Targeted allelic expression profiling in human islets identifies cis-regulatory effects for
multiple variants identified by type 2 diabetes genome-wide association studies
Running title: - Type 2 diabetes GWAS genes allelic islet levels
Jonathan M. Locke1, Gerald Hysenaj1, Andrew R Wood1, Michael N Weedon1, Lorna W
Harries1
1
Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter,
EX2 5DW
Corresponding author:
Lorna W Harries
Room L3/20
RILD Building
University of Exeter Medical School
Barrack Road
Exeter
EX2 5DW
Tel: 01392 406749
Fax: 01392 408385
Email: l.w.harries@exeter.ac.uk
Word count: 3118
Figures: 3
Tables: 1
Genome-wide association studies (GWAS) have identified variation at >65 genomic loci
associated with susceptibility to type 2 diabetes, but little progress has been made to
date in elucidating the molecular mechanisms behind most of these associations. Allelic
expression profiling is a powerful technique for identifying cis-regulatory variants
controlling gene expression, using samples heterozygous at transcribed single nucleotide
polymorphisms (SNPs) to distinguish and measure levels of transcripts derived from
each allele. Exonic SNPs, suitable for measuring mRNA levels and in high linkage
disequilibrium (LD) with 65 lead type 2 diabetes GWAS SNPs, were identified in 18
RefSeq genes. For 14 of these genes allelic expression could be determined using RNA
and DNA isolated from islets of 36 white, non-diabetic donors. A significant allelic
expression imbalance (AEI) was identified for 7 genes (ANPEP, CAMK2B, HMG20A,
KCNJ11, NOTCH2, SLC30A8 and WFS1). For 5 of these genes allelic expression could
be determined using other linked exonic SNPs, with significant AEI confirmed in each
case. This study provides one of the first pieces of evidence supporting the hypothesis
that changes to cis-regulation of gene expression are involved in a large proportion of
common variant associations with susceptibility to type 2 diabetes.
Genome-wide association studies (GWAS) have discovered germline genetic variation
associated with type 2 diabetes risk (1-4). One of the largest GWAS, involving DNA taken
from individuals of European descent, and conducted by the DIAGRAM (DIAbetes Genetics
Replication and Meta-analysis) consortium identified 65 loci associated with type 2 diabetes
risk (1). However for most of these loci, the precise identity of the affected gene and the
molecular mechanisms underpinning the altered risk are not known.
Similar to other complex polygenic diseases, the lack of protein-coding variation at the
majority of these loci has led many to believe that the disease-associated variation must result
in changes in the level of gene expression. This is predicted to affect the amount or nature of
protein produced resulting in compromised cellular function and ultimately altered disease
risk. It is this hypothesis that has led many to conduct expression quantitative trait loci
(eQTL) studies, where the disease-associated genotype is correlated with expression of cislinked transcripts. Whilst there have been some successes when using such an approach (5;
6), the presence of multiple known and unknown confounding factors that can affect gene
expression means few disease-associated loci are robustly associated. Allelic expression
profiling provides an alternative and direct way to measure the effect of cis-regulatory
variation on gene expression. By measuring transcripts from each allele of a gene (using a
SNP as a marker to differentiate between the two mRNA copies) the effect of trans-acting
factors on gene expression is essentially removed as the output from one allele serves as a
within-sample control for the other.
The known tissue-specificity of gene expression regulation means that the most informative
studies will measure transcript levels in the specific tissue(s) relevant to the disease. In the
case of type 2 diabetes, characterisation of physiological responses (e.g. stimulus-induced
insulin secretion, insulin sensitivity) suggests most loci are associated with defects in
pancreatic beta cell function (2; 3; 7). Therefore there is a real need to measure gene
expression in human beta cells (or whole islets as these have been shown to be a suitable
proxy (8)). There have however been very few reports linking type 2 diabetes-associated
variation with islet gene expression using the classical eQTL approach (9; 10).
In this report we provide one of the first pieces of evidence to support the theory that for a
large proportion of loci associated with predisposition to type 2 diabetes the variants affect
the expression of known protein-coding genes. Furthermore by pinpointing genes with allelic
expression imbalances we highlight the genes likely to be involved in disease pathogenesis.
