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Supporting Online Material for

Variation in Transcription Factor Binding Among Humans Maya Kasowski, Fabian Grubert, Christopher Heffelfinger, Manoj Hariharan, Akwasi

Asabere, Sebastian M. Waszak, Lukas Habegger, Joel Rozowsky, Minyi Shi, Alexander E. Urban, Mi-Young Hong, Konrad J. Karczewski, Wolfgang Huber, Sherman M. Weissman,

Mark B. Gerstein, Jan O. Korbel,† Michael Snyder†

†To whom correspondence should be addressed. E-mail: [email protected] (J.O.K.);

[email protected] (M.S.)

Published 18 March 2010 on Science Express

DOI: 10.1126/science.1183621

This PDF file includes:

Materials and Methods Figs. S1 to S16 Tables S1 to S20 References

Supporting Online Material (SOM)

Materials and Methods

ChIP Seq. Chromatin immunoprecipitation for NFκB was carried out as previously described

(S1, S2). Biological replicates were grown in separate batches and at separate times. 3 – 5

different cell lines were grown in parallel at any given time. Briefly, 2 x 10^8 cells were grown to

a density of 0.6-0.8 x 10^6/mL in 15% fetal bovine serum and treated with 25 ng/mL human

recombinant TNF-alpha (eBioscience #14-8329, San Diego, CA) for six hours at 37°C, 5% CO2.

After stimulation, cells were cross-linked in 1% formaldehyde for 10 minutes at room

temperature. Nuclear lysates were sonicated using a Branson 250 Sonifier (power setting 2, 100%

duty cycle for 7 × 30-s intervals), such that the chromatin fragments ranged from 50-2000kb.

Clarified lysates were divided in half and treated overnight at four degrees Celsius with 8µg of

either anti-NFκB p65 (C-20) rabbit polyclonal antibody or normal rabbit IgG (Santa Cruz

Biotechnology Sc-2027, Santa Cruz, CA). Protein-DNA complexes were captured on Protein A

agarose beads (Millipore #16-156, Billerica, MA) and eluted in 1% SDS TE buffer at 65°C.

Following cross-link reversal and purification, the ChIP DNA sequencing libraries were

generated according to Illumina DNA Sample Kit Instructions (Illumina Part # 0801– 0303, San

Diego, CA). The protocol was modified such that enzymes were obtained from other suppliers, as

described (S3). Libraries were sequenced on an Illumina Genome Analyzer II. PolII ChIP DNA

and libraries were prepared as described for NFκB, except that cells were not stimulated with

TNF-alpha. DNA was immunoprecipitated with either 24µg of mouse monoclonal 8WG16

antibody (Covance MMS-126R, Princeton, NJ) or normal mouse IgG (Millipore #12-371).

Analysis of the ChIP-data. To systematically analyze the ChIP Seq data we established the

computational analysis pipeline presented in Fig. S1B. (1) Read mapping. ChIP-Seq reads were

mapped onto unique (i.e. unambiguous) positions on the genome using ELAND

(www.illumina.com), allowing for up to two mismatches. Table S16 displays total numbers of

reads analyzed and Table S17 displays numbers of unambiguously mappable reads for each

replicate. (2) Identification of BRs. To identify BRs we first identified peaks in the mapped ChIP

Seq data by applying the PeakSeq algorithm using default parameters and a stringent P-value

threshold of 0.001 (S2). Second, we clustered peaks from different replicates of the same

individual into BRs by joining hits that intersected in their genomic coordinates. Peaks not

replicable in at least 2 replicates of the same individual were discarded (note that this step avoids

considering non-replicable peaks that e.g. may accumulate statistically in the presence of a large

number of reads). Third, we clustered and joined BRs across individuals by combining

intersecting events into discrete BRs. Figure S12 shows the size distribution of NFκB and PolII

BRs, and Fig. S3A displays a genome-wide view of BR occupancy. (3) Identifying significant

differences in binding. The intensity of signal was determined for each BR and replicate as the

sum of sequenced DNA reads mapping into the BR. To correct for differences in sequencing

depth between replicates, we normalized numbers of reads in a BR across all replicates and

samples using quantile normalization (function “normalize.quantiles”; available in R; www.R-

project.org). This adjusts differences in sequencing depth, i.e. by reasonably scaling both the

number of reads per replicate and the number of reads falling into BRs. BRs that differed between

each individual were conservatively identified with a pair-wise statistical comparison, i.e.

ANOVA, using a generalized linear model with Poisson error distribution (www.R-project.org).

Regions were considered differentially bound when the ANOVA P-value was < 0.05/N

(Bonferroni adjustment), with N being the number of pair-wise comparisons made (e.g., 857,745

= 19,061 * 10 * 9 / 2 in the case of PolII). Using this ANOVA approach we estimated that in pair-

wise comparisons between individuals 7.5% and 25%, respectively, of all NFκB and PolII BRs,

are differentially bound (Fig. S3C). Often, this involves fold-changes in BR occupancy (i.e., fold-

changes in terms of normalized read-counts within BRs) of >2 fold (Fig. S3D).

In each ANOVA test the pair-wise comparison between samples involved exactly three

replicates for a sample, to assure that results between different individuals are comparable. If

more than three replicates were available for a given sample, replicates were merged in the

following way: first, we calculated the Spearman correlation coefficients between all replicates in

a pair-wise fashion (Table S2); second, we kept the best two replicates and merged reads from all

remaining replicates into the “novel third” replicate.

We also assessed whether the amount of reads gathered per replicate had considerable

effects on our main results. Specifically, we reduced mapped reads of randomly picked single

replicates for both the NFκB and PolII experiments, and re-computed fractions of

significantly varying BRs. Notably, we found that even when randomly reducing the numbers

of mapped reads considerably, i.e., by factors of 2 or 3, fractions of differentially bound BRs

remained unchanged for NFκB and PolII, i.e. at 7.5% and 25%.

Furthermore, while the abovementioned generalized linear model based ANOVA test

was designed to conservatively identify specific, highly significant regions, we also applied

alternative approaches to arrive at an estimate of the overall fraction of differentially bound BRs.

To this end, we applied two alternative statistical tests for differential binding to the square root

transformed normalized read counts for each BR and pair of individuals: the ordinary t-test and

the limma moderated t-test (S4). The square root transformation was used to achieve approximate

homogeneity of variance. On the resulting set of p-values we applied Storey's algorithm (S5, S6)

for the estimation of π0 – the fraction of true null hypotheses – as implemented in the R package

(www.R-project.org) (S7) “qvalue”, using both alternative parameter options “smoother” and

“bootstrap”. The π 0 estimates of all analyses (ordinary t, limma-t, smoother, bootstrap) closely

agreed, resulting in average values of (1-π0) across all 45 pairs of individuals above our

conservative point estimates that at least 7.5% and 25%, respectively, of the NFκB and PolII BRs

are differentially bound when assessed across individuals. This analysis confirms that our

generalized linear model (ANOVA) based lower limit estimates of differences in binding between

individuals were very reasonable and applicable in this context.

Effect of SNPs on read-mapping. We also assessed the effect of SNPs on read-mapping, i.e. to

assess to what extent findings in this study may have been confounded by biases owing to the

mapping of reads onto the human reference genome, which presumably contains a mixture of

minor and major SNP alleles. In this regard, as mentioned above we applied the ELAND read

mapping algorithm allowing for up to two mismatches, which enabled us to detect reads

accurately even if they intersected with SNPs. Nevertheless, we realize that the choice of the

reference genome (in our case hg18) will in each research study likely introduce some biases, i.e.

by slightly favoring the detection of alleles present in the reference. In order to assess the effect of

this bias we carried out an analysis (Figure S2B) in which we excluded all reads that directly

intersect with SNP coordinates. Following the SNP exclusion, and subsequent quantile

normalization, we found that the results reported in Figure 1B of the main text remain – i.e., SNP

differences amongst individuals lead to an enrichment in differential TF binding (even if reads

directly mapping to SNP coordinates are excluded). The results shown in Figure S2B (1-3% loss

in enrichment) suggest that the effects of mapping bias are small.

Analysis of ChIP Seq signals for specifically occupied and non-occupied BRs in single

individuals. Occupied and unoccupied BRs (i.e., possible “gains” and “losses”) were identified

by intersecting BRs with peaks identified in the respective individuals using PeakSeq with a

threshold of P<0.001. We analyzed the losses in detail and found that in a large number of losses,

i.e. 40% and 53% of the BRs lost in one individual for NFκB and PolII, respectively, no peaks

were detectable even when applying more relaxed PeakSeq threshold criteria (P<0.01). This

implies that the recorded losses are presumably not due to “threshold effects”, as even with

relaxed scoring criteria no peaks were detectable in regions scored as ‘losses’.

Comparison of Chimpanzees and Human ChIP Data. In addition to examining variation

between individuals we also performed a general analysis of transcription factor binding between

humans and chimpanzees. Binding sites for PolII were mapped in a chimpanzee lymphoblastoid

cell line using ChIP-Seq with the same protocols and antibody used for the human samples.

The ChIP-Seq data were mapped onto the respective syntenic BRs in the human genome. For

15,418 (81%) of the human BRs, syntenic regions could be unambiguously identified in the

chimpanzee genome and peaks in these regions could thus be mapped onto the human ones.

Binding differences between the chimpanzee cells and each of the ten human samples were then

scored using the same criteria as described above (i.e., using the human BRs as a basis). While

many sites were shared between humans and chimp (Fig. S11AC) we found that on average 32%

of the PolII sites in synteny between chimp and human were differentially bound between

chimpanzees and individual humans (e.g., Fig. 2A, Fig. S11B), figures that are higher than the

intraspecies variation described above.

ChIP-quantitative PCR. Primers were designed using primer3 (S8)

(http://frodo.wi.mit.edu/primer3/) with a desired PCR amplicon length between 150 bp and 250

bp (see Table S18). All primers were checked with BLAT (S9) (http://genome.ucsc.edu/) to avoid

known SNPs that could influence primer hybridization. The primers were tested to yield a unique

product using insilico PCR (S10) (http://genome.ucsc.edu/). Primer efficiencies were determined

by calculating the standard curve of a serial dilution (6 times, 10-fold) of pooled gDNA. The

products were run on an agarose gel to make sure they gave no additional bands except for the

expected product. All experiments were preformed in triplicate on the Roche LightCycler 480

platform with LightCycler 480 SYBR Green I Master (cat# 04707516001, Indianapolis, IN).

Each well of a PCR plate contained a total volume of 20 µL and 1 ng ChIP DNA. Primers were

added to a final concentration of 400nM. The PCR protocol is as follows: (1) 5min at 95 °C; (2)

45 cycles of 5 s at 95 °C, 10 s at 60 °C, 30 s at 72 °C. PolII ChIP-quantitative PCR was

conducted on 4 biological replicates of sample GM12891. We determined the relative enrichment

for each target locus compared to a reference locus using the Delta-Delta-Ct-Method (2-ΔΔCt). We

selected a region that did not show any PolII binding as a control locus. To correlate the ChIP-

qPCR with ChIP-Seq data we counted the number of sequencing reads in an interval

corresponding to the location of the PCR amplicons. Figure S13 shows the correlation between

ChIP-qPCR and ChIP-Seq in 19 loci for PolII.

Quantitative PCR for allelic discrimination. The qPCR for allelic discrimination followed the

same protocol as the ChIP-quantitative PCR with a few adjustments. Each PCR reaction

contained 6 ng of genomic DNA. The control sample consisted of pooled genomic DNA from

seven individuals. The fold change was calculated using the Delta-Delta-Ct-Method (2-ΔΔCt) with

the same control locus as above.

