Genome Biology 2008, 9:R170
2008Chenet al.Volume 9, Issue 12, Article R170
FitSNPs: highly differentially expressed genes are more likely to
have variants associated with disease
Rong Chen*†‡, Alex A Morgan*†‡, Joel Dudley*†‡, Tarangini Deshpande§,
Li Li†, Keiichi Kodama*†‡, Annie P Chiang*†‡ and Atul J Butte*†‡
Addresses: *Stanford Center for Biomedical Informatics Research, 251 Cmpus Drive, Stanford, CA 94305, USA. †Department of Pediatrics,
Stanford University School of Medicine, Stanford, CA 94305, USA. ‡Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA 94304,
USA. §NuMedii Inc., Menlo Park, CA 94025, USA.
Correspondence: Atul J Butte. Email: firstname.lastname@example.org
© 2008 Chen et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Finding candidate disease SNPs<p>Differential expressed genes are more likely to have variants associated with disease. A new tool, fitSNP, prioritizes candidate SNPs from association studies.</p>
Background: Candidate single nucleotide polymorphisms (SNPs) from genome-wide association
studies (GWASs) were often selected for validation based on their functional annotation, which
was inadequate and biased. We propose to use the more than 200,000 microarray studies in the
Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.
Results: We analyzed all human microarray studies from the Gene Expression Omnibus, and
calculated the observed frequency of differential expression, which we called differential expression
ratio, for every human gene. Analysis conducted in a comprehensive list of curated disease genes
revealed a positive association between differential expression ratio values and the likelihood of
harboring disease-associated variants. By considering highly differentially expressed genes, we were
able to rediscover disease genes with 79% specificity and 37% sensitivity. We successfully
distinguished true disease genes from false positives in multiple GWASs for multiple diseases. We
then derived a list of functionally interpolating SNPs (fitSNPs) to analyze the top seven loci of
Wellcome Trust Case Control Consortium type 1 diabetes mellitus GWASs, rediscovered all type
1 diabetes mellitus genes, and predicted a novel gene (KIAA1109) for an unexplained locus 4q27.
We suggest that fitSNPs would work equally well for both Mendelian and complex diseases (being
more effective for cancer) and proposed candidate genes to sequence for their association with
597 syndromes with unknown molecular basis.
Conclusions: Our study demonstrates that highly differentially expressed genes are more likely
to harbor disease-associated DNA variants. FitSNPs can serve as an effective tool to systematically
prioritize candidate SNPs from GWASs.
A major goal of biomedical research is to identify genes that
contribute to the molecular pathology of specific diseases.
This process has been accelerated by two types of high-
throughput studies: genome-wide association studies
(GWASs) and gene expression microarray studies. A GWAS
Published: 5 December 2008
Genome Biology 2008, 9:R170 (doi:10.1186/gb-2008-9-12-r170)
Received: 17 June 2008
Revised: 26 September 2008
Accepted: 5 December 2008
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2008/9/12/R170
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.2
Genome Biology 2008, 9:R170
scans a genome for single nucleotide polymorphisms (SNPs)
associated with disease, whereas microarrays identify genes
that are differentially expressed between disease and control
samples. These methods have been integrated into molecular
profiling to identify expression quantitative trait loci and to
build pathways that are involved in various diseases, includ-
ing type 2 diabetes [1,2], atherosclerosis , dystrophic car-
diac calcification , metabolic disorders , and
cardiovascular disorders . To lower the cost, GWASs are
frequently designed as a two-stage study ; first is a stage
involving identification of candidate SNPs, and then a valida-
tion stage is conducted, in which the effect of the candidate
SNPs in a larger population is determined. However, in a
recent two-stage GWAS of prostate cancer, most of the SNPs
determined to be significant were not even ranked in the top
1,000 SNPs in the identification stage , which suggests that
existing candidate SNP prioritization methods, which are
largely based on known functional annotations, are inade-
There are many candidate gene and SNP prioritization meth-
ods, including the use of sequence information [8,9], protein-
protein interaction networks [10,11], literature and ontology
[12,13], and various combination of these methods . For a
detailed description of the available tools, the reader is
referred to comprehensive reviews [15,16]. Gene expression is
often taken into consideration when prioritizing candidate
genes or SNPs, but this is most often within the context of the
specific disease, such as disease-related anatomical regions
and tissue specificity [17-20], conserved co-expression ,
coherent expression profile with known disease-associated
genes , or several expression datasets in model organisms
. These disease-specific gene expression prioritization
methods are somewhat informative, but they are cumber-
some, requiring extensive manual work. Given that there are
more than 200,000 microarray studies included in the
National Center for Biotechnology Information's Gene
Expression Omnibus (GEO)  and more than 10,000 dis-
ease-associated DNA variants in the Genetic Association
Database (GAD)  and Human Gene Mutation Database
(HGMD) , we hypothesize that a more general (and there-
fore more systematic) link exists between a gene's expression
and the likelihood that it is associated with disease.
