Localization of a long-range cis-regulatory element
of IL13 by allelic transcript ratio mapping
Julian T. Forton,1,2,4Irina A. Udalova,2,3Susana Campino,2Kirk A. Rockett,2
Jeremy Hull,1,2and Dominic P. Kwiatkowski1,2
1Department of Paediatrics, Oxford University, Oxford OX3 7BN, United Kingdom;2Wellcome Trust Centre for Human Genetics,
Oxford University, Oxford OX3 7BN, United Kingdom
It appears that, for many genes, the two alleles possessed by an individual may produce different amounts of
transcript. When such allelic differences in transcription are observed for some individuals but not others, a plausible
explanation is genetic variation in the cis-acting elements that regulate the gene in question. Here we describe a novel
analytical approach that uses such observations, combined with genotyping data from the HapMap project, to define
the genomic location of cis-acting regulatory elements. When applied to the human 5q31 chromosomal region, where
complex regulatory mechanisms are known to exist, we demonstrate the sensitivity of this approach by locating a
highly significant cis-regulatory element operating on IL13 at long range from a position 250 kb upstream from the
gene (P = 2 × 10−6). As this method is unaffected by other sources of variation, such as environmental and
trans-acting genetic factors, it provides a tractable approach for dissecting the complexities of genetic variation in
[Supplemental material is available online at www.genome.org.]
It is now possible to identify regions of the human genome that
determine individual variation in gene expression, by combining
the powerful techniques of genome-wide expression profiling,
genome-wide SNP genotyping, and genetic association analysis
(Cheung et al. 2005; Stranger et al. 2005). The next step is to use
such findings to identify functional elements that regulate gene
expression. Here we explore a way of mapping genetic determi-
nants of gene expression that is complementary to genetic asso-
ciation analysis and may in some circumstances be more robust.
Imagine a functional polymorphism (F) that affects the ex-
pression of gene X. The aim of genetic association analysis is to
detect a significant difference in gene X expression between in-
dividuals of different F genotypes. However, if the expression of
gene X is affected by multiple genetic or environmental factors,
these are potential confounders that might obscure or distort the
association of gene X expression with F genotypes.
The number of potential confounders can be reduced by
using allelic transcript quantification to focus on cis-regulatory
effects, where F acts only on the copy of gene X that lies on the
same chromosome (Goldsborough and Kornberg 1994; Knight et
al. 2003; Buckland 2004; Pastinen and Hudson 2004; Pastinen et
al. 2004, 2005). This approach can be applied if there exists some
polymorphism (typically a SNP) on the transcript of gene X (here
called transcript marker T) that allows us to compare the abun-
dance of transcripts carrying different T alleles within heterozy-
gous individuals. For each heterozygous individual, we calculate
the ratio of the different T alleles, here called the allelic transcript
ratio (ATR). We would expect ATR measurements to be largely
unaffected by trans-acting genetic factors and to be relatively
robust against environmental confounders, because allelic com-
parisons are always made within individuals, not between indi-
viduals. The purpose of the present study was to develop a simple
way of using ATR measurements to map the genomic location of F.
We use as an example chromosome 5q31, a region that is rich in
immune genes and has been implicated in several common dis-
eases (Marquet et al. 1996; Rihet et al. 1998; Rioux et al. 2001).
Patterns of gene expression in this region are determined by com-
plex long-range regulatory mechanisms, making it an interesting
test case (Ansel et al. 2003; Fields et al. 2004; Spilianakis and
Flavell 2004). We performed allelic transcript quantification on
the genes CSF2, SLC22A4, RIL (currently known as PDLIM4),
IRF1, IL13, and IL4, using immortalized lymphoblastoid B-cell
lines from 16 unrelated individuals. The most striking deviation
from an ATR of 1 was observed for rs20541, a transcript marker of
IL13. A strong effect was observed in two out of eight individuals
who were heterozygous for rs20541, and this was confirmed by
further experiments using three other transcript markers of IL13
that were in phase with rs20541 (Fig. 1A). This observation raised
the possibility that some but not all individuals who are hetero-
zygous for the transcript marker rs20541 are also heterozygous
for a functional polymorphism F with cis-regulatory effects on
IL13. In an individual who is homozygous for cis-regulatory poly-
morphism F, we expect an ATR of 1, while in an individual who
is heterozygous for F, we expect an ATR that can be either above
or below 1, depending on the phase relationship of F with the
transcript marker T (Fig. 2A).