RNA Seq. Total RNA was obtained from lymphoblastoid cell lines grown under the same

conditions as described above (ChIP-Seq). Extraction of RNA was performed for each cell line

after 6 h stimulation with TNF-alpha and without stimulation, respectively. Total RNA was

extracted from lymphoblastoid cell lines using Trizol reagent according to the manufacturers

instructions and then purified using the Qiagen RNeasy kit. Purified total RNA was poly(A)-

enriched using two cycles of selection with oligo(dT) cellulose (Ambion MicroPurist-kit, Foster

City, CA). The Poly(A)-RNA was fragmented with 10 x Fragmentation Buffer (Ambion,

#AM8740) for 5 min at 70 °C. Aliquots of total, Poly(A)-enriched and fragmented RNA were run

on an Agilent 2100 Bioanalyzer to test for quality of starting RNA, efficiency of Poly(A)

enrichment and the expected size distribution of the fragments after fragmentation.

Doublestranded cDNA synthesis was primed with random hexamers using the Invitrogen

Superscript II kit for first and second strand synthesis according to manufacturers instructions.

The reaction then was purified with QIAquick PCR spin column (Qiagen, #28106, Valencia,

CA). The cDNA libraries were constructed and sequenced as described previously (S3). In brief:

cDNA was size-selected on 2% agarose E-gels (Invitrogen, Carlsbad, CA) end-repaired and

phosphorylated with the End-It kit from Epicentre (Cat# ER0720, Madison, WI). After treatment

with Klenow fragment (NEB, Cat# M0212s, Ipswich, MA) and dATP Illumina adapters were

ligated to the protruding 3_-‘A’ base (LigaFast, Promega Cat#M8221, Madison, WI). DNA was

amplified with Illumina genomic DNA primers 1.1 and 2.1 for 15 cycles using the following

program: 1. 30 s at 98 °C, 2. 15 cycles of 10 s at 98 °C, 30 s at 65 °C, 30 s at 72 °C, and 3. a 5-

min extension at 72 °C. The product was run on 2% agarose E-gels (Invitrogen) to isolate the

libraries from residual primers and adapters. Concentrations and A260/A280 ratios were

determined using a NanoDrop ND-1000 spectrophotometer. Purified library DNA was captured

on an Illumina flowcell for cluster generation and sequenced on an Illumina Genome Analyzer II

following the manufacturer’s protocols.

For four cell lines biological replicates were performed. The RNA-Seq data was highly

reproducible as indicated by Spearman correlations (see Figure S14).

Mapping of RNA-Seq reads. A three-step approach was adopted to map the RNA-Seq reads to

the genome. First, reads were aligned to a splice junction library consisting of all possible unique

pair-wise splice junctions within each transcript of the UCSC genes annotation set (S11). Note

that the UCSC genes and RefSeq annotation sets are very similar and they both represent a

moderately conservative set of transcripts. Only reads that mapped to a unique splice junction

were retained. In a second step, the remaining reads were aligned to the human genome (hg18)

and all unique hits were obtained. Bowtie was used for both alignment steps allowing up to two

mismatches (S12). Lastly, the reads that did not align in the previous two steps were aligned to

the genome using less stringent parameters. In this step reads were allowed to map up to five

genomic locations. One of these locations was selected according to the read density of the

uniquely mapped reads contained within non-overlapping bins (100bp) across the genome.

Quantification of gene expression. The level of gene expression was quantified by intersecting

mapped reads (see Table S19 for statistics) with the composite gene models of the UCSC Genes

annotation set (S11). The composite gene models were obtained by taking the union of all the

exons from the various transcript isoforms of a particular gene. The expression values were

determined by summing the nucleotide overlaps from all the mapped reads that intersect with a

composite gene model divided by the length of the composite gene model and the total number of

mapped reads in millions.

Correlation of RNA expression levels and transcription factor binding differences. Binding

sites that were located within a gene or up to one kb upstream or downstream were assigned to

genes. For each pair-wise set of individuals, the ratio of peak counts (after normalization) was

correlated with the ratio of gene expression values (Fig. 3B, Fig. S9B). For these sets of ratios the

Spearman correlation coefficient were computed for PolII and NFκB (expression values for the

unstimulated and stimulated RNA samples were used for PolII and NFκB, respectively) (see

Table S9). In addition, we restricted the set of binding sites to those that contained a SNP within a

known motif for NFκB, STAT1 (for NFκB binding sites), TATA, CAAT, and GC (for PolII

binding sites) and computed corresponding Spearman correlations between binding and

expression (see Figure 3C and Table S9). We also computed the counts of BRs that fell above or

below two standard deviations in binding ratio distribution as well as the gene expression ratio

distribution. These counts are shown in the four corners of Figure 3B and Figure S9B (horizontal

and vertical lines indicate +/- two standard deviations). We also computed the significance of

association of binding versus expression using a Fisher’s exact test using these four counts for

each data set. Example loci in which NFκB and PolII binding, respectively, correlates with RNA

expression levels are shown in Fig. 3A and Fig. S9A.

Retrieval of genomic variation data. SNPs for 8 out of 10 individuals analyzed in our study

(i.e., GM10847, GM12878, GM12892, GM12891, GM18505, GM18526, GM18951, and

GM19099) were obtained from the April 2009 release of the 1000 Genomes project (see

ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/2009_04/). In addition, CNVs from the study of

McCarroll and coworkers (S13) (8 individuals: GM10847, GM12878, GM12892, GM18505,

GM18526, GM18951, GM19099, and GM19193) were obtained from the Database of Genomic

Variants (http://projects.tcag.ca/variation/). In order to identify additional CNVs in our samples

we carried out high-resolution comparative genome hybridization (HRCGH) experiments (S14) in

eight samples (i.e., GM10847, GM12878, GM12891, GM12892, GM15510, GM18526,

GM18951, and GM19193) by hybridizing and scoring high-resolution Affymetrix microarrays in

seven samples and using the eighth (GM10847) as a reference in the HR-CGH experiments. CNV

calls from high-resolution Affymetrix microarrays and McCarroll et al. (S13) were combined as

described further below. We further obtained paired-end mapping based SV calls from Korbel et

al. (S15) and Kidd et al. (S16) (for GM15510, GM12878, and GM18505) and merged these SV-

calls in the following way: i.e., we focused on deletions and inversions reported in both studies,

as for these the reported SV size ranges overlap (this is not the case for insertions, which we thus

removed from the call lists). We further removed all SVs <8kb as these fall below the detection

range of Kidd and coworkers. In addition, we removed SVs >200kb reasoning that these might be

of relatively low confidence. The remaining SVs were combined into a coherent set. When

sample-specific calls intersected we used the Korbel et al. call, since the Korbel et al. set is

predicted to have a higher breakpoint resolution than the Kidd et al. set, owing to the relatively

short paired-end insert size used in Korbel et al. The merged SV set contained 691 SV-deletion

calls and 183 SV-inversion calls.

Comparison of binding differences between intergenic regions and regions nearby a

transcriptional start site (TSS). We used RefSeq genes as well as annotated start sites of RefSeq

genes to define intergenic regions and TSS-associated regions. In particular, regions within 1000

bp of a TSS were defined as TSS-associated regions. Furthermore, regions neither intersecting

with a TSS-associated region nor with RefSeq annotated exons or introns were considered as

intergenic. Permutation tests were performed 10,000 times; i.e. we randomly called BRs in

intergenic and TSS-associated regions as “significantly different in binding” and compared the

according results to the actual data. Note that it was previously shown (S2) that the fraction of

mappable bases when aggregated across the TSSs of all genes is only slightly elevated (~10%)

compared to the genomic average (i.e. intergenic regions), and we reasoned that this weak

difference in mappability has no major effect on our analysis results in terms of differences in

binding between TSSs and intergenic regions.

Array-CGH microarray design. The Affymetrix HG49m-T-v1 single-channel research array set

covers the unique regions of the human reference genome (Build 36; hg18) with ~32M step-wise

interdigitated 49-mer probe sequences across the three array designs termed “a”, “b” and “c.” The

average inter-probe distance is ~147 nt.

Array-CGH microarray experiment. The array-CGH experiments were carried out as

described in the following. gDNA was obtained from lymphoblastoid cell lines grown under the

conditions described above using the Qiagen QIAamp kit. The Affymetrix HG49m-T-v1 set of 3

arrays with a total of approximately 32 million 49-mers was probed using a protocol similar to the

protocol for the commercially available Affymetrix SNP 6.0 arrays and with the reagents from

the kit for probing these arrays (Affymetrix, Inc., Santa Clara, CA). For the probing of the

HG49m-T-v1 set the restriction digestion step is omitted. Genomic DNA (using a 60 µl reaction

volume and 60 µg input genomic DNA) is DNAse fragmented, biotin-labeled using Td-

transferase and hybridized to the arrays for 40 hours at 50° C. Washing, staining and scanning

follow standard Affymetrix procedure.

Array-CGH data analysis and inference of CNVs. Binary data files (CEL) for each DNA

sample across the three array designs were obtained with the GCOS software

(www.affymetrix.com/). Individual probe intensities were accessed and analysed with custom-

written R scripts (S7) and functions available in the package aroma.affymetrix (S17). In order to

combine fluorescence intensity values from the three chip designs and due to the high probe

density we have performed a six-step normalization procedure for each design and chromosome

separately: (1) Raw probe intensity values were transformed into log2 space; (2) log2 intensities

were centred around zero by subtracting the mean log2-intensity to remove shifts in the intensity

distributions among array designs; (3) log2 ratios between the test and a reference array design

were calculated; (4) we corrected for local GC-content effects on log2 ratios similar to Diskin and

coworkers (S18): We tiled the genome into m non-overlapping 100kb windows and calculated the

average GC percentage G for each window. For each of the m windows we calculated the median

log2 ratio Y (requiring at least 10 probes), and fitted a robust linear regression model: Yj = a + b

* Gj + c * Gj * Gj with model parameters a, b and c estimated by iterated re-weighted least

squares in R (rlm; MASS package). For each probe we calculated the expected log2 ratio value

based on the GC percentage in a 100kb window around the probe and with the estimated

regression parameters. Probe ratios were normalized by subtracting the expected log2 ratios

(residuals in the regression model) from the observed log2 ratios; (5) long-range spatial ‘wave’

effects in log2 ratios were removed using a similar approach as described by Marioni and

coworkers (S19): We fitted a loess curve to each chromosome (S20) and set the proportion of data

points used for the local regression moving windows such that it corresponded to 5Mb genomic

windows; (6) in order to remove differences in intensity scales between chip designs we divided

the log2 ratios by the standard deviation (S21). After normalization, array design intensity files

were combined and CNVs were predicted using the BreakPtr algorithm and the ‘core’ 3- state

(loss/normal/gain) Hidden Markov Model (HMM) architecture (S15). The following HMM

parameterization was used: Due to our normalization procedure (step #6) the standard deviation

was set to 1 for all three states. Normal state expectation value was set to zero (step #2). Loss and

Gain expectation model parameters were estimated to be -1 (= log2 1/2) and 1 (= log2 4/2),

respectively. Transition probabilities pij between states were estimated to be 1E-5 since recent

reports (S15) estimate ~600 CNV events with ~3kb or larger in size per individual (pij = 0.5 *

600 CNVs/30M probes). The CNV predictions were further filtered by stringent criteria: (1) Each

CNV is supported by at least 15 probes (i.e. 5 supporting probes from each array design), (2) only

CNVs >2,250bp are reported (= ~150 nt inter-probe distance * 15 probes) and (3) by an average

absolute log2 ratio of >0.7. Affymetrix HG49-T-v1 CNV calls of chromosome 3 are shown in

Figure S15A.