RESEARCH DESIGN AND METHODS
Tissue and nucleic acid extraction
Snap-frozen islets were purchased from ProCell Biotech (Newport Beach, CA, USA) where
islets had been collected with ethical permission at source. Donor characteristics are
presented in Supplementary Table 1. Islet purity and viability was determined by dithizone
and fluorescein diacetate/propidium iodide staining, respectively. RNAlater-ICE (Life
Technologies, Carlsbad, CA, USA) was used to transition the tissue to a state where RNA
could be extracted using the miRVana miRNA isolation kit, as per the manufacturers’
instructions. Small amounts of genomic DNA were co-eluted upon RNA extraction. Wholegenome amplification was subsequently carried out using the REPLI-g Mini kit (Qiagen,
Venlo, Netherlands).
Genotyping and quantitative reverse transcriptase-PCR (qRT-PCR)
To find heterozygous samples suitable for allelic expression measurements whole genome
amplified genomic DNAs were first genotyped. In cases where the transcribed SNP is more
common in the CEU population than the lead type 2 diabetes SNP at that locus (according to
1000 Genomes Project, Phase 1, CEU population) the lead SNP was also genotyped (by
Sanger sequencing) and only samples heterozygous at both loci were included for further
analysis. For allelic expression analyses total RNA was DNase-treated using the Turbo
DNA-free kit (Life Technologies). DNase-treated RNA was subsequently reverse transcribed
using the SuperScript VILO cDNA synthesis system (Life Technologies). For each SNP
duplicate wells were used to amplify cDNA and ~5ng genomic DNA in all heterozygous
samples. Following amplification wells with a cycle threshold (Ct) value >36 were excluded
from our analysis. To calculate dCt values the difference in Ct value for each reporter dye
(VIC/FAM), detecting each SNP allele, was determined. Subsequently, ddCt values were
calculated by determining the difference between the dCt and the mean dCt of all genomic
DNA samples. Genomic DNA samples should show a 1:1 allelic ratio and thus any departure
from 0 illustrates unequal amplification of alleles which must be corrected for. These ddCt
values were then transformed to 2-ddCt values to take into account the logarithmic nature of
PCR. Mean average allelic expression measurements, determined from two independent
cDNAs reverse transcribed and amplified on different days, are presented. All genotyping
and allelic expression experiments were performed with TaqMan SNP Genotyping assays
and TaqMan Genotyping Master Mix (both Life Technologies). Inventoried TaqMan SNP
genotyping assays were used where possible (amplicon mapping 100% to exonic sequence).
If not available or not meeting our design specifications custom TaqMan SNP genotyping
assays were designed and purchased where possible. For the islet eQTL analysis inventoried
TaqMan Gene Expression assays and TaqMan Fast Advanced Master Mix (both Life
Technologies) and were used. Relative expression of target genes was normalised to the
geometric mean of 5 housekeeping genes (ACTB, B2M, GUSB, HMBS, RPL11). All TaqMan
assays were run on the ABI7900HT platform (Life Technologies). Assay IDs and
primer/probe sequences for TaqMan SNP Genotyping and TaqMan Gene Expression assays
are shown in Supplementary Table 2.
Statistical analysis
Paired two-tailed T-tests, comparing genomic DNA and cDNA values from the same donor,
were used to determine statistical significance for allelic expression. Allelic and total gene
expression values were normally distributed upon log2 transformation and thus permitted the
use of parametric statistics.
Pearson correlation coefficients were used to ascertain the
robustness of our allelic expression measurement.
RESULTS
Bioinformatic analysis of type 2 diabetes GWAS loci to identify exonic SNPs for allelic
expression profiling in human islets
Large-scale association studies conducted by DIAGRAM, in individuals overwhelmingly of
European descent, have reported 65 lead SNPs associated with susceptibility to type 2
diabetes (1). Figure 1 illustrates how these SNPs and closely correlated proxy SNPs were
systematically selected for allelic expression analysis. In brief 1525 proxy SNPs (r2>0.8,
CEU, 1000 Genomes Phase 1) were found. Of these SNPs (lead + proxies) 45/1590 (2.8%)
map to exons of 23 human RefSeq genes. For 18 of these genes, TaqMan SNP genotyping
assays could be designed to map entirely to exonic sequence, thus allowing for amplification
and measurement of mature (i.e. spliced) mRNA species and normalisation of allelic
expression using genomic DNA from the same individual. After exclusion of SNPs with <4
heterozygotes (rs1801282, PPARG; rs3734621, KIF6) and assays where >50% cDNA
samples yielded Ct values >36 (rs2793823, ADAM30; rs7377, SRGN), indicating very low
levels of gene expression, allelic expression could be determined for 14 genes in samples
from 36 white, non-diabetic donors.