The above findings are typical of allelic transcript quantifi-
cation studies that have been reported for a growing number of
genes (Pastinen et al. 2004). Cis-acting regulation can act over
hundreds of kilobases (Lettice et al. 2002; West and Fraser 2005),
and this adds to the difficulty of identifying the functional ele-
ment precisely. Conventional linkage disequilibrium mapping
approaches cannot be applied to allelic transcript ratio data, but
by inspecting haplotypes across the genomic region surrounding
3Present address: Kennedy Institute of Rheumatology, Imperial Col-
lege, London, UK.
E-mail Julian.firstname.lastname@example.org; fax 44-1865-220479.
Article published online before print. Article and publication date are at http://
17:82–87 ©2007 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/07; www.genome.org
T, we can identify SNP markers that are correlated with allele-
specific expression (Pastinen et al. 2005). The problem we address
in this study is how to most effectively screen across a wide ge-
nomic region to determine the location of F.
We propose a simple method of allelic transcript ratio map-
ping. For any SNP marker M, with alleles M1 and M2, in the
genomic region of T we can measure ATR in a group of individu-
als who are heterozygous for T, and determine pairwise haplo-
types between M and T in each individual. In individuals who are
heterozygous for M, this haplotypic information allows us to
establish the phase of the ATR with respect to M, and to derive a
marker-specific transcript ratio (MTR), that is, the relative abun-
dance of transcripts derived from the M1 chromosome compared
to the M2 chromosome (Fig. 2B).
In individuals who are homozygous for M, the distribution
of ATR values provides a measure of experimental variation that
cannot be attributed to the allele-specific effects of M. We call
this the nonspecific transcript ratio (NTR). We then derive some
measure of the statistical significance of the MTR taking the NTR
into account—we call this the ATR mapping metric. The P-value
of a t-test comparison of the MTR and NTR distributions is used
here as a simple example of a mapping metric. By charting the
ATR mapping metric for a set of SNP markers across the genomic
region surrounding T, we can build up a picture of the location of F.
To test this method on the IL13 locus in a model system, we
used immortalized lymphoblastoid B-cell lines derived from 90
individuals of European ancestry that form part of the HapMap
project (Altshuler et al. 2005). Allelic transcript quantification of
the IL13 transcript polymorphism rs20541 was carried out in 22
unrelated individuals who were heterozygous for this SNP, both
in non-activated cells and after activation with PMA/ionomycin.
Individuals with extreme ATR values were found to give consis-
tent results when the test was repeated
on different cell aliquots (Supplemental
Fig. 1; Supplemental Table 1). As an ex-
perimental control, we studied the IRF1
transcript polymorphism rs839, and ob-
served greater variation in the ATR for
IL13 than for IRF1 (Fig. 1B), consistent
with our earlier data (Fig. 1A). Genotyp-
ing data and pedigree information ob-
tained from the HapMap database
(http://www.hapmap.org) allowed us to
derive pairwise haplotypes for SNP
markers in the region surrounding the
IL13 transcript polymorphism. Using
this haplotypic information, we calcu-
lated MTR and NTR values for each SNP
marker, and from a comparison of these
values by t-test, we derived a log prob-
ability to use as the ATR mapping met-
We examined 300 SNPs across an
∼1200-kb region centered on IL13 (Fig.
3). This revealed a region located 200–
300 kb upstream of IL13 containing nine
SNP markers with marked differences be-
tween MTR and NTR in non-activated
cells (t-test, P = 2 ? 10?6; permutation
analysis, P < 1 ? 10?5; Mann Whitney,
P = 4 ? 10?4) (Fig. 3A) and somewhat
less marked differences in activated cells
(t-test, P = 2 ? 10?4; permutation analysis, P < 1 ? 10?3; Mann
Whitney, P = 7 ? 10?3) (Fig. 3B).