Recognizing the wealth of gene expression data in public
repositories, we propose an integrative genomics method to
systematically prioritize DNA markers that aims to accelerate
the identification of novel causative genes and variants. Here,
we analyzed every available human microarray study in GEO;
we calculated the frequency of differential expression for
every gene; and we found that the more often a gene was dif-
ferentially expressed, the more likely it was that it contained
disease-associated variants. Based on this discovery, we
derived a list of functionally interpolating SNPs (fitSNPs)
from differential gene expression, and we showed how fit-
SNPs could have been used to successfully prioritize genes
from type 1 and type 2 diabetes mellitus GWASs, as well as
previously identified Online Mendelian Inheritance in Man
(OMIM) loci with unknown molecular basis.
Highly differentially expressed genes are more likely to
harbor disease-associated variants
In order to determine whether differentially expressed genes
are genetically associated with disease, we downloaded all
476 curated human GEO datasets to serve as our human gene
expression set. The probes from these GEO datasets, which
include groups of microarrays organized by experimental var-
iable (for example, time, tissue, agent, temperature, and so
on), were annotated with the latest National Center for Bio-
technology Information Entrez Gene annotations using
AILUN . We conducted 4,877 group-versus-group com-
parisons using significance analysis of microarrays (SAM)
 and obtained a list of 19,879 genes that were differen-
tially expressed with q value under 0.05 in one or more exper-
iments. We then created a list of curated human disease-
associated genes by combining GAD  and HGMD ,
resulting in a list of 3,221 genes with disease-associated vari-
We compared our list of differentially expressed genes with
the list of genes with disease-associated variants, and we
found that 99% of disease-associated genes were differen-
tially expressed in one or more GEO datasets, with 14% spe-
cificity (Additional data file 1). The likelihood of having
variants associated with disease was 12 times higher among
differentially expressed genes than among constantly
expressed genes (P < 0.0001, Fisher's exact test), whereas the
likelihood of having a nonsynonymous coding SNP was 1.6
times higher among differentially expressed genes than
among constantly expressed genes.
In order to characterize better the relationship between DNA
variance and expression in all human genes, we tested
whether genes differentially expressed in multiple microarray
studies are more likely to have disease-associated variants.
For each gene, a differential expression ratio (DER) was cal-
culated as the count of GEO datasets in which it was differen-
tially expressed (q value ≤ 0.05) divided by the count of GEO
datasets in which it was measured. The calculation was
restricted to genes that were measured in at least 5% of all
The precision of rediscovering a disease gene was 16% for
genes with a DER greater than 0. This precision improved
gradually to 28% when the DER was greater than 0.62, and
then increased dramatically to 100% when the DER was
greater than 0.72 (Figure 1). As a control, a similar graph is
also plotted in Figure 1 for constantly expressed genes with a
DER less than the cutoffs used. The more GEO datasets in
which a gene was constantly expressed, the less likely it was
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.3
Genome Biology 2008, 9:R170
to contain disease-associated variants. As an additional con-
trol, we randomly shuffled disease labels for all genes 10,000
times, and the precision of rediscovering disease genes
remained at the predicted 16%. Compared with constantly
expressed or randomly shuffled disease genes, the more often
a gene was differentially expressed, the more likely it was that
it contained DNA variants associated with diseases.
In a receiver operating characteristic curve constructed to
rediscover disease genes using the DER values, a DER value ≥
0.55 exhibited the best performance, with 79% specificity and
37% sensitivity. As shown in Figure 2, genes with DER ≥ 0.55
were 2.25 times more likely to harbor disease-associated var-
iants than others (P < 0.0001, Fisher's exact test). Varying the
threshold, we achieved 56% specificity and 65% sensitivity at
DER ≥ 0.50, and 93% specificity and 16% sensitivity at DER ≥
DER distinguishes true type 1 diabetes mellitus genes
from false positive genes in GWASs
The likelihood of harboring disease-associated variants in
genes with high DER values could be used to prioritize candi-
date SNPs from GWASs. To lower the cost, GWASs are often
designed as a two-stage experiment: identifying candidate
SNPs and then validating them in a larger population. Most
often, functionally important genes are manually selected
from the loci around positive SNPs for sequencing or high-
quality genotyping in a larger population. This prior knowl-
edge based gene prioritization method is not only time con-
suming but is also likely to miss novel genes. Indeed,
associations for a large number of candidate genes from iden-
tification stage of GWASs were found to be false positives in
the validation stage. A test to distinguish true disease genes
from these false-positive genes will demonstrate the prioritiz-
ing power of DER in GWASs.
Use of differentially and constantly expressed genes to rediscover disease genes
Use of differentially and constantly expressed genes to rediscover disease genes. The DER was calculated as the count of GEO datasets in which a gene
was differentially expressed divided by the count of GEO datasets in which it was measured. For any cutoff x, differentially expressed genes were defined
as genes with DER > x, whereas constantly expressed genes were defined as genes with DER <x. The precision/recall graphs show that the likelihood of
harboring disease mutations for a gene increases when its DER value increases. For the control, we shuffled disease labels 10,000 times among all genes
and obtained a predicted precision of 16%. DER, differential expression ratio; GEO, Gene Expression Omnibus.