Comparison and merging of microarray based CNV calls. The microarray-based CNV calls

used in our analyses were retrieved from two distinct, complementary Affymetrix platforms that

were designed using different array-design strategies. Whereas the 30 Million feature Affymetrix

HG49m-T-v1 arrays probe non-repetitive genomic regions with an unbiased high-resolution

probe tiling, the Affymetrix 6.0 platform described in McCarroll et al. (S13) interrogates selected

genomic regions (i.e., CNV regions) at high resolution and the rest of the genome at relatively

low resolution (i.e., with ~800,000 probes). Despite the distinct array-designs we assessed the

intersection between individual-calls made with the two platforms to judge the potential value

resulting from merging both sets. In particular, we found that 1,941 CNV calls made with the

HG49m-T-v1 Affymetrix platform intersected with 403 (51%) of the 787 sample specific calls

McCarroll et al. made in the samples GM12878, GM12892, GM18526, GM18951, GM19193

(the sample subset for which calls from both platforms were available). We concluded that there

is a reasonable overlap in sample-specific calls between both platforms, and that merging calls

from both provides a suitable list for our analysis. We thus went on combining calls from both

sets, by merging all calls and giving McCarroll et al. calls priority in case CNV calls intersected

between both datasets. The size distribution of CNVs is shown in Figure S15B. We observed rare

cases in which SV copy-numbers were likely called inaccurately, as evidenced by an abundance

of genomic sequencing reads matching into loci reported as homozygous deletions by previous

studies (one example is shown in Fig. S16A.)

Analysis of conflicting CNV calls. As indicated above, in some instances conflicting calls were

observed between the array data (S13) and our DNA sequencing data. These were resolved as

follows. For NFκB in the region shown in Fig. S8 both expression and array data suggest that

individual GM12878 carries a homozygous deletion. However, the ChIP-Seq signal track

indicates the presence of background reads. Sequence data from Xue et al. (S22) was used to

resolve the reads. In the case of PolII (Fig. 2A), array data for individuals GM12891 and

GM15510 suggest that each individual carries one and two copies of the reference allele,

respectively. However, ChIP-Seq and RNA-Seq data suggest that both individuals carry

homozygous deletions. This was resolved using qPCR, which was consistent with homozygous

deletions in both (Fig. S16B). Fig. S16A displays a conflicting call between array data and ChIP-

Seq data. The array data suggested a homozygous deletion in GM19099, while the Chip-Seq

signal track revealed a peak. We determined the copy number independently by qPCR and found

that the deletion was heterozygous rather than homozygous. This result was concordant with the

ChIP signal. For the remainder of the individuals, the PolII array data was in agreement with the

binding and expression data.

DNA motifs in BRs. To check for presence of known motifs associated with NFκB and PolII

binding in the BRs, we used the position weight matrices (PWMs) of the motifs from JASPAR

and TRANSFAC databases. All sequences within the BR were scanned using the MAST program

in the MEME suite (v 4.1.0) (S23). We used a stringent P-value of P< 0.001 to identify the

motifs.

To check for presence of novel motifs in the NFκB and PolII BRs, we generated 10 sets

each of 1000 randomly picked sequences 200nt and 400nt within the respective peak centers,

totaling 20 sets each for NFκB and PolII. The MEME software (v 4.1.0) (S24) was used to

identify de novo motifs in each of the sets. Those motifs that were present in at least 5 of the sets

were identified as novel motifs associated NFκB or PolII binding sites.

Functional categorization of genes around differentially occupied BRs. To test for functional

enrichment among genes that are differentially bound by NFκB, we chose genes flanking 3kb

around peaks that showed a two-fold binding difference as a test list and genes flanking 3kb

around peaks that showed no differential binding as a background list. This was run through the

DAVID server (S25, S26) for finding enrichment of genes based on the Panther Biological

Process categorization (v 6.1) (S27). The same procedure was followed for testing functional

enrichment of genes flanking PolII binding sites, with the exception that the genes flanking 1kb

of the binding sites was used.

When comparing the chimp and human datasets, we first mapped the binding sites in

chimp to syntenic regions in humans (using liftover, with the requirement that at least 97% of

bases had to be mappable) and then mapped the flanking genes as described above.

Datasets

Coordinates of peaks and BRs for all individuals along with signal strengths and P-values (i.e.,

~100 MB of data) have been made available online (i.e., the data can be retrieved from:

http://www.embl.de/~korbel/ChIP_Seq/Snyder_Korbel_Labs_Variation/). Data sets are available

at GEO: GSE19486.

Discussion of Specific Examples. Several of the variable BRs identified in this study involve

loci of relevance to human health. For example, we observed that PolII binding at ZNF804A is

highly variable (Fig. S1A). This locus is of strong interest, as it was identified in GWAS of

schizophrenia (S28). The function of ZNF804A is unknown. Future studies might investigate the

influence of variable TF binding and expression of this protein on the phenotype (particularly in

the central nervous system). Pol II also showed variable binding at the RPS26, BLK, SP140

genes, which have been associated with type I diabetes, systemic lupus erythematosus, chronic

lymphatic leukemia, respectively (S29). NFκB showed variation in binding at ORMDL3,

PTGER4, and LOC253039, loci associated with asthma, Crohn’s disease, and rheumatoid

arthritis, respectively (S29).

NFκB binding is variable at the UGT2B17 locus, which is copy number variable. This

gene encodes an enzyme that metabolizes steroids including testosterone. Individuals with fewer

copies have higher levels of urine testosterone, a trait that can influence doping test results (S30).

Xue et al. sequenced the CNV breakpoint in 91 HapMap individuals, and found that the

frequency of the CNV varied by geographical ancestry (S22). A recent study found that copy

number status at UGT2B17 influences the risk of developing graft verses host disease (GVHD)

following a bone marrow transplant (S31). Recipients who carry homozygous deletions were

more likely (Odds Ratio = 2.5) to develop GVHD when receiving a transplant from an individual

who carries the gene. The enzyme is expressed extracellularly in several tissues that are involved

in GVHD. We show that UGT2B17 is variably bound by NFκB. It is possible that NFκB activity,

which is upregulated in inflammation, could increase the expression of UGT2B17, leading to an

increase in the likelihood or severity of an immune reaction targeting the tissue expressing the

enzyme.

Figure Legends

Figure S1: Approach for identifying and analyzing variations in NFκB and PolII transcription

factor binding in several human genomes and a single chimpanzee genome. (A) Signal tracks

indicating binding variability at CD2 and ZNF804A loci in humans. The different colors indicate

distinct ethnic groups. (B) Computational analysis approach used for identifying variations in

binding and for correlating these with genetic variation, gene expression, and with PolII binding

in a chimpanzee.

Figure S2. (A) ChIP-Seq experiments are highly replicable as indicated by Spearman correlations

between normalized replicate read counts in factor BRs. Shown are two examples for both NFκB

and PolII, respectively. (B) Enrichment in differential binding of BRs following the exclusion of

reads mapping directly onto SNPs to correct for possible reference biases. Although some BRs

fell below the significance threshold as a result of excluding reads mapping onto SNPs (to correct

for allele ascertainment biases that may have been caused by the presently used human reference

genome sequence), overall the observed trend remains, i.e. SNP differences in BRs lead to a

marked enrichment in differential binding among individuals. (Compared with the uncorrected

data, displayed in Fig. 1B in the main text, the relative enrichments for NFκB and PolII are

reduced, on average, by 3% and 1%, respectively.) *All enrichments displayed in the above

figure are significant with P<0.001 based on permutation tests involving 10,000 permutations.

Figure S3: Genome-wide binding variability and occupancy of NFκB and PolII BRs in humans.

(A) Genome-wide map indicating the occupancy of NFκB and PolII BRs in 10 individuals.

Lengths of lines are proportional to the number of individuals in which the BR is occupied (based

on detecting with the PeakSeq algorithm peaks with P<0.001 falling into the respective BR). (B)

Cumulative plot summarizing the data in panel A. (C) Proportions of differentially occupied

NFκB and PolII BRs identified in pairwise comparisons between human samples, as scored with

our ANOVA approach. (D) Fold changes observed in differentially occupied BRs.

Figure S4: Evidence for cooperativity between neighboring binding sites. (A) Distribution of

Spearman correlation coefficients of binding differences between neighboring BRs, recorded

across ten individuals. Adjacent PolII BRs (upper panel) correlate in terms of binding, as well as

are neighboring NFκB BRs (panel in the middle). Furthermore, the bottom panel indicates an

association in binding among neighboring PolII and NFκB BRs, even though the NFκB and PolII

data are from TNFα-treated and untreated cells, respectively. (B) Signal tracks of example

regions several kilobase in size in which neighboring binding peaks are correlated; one possible

explanation is cooperativity in binding. (C) Fold changes in binding, i.e. ratios between quantile

normalized numbers of reads falling into a BR recorded in pair-wise comparisons between

samples, are correlated between neighboring BRs.

Figure S5: On average intergenic BRs were weaker in signal than regions within 1kb of a TSS,

with a much more pronounced effect observed for PolII than for NFκB. Displayed are histograms

of peak sizes (i.e. numbers of normalized reads falling into a BRs) in TSSs and intergenic

regions. The intergenic occupancy observed for NFκB is consistent with its role as an enhancer.

Similarly, PolII is expected to be strongly associated with promoters, especially given that the

anti-PolII ChIP antibody is specific for the form of PolII present at the initiation complex.

Figure S6. (A) Signal tracks of a NFκB motif demonstrate effects of B-SNPs on TF binding, with

correlations in the expected direction (i.e., with “correct trend”) according to the SNP genotype

and the motif sequence. (B) Correlation between difference in binding and B-SNPs altering

positional weight matrix (PWM) scores of a motif. (C) Summary of NFκB and PolII BRs for

which genetic variation is found associated with differential TF binding. In cases for which both

SNPs and B-SVs (e.g., CNVs or inversions) affect a region, only the B-SV contribution is

indicated.

Figure S7: DNA motifs identified at (A) NFκB BRs and (B) PolII BRs using de novo motif

enrichment analysis with the MEME tool.

Figure S8: Signal tracks demonstrate considerable effects of B-SVs (here CNVs identified with

microarrays) on NFκB binding.

Figure S9: Correlation and effect sizes of TF binding and gene expression. (A) Examples

showing correlation of binding and expression for NFκB and Pol II. (B) Regions with binding

variation correlate with differences in expression. Dark blue dots: NFκB BRs displaying

significant differences in binding in pair wise comparisons between individuals; light blue dots:

other BRs. The black lines demarcate data points that either fall two standard deviations below or

above the binding ratio distribution or the gene expression ratio distribution, respectively.

Indicated counts represent data points falling into the four corners for each data set.

Figure S10. Segregation of binding in a parent offspring trio of European ancestry and candidate

transgression events. (A) Signal tracks of example regions in which the binding pattern appears to

segregate consistent with Mendelian inheritance. (B) Signal tracks of an example region in which

the child displays a strong increase in NFκB binding relative to the parents, which is indicative of

transgression. (C) Signal tracks of an example region in which the child displays a strong increase

in Pol II binding relative to the parents. C: child; M: mother; F: father.

Figure S11. Comparison of PolII binding between humans and a chimpanzee individual. (A)

Signal tracks of an example region that is bound in both humans and the chimpanzee. (B) Signal

tracks demonstrating a peak identified in the chimpanzee only. (C) Comparison of overlap in

binding on syntenic regions between chimpanzee and human samples. BRs are ordered, on the X-

axis, according to the cumulative number of reads recorded in a BR in the human samples.