These nine SNPs fall within a genomic block of high linkage
disequilibrium (Fig. 3B), and their minor alleles appear to be spe-
cific for a long-range haplotype spanning the genes APXL2 (cur-
rently known as SHROOM1), GDF1, QP-C (currently known as
UQCRQ), LEAP2, and AFF4 (haplotype A4 in Fig. 3C). By analogy
with MTR, we can use individuals who are heterozygous for a
given haplotype to estimate the allelic expression ratio between
that haplotype and all other haplotypes, which we call the hap-
lotype-specific transcript ratio (HTR). Haplotype A4 has an aver-
age HTR of 1.28 (95% CI, 1.19–1.37) in non-activated cells and
1.26 (95% CI, 1.15–1.37) in activated cells. The t-test for average
HTR in A4 haplotype carriers versus non-A4-haplotype carriers
was significant in both resting cells (P = 2 ? 10?5), and in acti-
vated cells (P = 8 ? 10?4).
These IL13 data highlight a crucial feature of ATR mapping,
namely, that this method does not require F to be in linkage
disequilibrium with T. If F is in strong linkage disequilibrium
with T, then ATR values observed in different individuals will
tend to be consistently different from 1. If F is not in linkage
disequilibrium with T, then ATR values observed in different in-
dividuals will range above and below 1, depending on the hap-
lotypic relationship of F and T in the individual being tested, and
there may be many ATR values close to 1 arising from individuals
who are heterozygous for T but homozygous for F. This has in-
teresting implications for large-scale efforts to use allelic tran-
script quantification to screen for cis-regulatory factors across the
whole genome (Pastinen et al. 2004, 2005). By focusing on loci
where the ATR is consistently different from 1, we will detect
functional polymorphisms that are in strong linkage disequilib-
rium with the transcript marker, and this tends to bias the ex-
covering six genes across the 5q31 cytokine cluster. Four transcript SNPs in complete linkage disequi-
librium across IL13 demonstrate consistent ATRs in all cell lines tested, suggesting that these results are
real and not a product of asymmetrical primer affinity or extension reaction bias. (B) Allelic transcript
ratios observed in 22 CEPH cell lines used in the HapMap resource show more variation in ATR for IL13
(using rs20541 as transcript SNP) than for IRF1 (using rs839), in both non-activated (N/A) and acti-
vated (A) cell aliquots. In each plot, the median allelic transcript ratio, the interquartile range, and the
full range are shown for the group of cells assayed.
(A) Allelic transcript ratios (?SEM) observed in 16 CEPH cell lines, for 10 transcript SNPs
Localizing cis regulation for IL13 by ATR mapping
perimental design toward the detection of cis-regulatory factors
that operate over a short range. It is important not to reject loci
where the ATR ranges widely above and below 1 because, as we
have observed for IL13 (Figs. 1B and 3), this may indicate a func-
tional polymorphism that acts on but is not in linkage disequi-
librium with the transcript marker, and by investigating such loci
we may discover cis-regulatory factors that operate at long range.
Since ATR data are determined by cis-acting regulation and
are potentially refractory to trans-acting effects (which will affect
absolute expression but not the allelic transcript ratio), genetic
diversity on a different chromosome should not influence the
variation observed in ATR data for IL13. We therefore applied the
ATR mapping metric for the IL13 data (chromosome 5) to 10,000
consecutive SNPs on chromosome 20 (available for the same cell
lines from the HapMap resource), to gain further insight into the
potential false discovery rate of the test. Because the ATR map-
ping metric relies on phasing of the transcript SNP with each
marker SNP being tested, we “transferred” the IL13 transcript SNP
to chromosome 20 “in silico” for this simulation, so that it could
be phased with each SNP in the analysis. Of the 10,000 SNPs on
chromosome 20, only eight independent loci reached signifi-
cance at or above the level of that seen for the nine SNPs iden-
tified 250 kb upstream of IL13 (Fig. 4), giving a false discovery
rate for the ATR mapping metric, at a designated significance
level of P < 2 ? 10?6, of 1 in 1250 SNPs.
To corroborate the finding of a potential long-range cis-
regulator for IL13, we interrogated publicly available human cell
line expression data at Gene Expression
Omnibus, the NCBI gene expression
and hybridization array data repository
(Edgar et al. 2002). We sourced quanti-
tative expression data for IL13 from ex-
pression profiles performed on 15 CEPH
families using a 25K human gene oligo-
nucleotide microarray (Monks et al.