Differentially expressed genes
00.20.4 0.6 0.8 1
Constantly expressed genes
Randomly shuffled disease labels for all genes
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.4
Genome Biology 2008, 9:R170
We first evaluated the performance in type 1 diabetes mellitus
(T1DM). Within the top seven T1DM loci (6p21, 12q24, 12q13,
16p13, 18p11, 12p13, and 4q27) identified from the Wellcome
Trust Case Control Consortium (WTCCC) GWAS , 21
genes were reported with genotyping results in two follow-up
studies [30,31]. Table 1 lists their DER values along with their
validation results. As shown in Figure 3, the DER values of
T1DM genes were significantly higher than those for false-
positive genes (P = 0.003, t-test), with clear separation of the
25th to 75th percentile ranges. Among the ten genotyped can-
didate genes with DER ≥ 0.55, all but ITPR3 were validated as
true T1DM genes. Of the 11 genotyped genes with DER < 0.55,
all but three (HLA-DPB1, C12orf30, and KIAA0350) were
found to be unassociated with T1DM. We successfully distin-
guished true T1DM genes from false positives with 89% spe-
cificity and 75% sensitivity (P = 0.02, Fisher's exact test). If
we only genotype genes with DER ≥ 0.50, then we identify all
true T1DM genes, with a 56% false discovery rate.
DER distinguishes true type 2 diabetes mellitus genes
from false-positive genes in GWASss
To validate the robustness of this method, we applied it to
another disease, namely type 2 diabetes mellitus (T2DM),
which had been studied in six large-scale GWASs [29,32-36]
and tens of targeted association studies in more than 20 pop-
ulations. We extracted all significant T2DM genes described
in the abstracts, and limited the list to those with significant
association in at least three different populations, and derived
15 widely accepted T2DM genes (Table 2). We also retrieved
SNPs that were reported to exhibit significant association in
the identification stage but no association in the validation
stage in a large-scale T2DM GWAS . We annotated these
Performance of rediscovering disease genes by DER
Performance of rediscovering disease genes by DER. Genes with DER ≥ 0.55 were predicted to be disease genes, and compared with genes with disease-
associated DNA variants listed in GAD and HGMD. P values were calculated using Fisher's exact test. DER, differential expression ratio; GAD, Genetic
Association Database; GEO, Gene Expression Omnibus; HGMD, Human Gene Mutation Database.
Genes in 476 GEO human data sets (22565)()
P value < 0.0001
Specificity = 79%
Sensitivity = 37%
Odds Ratio = 2.25
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.5
Genome Biology 2008, 9:R170
negative SNPs with their associated genes using Entrez
dbSNP, and we removed those without gene annotations, and
derived 13 negative genes. As shown in Table 2, DER ≥ 0.55
successfully distinguished T2DM genes from negative genes
with 85% specificity and 60% sensitivity (P = 0.02, Fisher's
FitSNPs predicts T1DM genes directly from the top
seven WTCCC T1DM loci
The robustness of DER to distinguish disease genes from false
positives in T1DM and T2DM GWASs led us to hypothesize
that it may also be used to predict disease genes directly from
the loci identified from GWASs. To facilitate the visualization
of DER values along with GWAS results on the human
genome, we created a tool called functionally interpolating
SNPs (fitSNPs) . It is a list of human SNPs with DER val-
ues assigned according to their associated genes. It can be
easily loaded into the University of California Santa Cruz
(UCSC) genome graph  and visualized on the human
genome along with a wealth of preloaded or user-defined
genomic data, such as GWAS results. We called the tool 'func-
tionally interpolating SNPs' because it not only infers the like-
lihood of disease association for all human SNPs but also
suggests potential diseases to guide functional studies. In the
Gene page of the FitSNPs server, clicking the DER value for
any gene will display all biologic and clinical conditions in
which it was found to be differentially expressed, with statis-
tical comparisons and filter/sort functions .
We therefore examined each of the top seven WTCCC T1DM
loci on the UCSC genome browser to evaluate whether we
could predict T1DM genes using fitSNPs. The hypothesis is
Distinguishing T1DM genes from false positives in the top seven loci from GWASs using DER
Distinguishing T1DM genes from false positives in the top seven loci from GWASs using DER. Genes in the top seven loci from the WTCCC T1DM
GWASs are reported with validation results. False-positive genes were shown as positive in the initial scan but found to be unassociated with T1DM in the
follow-up validation studies. T1DM genes had significantly higher DER values than did false positive genes (P = 0.003). The mean DER values for T1DM and
false-positive genes were 0.59 and 0.50, respectively. DER, differential expression ratio; GWAS, genome-wide association study; T1DM, type 1 diabetes
mellitus; WTCCC, Wellcome Trust Case Control Consortium.