Strongly bound human regions are more frequently bound in the corresponding syntenic chimp

regions (assessed by subjecting the chimp reads to PeakSeq with a stringent P-value threshold of

P<0.001) than weakly bound human regions. Syntenic regions were identified by using the

liftover tool available at the UCSC genome browser, requiring that 97% of bases must be

mappable during the liftover process. (D) Analysis of fold changes in binding between humans

and chimp indicates correlation of human binding variation and human-chimp binding

divergence. We analyzed the average fold-change in binding (i.e., normalized read counts falling

into BRs) for BRs showing no variable binding vs. such showing differential binding in at least

one pair-wise comparison between individuals (left panel). Furthermore, we binned the data

according to the number of pairs of individuals identified per BR for which differential binding

was detected (right panel), showing that polymorphic BRs (such with differences within humans)

are generally likely to be divergent (show differences between human and chimp).

Figure S12. Sizes of NFκB and PolII BRs.

Figure S13. (A) Correlation between ChIP-seq and ChIP-qPCR data. We determined the relative

enrichment of PolII binding at 19 loci. In order to compare with the ChIP-Seq data we calculated

the number of sequencing reads that were found in the corresponding PCR amplicon location. A

strong correlation between ChIP-Seq and ChIP-qPCR was observed (Spearman = 0.96). (B)

Reproducibility of ChIP-qPCR data: A strong correlation between four biological replicates of

GM12891 was observed at 19 loci. Shown here is the correlation between two replicates

(Spearman = 0.99).

Figure S14: RNA-Seq experiments are highly replicable as indicated by Spearman correlations.

Shown are four examples of correlations of expression values between replicates in uninduced

cells.

Figure S15: (A) Affymetrix HG49-T-v1 CNV calls on chromosome 3. Blue bars indicate

deletions and green bars indicate duplications. (B) CNV size distribution (Affymetrix HG49-T-v1

based calls).

Figure S16: qPCR analysis of conflicting CNV calls. Results are displayed as fold changes of

copy number relative to the reference genome. (A) Example of a conflicting CNV call on

chromosome 1. NFκB ChIP-Seq data for individual GM19099 suggest presence of the region,

despite the fact that McCarroll et al. (6) have called this region homozygously deleted (i.e.,

assigned the region chr1:147303148-147526040 a copy-number of zero). qPCR results indicate

individual GM19099 carries a heterozygous deletion. (B) qPCR results for the GSTM1 locus.

Tables and Legends

Table S1. Characteristics of the cell lines. YRI: Yoruba in Ibadan, Nigeria; JPT: Japanese in

Tokyo, Japan; CHB: Han Chinese in Beijing, China; WE: Western European; CEU: Utah

residents with ancestry from northern and western Europe (also referred to as CEPH).

Cell Line Gender Origin GM19193 Female YRI GM19099 Female YRI GM18505 Female YRI GM18951 Female JPT GM18526 Female CHB GM15510 Female WE GM10847 Female CEU GM12878 Female CEU GM12892 Female CEU GM12891 Male CEU AG18359 Female -

Table S2. Spearman correlations of biological replicates.

Experiment Spearman Sample (Replicate) Sample (Replicate) ChIP-Seq (PolII) 0.98 GM10847 (0) GM10847 (1) ChIP-Seq (PolII) 0.97 GM10847 (0) GM10847 (2) ChIP-Seq (PolII) 0.97 GM10847 (1) GM10847 (2) ChIP-Seq (PolII) 0.96 GM12878 (0) GM12878 (2) ChIP-Seq (PolII) 0.96 GM12878 (0) GM12878 (4) ChIP-Seq (PolII) 0.96 GM12878 (2) GM12878 (4) ChIP-Seq (PolII) 0.99 GM12891 (0) GM12891 (3) ChIP-Seq (PolII) 0.98 GM12891 (0) GM12891 (4) ChIP-Seq (PolII) 0.97 GM12891 (3) GM12891 (4) ChIP-Seq (PolII) 0.99 GM12892 (1) GM12892 (2) ChIP-Seq (PolII) 0.88 GM12892 (1) GM12892 (3) ChIP-Seq (PolII) 0.90 GM12892 (2) GM12892 (3) ChIP-Seq (PolII) 0.96 GM15510 (1) GM15510 (2) ChIP-Seq (PolII) 0.90 GM15510 (1) GM15510 (3) ChIP-Seq (PolII) 0.91 GM15510 (2) GM15510 (3) ChIP-Seq (PolII) 0.96 GM18505 (0) GM18505 (1) ChIP-Seq (PolII) 0.96 GM18505 (0) GM18505 (4) ChIP-Seq (PolII) 0.89 GM18505 (1) GM18505 (4) ChIP-Seq (PolII) 0.95 GM18526 (1) GM18526 (2) ChIP-Seq (PolII) 0.97 GM18526 (1) GM18526 (3) ChIP-Seq (PolII) 0.98 GM18526 (2) GM18526 (3) ChIP-Seq (PolII) 0.98 GM18951 (0) GM18951 (2) ChIP-Seq (PolII) 0.98 GM18951 (0) GM18951 (3) ChIP-Seq (PolII) 0.97 GM18951 (2) GM18951 (3) ChIP-Seq (PolII) 0.96 GM19099 (1) GM19099 (2) ChIP-Seq (PolII) 0.93 GM19099 (1) GM19099 (3) ChIP-Seq (PolII) 0.91 GM19099 (2) GM19099 (3) ChIP-Seq (PolII) 0.96 GM19193 (0) GM19193 (2) ChIP-Seq (PolII) 0.96 GM19193 (0) GM19193 (3) ChIP-Seq (PolII) 0.98 GM19193 (2) GM19193 (3) ChIP-Seq (PolII) 0.97 AG18359 (1) AG18359 (2) ChIP-Seq (PolII) 0.94 AG18359 (1) AG18359 (3) ChIP-Seq (PolII) 0.93 AG18359 (2) AG18359 (3) ChIP-Seq (NFκB) 0.94 GM12878 (0) GM12878 (2) ChIP-Seq (NFκB) 0.83 GM12878 (0) GM12878 (4) ChIP-Seq (NFκB) 0.79 GM12878 (2) GM12878 (4) ChIP-Seq (NFκB) 0.72 GM12891 (1) GM12891 (2) ChIP-Seq (NFκB) 0.67 GM12891 (1) GM12891 (3) ChIP-Seq (NFκB) 0.95 GM12891 (2) GM12891 (3) ChIP-Seq (NFκB) 0.91 GM18505 (0) GM18505 (1) ChIP-Seq (NFκB) 0.93 GM18505 (0) GM18505 (5) ChIP-Seq (NFκB) 0.89 GM18505 (1) GM18505 (5) ChIP-Seq (NFκB) 0.90 GM10847 (0) GM10847 (1) ChIP-Seq (NFκB) 0.93 GM10847 (0) GM10847 (4) ChIP-Seq (NFκB) 0.85 GM10847 (1) GM10847 (4) ChIP-Seq (NFκB) 0.86 GM18951 (0) GM18951 (1) ChIP-Seq (NFκB) 0.86 GM18951 (0) GM18951 (2) ChIP-Seq (NFκB) 0.91 GM18951 (1) GM18951 (2)

ChIP-Seq (NFκB) 0.86 GM19099 (1) GM19099 (2) ChIP-Seq (NFκB) 0.83 GM19099 (1) GM19099 (3) ChIP-Seq (NFκB) 0.81 GM19099 (2) GM19099 (3) ChIP-Seq (NFκB) 0.91 GM19193 (1) GM19193 (2) ChIP-Seq (NFκB) 0.83 GM19193 (1) GM19193 (3) ChIP-Seq (NFκB) 0.77 GM19193 (2) GM19193 (3) ChIP-Seq (NFκB) 0.84 GM18526 (1) GM18526 (2) ChIP-Seq (NFκB) 0.85 GM18526 (1) GM18526 (3) ChIP-Seq (NFκB) 0.90 GM18526 (2) GM18526 (3) ChIP-Seq (NFκB) 0.90 GM12892 (1) GM12892 (2) ChIP-Seq (NFκB) 0.90 GM12892 (1) GM12892 (3) ChIP-Seq (NFκB) 0.90 GM12892 (2) GM12892 (3) ChIP-Seq (NFκB) 0.92 GM15510 (0) GM15510 (4) ChIP-Seq (NFκB) 0.90 GM15510 (0) GM15510 (5) ChIP-Seq (NFκB) 0.95 GM15510 (4) GM15510 (5)

RNA-Seq (not NFκB induced) 0.97 GM10847 (1) GM10847 (2) RNA-Seq (not NFκB induced) 0.97 GM12878 (1) GM12878 (2) RNA-Seq (not NFκB induced) 0.97 GM18505 (1) GM18505 (2) RNA-Seq (not NFκB induced) 0.97 GM18526 (1) GM18526 (2)

Table S3. In order to compare the distributions of Spearman correlation coefficients between

neighboring binding regions across the ten individuals separated by specific distance intervals we

performed Kolmogorov-Smirnov tests between these pairwise distributions. Using a

Kolmogorov-Smirnov test we calculated a P-value that measures the similarity between the

distribution of binding regions for different separations where a small P-value indicates a

significant difference between the distributions. This analysis was performed for the distributions

comparing neighboring NFKB binding regions, neighboring PolII binding sites, and the

association between neighboring PolII and NFKB binding regions.

Neighboring NFKB binding regions Distributions compared in the KS test P-value all 0-1 3.38E-32 all 1-10 3.05E-30 all 10-100 2.99E-02 all 100-1000 7.20E-41 0-1 1-10 1.48E-06 0-1 10-100 1.68E-25 0-1 100-1000 2.11E-65 1-10 10-100 3.57E-18 1-10 100-1000 1.86E-83 10-100 100-1000 3.69E-43 Neighboring Pol II binding regions Distributions compared in the KS test P-value all 0-1 1.31E-129 all 1-10 5.20E-24 all 10-100 5.25E-13 all 100-1000 1.35E-42 0-1 1-10 5.61E-50 0-1 10-100 7.70E-157 0-1 100-1000 3.72E-187 1-10 10-100 1.40E-46 1-10 100-1000 4.65E-77 10-100 100-1000 2.89E-18 Neighboring NFKB / Pol II binding regions Distributions compared in the KS test P-value all 0-1 4.69E-02 all 1-10 4.89E-01

all 10-100 2.50E-08 all 100-1000 1.21E-20 all overlap 2.80E-25 0-1 1-10 1.38E-02 0-1 10-100 1.02E-04 0-1 100-1000 2.70E-10 0-1 overlap 3.43E-01 1-10 10-100 2.73E-02 1-10 100-1000 9.71E-11 1-10 overlap 1.13E-14 10-100 100-1000 6.24E-07 10-100 overlap 6.16E-40 100-1000 overlap 9.23E-49

Table S4. Correlation between difference in binding and B-SNPs altering positional weight

matrix (PWM) scores of a motif. Spearman correlations have been calculated from pair-wise

comparisons of samples, i.e. between the magnitude in binding difference calculated as the ratio

between DNA reads falling into a peak region and the difference in PWM scores between

individuals owing to B-SNPs. Motif PWMs were downloaded from JASPAR and are displayed in

Table S20. The NFκB-motif, TATA-box, STAT1-motif, CAAT-box, and GC-box are

summarized as sequence logos in Fig. 1A and Fig. S6A. The ABC test was also tested on the

following additional motifs in PolII BRs, without revealing significant results: DCE_S_III, INR,

MTE, MED-1, XCPE1, DCE_S_II, DPE, BREu, DCE_S_I, BREd (PWMs were obtained from

the JASPAR database and motifs mapped with MAST). Furthermore, the C/EBP motif (obtained

from the TRANSFAC database) was tested on NFκB BRs, without revealing significant results.

Factor Binding motif Spearman correlation P-value

NFκB NFκB-motif 0.38 <2.2E-16

NFκB STAT1-motif 0.15 3.62E-16

PolII CAAT-box 0.13 6.93E-05

PolII TATA-box 0.01 non-significant

PolII GC-box 0.02 non-significant

Table S5. CNV calls generated with the Affymetrix HG49m-T-v1 early access array set.

