 binding motif that is also associated with Crohn’s disease.
(D) bQTL for JunD and PU.1 in a CTCF binding motif that is also associated with inflammatory bowel disease.
(E) Comparing enrichments for asthma-associated SNPs: all bound SNPs (left) and bQTLs (right).
See also Figure S6 and Table S2.
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9. Figure S1
Data QC and Investigation of bQTLs in Motifs, Related to Figure 1 and Table S1
(A) Comparison of post-ChIP allele frequencies between biological replicates.
(B) Pre-ChIP versus post-ChIP allele frequency plots (similar to Figure 1B.)
(C) QQ plots. p values were calculated using our modified binomial test.
(D) bQTLs in TF-binding motifs. For each TF, we calculated what fraction of bQTLs fall within a motif for that TF, as well as what fraction of all SNPs within predicted motifs for that TF were detected as bQTLs.
(E) GC content biases of bQTLs outside motifs. To explore the idea that GC content flanking a TF binding site can play an important role in affinity, we compared the GC content of high-affinity (blue) versus low-affinity (orange) alleles for bQTLs at varying distances from the TF’s motif. We observed one case of high-affinity alleles with increased GC content with 25 bp of the motif (NF-κB), and one case with increased AT content within 25 bp of the motif (Stat1). We did not observe any significant biases further from the motif, for any TF. The numbers at each point indicate the number of SNPs with G or C as the high-affinity allele, or low-affinity allele.
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10. Figure S2
Comparison of Our PU.1 bQTLs with Those Previously Reported, Related to Figure 2
At each cutoff for our bQTL p value, we tested the enrichment of bQTLs with QTLs for histone modifications (hQTLs). Our goal was to find a cutoff where the enrichments are similar for all three hQTLs reported. Overall, our bQTLs had slightly higher enrichment in 2/3 comparisons.
(A) For H3K4me1 QTLs, our enrichment at 5x10−5 is slightly lower than that of the previously reported PU.1 bQTLs.
(B) For H3K4me3 QTLs, our enrichment at 5x10−5 is slightly higher than that of the previously reported PU.1 bQTLs.
(C) For H3K27ac QTLs, our enrichment at 5x10−5 is slightly higher than that of the previously reported PU.1 bQTLs.
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11. Figure S3
bQTL Enrichment and Directionality Agreement with Other QTLs, Related to Figure 3
(A) Enrichment in overlaps between bQTLs and other QTLs. Enrichment was measured compared to non-bQTLs that are bound by the TF, to control for any biases in the locations of TF binding sites.
(B) Directionality agreement between bQTLs and mQTLs. Lower DNA methylation is associated with higher binding, but the agreement is not as strong as for other chromatin QTLs (Figures 3A and 3B), as expected since DNA methylation can be associated with either activation or repression. mQTLs are from Banovich et al. (2014).
(C) Directionality agreement for bQTLs outside of motifs. Similar to Figure 3, except only for bQTLs outside of consensus motifs for the corresponding TF. The agreements are almost identical in every case, indicating the bQTLs outside motifs are not enriched for false positives.
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12. Figure S4
Enrichment in Overlaps between Our bQTLs and SNPs with Allele-Specific CTCF Binding, Related to Figure 4
Enrichment in overlaps between bQTLs and allele-specific CTCF QTLs. Enrichment was measured similar to Figure S3A.
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13. Figure S5 Chromosomal Contacts around CCND1, Related to Figure 5
Hi-C data from an LCL (Rao et al., 2014) are plotted as the observed/expected ratio. Three SNPs disrupting enhancers are indicated in blue. The entire region encompassing these SNPs and CCND1 is enriched for contacts.
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14. Figure S6
Comparison of bQTL/GWAS Overlap versus Bound SNP/GWAS Overlap, Related to Figure 6 and Table S2
We focused on autoimmune diseases, because of their relevance to LCLs. Seven out of the 12 had negative correlations between the TF-specific enrichments for bQTLs/GWAS overlaps and the bound SNP/GWAS overlaps (median r = −0.03), indicating that inferences based on all SNPs bound by a TF may lead to misleading results about associations between TF binding and disease risk, since most of these SNPs do not affect binding.
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