T1DM genesFlase positive genes
25th percentile MinimumMean
Median Maximum 75th percentile
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.6
Genome Biology 2008, 9:R170
that a gene with a significantly higher DER value than other
genes in the vicinity will probably explain the observed dis-
ease association from the locus.
In 12q13, ERBB3 is the only gene with high scores in both the
WTCCC T1DM GWAS and fitSNPs, and this gene was indeed
found to contain rs2292239, which is the only confirmed
T1DM marker within this region. In 18p11, PTPN2 is the only
gene suggested by fitSNPs (DER = 0.64), and it was con-
firmed to explain the association with T1DM for this region.
In 16p13, we predicted SOCS1 to be the most significant gene
(DER = 0.64), and the follow-up study showed that it con-
tains the validated marker rs243329 (-log10P = 4.19). How-
ever, we missed KIAA3350 (DER = 0.5) from 16p13, which
has a confirmed association with T1DM and a higher -log10P
than SOCS1. In 12p13, no gene has a high score in both GWAS
and fitSNPs, which is consistent with the fact that no associa-
tion was found in the follow-up parent-child trio study .
Within 12q24, SH2B3 and ALDH2 have high scores in both
T1DM and fitSNPs, and indeed SH2B3 was confirmed to con-
tain a mutation in R262W that explains the association with
T1DM in this region in the follow-up study . The associa-
tion of SH2B3 with T1DM is somewhat fortuitous because it
was originally excluded based on data quality. Only upon
recovering additional, poorly clustered nonsynonymous
SNPs was it screened for association. This highlights an inad-
equate prioritization approach, which currently is based on
existing functional annotations. This gene prioritization
problem is addressed by fitSNPs because it is not biased by
existing functional annotations. It is not clear whether there
was any follow-up study on mitochondrial aldehyde dehydro-
genase 2 (the protein encoded by ALDH2), which detoxifies
aldehydes generated by alcohol metabolism and lipid peroxi-
dation in the mitochondrial matrix. The association of inac-
tive ALDH2 genotype with maternal inheritance of T1DM,
previously reported in a Japanese population , suggests
that it may also play a role in T1DM.
Within 4q27, IL2, IL21, and TENR were selected for deep
sequencing in the T1DM follow-up study because of the asso-
ciation of T1DM susceptibility with IL2 in nonobese diabetic
mice. However, no T1DM marker had been found in these
three genes, and the T1DM association of 4q27 remains unex-
plained. Figure 4 shows the fitSNPs DER values along with
T1DM GWAS -log10P at 4q27 on the UCSC genome browser
. We found that KIAA1109's DER value (0.63) is much
greater than those for all other genes in 4q27, including IL2
(0.48), IL21 (0.46), and TENR (0.54). It is flanked by two
most significant T1DM GWAS SNPs, and is highly likely to be
associated with T1DM. The -log10P curve within KIAA1109
was missing because it was not listed in the genotyping array
used in the WTCCC T1DM GWAS (Affymetrix 500K SNP
array; Affymetrix Inc., Santa Clara, CA, USA).
Interestingly, the 4q27 region has also been found to be asso-
ciated with celiac disease  and rheumatoid arthritis ,
suggesting that it might be a general risk factor for multiple
autoimmune diseases. It has been reported that rs13119723 in
KIAA109 has the most significant association with celiac dis-
ease outside the HLA region (P = 2 × 10-7) . By examining
our annotated microarray database of disease versus normal
gene expression datasets , we found that KIAA1109 was
significantly downregulated in peripheral blood cells in juve-
nile rheumatoid arthritis in two independent studies [44,45].
Additionally, the GNF SymAtlas lists it as being highly
expressed in T cells . Therefore, KIAA1109 is a valuable
gene for further investigation in T1DM and other autoim-
DER values for T1DM and false positive genes in the top 7
WTCCC T1DM loci
Yes 0.68True positive
Yes 0.61True positive
Yes0.54 False negative
Yes0.52 False negative
aThe positive candidate genes from WTCCC GWAS with reported
validation results. bValidated to be associated or unassociated with
T2DM in the high-quality genotyping. cThe predicted result using DER
≥ 0.55. DER, differential expression ratio; GWAS, genome-wide
association study; T1DM, type 1 diabetes mellitus; WTCCC,
Wellcome Trust Case Control Consortium.
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.7
Genome Biology 2008, 9:R170
DER values for T2DM and false positive genes from GWAS
Locus or SNPGene Associated in populations DERCorrect?a
Finish. , Korean , Mexican. , Tunisian 0.57True positive
Caucasian. , Finish. , German. , Indian Sikhs. , Japanese. , Mexican. 0.53 False negative
Asian. , Caucasian. , Chinese , Danish. , French. , German. , Hispanic. ,
Indian Sikhs. , Japanese. , Norwegian. 
Asian. , Ashkenazi Jewish. , Caucasian. , Chinese , German. , Hispanic. ,
Japanese. , Norwegian. 