Reference (for scoring) Sample

Total # CNVs

Total # Duplications

Total # Deletions

GM10847 GM12878 351 176 175 GM12891 325 138 187 GM12892 347 176 171 GM15510 292 149 143 GM18526 441 209 232 GM18951 375 220 155 GM19193 427 229 198

Table S6. Numbers of differentially bound BRs affected by B-SVs in at least one pairwise

comparison between individuals. CNVs identified using microarrays and deletions detected by

paired-end mapping (SV-deletions) are only displayed if the differential binding occurred in the

expected direction, based on the altered copy-number of the region, i.e. with "correct trend".

(*) We did not consider 1,426 SV-inversions intersecting with significantly differing PolII BRs,

as SV-inversions did not enrich for significant binding in the PolII data (see Figure 2B).

NFκB PolII

Copy-number imbalanced SVs (CNVs) identified by microarrays 72 169 SV-deletions identified by paired-end mapping 22 61 CNVs and SV-deletions combined (redundancy-filtered) 83 206 SV-inversions identified by paired-end mapping 357 - (*) all SVs (redundancy-filtered) 423 206

Table S7. Fraction of BRs for which differential binding coincides with genetic variation (*)

Type of genetic variation (*) NFκB PolII

Any SNP-differences in BR [range] 34.1% [25.1% - 47.4%] 24.9% [14.7% - 32.2%]

SNP with right trend in transcription factor binding motif [range] 0.71% [0.49% – 0.88%] 0.57% [0.40% – 0.73%]

CNVs with right trend identified with microarrays [range] 0.53% [0.31% – 0.85%] 0.28% [0.14% – 0.36%]

SV-indels with right trend identified by paired-end mapping [range] 0.15% [0.08% - 0.20%] 0.11% [0.07% – 0.14%]

Unbalanced structural variants, i.e. CNVs and SV-indels, with right trend [range]

0.58% [0.36% – 0.85%] 0.30% [0.14% – 0.38%]

Inversions [range] 2.39% [1.50% – 4.06%] 2.36%(**) [1.66% – 3.37%]

All types of genetic variation intersecting with BRs, following redundancy removal, i.e. regions intersecting with both SNPs and SVs are accounted for once only [range]

35.2% [26.0% - 48.4%] 25.7% [15.2% - 33.0%]

(*) displayed are averages for all individuals in which genetic data was available; e.g. SNP-based analyses exclude GM15510 and GM19193 for which no SNP genotype data was available [ranges indicated results for different individuals]; (**) not displayed in Table S6 and Figure S6C, as no enrichment detected in PolII experiments

Table S8. SNPs falling into transcription factor binding DNA motifs, coinciding with

differentially bound BRs in at least one pair-wise comparison between individuals.

NFκB -motif

STAT1-motif

CAAT-box

TATA-box

GC-box

Sum

SNPs intersecting motifs associated with significant differences in binding in at least one pair-wise comparison between individuals

97 98 77 59 593 924

SNPs intersecting motifs associated with significant differences in binding with correct trend in at least one pair-wise comparison between individuals

79 71 60 40 427 677

SNPs associated with a decreased position weight matrix (PWM) quality (relative to the reference genome, build 36) leading to a significant decrease in binding compared to individuals with the reference allele

72 55 46 34 349 556

SNPs associated with improved PWM quality (relative to build 36) leading to a significant increase in binding compared to individuals with the reference allele

7 16 14 6 78 121

Table S9. Correlations between binding and expression values recorded in pair-wise comparisons

between individuals.

Spearman correlations between binding and

expression Pearson correlations between

binding and expression

Overall: Correlation P-value Correlation P-value

NFκB 0.475 0.00E+00 0.448 0.00E+00 PolII 0.461 0.00E+00 0.519 0.00E+00

Binding sites with SNPs in

motifs: Correlation P-value Correlation P-value

NFκB: NFκB 0.546 1.57E-12 0.538 2.48E-11 NFκB: STAT1 0.543 6.43E-07 0.548 3.03E-07

PolII: GC 0.479 1.25E-64 0.514 1.56E-75 PolII: TATA 0.63 6.31E-13 0.757 9.14E-21 PolII: CAAT 0.816 5.81E-48 0.784 5.73E-42

Table S10. Correlations between SVs and transcription factor binding as well as gene expression.

Unbalanced SVs (CNVs, and SV-DELs) were merged, and for each pair-wise set of individuals,

the BRs associated with SVs were extracted and associated with a gene if a BR was within 1kb of

the gene. The normalized read counts, expression values, and copy number values were then

obtained for each individual pair. SV-DELs were assigned a copy-number value of 1;

pseudocounts of 1 were used on the copy-number ratios in order avoid infinite numbers. The copy

number ratios were correlated with the gene expression ratios and with the read count ratios for

BRs.

Spearman Pearson

Correlation P-value Correlation P-value

NFKB 0.524 4.41E-32 0.462 2.33E-24 Correlation between SV/CNV ratios and

binding ratios PolII 0.458 2.85E-64 0.314 2.02E-29

NFKB 0.303 1.15E-10 0.353 3.25E-14 Correlation between SV/CNV ratios and

expression ratios PolII 0.466 7.90E-67 0.500 4.18E-78

Table S11. Transgressive segregation of NFκB occupancy in a parent offspring trio of European

ancestry. NFκB binding sites that are occupied in the trio daughter (GM12878), but not in either

parent (GM12891, GM12892) are listed. These were identified by scanning for BRs intersecting

with a peak identified by PeakSeq (P<0.001) in ChIP Seq replicates of the daughter only; we

confirmed that no spurious peaks were present in the parents by scanning with a very sensitive

threshold of P<0.05. The table displays for each transgression candidate the number of replicates

that demonstrate binding (P<0.001) in the daughter and the number of replicates with binding

(P<0.05) in either parent (i.e., 0 by definition for all transgression candidates).

Number of replicates

demonstrating binding

Coordinates

Daughter (GM12878)

“ON”

Father (GM12891)

“OFF”

Mother (GM12892)

“OFF” chr3:46959869-46960702 1 0 0 chr3:48128945-48129503 2 0 0 chr3:50250255-50250864 3 0 0 chr3:56926654-56927782 1 0 0 chr3:67208308-67208751 2 0 0 chr3:72211985-72212313 2 0 0 chr3:75312179-75312838 2 0 0 chr3:79400282-79400622 2 0 0 chr3:102763121-102763807 2 0 0 chr3:103239982-103240536 2 0 0 chr3:108723985-108724756 1 0 0 chr3:113700486-113701424 3 0 0 chr3:117239483-117240407 2 0 0 chr1:176816834-176817916 2 0 0 chr1:12418257-12419059 2 0 0 chr3:123259810-123260251 2 0 0 chr3:123790856-123791353 2 0 0 chr3:136157948-136158377 2 0 0 chr1:12421081-12421575 2 0 0 chr1:179144308-179145905 1 0 0 chr3:145049360-145050418 2 0 0 chr3:150780237-150780937 2 0 0 chr3:159844765-159845467 2 0 0 chr3:161164583-161165238 2 0 0 chr3:161599405-161600666 1 0 0 chr3:162970802-162971895 2 0 0 chr3:171140789-171141546 2 0 0

chr3:172449268-172449751 2 0 0 chr3:173009803-173011069 1 0 0 chr3:178402112-178402711 1 0 0 chr3:178515444-178515796 2 0 0 chr3:181156632-181157491 3 0 0 chr3:181529534-181530329 1 0 0 chr3:184716485-184717120 2 0 0 chr3:184792305-184793074 1 0 0 chr3:186786077-186787327 1 0 0 chr3:189119501-189120059 2 0 0 chr3:192448547-192449051 2 0 0 chr3:192496299-192497004 3 0 0 chr3:195873717-195874791 2 0 0 chr3:197498389-197499639 1 0 0 chr3:198605692-198606274 3 0 0 chr3:199160931-199161967 3 0 0 chr4:3759513-3759898 1 0 0 chr4:13788416-13789623 1 0 0 chr4:15566949-15567376 2 0 0 chr4:25485447-25486505 1 0 0 chr4:32170940-32171435 2 0 0 chr1:182192311-182192803 3 0 0 chr4:37393760-37394589 2 0 0 chr4:38201894-38202462 1 0 0 chr4:39031486-39032364 2 0 0 chr4:39302260-39302572 2 0 0 chr4:40012937-40014301 2 0 0 chr1:183102552-183103188 1 0 0 chr4:40326184-40327979 2 0 0 chr4:40362647-40363031 2 0 0 chr4:40369491-40370273 2 0 0 chr4:47195531-47196185 2 0 0 chr4:47835730-47836104 2 0 0 chr4:48850108-48853423 2 0 0 chr4:54201436-54202557 2 0 0 chr4:59012670-59012994 2 0 0 chr4:67946747-67949408 2 0 0 chr4:74673874-74675517 2 0 0 chr4:75798503-75798854 2 0 0 chr4:76149004-76149711 2 0 0 chr4:76279701-76280183 2 0 0

chr9:114858684-114859193 1 0 0 chr9:115383390-115384488 1 0 0 chr9:116290382-116290976 2 0 0 chr9:120197474-120197951 2 0 0 chr10:4078225-4078614 1 0 0 chr9:120331363-120332168 2 0 0 chr9:126217334-126217921 2 0 0 chr9:127448152-127449295 2 0 0 chr9:132736114-132736809 2 0 0 chr9:133542654-133544127 1 0 0 chr9:133869048-133869478 1 0 0 chr9:136019313-136020515 2 0 0 chr9:136435382-136436239 1 0 0 chr9:138553038-138554702 2 0 0 chr9:139759032-139759585 2 0 0 chr9:139771749-139772386 2 0 0 chrX:7189711-7190387 2 0 0 chrX:10047353-10047953 3 0 0 chrX:11687136-11688506 1 0 0 chr10:6349029-6349630 2 0 0 chrX:15079323-15080008 2 0 0 chrX:15132135-15132562 2 0 0 chrX:30499798-30500676 2 0 0 chrX:31106036-31106779 2 0 0 chrX:37530377-37531964 2 0 0 chrX:40607932-40608256 2 0 0 chrX:41447650-41448064 2 0 0 chrX:46398291-46398625 2 0 0 chrX:46437388-46437945 2 0 0 chr10:7566413-7567474 1 0 0 chrX:46699056-46699594 2 0 0 chrX:61634402-61634769 2 0 0 chrX:78280795-78281555 2 0 0 chrX:107244385-107244756 2 0 0 chrX:130774623-130775343 2 0 0 chrX:130778207-130779191 2 0 0 chrX:134277442-134277971 2 0 0 chrX:135624715-135625151 2 0 0 chrY:57411160-57411609 2 0 0 chr10:13370162-13370602 1 0 0 chr10:16652558-16653186 2 0 0

chr1:167928368-167929282 3 0 0 chr3:5215713-5216517 2 0 0 chr3:7221138-7221959 1 0 0 chr1:167947039-167948161 1 0 0 chr1:168309959-168310904 2 0 0 chr3:14442517-14443084 1 0 0 chr1:168774075-168775428 2 0 0 chr3:16606424-16607062 2 0 0 chr3:19576225-19576813 2 0 0 chr3:23043408-23043766 2 0 0 chr3:23966108-23967037 2 0 0 chr3:34167467-34167816 2 0 0 chr3:36934513-36934949 2 0 0 chr3:39271742-39272743 2 0 0 chr3:40977574-40978384 2 0 0 chr3:41978090-41979528 2 0 0 chr3:43241182-43241573 2 0 0 chr3:43645734-43646182 2 0 0

Table S12. Transgressive segregation of PolII occupancy in a parent offspring trio of European

ancestry. PolII binding sites that are occupied in the trio daughter (GM12878), but not in either

parent (GM12891, GM12892) are listed. Displayed are the number of replicates that demonstrate

binding (P<0.001) in the daughter and the number of replicates that demonstrate binding (P<0.05)

in either parent.