2004). Using only the unrelated indi-
viduals in this cohort, we sourced geno-
type status at rs11739417 for 30 indi-
viduals using publicly available geno-
type data from the HapMap and
Perlegen resources. Expression levels are
correlated with genotype at rs11739417
in Figure 5, and demonstrate a reduction
in mean IL13 production for AG hetero-
zygotes compared to AA homozygotes
(t-test, P = 0.016). These independent
expression data support the conclusions
from ATR mapping, namely, that allele G
at rs11739417 (and haplotype A4 to
which allele G is unique) is associated
with a reduction in IL13 expression.
In addition to the 200–300-kb up-
stream region, there may exist other cis-
regulatory polymorphisms that deter-
mine IL13 expression. For example, the
region ∼400–500 kb downstream from
IL13 shows several potentially interest-
ing differences between MTR and NTR
When we focused on the proximal
100-kb region containing IL13 and its
flanking genes RAD50 and IL4, single-
locus analysis revealed no striking effects, but there were poten-
tially interesting haplotypic effects (Fig. 3D). We analyzed com-
mon haplotypic groups across this region in 22 unrelated indi-
viduals, after removing the potential confounding effect of the
distal haplotype A4 (Fig. 3C) by excluding four individuals
known to be A4 heterozygotes. One haplotype (B6) (see Fig. 3D)
emerged from this analysis as having a possible effect on gene
expression that was much more apparent in activated cells
(HTR = 0.78, 95% CI, 0.65–0.91) than in non-activated cells
(HTR = 0.93, 95% CI, 0.76–1.10). The t-test for average HTR in B6
haplotype carriers versus non-B6-haplotype carriers was signifi-
cant in activated cells (P = 9 ? 10?3) but not in resting cells. The
direction of HTR distortion in B6 haplotype carriers upon acti-
vation was consistent in all lines (paired t-test, P < 5 ? 10?3).
We demonstrate a method of ATR mapping that is capable of
detecting functional polymorphism acting on a gene, irrespec-
tive of whether it lies proximally to and in high LD with the
gene, or operates at long range and is in low LD with the gene. A
pattern of unidirectional ATR distortion in the majority of the
individuals assayed would be consistent with the presence of
cis-regulatory polymorphism in high LD with the gene, whereas
bidirectional ATR distortion in a subgroup of the individuals as-
sayed would be consistent with cis-regulatory polymorphism in
low LD with the gene.
T will be expected to deviate from 1. The direction of ATR deviation in a given cell line will depend on
the phase relationship between F and T. In the example shown in A, allele F1 up-regulates gene
expression. The observed ATR, T1:T2, is therefore >1 when it represents F1:F2 and <1 when it repre-
sents F2:F1. (B) In individuals who are heterozygous for M, the ATR is phase-corrected with respect to
M so that in all cases it represents the relative abundance of transcripts derived from the M1 chromo-
some compared to the M2 chromosome. We call the phase-corrected ATR the marker-specific tran-
script ratio (MTR) and for a marker SNP M in high LD with the functional SNP F, but in low LD with the
transcript SNP T, MTR values will cluster where ATR values may not. In individuals who are homozygous
for M, ATR values provide a measure of experimental variation that cannot be attributed to the
allele-specific effects of M. We call this the non-specific transcript ratio (NTR). The ATR Mapping Metric
is a two-tailed t-test with unequal variance for the comparison ATR values in the two groups MTR and
NTR. The Log10s of all ATR values are used in the t-test. A significant P-value implies that the variation
seen in the observed ATR is attributable to the allele-specific effects at M.
(A) If a cis-acting polymorphism F is heterozygous, the observed ATR at transcript Marker
Forton et al.
For IL13 we identify distal cis-regulatory polymorphism
highlighted by nine SNPs all common to a single haplotype span-
ning the genes from APLX2 to AFF4. Complex mechanisms of
gene regulation have been described in the 5q31 gene region. A
three-dimensional chromatin configuration that approximates
regulatory elements and target genes has been proposed at 5q31
(Ansel et al. 2003; Fields et al. 2004; Spilianakis and Flavell 2004),
and it is conceivable that the distal cis-regulatory element de-
scribed here may also integrate into such a multiloop chromatin
structure, or may yet be part of a higher-order configuration.