Validation of TaqMan SNP genotyping assays as a robust method for determining
allelic expression
The reproducibility of the allelic expression measurement was determined by comparing
values generated from independent cDNAs that were reverse transcribed and amplified by
real-time PCR on different days and PCR plates (r2=0.72, p=1.8 x 10-62; Fig. 2A). To further
demonstrate the robustness of this technique allelic expression values for 5 genes were
compared between 2 linked SNPs residing in the same gene (r2=0.67, p=5.7 x 10-16; Fig. 2B).
Identification of allelic expression imbalance at GWAS loci
Significant (unadjusted p<0.05) allelic expression imbalance (AEI) was observed for 7/14
genes tested (Table 1, Fig. 3A,C,E,G,I,K,L). For 5 of the genes where significant imbalance
was observed (ANPEP, KCNJ11, NOTCH2, SLC30A8, WFS1), there was at least one other
exonic proxy SNP which was used for validation of our findings. For all five of the tested
SNPs significant (unadjusted p<0.05) AEI in a direction consistent with the results from the
first SNP was observed (Table 1, Fig. 3B,D,F,H,J). We were unable to validate our results
for CAMK2B and HMG20A due to the position of the only other proxy exonic SNP on the
transcript (SNP either too close to a splice site to allow for assay to be designed to measure
mature mRNA, or near a sequence of low complexity precluding assay design).
Islet eQTL analysis provides further evidence to support ANPEP as being the causal
gene at this locus
One limitation of our approach is its reliance on the presence of exonic proxy SNPs, thus
preventing testing of all candidate genes at a locus. Of the 7 genes where we identified
significant AEI ANPEP exhibited by far the largest imbalance, with both SNPs tested
showing ~50% difference between mRNA alleles. Therefore we predicted that we had most
power to see an eQTL effect at this locus versus the other 6 where far lower AEIs were
observed.
Using real-time PCR we measured the expression of ANPEP and four
neighbouring RefSeq genes, the two immediately 5’ of ANPEP (MESP1, MESP2) and the
two immediately 3’ of ANPEP (AP3S2, ARPIN) in the 36 islet samples. Consistent with our
AEI findings of increased levels of ANPEP mRNA originating from the risk allele we
measured 2.2-fold higher levels of ANPEP expression in individuals homozygous for the risk
(G) allele of rs2007084 compared to heterozygous individuals (p=0.03) (Fig. 4A). We found
no association with rs2007084 genotype for 3 of the other genes at this locus (AP3S2, p=0.61;
ARPIN, p=0.11; MESP1, p=0.97) (Fig. 4B-D). Very low levels of MESP2 transcripts (all
samples either mean Ct>38 or not amplified) meant analysis of MESP2 expression with
respect to genotype was not possible. In summary, this eQTL analysis supports our AEI
findings for ANPEP and provides further evidence that the type 2 diabetes-associated risk
allele at this locus exerts its detrimental effect via increasing ANPEP expression.
DISCUSSION
In this study we report that half of the type 2 diabetes SNPs examined had effects on allelic
expression of nearby genes, consistent with a role for such variation in cis-regulation of islet
gene expression. This figure is greater than genome-wide analyses of AEI which have
generally reported 4-8% of common SNPs associated with the expression of at least one
transcript (11; 12).
An enrichment of significant genotype effects on cis-linked gene
expression has been reported for complex disease traits (13; 14) and our results suggest that
effects on gene expression may play a significant role in mediating the genotype associations
with type 2 diabetes.
We are aware of only one other published study reporting associations between gene
expression levels and multiple type 2 diabetes-associated lead SNPs. Using global microarray
data measuring gene expression in islets from 63 donors Taneera et al. reported a significant
(p<0.001) cis-eQTL for 5/47 (11%) type 2 diabetes-associated SNPs (10). Unfortunately
none of the cis-eQTLs identified in their study could be tested in our study due to a lack of
SNPs meeting our inclusion criteria (exonic and assay mapping purely to exonic sequence).