Number of replicates

demonstrating binding

Coordinates

Daughter (GM12878)

“ON”

Father (GM12891)

“OFF”

Mother (GM12892)

“OFF” chr1:117450810-117451222 2 0 0 chr2:38156034-38158074 3 0 0 chr1:117465482-117466508 1 0 0 chr2:46309533-46311471 4 0 0 chr2:46432276-46432779 2 0 0 chr2:54642455-54643318 3 0 0 chr2:54647979-54648894 2 0 0 chr2:57886866-57887526 3 0 0 chr2:57962088-57963964 2 0 0 chr2:62414694-62415546 3 0 0 chr2:64406576-64407466 2 0 0 chr2:65468713-65469728 2 0 0 chr2:70241521-70242131 3 0 0 chr2:73349413-73350608 2 0 0 chr1:144217331-144217636 2 0 0 chr2:75135804-75137397 2 0 0 chr2:85933670-85934775 3 0 0 chr2:85969414-85970199 3 0 0 chr2:88636189-88637041 2 0 0 chr2:96534391-96535063 2 0 0 chr2:96571979-96572292 2 0 0 chr2:97974086-97974760 2 0 0 chr2:98452680-98453420 2 0 0 chr2:98459059-98460127 2 0 0 chr2:108558096-108558503 4 0 0 chr2:112371880-112373110 2 0 0 chr2:113199975-113200921 2 0 0 chr2:123213824-123215967 4 0 0 chr2:131201281-131201951 1 0 0 chr2:132003199-132003894 1 0 0 chr2:136531045-136531965 2 0 0 chr2:162638488-162639315 2 0 0 chr2:171552941-171553517 2 0 0 chr2:196106827-196107495 2 0 0 chr2:196108583-196109526 4 0 0

chr2:196978156-196979200 5 0 0 chr2:216404311-216404698 2 0 0 chr2:218895762-218896883 1 0 0 chr2:219456272-219457083 3 0 0 chr2:225491237-225492896 2 0 0 chr2:228378603-228379224 2 0 0 chr2:231223590-231223989 3 0 0 chr2:232260262-232260617 2 0 0 chr2:233681601-233682386 3 0 0 chr2:237149534-237151931 3 0 0 chr2:238471733-238472461 2 0 0 chr2:238631782-238632248 2 0 0 chr1:150078442-150079496 4 0 0 chr2:239956242-239956800 2 0 0 chr20:3727685-3728713 1 0 0 chr20:6695919-6697192 1 0 0 chr20:34907006-34907842 2 0 0 chr20:48564681-48565360 4 0 0 chr20:48867676-48868823 2 0 0 chr20:48971868-48972375 3 0 0 chr20:49530305-49531855 3 0 0 chr20:49580451-49581743 3 0 0 chr20:51658099-51659036 2 0 0 chr20:51786781-51787767 2 0 0 chr1:153083100-153083389 2 0 0 chr20:52113838-52115499 3 0 0 chr20:55628585-55629350 1 0 0 chr20:61116330-61117018 1 0 0 chr21:25712108-25712708 2 0 0 chr21:31852930-31854471 3 0 0 chr21:39679477-39680443 4 0 0 chr21:42716342-42717143 3 0 0 chr21:43446626-43447324 4 0 0 chr21:43599720-43600678 2 0 0 chr21:44309787-44310082 2 0 0 chr22:16859230-16859585 2 0 0 chr22:16972907-16973529 3 0 0 chr22:18259046-18259404 2 0 0 chr22:21369349-21371063 3 0 0 chr22:21526020-21529377 7 0 0 chr22:21571799-21572398 7 0 0 chr22:21606947-21609367 3 0 0 chr1:154333236-154333979 1 0 0 chr22:29161541-29162142 3 0 0 chr1:154360057-154360565 1 0 0 chr22:35105608-35105974 2 0 0 chr22:35599762-35600429 2 0 0

chr8:144423285-144423612 2 0 0 chr8:145567504-145567808 2 0 0 chr9:20630339-20631188 4 0 0 chr9:33120587-33121618 3 0 0 chr9:33437106-33437725 1 0 0 chr9:37134253-37135219 4 0 0 chr9:37519061-37519562 1 0 0 chr9:67908189-67909559 2 0 0 chr9:93899719-93901518 4 0 0 chr9:94863655-94864760 2 0 0 chr9:94921860-94922754 2 0 0 chr9:99376997-99377387 1 0 0 chr9:107305429-107306330 3 0 0 chr9:108664581-108667485 7 0 0 chr9:111212857-111214365 2 0 0 chr9:111958895-111959571 2 0 0 chr10:6218143-6218859 2 0 0 chr9:115320957-115322796 3 0 0 chr10:6222443-6224106 3 0 0 chr9:115380959-115382252 2 0 0 chr9:116307354-116307711 2 0 0 chr9:116406807-116407531 2 0 0 chr9:116409746-116410982 3 0 0 chr9:122735496-122736014 4 0 0 chr9:125811894-125813113 2 0 0 chr10:6315792-6316661 2 0 0 chr10:6349201-6349654 3 0 0 chr10:6352328-6353625 3 0 0 chr9:127502877-127504055 5 0 0 chr9:129517532-129518110 1 0 0 chr9:129920619-129920845 2 0 0 chr9:130810554-130811152 2 0 0 chr9:131399155-131400245 1 0 0 chr1:12063546-12064812 3 0 0 chr9:133492926-133494701 1 0 0 chr9:134998669-134999774 2 0 0 chr9:135770656-135771038 1 0 0 chr9:136797369-136798875 2 0 0 chr9:138126751-138126984 2 0 0 chr9:138387697-138388187 3 0 0 chr9:139142491-139143412 2 0 0 chr9:139183436-139184121 1 0 0 chr9:139283193-139284126 2 0 0 chr9:139760911-139761439 2 0 0 chrX:9938618-9940804 4 0 0 chrX:11695487-11699489 7 0 0 chr1:12068244-12068874 1 0 0

chrX:12958214-12958621 2 0 0 chrX:13621174-13622195 2 0 0 chrX:13622708-13624184 3 0 0 chrX:15085636-15087591 2 0 0 chrX:19494490-19495060 2 0 0 chrX:39559791-39560295 1 0 0 chrX:41665721-41666118 2 0 0 chrX:53129749-53130378 2 0 0 chrX:56808384-56809256 1 0 0 chrX:74659299-74660159 4 0 0 chrX:84520955-84521633 5 0 0 chrX:105932137-105933064 1 0 0 chrX:111773482-111776215 7 0 0 chrX:114701412-114703239 4 0 0 chrX:130690087-130691482 3 0 0 chrX:130708155-130709444 2 0 0 chrX:153231432-153232267 2 0 0 chrX:153242799-153244184 2 0 0 chr10:13847622-13848726 2 0 0 chr10:17698700-17699831 1 0 0 chr10:18727916-18730106 4 0 0 chr10:23039060-23039772 3 0 0 chr1:14715460-14717201 3 0 0 chr10:31066676-31067818 2 0 0 chr10:49158138-49158480 2 0 0 chr10:70483577-70484048 2 0 0 chr10:70494720-70496110 4 0 0 chr1:16650950-16651314 2 0 0 chr10:76830592-76834798 4 0 0 chr10:81945872-81947233 1 0 0 chr10:82208710-82209618 3 0 0 chr10:85918107-85918922 2 0 0 chr10:97143982-97144272 2 0 0 chr1:19675751-19676839 4 0 0 chr1:21533047-21534080 7 0 0 chr10:109663045-109665541 6 0 0 chr1:22218007-22218471 2 0 0 chr10:121910871-121911189 2 0 0 chr11:508717-509206 1 0 0 chr11:805893-806276 2 0 0 chr1:23723722-23725220 2 0 0 chr11:1840319-1841709 2 0 0 chr11:7643309-7644399 2 0 0 chr11:14240155-14241121 2 0 0 chr11:19754800-19756387 4 0 0 chr11:44578180-44580502 5 0 0 chr1:24064234-24065071 2 0 0

chr19:43502135-43502726 1 0 0 chr19:48871289-48871652 2 0 0 chr19:48943439-48944842 4 0 0 chr19:50614501-50614938 4 0 0 chr19:50924777-50925329 2 0 0 chr19:51071379-51072351 2 0 0 chr19:52019483-52019741 3 0 0 chr19:52452232-52452899 2 0 0 chr19:53915578-53916217 1 0 0 chr19:54055001-54055533 2 0 0 chr19:54322904-54323273 1 0 0 chr19:54880563-54881546 2 0 0 chr1:112743553-112744075 3 0 0 chr19:56794202-56794900 2 0 0 chr19:59591429-59592530 5 0 0 chr19:63715573-63715891 2 0 0 chr2:7782118-7784180 4 0 0 chr2:7784442-7788194 2 0 0 chr2:7789164-7790871 2 0 0 chr2:8360973-8361540 2 0 0 chr2:8369502-8371123 4 0 0 chr2:8466123-8467111 3 0 0 chr2:8479172-8479883 3 0 0 chr2:8533717-8536669 5 0 0 chr2:8538355-8541784 6 0 0 chr2:8542073-8544628 4 0 0 chr2:8571554-8574541 4 0 0 chr2:8583310-8584538 2 0 0 chr2:8641130-8641826 4 0 0 chr2:9149899-9150914 2 0 0 chr2:11928134-11928725 2 0 0 chr2:12129805-12132259 4 0 0 chr2:20487779-20488701 1 0 0 chr2:27286119-27286707 2 0 0 chr2:27518714-27519566 1 0 0 chr2:30703273-30704063 6 0 0 chr1:117169938-117171935 4 0 0 chr2:31332114-31333060 2 0 0 chr2:34755860-34756603 3 0 0

Table S13. RNA expression analysis for transgression candidates. Contingency tables show that

mRNA expression values measured in nearby genes frequently are consistent with transgression.

The analysis was carried out for 562 NFκB sites and 520 PolII sites with a nearby gene; other*:

expression values are zero or not strictly > or <; C: child; M: mother; F: father.

RNA Expression Analysis for Transgression Candidates

NFκB PolII

C<M C>M C<M C>M

C<F 101 91 C<F 58 71

C>F 69 277 C>F 59 323

Other* 24 Other* 9

Fisher Exact Test (2-tail) 1.50E-14

Fisher Exact Test (2-tail) 7.23E-11

Table S14. BRs specifically occupied or unbound in a human population, i.e. "gains" and

"losses" of binding events. For NFκB, losses and gains were scored using a majority vote

criterion, i.e. in one population relative to both other populations. For PolII, where we ChIP-Seq

data for a chimpanzee and interpreted these as an “outgroup”, gains and losses are indicated

relative to chimpanzee peaks mapping onto syntenic PolII BRs in humans (chimp peaks were

therefore scored with a sensitive threshold using PeakSeq, i.e. P<0.05; human peaks were

identified with PeakSeq, using a stringent threshold with P<0.001).

Factor Population Specific gains Specific losses NFκB CEU 0 0 NFκB YRI 5 0 NFκB JPTCHB 0 9 PolII CEU 4 0 PolII YRI 7 1 PolII JPTCHB 2 54

Table S15. Enrichment of PANTHER gene functional categories uniquely bound by PolII in the

human samples and not in chimpanzee, identified using the DAVID server

(http://david.abcc.ncifcrf.gov/). Shown are the top 50 categories. P-values were calculated using a

Fischer Exact test and are equivalent to David server EASE scores according to

(http://david.abcc.ncifcrf.gov/helps/functional_annotation.html). Corrected P-values were

calculated using the Benjamini-Hochberg correction.