Asian. , African. , Caucasian. , Chinese , Hispanic. , Japanese. , Norwegian.
Asian. , Caucasian. , Chinese , Danish. , French , Japanese. 0.59True positive
Asian. , Caucasian. , Chinese , Danish. , French , Japanese. , Norwegian.
0.49 False negative
Asian. , Caucasian. , Chinese , Danish. , German. , Japanese. , Norwegian.
Caucasian. , Chinese , Danish. , Japanese. , Korean. 0.61True positive
Caucasian, Chinese , Danish. , Japanese. 0.54False negative
African. , Ashkenazi Jewish. , Asian. , Caucasian. , Chinese. , German. ,
Hispanic. , Indian Sikhs. , Japanese. , Spanish, UK white. 
Arab. , Caucasian. , Czech , Japanese. 0.39 False negative
Singaporean. , European. , Japanese.  0.6True positive
Asian. , Caucasian. , Indian Sikhs. , German. , Japanese. , Norwegian. 0.55True positive
Amish. , Ashkenazim , Danish. , Finish. , Swedish. , Mexican. , Norwegian.
, UK Caucasian. 
No 0.4True negative
aThe predicted result using DER ≥ 0.55. DER, differential expression ratio; GWAS, genome-wide association study; T2DM, type 2 diabetes mellitus.
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.8
Genome Biology 2008, 9:R170
Interpreting T1DM GWAS findings at 4q27 using fitSNPs
Interpreting T1DM GWAS findings at 4q27 using fitSNPs. The region 4q27 has been identified as a risk factor area for T1DM, celiac disease, and
rheumatoid arthritis. IL2, IL21, and TENR were selected based on prior knowledge for sequencing in the follow-up studies, but no association was found.
KIAA1109 has a much higher fitSNPs DER value than all other genes in the region, and is flanked by two significant T1DM GWAS SNPs (-log10P >5). We
predicted that this gene may explain the T1DM association in this region. The GWAS -log10P curve for KIAA1109 is missing because it was not listed in the
Affymetrix 500 K SNP array used for the GWAS. DER, differential expression ratio; fitSNPs, functionally interpolating single nucleotide polymorphisms;
GWAS, genome-wide association study; SNP, single nucleotide polymorphism; T1DM, type 1 diabetes mellitus.
Chromosome bands localized by FISH mapping clones
Case control consortium type 1 diabetes trend -log10 P-value
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.9
Genome Biology 2008, 9:R170
mune diseases, and we predict that it is likely to explain the
T1DM association in 4q27.
Comparing DER values among different types of
The success of these three validation studies demonstrates
that fitSNPs could be used not only to prioritize different loci
from GWASs but also to prioritize genes from each locus.
Before applying fitSNPs to all diseases, one important ques-
tion is whether genes associated with different type of dis-
eases have different DER values. We downloaded lists of
disease genes for Mendelian diseases (highly penetrant dis-
eases caused by a single mutation), complex diseases, and
cancer, which were compiled by Ran Blekhman and cowork-
ers . As shown in Table 3, no significant DER difference
were observed between Mendelian and complex disease
genes (0.53 versus 0.54; P = 0.2, t-test). Cancer genes exhib-
ited significantly higher DER values (0.56) than did both
Mendelian (P < 0.0001, t-test) and complex disease genes (P
= 0.001, t-test). Furthermore, all types of disease genes exhib-
ited significantly higher DER values than did nondisease
genes (P < 0.0001, t-test). These findings suggest that fitSNPs
could be used to prioritize disease genes for both Mendelian
and complex diseases, and would be even more effective in
prioritizing cancer genes.
FitSNPs predicts disease genes in OMIM loci with
unknown molecular basis
FitSNPs could be used not only to prioritize disease genes
from GWASs for multiple disease types, but also to predict
disease associations for genes with high DER values. There
are 5,253 human genes with DER ≥ 0.55. Of these, 23% have
known variants for various diseases according to GAD and
HGMD. The remaining 4,052 genes have not yet been shown
to associate with any diseases through mutations or polymor-
phisms, making them promising leads. To systematically pre-
dict disease associations for them, we searched OMIM and
found that 830 diseases and syndromes have been linked to
cytogenetic locations but not specific genes. From these
cytogenetic locations, we predicted 3,331 highly differentially
expressed genes with DER ≥ 0.55 in 610 diseases. From this
group, 2,586 genes, which are currently not associated with
any disease according to GAD and HGMD, were predicted to
be associated with 597 diseases .