A comparison of the results from their study and ours illustrates some of the advantages and
disadvantages of the respective techniques. Whilst our targeted allelic expression approach
identifies a higher percentage (50% versus 11%) of genes regulated by cis-acting variation
our approach is limited to genes with exonic SNPs in high linkage disequilibrium (r2>0.8)
with the lead type 2 diabetes-associated SNP. Indeed this dependence will also often preclude
examining isoform-specific effects which may be substantial (15) and an examination of all
candidate genes at a locus. The classical cis-eQTL approach is not dependent on transcribed
exonic SNPs and thus can examine many more of the disease-associated loci and the
expression of any of the closely-residing genes and their isoforms. However the presence of
known and unknown trans-acting confounding factors, which cannot all be accounted for in
regression analyses, adds significant variation to the gene expression measurements and
likely contributes to the lower number of significant associations between genotype and
expression. To enable a greater number of type 2 diabetes-associated loci to be examined by
the more powerful allelic expression approach future studies may consider using intronic
SNPs to measure allelic expression.
Allelic expression measurements calculated from
intronic SNPs have been shown to be highly correlated with values calculated from linked
exonic SNPs (16).
Whilst this is, to our knowledge, the first report of a systematic use of allelic expression
measurements in human islets to identify likely causal genes at type 2 diabetes GWAS loci,
there are other published studies that have reported allelic imbalance in human islets at single
loci. Kulzer et al. report allelic expression imbalance for two SNPs in ARAP1 (17), one of
which (rs11603334) was also tested in our study. They report significant AEI for rs11603334
in 6 individuals with a mean AEI = 12.6% whereas our study fails to identify significant AEI
(p=0.54) in the 7 heterozygous individuals tested. It is difficult to understand the reasons for
such disparate findings, given that allelic expression should remove much of the confounding
effects of trans-acting factors, but it may be that differences in methodology (TaqMan versus
MALDI-TOF) and the small sample sizes (n=6-7) explain the lack of replication. Another
study by Nica et al. analysed allelic expression for type 2 diabetes GWAS implicated genes
using RNA-Seq in pancreatic beta cells from 11 donors. In support of our findings they also
report AEI for rs5215 (KCNJ11) and rs11558471 (SLC30A8), albeit only in four samples and
one sample, respectively (8).
The lack of reports detailing allelic expression imbalances for type 2 diabetes GWAS genes
may be explained by the widespread use of non-targeted RNA-Seq methodologies. Whilst
whole transcriptome RNA-Seq approaches provide researchers with an unparalleled resource
to investigate all transcripts, its lack of targeting of specific transcripts means the sequencing
depth at loci of interest is not as high as could be achieved using targeted approaches. An
examination of two recent studies using RNA-Seq methods to quantify the islet transcriptome
(18; 19) shows that the median RPKM (reads per kilobase per million mapped reads) for the
genes at the 65 type 2 diabetes-associated GWAS loci is 8-9 RPKM. If one considers that
current read lengths are ~50bp and typical human islet sequencing depths vary, but are
perhaps currently ~50 million mapped reads per sample, then an average 25 reads will map to
one exonic SNP. Fontanillas et al. calculate that 200 reads (equivalent to 8 samples) would
give 80% power to detect AEI of 1.5, whilst to detect an AEI =1.25 >500 mapped reads (>20
samples) would be needed (assuming a liberal type 1 error rate α = 5%) (20). In our study we
found an AEI ≤1.25 for 5/7 loci where AEI was significant. Therefore to replicate our
findings using current non-targeted RNA-Seq technologies would likely require a number of
heterozygous individuals which, given the difficulty in collecting large cohorts of human
islets, would be difficult to procure in the near future.
The significance of small AEIs (≤1.25) at 5 of the loci deserves discussion. Are such
seemingly small imbalances of significant consequence to islet function? At one of these loci
(SLC30A8) a missense SNP (rs13266634; W325R) is in complete LD (r2=1, D’=1) with the
lead SNP (rs3802177) also examined for AEI. There is some evidence that functional nonsynonymous variants have co-evolved with regulatory variants so as to strengthen or diminish
their functional impact (21). This would suggest that the increase in expression of the risk
versus non-risk allele is not directly altering disease risk in such cases, but rather moderating
the effect of the non-synonymous variant, which is primarily responsible for the molecular
deficit increasing disease risk. However, with respect to SLC30A8 it has recently been
reported that heterozygous loss-of-function mutations, which are likely null alleles, protect
against type 2 diabetes (22). This is strong evidence that the precise levels of SLC30A8 are
important for beta cell function. Given that we measured increased levels of SLC30A8
transcripts originating from the risk, versus non-risk allele, one could speculate that it is the
increase in SLC30A8 levels, rather than the presence of the missense variant, which increases
type 2 diabetes risk. Indeed the sole study, to date, that examined the functional effects of the
missense variant on zinc transporter activity, reported only a mild attenuation in zinc ion
transporter activity in cells overexpressing the risk (R) allele of W325R, compared to cells
expressing the non-risk (W) allele (23).