Term PValue Fold

Enrichment Corrected P-Value

BP00040:mRNA transcription 4.13E-141 1.326550824 9.08E-139 BP00031:Nucleoside, nucleotide and nucleic acid metabolism 8.28E-123 1.424384233 1.82E-120 BP00289:Other metabolism 3.80E-104 1.436538093 8.37E-102 BP00063:Protein modification 1.01E-91 1.420469781 2.22E-89 BP00071:Proteolysis 2.00E-76 1.272671796 4.40E-74 BP00064:Protein phosphorylation 4.65E-75 1.428096098 1.02E-72 BP00104:G-protein mediated signaling 1.96E-71 1.311191795 4.31E-69 BP00143:Cation transport 7.20E-69 1.288598075 1.58E-66 BP00067:Protein glycosylation 2.72E-51 1.408489852 5.98E-49 BP00060:Protein metabolism and modification 1.36E-48 1.288663932 2.98E-46 BP00036:DNA repair 3.67E-48 1.444826013 8.07E-46 BP00286:Cell structure 4.22E-38 1.240024933 9.28E-36 BP00034:DNA metabolism 7.17E-33 1.47371212 1.58E-30 BP00125:Intracellular protein traffic 1.68E-32 1.445980606 3.69E-30 BP00206:Chromosome segregation 8.71E-32 1.533813774 1.92E-29 BP00179:Apoptosis 1.56E-30 1.425425056 3.43E-28 BP00142:Ion transport 1.64E-28 1.251791448 3.60E-26 BP00141:Transport 5.50E-28 1.322444025 1.21E-25 BP00197:Spermatogenesis and motility 2.98E-25 1.298805353 6.55E-23 BP00276:General vesicle transport 3.83E-24 1.36459585 8.42E-22 BP00282:Mitosis 4.83E-24 1.376896949 1.06E-21 BP00103:Cell surface receptor mediated signal transduction 1.08E-23 1.216467909 2.37E-21 BP00250:Muscle development 2.69E-23 1.469431467 5.92E-21 BP00193:Developmental processes 1.88E-22 1.242791829 4.14E-20 BP00199:Neurogenesis 9.03E-22 1.335005151 1.99E-19 BP00014:Amino acid biosynthesis 1.19E-21 1.461689187 2.63E-19 BP00203:Cell cycle 6.38E-20 1.567990895 1.40E-17 BP00111:Intracellular signaling cascade 1.79E-19 1.3173694 3.94E-17 BP00211:Miscellaneous 8.02E-19 1.319588127 1.76E-16 BP00054:tRNA metabolism 4.78E-18 1.534136546 1.05E-15 BP00285:Cell structure and motility 2.92E-17 1.241374881 6.43E-15 BP00062:Protein folding 3.90E-17 1.419115164 8.57E-15

BP00112:Calcium mediated signaling 2.69E-16 1.450557679 4.88E-14 BP00041:General mRNA transcription activities 1.72E-16 1.364783811 4.88E-14 BP00069:Protein disulfide-isomerase reaction 3.74E-16 1.576603994 7.33E-14 BP00145:Small molecule transport 6.70E-16 1.385910793 1.47E-13 BP00246:Ectoderm development 9.05E-16 1.408108279 1.95E-13 BP00201:Skeletal development 2.92E-15 1.422363741 6.35E-13 BP00101:Sulfur metabolism 3.01E-15 1.385695483 6.59E-13 BP00070:Protein-lipid modification 3.31E-15 1.588717211 7.33E-13 BP00273:Chromatin packaging and remodeling 7.43E-15 1.295422353 1.64E-12 BP00048:mRNA splicing 9.51E-15 1.091360632 2.10E-12 BP00150:MHCI-mediated immunity 2.57E-14 1.110422643 5.64E-12 BP00136:Other intracellular protein traffic 2.95E-14 1.559393175 6.47E-12 BP00248:Mesoderm development 3.65E-14 1.297295892 8.04E-12 BP00207:Cell cycle control 8.92E-14 1.463231076 1.96E-11 BP00224:Cell proliferation and differentiation 3.12E-13 1.248151874 6.86E-11 BP00166:Neuronal activities 3.75E-13 1.465648308 8.25E-11 BP00001:Carbohydrate metabolism 1.02E-12 1.329209804 2.24E-10 BP00126:Exocytosis 1.92E-12 1.427927093 4.23E-10

Table S16. Summary of the results of Illumina sequencing of NFκB and PolII ChIP DNA.

Species Cell Line Factor Number of Replicates

Total Uniquely Mapped

ChIP Reads

Total Uniquely

Mapped IgG Reads

Human GM19193 NFκB 3 27,134,713 13,389,245 Human GM19193 PolII 4 23,448,488 15,597,850 Human GM19099 NFκB 3 27,132,498 14,446,085 Human GM19099 PolII 3 26,526,646 39,776,451 Human GM18505 NFκB 5 37,378,258 14,746,327 Human GM18505 PolII 4 35,831,337 11,791,866 Human GM18951 NFκB 4 43,819,021 12,855,821 Human GM18951 PolII 4 24,400,211 23,207,845 Human GM18526 NFκB 3 19,822,633 13,139,806 Human GM18526 PolII 3 13,850,344 39,776,451 Human GM15510 NFκB 5 37,901,453 13,738,416 Human GM15510 PolII 3 14,365,363 17,558,771 Human GM10847 NFκB 5 32,273,969 14,343,062 Human GM10847 PolII 4 23,031,305 14,283,489 Human GM12878 NFκB 4 48,478,730 24,803,979 Human GM12878 PolII 7 52,955,104 11,435,911 Human GM12892 NFκB 3 15,671,482 12,546,767 Human GM12892 PolII 3 24,215,247 12,400,429 Human GM12891 NFκB 3 26,295,924 14,604,360 Human GM12891 PolII 6 59,330,752 13,618,345

Chimpanzee AG18359 PolII 3 46,039,315 22,067,145

Table S17. Summary of the depth of sequencing for ChIP replicates. Displayed is the total

number of uniquely mapped reads for each ChIP sample.

Species Cell Line Factor Rep 1 Rep 2 Rep 3 Rep 4 Rep 5 Rep 6 Rep7

Human GM19193 NFκB 7,980,529 11,701,274 7,452,910 Human GM19193 PolII 3,881,248 6,563,574 8,188,275 4,815,391 Human GM19099 NFκB 7,042,689 13,103,447 6,986,362 Human GM19099 PolII 6,721,103 5,331,071 14,474,472 Human GM18505 NFκB 7,213,490 5,228,841 10,032,055 5,513,398 9,390,474 Human GM18505 PolII 9,534,445 5,758,319 7,166,980 13,371,593 Human GM18951 NFκB 5,259,923 14,156,434 15,037,202 9,365,462 Human GM18951 PolII 3,301,799 6,549,033 9,840,160 4,709,219 Human GM18526 NFκB 7,693,884 6,356,152 5,772,597 Human GM18526 PolII 4,379,494 4,528,777 4,942,073 Human GM15510 NFκB 6,611,307 4,050,607 6,373,991 10,248,789 10,616,759 Human GM15510 PolII 4,419,068 5,691,699 4,254,596 Human GM10847 NFκB 4,723,812 4,005,966 4,491,879 9,079,639 9,972,673 Human GM10847 PolII 4,847,246 3,511,138 7,387,899 7,285,022 Human GM12878 NFκB 12,573,662 16,406,889 10,153,862 9,344,317 Human GM12878 PolII 6,188,080 6,662,657 6,193,587 8,095,962 12,518,153 8,300,928 4,995,737 Human GM12892 NFκB 4,130,207 4,300,969 7,240,306 Human GM12892 PolII 7,273,170 9,582,006 7,360,071 Human GM12891 NFκB 5,435,404 9,802,466 11,058,054 Human GM12891 PolII 12,106,525 13,464,751 13,790,971 6,185,707 6,947,187 6,835,611

Chimpanzee AG18359 PolII 14,082,286 14,847,191 17,109,838

Table S18. qPCR primer design.

Primer pair Forward Reverse

Pol_II_P1 TGAGCAAATCCTGAGCCTTC TCCTTCTTCTGTGCCTTGGT Pol_II_P2 CCCAGAGCACATCCTCCTAC TCCTTCCTTACCTGGGAACC Pol_II_P3 GTCAGGCTGGAACTGAGGAG CAGCAAGGACAGCACCTACA Pol_II_P4 CACTGTTCTCCACGTCACTCA GGATGTTGAACGCATTTGG Pol_II_P5 CCTCTTCTCCCAGAATGCAG ACTGGCTGTCGTTGCAGTTA Pol_II_P6 TAGGCAGTCCAGTCCTGACC CACAGAGTTGTGGCTGGAGA Pol_II_P7 CTGGGATCCCCTGTATGTGT CCACCGCAGGATTTAAAAGT Pol_II_P8 CGCTTAGCGTCCGTTAACTC GGCTGCCAGTTGAGTACGAG Pol_II_P9 GCTGGAATCAATCAGGAAGC AAGCTCTGGACACAGCCATT Pol_II_P10 AATGCCCACCAAATCAGAAC CAGCCTTCCTGTGTGGTTTT Pol_II_P11 GAGAGCGTTGGAGAATGAGG CAGCAGTCAGCCAATCAAAA Pol_II_P12 TTGGACAGGAGGTGTTAGGG GAGAACGAGCCTGGGAGAG Pol_II_P13 GAGGAGAGTGAAGCCAGGTG TGAGGCTATGACCCACTTCC Pol_II_P14 GTCTACGACTTCGGGTCTCG TGAGTTTCCGTCGAGCTTTT Pol_II_P15 TGTCCAGTCCAGGGTTAAGG GCTGCTGCATACAAAGTGGA Pol_II_P16 CTGGGAGGGCTCATCTGTAG TGTGTGTGTGACAGCTGGTG Pol_II_P17 GATATTGCTCCATGCCACCT GAAGACACAGGATGGGGAGA Pol_II_P18 GGAAGAGATTCAGCCAGCAC GCCTGTTTGTTTTGGTCTCC Pol_II_P19 AGCCCGAGTTTAGGAAGAGG GGAGGGGAAAGAGGAGAAAA

Table S19. Summary of the results of Illumina sequencing of RNA purified from both NFκB

induced and uninduced cells.

Mapped reads

Cell line NFκB Induced?

Total unmapped

reads

Splice junctions

unique

Genomic unique

Genomic multi Total Fraction

mapped

GM10847 N 23,580,592 1,080,557 10,738,985 4,167,373 15,986,915 67.8% GM12878 N 20,475,699 987,907 9,765,204 3,822,581 14,575,692 71.2% GM12878 Y 20,025,486 865,707 8,247,315 2,784,082 11,897,104 59.4% GM10847 Y 17,062,129 829,991 7,766,350 2,776,360 11,372,701 66.7% GM19099 N 24,119,720 830,414 8,264,383 2,619,794 11,714,591 48.6% GM19099 Y 24,225,493 977,855 9,964,647 3,055,967 13,998,469 57.8% GM18951 N 25,276,523 933,252 9,715,718 3,051,680 13,700,650 54.2% GM18951 Y 24,091,166 977,034 10,358,823 3,461,261 14,797,118 61.4% GM18505 N 22,060,380 989,220 9,803,240 3,401,619 14,194,079 64.3% GM18505 Y 24,532,625 983,350 9,930,227 3,538,275 14,451,852 58.9% GM12891 N 21,252,573 1,045,762 9,734,575 3,301,524 14,081,861 66.3% GM12891 Y 22,340,578 1,061,948 9,975,729 3,265,937 14,303,614 64.0% GM12892 N 22,054,940 811,403 7,878,989 2,814,246 11,504,638 52.2% GM12892 Y 19,594,855 802,057 8,087,011 2,793,112 11,682,180 59.6% GM18526 N 21,241,739 857,195 8,807,090 3,026,427 12,690,712 59.7% GM18526 Y 18,212,851 807,360 8,417,536 2,914,597 12,139,493 66.7% GM15510 N 20,254,461 933,473 9,113,174 3,071,732 13,118,379 64.8% GM15510 Y 19,871,738 929,774 9,205,247 3,128,667 13,263,688 66.7% GM19193 N 19,692,693 939,371 9,415,893 2,916,499 13,271,763 67.4% GM19193 Y 16,799,365 796,274 8,396,917 2,636,795 11,829,986 70.4%

Table S20. Motif PWMs downloaded from JASPAR.