For example, systemic lupus erythemetosus (SLE) is an
autoimmune disease with multiple organ involvement and a
genetic predisposition. Renal disease occurs in 40% to 75% of
SLE patients and up to 90% of childhood SLE patients, and
significantly contributes to morbidity and mortality. A
genome scan was performed with more than 300 microsatel-
lite markers in the 75 pedigrees that had SLE with nephritis,
and linkage was identified at 2q34-q35 with P = 0.000001
(SLEN2; OMIM %607966). To date, no gene in 2q34-q35 has
been associated with SLEN2. The DER for the gene OBSL1
(obscurin-like 1; DER = 0.71) is significantly greater than that
for all other genes (Figure 5). Actually, it has the second high-
est DER value among all human genes without known dis-
ease-associated variants. By examining our annotated
microarray database of disease versus normal gene expres-
sion datasets , we found that OBSL1 was significantly dif-
ferentially expressed in juvenile idiopathic arthritis (GEO
series 8650) and several kidney diseases, such as kidney can-
cer (GEO dataset 9) and kidney transplant rejection (GEO
dataset 724). Therefore, we suggest that OBSL1 might be
associated with SLEN2. Similarly, we suggest that the 2,586
genes predicted with DER values are top candidate genes for
the 597 syndromes in question.
We analyzed 476 human GEO datasets and calculated the fre-
quency of differential expression for every gene, which we
called the differential expression ratio (DER). The enrich-
ment analysis on a comprehensive list of curated disease
genes revealed a positive association between DER values and
the likelihood of harboring disease-associated mutations. We
were able to rediscover all disease genes with 79% specificity
and 37% sensitivity using a simple threshold of DER ≥ 0.55.
These highly differentially expressed genes were 2.25 times
DER value comparisons among Mendelian, complex, cancer, all disease genes and nondisease genes
(mean = 0.53, n = 931)
(mean = 0.54, n = 70)
(mean = 0.56, n = 324)
(mean = 0.53, n = 3,178)
(mean = 0.50, n = 16,698)
Mendelian 0.2<0.0001 0.4<0.0001
*P values were calculated using t-test. DER, differential expression ratio.
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.10
Genome Biology 2008, 9:R170
Prediction that OBSL1 is associated with systemic lupus erythematosus with nephritis through 2q34-q35
Prediction that OBSL1 is associated with systemic lupus erythematosus with nephritis through 2q34-q35. Systemic lupus erythemetosus with nephritis
(SLEN2; OMIM %607966) was identified to be associated with 2q34-q35 but without identification of specific genes. OBSL1 has a much higher DER value
(0.71) than those of all other genes from 2q34-q35. It was also found to be differentially expressed in juvenile idiopathic arthritis, kidney cancer, and kidney
transplant rejection. Therefore, we suggest that it should be sequenced for its potential association with SLEN2.
fitSNPs DE R
Chromosome bands localized by FISH mapping clones
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.11
Genome Biology 2008, 9:R170
more likely to harbor disease-associated variants than others.
The positive association between DER and our precision to
rediscover disease genes was consistently observed across
ranges of DER values, in spite of variable adjustments,
including adjusting the q value cutoff from 0.005 to 0.2, and
the removal of genes measured in fewer than 0% to 30%
microarray studies. Additionally, we analyzed disease genes
from three different human genetic association databases,
namely GAD, HGMD, and OMIM, individually and observed
the same DER-related increase in precision. We also used the
absolute GEO dataset counts instead of the DER to rediscover
disease genes and observed the same pattern. The majority of
476 GEO datasets are genome-wide experiments; 98% of
GEO datasets contained more than 5,000 probes, and 89%
contained more than 10,000 probes, which are unlikely to be
targeted arrays. These results demonstrated a robust associa-
tion between differential expression and disease variants.
Based on the observed associations, we created a tool called
fitSNPs to prioritize disease genes from candidate GWAS loci.
First, we successfully distinguished true disease genes from
false positives (positive SNPs from initial scan subsequently
found to be negative during validation) for T1DM GWASs
with 89% specificity and 75% sensitivity, and T2DM GWASs
with 85% specificity and 60% sensitivity. We then directly
rediscovered true T1DM genes by analyzing the top seven loci
of WTCCC GWAS initial scan results using fitSNPs. Further-
more, in an unexplained locus (4q27), fitSNPs predicted that
a novel gene, KIAA1109, may explain the association for
T1DM and several autoimmune diseases. We also examined
the findings of a segmental copy number variation (CNV)
study , which was performed using a whole-genome til-
ing-path bacterial artificial chromosome array to detect a gain
or loss of more than 40 kilobases in 93 human samples. The
results were uploaded into the UCSC genome browser as a
custom track. Using the custom track, we found a CNV in
KIAA1109, suggesting that CNV might play a role in T1DM.
Although there are existing gene prioritization methods, this
is the first to describe the use of differential expression to sys-
tematically prioritize candidate genes or SNPs. We acknowl-
edge that no single gene prioritization method is perfect and
suggest that fitSNPs can also be used in a complementary
manner with other prioritization methods. Given that there
are more than 100 published GWASs, we believe that fitSNPs
can serve as an effective tool to systematically prioritize can-
didate SNPs from them.