If we consider the other 6 loci where we observe significant AEI, two (ANPEP, KCNJ11)
have at least one missense variant within that gene and in high LD (r2>0.8) with the lead
SNP, whilst at the NOTCH2 locus there is a missense SNP in a nearby gene (ADAM30).
Whether the SNP in ADAM30 is of consequence to the type 2 diabetes association is very
questionable given its reported testis-specific expression (24), and the very low expression
we and others (8; 19), have observed in human islets.
Furthermore, small changes in
expression of NOTCH2, as identified in this study, may be of significant consequence to the
beta cell with experiments in mice showing that when only one copy of NOTCH2 is
conditionally removed from Ngn3-positive cells (i.e. endocrine progenitors), the cells are
diverted to an acinar fate (25). Therefore it may be hypothesised that carriers of NOTCH2
risk alleles have reduced beta cell mass (and hence increased type 2 diabetes susceptibility)
but difficulties in accurately measuring beta cell mass in large cohorts of individuals mean
this theory is currently not easily amenable to testing. With regards to ANPEP the one proxy
SNP (rs17240268) resulting in a non-synonymous substitution does not affect an
evolutionary well-conserved residue. Therefore this increases the likelihood that the ANPEP
expression changes we identified, via allelic expression and eQTL analyses, are causally
involved in the association between SNPs at this locus and risk of type 2 diabetes. The
ANPEP gene encodes the protein, aminopeptidase N, a broad specificity membraneassociated peptidase. Its widespread expression and involvement in many cellular processes
(e.g. proliferation, apoptosis, antigen presentation, differentiation, angiogenesis, chemotaxis)
(26) makes it difficult to predict exactly how increased expression predisposes to type 2
diabetes. Indeed this may partially explain why investigators sought to proffer the
neighbouring gene, AP3S2, as the causal gene at this locus, this despite the fact that the lead
and all proxy SNPs, as defined in this paper, map within the ANPEP gene. The results of this
study will hopefully stimulate research into the role of aminopeptidase N in the pathology of
diabetes.
For three genes (CAMK2B, HMG20A and WFS1) there are no non-synonymous variants in
high LD (r2>0.8), strongly suggesting that at these loci, at least, alterations in gene
expression are primarily involved in affecting type 2 diabetes risk. Our finding of a small
AEI for WFS1 may be of significant consequence to the beta cell given that homozygous null
alleles in WFS1 cause Wolfram syndrome, a rare, recessive disorder characterised by earlyonset diabetes with a non-autoimmune origin (27), and a heterozygous mutation segregates
with adult-onset diabetes in a large family (28). Our most unexpected finding is perhaps the
significant AEI for CAMK2B. This gene is in close proximity to GCK, the gene which
encodes glucokinase, a key enzyme involved in the first rate-limiting step of glucose
metabolism in the beta cell, and a gene where heterozygous inactivating mutations cause
persistent, mild hyperglycaemia and homozygous inactivating mutations cause permanent
neonatal diabetes (29). Characterisation of the effects of the type 2 diabetes-associated SNP
at this locus on glycaemic traits show a strong association with fasting glucose (7), further
implicating GCK as the causative gene. One cannot rule out however a role for variation in
CAMK2B levels affecting beta cell function given that this gene encodes a
calcium/calmodulin-dependent protein kinase that has been shown to be involved in coupling
Ca2+ entry with insulin secretion (30-32), and that this isoform is the predominant form in
pancreatic beta cells (31; 33). Little is known about HMG20A function in human islets but
its involvement in neuronal differentiation (34; 35) may point towards a similar function in
the neuronal-like beta cell.