GC-Box Position: A C G T

1 0.371818 0.146364 0.182727 0.299091 2 0.353636 0.113636 0.408182 0.124545 3 0.182727 0.022727 0.560909 0.233636 4 0.244545 0.004545 0.750000 0.000909 5 0.000909 0.000909 0.997273 0.000909 6 0.008182 0.000909 0.990000 0.000909 7 0.197273 0.619091 0.000909 0.182727 8 0.168182 0.004545 0.815455 0.011818 9 0.004545 0.011818 0.808182 0.175455

10 0.288182 0.000909 0.622727 0.088182 11 0.084545 0.062727 0.699091 0.153636 12 0.000909 0.604545 0.128182 0.266364 13 0.073636 0.313636 0.190000 0.422727 14 0.146364 0.088182 0.397273 0.368182 TATA-Box

Position: A C G T 1 0.157051 0.372436 0.390385 0.080128 2 0.041667 0.11859 0.046795 0.792949 3 0.903205 0.000641 0.005769 0.090385 4 0.008333 0.026282 0.005769 0.959615 5 0.908333 0.000641 0.013462 0.077564 6 0.687821 0.000641 0.000641 0.310897 7 0.948026 0.008553 0.026974 0.016447 8 0.569872 0.005769 0.113462 0.310897 9 0.398077 0.113462 0.403205 0.085256

10 0.144231 0.346795 0.385256 0.123718 11 0.213462 0.377564 0.328846 0.080128 12 0.210897 0.326282 0.328846 0.133974 13 0.210897 0.303205 0.328846 0.157051 14 0.175000 0.275 0.357051 0.192949 15 0.198077 0.259615 0.359615 0.182692 CAAT-Box

Position: A C G T 1 0.319602 0.31392 0.069602 0.296875 2 0.183239 0.296875 0.245739 0.274148 3 0.143466 0.268466 0.137784 0.450284 4 0.580966 0.007102 0.399148 0.012784 5 0.291193 0.035511 0.563920 0.109375 6 0.001420 0.984375 0.007102 0.007102

7 0.001420 0.990057 0.001420 0.007102 8 0.995739 0.00142 0.001420 0.001420 9 0.677557 0.046875 0.120739 0.154830

10 0.098011 0.00142 0.086648 0.813920 11 0.132102 0.512784 0.336648 0.018466 12 0.660511 0.035511 0.296875 0.007102 NFKB-Motif

Position: A C G T 1 0.013158 0.223684 0.592105 0.171053 2 0.013158 0.013158 0.907895 0.065789 3 0.013158 0.013158 0.960526 0.013158 4 0.592105 0.013158 0.381579 0.013158 5 0.539474 0.171053 0.223684 0.065789 6 0.118421 0.013158 0.013158 0.855263 7 0.013158 0.013158 0.013158 0.960526 8 0.013158 0.118421 0.013158 0.855263 9 0.013158 0.960526 0.013158 0.013158

10 0.013158 0.960526 0.013158 0.013158 STAT1-Motif

Position: A C G T 1 0.328125 0.078125 0.578125 0.015625 2 0.140625 0.015625 0.828125 0.015625 3 0.703125 0.015625 0.265625 0.015625 4 0.953125 0.015625 0.015625 0.015625 5 0.890625 0.078125 0.015625 0.015625 6 0.515625 0.203125 0.203125 0.078125 7 0.078125 0.390625 0.140625 0.390625 8 0.015625 0.015625 0.953125 0.015625 9 0.953125 0.015625 0.015625 0.015625

10 0.953125 0.015625 0.015625 0.015625 11 0.953125 0.015625 0.015625 0.015625 12 0.015625 0.890625 0.078125 0.015625 13 0.078125 0.203125 0.015625 0.703125 14 0.265625 0.140625 0.453125 0.140625

Supplementary Tables

Table S1. Characteristics of the cell lines.

Table S2. Spearman correlations of biological replicates.

Table S3. In order to compare the distributions of Spearman correlation coefficients between

neighboring binding regions across the ten individuals separated by specific distance intervals we

performed Kolmogorov-Smirnov tests between these pairwise distributions. Using a

Kolmogorov-Smirnov test we calculated a P-value that measures the similarity between the

distribution of binding regions for different separations where a small P-value indicates a

significant difference between the distributions. This analysis was performed for the distributions

comparing neighboring NFKB binding regions, neighboring PolII binding sites, and the

association between neighboring PolII and NFKB binding regions.

Table S4. Correlation between difference in binding and B-SNPs altering positional weight

matrix (PWM) scores of a motif. Spearman correlations have been calculated from pair-wise

comparisons of samples, i.e. between the magnitude in binding difference calculated as the ratio

between DNA reads falling into a peak region and the difference in PWM scores between

individuals owing to B-SNPs. Motif PWMs were downloaded from JASPAR and are displayed in

Table S20. The NFκB-motif, TATA-box, STAT1-motif, CAAT-box, and GC-box are

summarized as sequence logos in Fig. 1A and Fig. S6A. The ABC test was also tested on the

following additional motifs in PolII BRs, without revealing significant results: DCE_S_III, INR,

MTE, MED-1, XCPE1, DCE_S_II, DPE, BREu, DCE_S_I, BREd (PWMs were obtained from

the JASPAR database and motifs mapped with MAST). Furthermore, the C/EBP motif (obtained

from the TRANSFAC database) was tested on NFκB BRs, without revealing significant results.

Table S5. CNV calls generated with the Affymetrix HG49m-T-v1 early access array set.

Table S6. Numbers of differentially bound BRs affected by B-SVs in at least one pairwise

comparison between individuals. CNVs identified using microarrays and deletions detected by

paired-end mapping (SV-deletions) are only displayed if the differential binding occurred in the

expected direction, based on the altered copy-number of the region, i.e. with "correct trend".

(*) We did not consider 1,426 SV-inversions intersecting with significantly differing PolII BRs,

as SV-inversions did not enrich for significant binding in the PolII data (see Figure 2B).

Table S7. Table S7. Fraction of BRs for which differential binding coincides with genetic

variation (*) displayed are averages for all individuals in which genetic data was available; e.g.

SNP based analyses exclude GM15510 and GM19193 for which no SNP genotype data was

available [ranges indicated results for different individuals]; (**) not displayed in Table S6 and

Fig. S6C, as no enrichment detected in PolII experiments.

Table S8. SNPs falling into transcription factor binding DNA motifs, coinciding with

differentially bound BRs in at least one pair-wise comparison between individuals.

Table S9. Correlations between binding and expression values recorded in pair-wise comparisons

between individuals

Table S10. Correlations between SVs and transcription factor binding as well as gene expression.

Unbalanced SVs (CNVs, and SV-DELs) were merged, and for each pair-wise set of individuals,

the BRs associated with SVs were extracted and associated with a gene if a BR was within 1kb of

the gene. The normalized read counts, expression values, and copy number values were then

obtained for each individual pair. SV-DELs were assigned a copy-number value of 1;

pseudocounts of 1 were used on the copy-number ratios in order avoid infinite numbers. The copy

number ratios were correlated with the gene expression ratios and with the read count ratios for

BRs.

Table S11. Transgressive segregation of NFκB occupancy in a parent offspring trio of European

ancestry. NFκB binding sites that are occupied in the trio daughter (GM12878), but not in either

parent (GM12891, GM12892) are listed. These were identified by scanning for BRs intersecting

with a peak identified by PeakSeq (P<0.001) in ChIP Seq replicates of the daughter only; we

confirmed that no spurious peaks were present in the parents by scanning with a very sensitive

threshold of P<0.05. The table displays for each transgression candidate the number of replicates

that demonstrate binding (P<0.001) in the daughter and the number of replicates with binding

(P<0.05) in either parent (i.e., 0 by definition for all transgression candidates).

Table S12. Transgressive segregation of PolII occupancy in a parent offspring trio of European

ancestry. PolII binding sites that are occupied in the trio daughter (GM12878), but not in either

parent (GM12891, GM12892) are listed. Displayed are the number of replicates that demonstrate

binding (P<0.001) in the daughter and the number of replicates that demonstrate binding (P<0.05)

in either parent.

Table S13. RNA expression analysis for transgression candidates. Contingency tables show that

mRNA expression values measured in nearby genes frequently are consistent with transgression.

The analysis was carried out for 562 NFκB sites and 520 PolII sites with a nearby gene; other:

expression values are zero or not strictly > or <; C: child; M: mother; F: father.

Table S14. BRs specifically occupied or unbound in a human population, i.e. "gains" and

"losses" of binding events. For NFκB, losses and gains were scored using a majority vote

criterion, i.e. in one population relative to both other populations. For PolII, where we ChIP-Seq

data for a chimpanzee and interpreted these as an “outgroup”, gains and losses are indicated

relative to chimpanzee peaks mapping onto syntenic PolII BRs in humans (chimp peaks were

therefore scored with a sensitive threshold using PeakSeq, i.e. P<0.05; human peaks were

identified with PeakSeq, using a stringent threshold with P<0.001).

Table S15. Enrichment of PANTHER gene functional categories uniquely bound by PolII in the

human samples and not in chimpanzee, identified using the DAVID server

(http://david.abcc.ncifcrf.gov/). Shown are the top 50 categories. P-values were calculated using a

Fischer Exact test and are equivalent to David server EASE scores according to

(http://david.abcc.ncifcrf.gov/helps/functional_annotation.html). Corrected P-values were

calculated using the Benjamini-Hochberg correction.

Table S16. Summary of the results of Illumina sequencing of NFκB and PolII ChIP DNA.

Table S17. Summary of the depth of sequencing for ChIP replicates. Displayed is the total

number of uniquely mapped reads for each ChIP sample.

Table S18. qPCR primer sequences.

Table S19. Summary of the results of Illumina sequencing of RNA purified from both NFκB

induced and uninduced cells.

Table S20. Motif PWMs downloaded from JASPAR.

Supplementary References

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66, 187 (2004). S7. R Development Core Team (2008). R: A language and environment for statistical

computing. R Foundation for Statistical Computing, Vienna, Austria. S8. S. Rozen, H. Skaletsky, Methods in molecular biology (Clifton, N.J.) 132, 365 (2000). S9. W. J. Kent, Genome Res 12, 656 (Apr, 2002). S10. W. J. Kent et al., Genome Res 12, 996 (Jun, 2002). S11. F. Hsu et al., Bioinformatics 22, 1036 (May 1, 2006). S12. B. Langmead, C. Trapnell, M. Pop, S. L. Salzberg, Genome Biol 10, R25 (2009). S13. S. A. McCarroll et al., Nat Genet 40, 1166 (Oct, 2008). S14. A. E. Urban et al., PNAS 103, 4534 (Mar 21, 2006). S15. J. O. Korbel et al., Science 318, 420 (Oct 19, 2007). S16. J. M. Kidd et al., Nature 453, 56 (May 1, 2008). S17. S. K. Bengtsson H, Bullard J, Hansen K, “aroma.affymetrix: An R framework for

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