containing nine SNP markers with marked differences between MTR and NTR in both (A) non-activated cells and (B) activated cells. These nine SNPs
fall within a genomic block of high linkage disequilibrium spanning APXL2 (SHROOM1), GDF1, QP-C (UQCRQ), LEAP2, and AFF4 and are all unique to
(C) haplotype A4, which has an average haplotype-specific transcript ratio (HTR) of 1.28 (95% CI, 1.19–1.37). (D) Haplotype analysis of the proximal
100-kb region containing IL13, IL4, and RAD50 highlighted haplotype B4 with an HTR much more apparent in activated cells (HTR = 0.78, 95% CI,
0.65–0.91) than in non-activated cells (HTR = 0.93, 95% CI, 0.76–1.10).
Allelic transcript ratio mapping for 300 SNPs across ∼1200 kb centered on IL13 revealed a region located 200–300 kb upstream of IL13
Localizing cis regulation for IL13 by ATR mapping
Replicating these findings for IL13 in other populations with less
extensive LD may localize the effect to a smaller region and help
in the focused application of reporter assays and EMSAs in the
assessment of transcription factor dynamics.
Single-locus analysis revealed no effects in the region proxi-
mal to IL13 despite the presence of a positive haplotype effect.
The ATR mapping metric proposed here has the greatest power to
detect a cis-regulatory effect in a Marker SNP (M) where there are
equal numbers of heterozygotes and homozygotes. Although as a
consequence, this metric is less well powered for the detection of
proximal cis-polymorphism existing in high LD with the tran-
script marker (T), it is likely that single-locus analysis would have
been successful in identifying the proximal effect seen for IL13
with a greater depth of SNP ascertainment.
In the context of complex gene regulation, it is possible
that those cell lines with observed ATR distortion may carry one
of a number of independent or interdependent cis-acting
components. The two regulatory elements identified with these
data appear to be independent effects that show different
characteristics, with the distal haplotype affording a constitu-
tive down-regulating effect and the proximal haplotype afford-
ing an inducible up-regulating effect. The context specificity of
If the complexity of cis-regulatory mechanisms is high, the
observed ATR for an individual may be a composite reflection of
multiple cis-acting elements. In this situation, the observed ATRs
for a group of cell lines are likely to cover a broad spectrum of
values, and the data may prove more difficult to resolve.
For all modes of quantifying cell line expression (Cheung et
al. 2003; Pastinen and Hudson 2004), the success of locating an
existing cis-regulator will be dependant on many factors, includ-
ing the complexity of the regulatory mechanisms that exist, the
context specificity of those regulatory processes in the cell lines
or tissues analyzed, on the stochastic sampling of individuals, on
cohort size, and on the haplotypic structure of the population
studied (Forton and Kwiatkowski 2006).
Nevertheless, the complexity of regulatory mechanisms that
are assayed using the ATR approach is significantly less than that
using traditional expression profiling as trans-acting regulation
and environmental confounders will not influence ATR measure-
ments. By focusing only on the cis-acting framework of gene
regulation, ATR measurement and mapping (Knight et al. 2003;
Pastinen et al. 2004, 2005) may provide a tractable approach for
dissecting the complexities of variation in gene expression.
Lymphoblastoid B-cell lines from the CEPH collection (Center
d’Etude de Polymorphisme Humaine) were sourced from the Co-
riell Repository. All 30 CEPH HapMap families and a further eight
CEPH family trios were used in analysis. Unrelated individuals
only were used in expression assays.
Cell culture and activation
Immortalized lymphoblastoid B-cells were cultured at 37°C in a
humidified, 5% CO2environment in RPMI 1640 with 10% fetal
calf serum, 200 mM L-glutamine, penicillin, and streptomycin.
Cell density was maintained between 200,000 and 800,000 cells/
mL. DNA was extracted from 20 million cell aliquots. For RNA
extraction and cDNA synthesis, cell cultures were set up in ali-
quots of 15 ml in T25 flasks at a cell density of 200,000 cells/mL.
Cells were activated at a cell concentration between 600,000 and
800,000 cells/mL (∼10 million cells) using PMA (200 mM) and
ionomycin (200 mM). Cells were harvested at 6 h post-activation.
Equivalent non-activated aliquots were prepared in parallel.