Whilst we have identified AEIs in genes implicated by type 2 diabetes GWAS in human
islets it is not altogether clear whether altered expression of these genes in islets is the
primary mechanism leading to changes in disease susceptibility. The most recent and largest
study to date examining the effect of T2D GWAS variants on continuous glycaemic traits
reports 12/36 loci examined showing defects in beta cell function, whilst only 4/36 were
associated with insulin sensitivity measures (7). Of the 12 loci associated with defects in beta
cell function we report AEI of SLC30A8 and CAMK2B at the SLC30A8 and GCK loci,
respectively. At the remaining 20 loci Dimas et al. report no discernible effect of the SNPs
on glycaemic traits; this includes the NOTCH2, KCNJ11 and WFS1 loci exhibiting AEI in
our study. Given the raft of measures used to assay beta cell function, in particular, one
might speculate that SNPs at these twenty loci impact on other cell types relevant to diabetes
pathogenesis. However it is worth noting that a number of loci harbouring genes where
mutations cause monogenic diabetes (HNF1A, HNF1B, KCNJ11, WFS1), and which do so
through a detrimental effect on beta-cell function, are included in this list with unknown
effects on glycaemic traits. Therefore one cannot rule out the possibility that the genes where
we have uncovered AEI in islets, but where there is no data currently to support an effect of
the type 2 diabetes associated SNP on beta cell function (either through physiological
analyses of SNPs on glycaemic measures or presence of monogenic forms of diabetes), do so
via an effect in this tissue. Furthermore whilst regulation of gene expression can be highly
tissue-specific the findings of significant numbers of cis-eQTLs that are tissue-independent
(shared across two or more tissues) mean that AEI observed in islets may be observed in
other disease-relevant tissues (36-38).
In summary, we report allelic expression imbalances that are consistent with type 2 diabetesassociated variation regulating the expression of cis-linked genes in human islets. For some
of the genes where significant AEI was identified (e.g. SLC30A8, WFS1) there is strong
evidence from human genetics that small changes in gene dosage may have significant
consequences for the pancreatic beta cell. For other genes with significant AEI (e.g. ANPEP,
HMG20A) their role is less well-defined and hence this study should provide a platform for
further work examining the effects of carefully manipulating the expression of these genes in
human islets.
Author contributions. J.M.L. and L.W.H. conceived and designed the experiments. J.M.L.,
G.H., A.R.W. and M.N.W. conducted experiments and/or analysed data. J.M.L., G.H.,
A.R.W., M.N.W. and L.W.H. interpreted the results. J.M.L. wrote the manuscript. L.W.H.
and M.N.W. reviewed and edited the manuscript. L.W.H. is the guarantor of this work and, as
such, had full access to all the data in the study and takes responsibility for the integrity of the
data and the accuracy of the data analysis.
Acknowledgments. We would like to thank Dr Tim McDonald and Professor Tim Frayling
(both University of Exeter Medical School) for helpful discussions during this study.
Funding. This work was supported by funding from the Medical Research Council (Grant
number MR/J006777/1).
Duality of interest. The authors would like to state that they have no conflicts of interest
relevant to the publication of this study.
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65 lead SNPs published by DIAGRAM consortium
1
Proxy SNPs identified using r2>0.8, Phase 1
1000 Genomes Project, CEU population
1590 SNPs (lead + proxy SNPs)
SNP annotation using SeattleSeq
Annotation 138
45 exonic SNPs in 23 RefSeq genes
TaqMan SNP genotyping assay maps 100%
to exonic sequence
36 exonic SNPs in 18 RefSeq genes.
One SNP assay per gene tested
36 genomic DNAs genotyped
16 SNPs with ≥5 heterozygotes
Allelic expression measured using human
islet RNA from 36 donors
14 assays where expression could be robustly detected (Ct values <36)
Figure 1 – Systematic identification of exonic SNPs suitable for allelic expression analysis in
human islets.
A
B
2
-2
1.5
1.5
1
0.5
0
-0.5
R² = 0.67
R² = 0.72
0
-1
-1.5
-2
log2 risk/non-risk allele
expression
Run 1
2
log2 risk/non-risk allele expression
SNP 2
log2 risk/non-risk allele expression
Run 2
2
1
0.5
0
-2
0
2
-0.5
-1
-1.5
log2 risk/non-risk allele
expression
SNP 1
Figure 2 – TaqMan SNP Genotyping assays as a robust method to determine allelic
expression. A: Correlation between allelic expression measurements determined from
independent cDNAs reverse transcribed and amplified on different days. B: Correlation
between allelic expression measurements calculated from SNPs in high linkage
disequilibrium with each other and residing within the same gene.