In theory, FitSNPs can also be used to design SNP arrays for
GWASs. It has been shown that tagSNPs could lower costs by
53% while capturing 80% of common SNPs in the African
population . In comparison, a DER of 0.48 achieved sim-
ilar sensitivity; 57% of genes in the genome have a DER value
larger than 0.48. They comprise 74% of genes known to have
disease-associated variants. A GWAS focusing on these genes
could lower experimental costs by 43% while covering at least
74% of disease genes. Therefore, fitSNPs could reduce GWAS
costs in a way comparable to that of tagSNPs, but with the
additional advantages of gene prioritization and direct link-
age to functional experiments. Furthermore, fitSNPs could be
combined with tagSNPs in the design of GWASs to further
reduce costs and to expedite the discovery of causative genes
and DNA variants.
To facilitate the use of fitSNPs, we developed a web server 
that retrieves DER values, and a comprehensive list of vali-
dated and predicted disease associations for all human genes
and their underlying microarray study results.
This study demonstrates that highly differentially expressed
genes are more likely to harbor disease-associated variants.
FitSNPs successfully distinguished true disease genes from
false positives of GWASs for multiple diseases, and can serve
as a powerful and convenient tool to prioritize disease genes
from GWASs. We further proposed 2,586 genes to sequence
for 597 syndromes with unknown molecular basis. With the
wealth of genomic, genetic, and disease databases in public
international repositories, we are now able to investigate sys-
tematically the molecular and genetic mechanisms of dis-
eases, make predictions, and validate them using commercial
kits and core facilities. To maximize their value, these molec-
ular measurements must be placed within the context of
physiology. A public repository of de-identified clinical meas-
urements will greatly accelerate this process .
Materials and methods
The GEO contains gene expression profiles for more than
200,000 individual microarray samples. They are assembled
into biologically meaningful and comparable GEO datasets
with manually annotated experimental details, such as varia-
bles that were studied in the experiment. All samples within a
GEO dataset were measured on the same platform with the
same background processing and normalization, and their
values were directly comparable. We downloaded, processed,
and annotated all GEO datasets from GEO, and obtained 476
human GEO datasets, in which both the GEO platform and
the GEO dataset were annotated as human.
Differentially expressed genes
Each GEO dataset was categorized into subsets annotated
with one of the 24 types, including disease state, genotype/
variation, strain, infection, development stage, age, time,
agent, dose, tissue, cell type, cell line, metabolism, stress,
growth protocol, protocol, gender, individual, isolate, shock,
species, specimen, temperature, and others. We performed
all possible subset-versus-subset comparisons in each com-
parison type in every GEO dataset, ignoring subsets with
fewer than three samples. For every comparison, we identi-
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.12
Genome Biology 2008, 9:R170
fied all differentially expressed probes using two class
unpaired analysis in the R package of SAM (SAMR) with ver-
sion 1.25 . We used all default parameters with standard
t-statistics: nperms = 50, fold > 0, and delta < 0.4. All differ-
entially expressed probes with q ≤ 0.05 were recorded and
annotated with the latest Entrez Gene IDs using AILUN .
For 4,552 out of 4,877 comparisons, at least one gene exhib-
ited a significant difference.
The DER was calculated for each Entrez Gene ID as the count
of GEO datasets in which it was differentially expressed
divided by the count of GEO datasets in which it was meas-
ured. Genes measured in fewer than 5% of GEO datasets were
Human genes with known disease-associated variants were
downloaded from HGMD Professional (Biobase) and GAD.
HGMD gene symbols were related to Entrez Gene IDs using
AILUN . Entrez Gene IDs were retrieved from GAD
entries with validated disease associations, and compared
with the latest Entrez Gene ID list to replace or remove out-
dated Gene IDs and nonhuman genes.
Differentially expressed genes versus disease genes
For a cutoff from 0 to 1 with an increment of 0.02, differen-
tially expressed genes with DER values greater than the cutoff
were compared with the list of disease genes to calculate the
precision and recall. Constantly expressed genes with DER
less than the cutoff were similarly evaluated. For the control,
the label of disease genes was also shuffled 10,000 times
within all human genes and compared with differentially
Comparison of DER values for T1DM genes with those
of false positives in GWASs
For each of top seven loci described in the WTCCC T1DM
GWAS , all genes with described validation results were
manually extracted from the paper and supplementary mate-
rials [30,31]. The DER values were compared between T1DM
and non-T1DM genes in accordance with the validation
Comparison of DER values for T2DM genes with those
of false positives in GWASs
T2DM genes were extracted from six T2DM GWASs [29,32-
36] and tens of association studies. Of them, genes associated
with T2DM in three or more populations were recorded as
true T2DM genes. False-positive SNPs were extracted from
Table S7 of the report of a T2DM GWAS , which were
found to be positive in the stage 1 GWAS but found to be unas-
sociated with T2DM during the validation phase, with p value
from permutation larger than 0.05. They were annotated with
Entrez Gene IDs using Entrez dbSNP . All SNPs without
gene annotations were removed.
FitSNPs  is a list of human Entrez Gene IDs with DER val-
ues. Genes with disease-associated variants and correspond-
ing diseases were retrieved from HGMD  and GAD .