Cell aliquots (15 mL) for RNA were centrifuged, the media was
decanted, and the cell pellet was lysed in TRIREAGENT. Cell ly-
sates were stored at ?80°C until RNA extraction. RNA was ex-
tracted as per the TRIREAGENT protocol, using chloroform, iso-
performed on 15 CEPH families using a 25K human gene oligonucleotide
microarray (Monks et al. 2004) are correlated with genotype at
rs11739417 and demonstrate a reduction in mean IL13 production for
AG heterozygotes compared to AA homozygotes (t-test, P = 0.016).
These independent expression data support the conclusions from ATR
mapping, namely, that allele G at rs11739417 (and haplotype A4 to
which allele G is unique) is associated with a reduction in IL13 expression.
Quantitative expression data for IL13 from expression profiles
10,000 consecutive SNPs on chromosome 20 identified only eight inde-
pendent loci with a significance at or above the level of that seen for the
nine SNPs identified 250 kb upstream of IL13, giving a false discovery rate
for the ATR mapping metric, at a designated significance level of
P < 2 ? 10?6, of 1 in 1250 SNPs.
Simulation of the ATR mapping metric for IL13 data on
Forton et al.
86 Genome Research
propanol, and ethanol precipitation. Total RNA was quantified Download full-text
using UV spectrophotometry (Nanodrop). Samples with an A260/
A280 ratio of 2.0 ? 0.2 were accepted. mRNA was extracted from
20-µg total RNA aliquots using the Dynabeads mRNA purifica-
tion kit, as per protocol, and cDNA was synthesized using Stra-
tascript reverse transcriptase with oligo(dT) primers. One micro-
liter of cDNA was derived from 100 ng of total RNA. Parallel RT
negative controls were generated in all cDNA syntheses.
Allele-specific transcript quantification
The Allelotype platform from Massarray (Sequenom) was used for
accurate relative quantification of allele-specific cDNA species
(Hacking et al. 2004; Elvidge et al. 2005). Primers were designed
using the dedicated software Spectrodesigner (Sequenom).
For a single cDNA assay, we performed four PCR replicates.
We performed four extension reaction replicates on each PCR
product, therefore producing 16 nested technical replicates per
assay. Each replicate was run using a 2-µL aliquot of cDNA (de-
rived from 200 ng of total RNA). Genomic DNA in 16 equivalent
nested technical replicates (4 µg per replicate) was assayed as a
control for each assay, using the same allelotype chip and primer
mix. RT negative cDNA controls for all assays were run in parallel.
For a given cDNA assay, the allelic transcript ratio was
calculated on each of the 16 technical replicates from the rela-
tive quantity of the two allele-specific cDNA species. The mean
allelic transcript ratio for the whole assay was then calculated and
normalized to the mean allelic transcript ratio for the genomic
controls. An assay was accepted for further analysis if the stan-
dard error of the mean for the 16 technical replicates was <10%.
SNP and haplotype analysis
HapMap release 16 was accessed at http://www.hapmap.org. All
other genotyping was performed using MassArray (Sequenom).
All haplotypes were generated with the software PHAMILY and
PHASE2.1.1 (Stephens et al. 2001) using family trios.
The ATR Mapping metric applied to the data in this study is a sim-
ple two-tailed t-test with unequal variance for the comparison of
ATR values in the two groups bracketed as MTR and as NTR for a
given SNP M. The Log10s of all ATR values were used in the t-test.
To generate a corrected P-value for multiple testing, per-
mutation analysis for SNPs of interest was performed by ran-
domizing observed ATRs with individuals. For the SNP of
interest, a two-tailed t-test with unequal variance for the
comparison of groups bracketed as MTR and as NTR was
performed for each of 10,000 permutations to generate a dis-
tribution of P-values. (The Log10s of all ATR values were used in
the t-test.) The corrected P-value was assigned from the position
of the observed P-value within this distribution of random P-values.
All statistics were programmed in visual Basic (Microsoft Ex-
cel) or obtained using SSPS.
We thank Evelyn Harvey for her contribution to initial ATR ex-
periments in the 5q31 region. This work was funded by a Well-
come Trust Clinical Research Training Fellowship (to J.T.F.) and
a MRC Program Grant (to D.P.K.).
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Received June 19, 2006; accepted in revised form October 18, 2006.
Localizing cis regulation for IL13 by ATR mapping