A
C
B
1.5
1
2
D
1
0.4
0.5
0.2
0
0
0
-0.5
-0.2
-0.5
-1
-0.4
G
H
1.5
1
0.5
0.5
0
log2 risk/non-risk allelic expression
-0.5
E
F
0.4
0.5
0.4
0.4
0.2
0
0.2
-0.5
0
-1
-0.2
0
-0.2
-0.4
-0.6
I
0.6
0.2
0
-0.2
-0.4
-0.6
-1.5
-0.4
-0.8
J
K
L
0.4
0.8
0.4
0.6
0.2
0.6
0.2
0.4
0.4
0
0.2
-0.2
0
-0.4
-0.8
-0.2
-0.6
-0.2
-1
-0.4
-0.8
-0.4
0
-0.2
-0.4
-0.6
0.2
0
Figure 3 – Significant allelic expression imbalance (AEI) identified for SNPs in 7 genes. A:
rs17240240 (ANPEP), B: rs41276922 (ANPEP), C: rs5219 (KCNJ11), D: rs5215 (KCNJ11),
E: rs699779 (NOTCH2), F: rs835575 (NOTCH2), G: rs11558471 (SLC30A8), H: rs3802177
(SLC30A8), I: rs1046320 (WFS1), J: rs1801206 (WFS1), K: rs1065359 (CAMK2B), L:
rs952472 (HMG20A). White circles = genomic DNA; white squares = cDNA. Dotted lines
show paired genomic DNA:cDNA samples.
log2 relative expression
A
B
C
D
*
Number of risk alleles
Figure 4 –rs2007084 genotype is significantly associated with (A) ANPEP expression but not
expression of neighbouring genes (B) AP3S2, (C) ARPIN, (D) MESP1 in human islet tissue
(n=36). *p<0.05 (T-test).
Table 1 – Allelic expression results for 14 type 2 diabetes GWAS implicated genes
DIAGRAM
Transcribed SNP (gene)
lead SNP
p
Adjusted*
Linkage
Mean
Unadjusted
lead
allelic
value
p value
transcribed
expression
SNP
r2 (D’)
rs10923931
rs699779 (NOTCH2)
0.99 (1.00)
0.79
4.8 x 10-5
9.1 x 10-4
rs5215
rs5219 (KCNJ11)
1.00 (1.00)
61.43
1.3 x 10-4
1.2 x 10-3
rs2007084
rs17240240 (ANPEP)
0.86 (0.95)
1.50
7.4 x 10-4
4.7 x 10-3
rs7177055
rs952472 (HMG20A)
0.91 (0.99)
1.14
2.1 x 10-3
6.7 x 10-3
rs10278336
rs1065359 (CAMK2B)
0.99 (1.00)
0.82
2.6 x 10-3
7.1 x 10-3
rs3802177
rs11558471 (SLC30A8)
0.97 (1.00)
1.12
6.2 x 10-3
1.5 x 10-2
rs4458523
rs1046320 (WFS1)
0.95 (0.99)
0.92
6.9 x 10-3
1.5 x 10-2
rs12427353
rs55834942 (HNF1A)
0.95 (0.99)
0.92
0.10
0.15
rs10203174
rs7578597 (THADA)
0.91 (0.98)
1.04
0.19
0.26
rs10401969
rs72999033 (HAPLN4)
0.90 (1.00)
1.13
0.27
0.34
rs7202877
rs13337017 (BCAR1)
0.93 (0.97)
1.07
0.29
0.34
rs516946
rs750625 (ANK1)
0.96 (0.99)
0.94
0.45
0.50
rs1552224
rs11603334 (ARAP1)
1.00 (1.00)
1.05
0.54
0.57
rs5215
rs11024271 (NCR3LG1)
0.88 (0.96)
0.97
0.65
0.65
rs2007084
rs41276922 (ANPEP)
0.83 (0.95)
1.51
1.6 x 10-3
6.7 x 10-3
rs10923931
rs835575 (NOTCH2)
0.99 (1.00)
0.70
1.9 x 10-3
6.7 x 10-3
rs5215
rs5215 (KCNJ11)
1.00 (1.00)
1.12
1.6 x 10-2
3.0 x 10-2
rs4458523
rs1801206 (WFS1)
0.93 (0.99)
0.93
3.1 x 10-2
5.4 x 10-2
rs3802177
rs3802177 (SLC30A8)
1.00 (1.00)
1.05
3.9 x 10-2
6.2 x 10-2
Validation
SNPs exhibiting significant AEI (unadjusted p<0.05) shown in bold.*Adjusted using
Benjamini-Hochberg false discovery rate