To facilitate integration between fitSNPs and GWASs on the
human genome, all reference SNPs were downloaded from
dbSNP  and assigned DER scores according to their asso-
ciated genes. For SNPs mapping to multiple genes, the high-
est DER value was selected. FitSNPs can be loaded into the
UCSC genome graph, in accordance with the instructions in
the GWAS page of the FitSNPs server . It will automati-
cally show up as a custom track in the UCSC genome browser
that can be compared with a wealth of genomic data, includ-
ing multiple GWAS study results.
Predicting T1DM genes from the top seven loci of
Both DER values and WTCCC T1DM GWAS -log10P were vis-
ualized in UCSC genome browser  for the top seven loci.
Genes with DER value ≥ 0.55 and -log10P >5 were predicted to
be T1DM genes, and compared with the validation findings.
Mapping diseases without known molecular basis to
All diseases in OMIM morbid map with a percentage preced-
ing their MIM numbers were considered to be Mendelian dis-
orders without known molecular association. Cytogenetic
locations of these diseases and all human genes were
retrieved from the OMIM morbid map and the Human Gene
Nomenclature Committee, respectively. Highly differentially
expressed genes with DER ≥ 0.55 were identified from the
cytogenetic location for each disease. Within them, genes that
have not been known to have disease-associated variants
were predicted to be associated with a corresponding disease.
CNV: copy number variation; DER: differential expression
ratio; fitSNPs: functionally interpolating single nucleotide
polymorphisms; GAD: Genetic Association Database; GEO:
Gene Expression Omnibus; GWAS: genome-wide association
study; HGMD: Human Gene Mutation Database; OMIM:
Online Mendelian Inheritance in Man; SAM: significance
analysis of microarrays; SLE: systemic lupus erythemetosus;
SNP: single nucleotide polymorphism; T1DM: type 1 diabetes
mellitus; T2DM: type 2 diabetes mellitus; UCSC: University
of California Santa Cruz; WTCCC: Wellcome Trust Case Con-
RC designed and performed the experiments, and wrote the
manuscript. AB provided the overall project guidance. AC
provided critical review and edited the manuscript. KK col-
lected T2DM gene lists and gave advice on the validation. AM,
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.13
Genome Biology 2008, 9:R170
JD, TD, and LL gave advice on the experiments. All authors
read and approved the manuscript.
Additional data files
The following additional data are available with the online
version of this paper. Additional data file 1 is an enrichment
comparison between genes differentially expressed in one or
more microarray studies and genes with disease-associated
Additional data file 1Comparison: genes differentially expressed ≥ 1 microarray study versus genes with disease-associated variantsPresented is an enrichment comparison between genes differen- tially expressed in one or more microarray studies and genes with disease-associated variants. Click here for file
This work was supported by Lucile Packard Foundation for Children's
Health, US National Library of Medicine (K22 LM008261), National Insti-
tute of General Medical Sciences (R01 GM079719), Howard Hughes Med-
ical Institute, and Pharmaceutical Research and Manufacturers of America
Foundation. We thank Alex Skrenchuk from Stanford University for com-
puter support, and Rohan Mallelwar and Ajit Thosar from Optra Systems
for website development.
1. Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J,
Carlson S, Helgason A, Walters GB, Gunnarsdottir S, Mouy M,
Steinthorsdottir V, Eiriksdottir GH, Bjornsdottir G, Reynisdottir I,
Gudbjartsson D, Helgadottir A, Jonasdottir A, Jonasdottir A, Sty-
rkarsdottir U, Gretarsdottir S, Magnusson KP, Stefansson H, Fossdal
R, Kristjansson K, Gislason HG, Stefansson T, Leifsson BG,
Thorsteinsdottir U, Lamb JR, et al.: Genetics of gene expression
and its effect on disease. Nature 2008, 452:423-428.
2.Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Staple-
ton DS, Argmann C, Schueler KL, Edwards S, Steinberg HA, Neto EC,
Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kend-
ziorski C, Attie AD: A gene expression network model of type
2 diabetes links cell cycle regulation in islets with diabetes
susceptibility. Genome Res 2008, 18:706-716.
3. Wang SS, Schadt EE, Wang H, Wang X, Ingram-Drake L, Shi W,
Drake TA, Lusis AJ: Identification of pathways for atherosclero-
sis in mice: integration of quantitative trait locus analysis and
global gene expression data. Circ Res 2007, 101:e11-e30.
4.Meng H, Vera I, Che N, Wang X, Wang SS, Ingram-Drake L, Schadt
EE, Drake TA, Lusis AJ: Identification of Abcc6 as the major
causal gene for dystrophic cardiac calcification in mice
through integrative genomics. Proc Natl Acad Sci USA 2007,
5.Chen Y, Zhu J, Lum PY, Yang X, Pinto S, Macneil DJ, Zhang C, Lamb
J, Edwards S, Sieberts SK, Leonardson A, Castellini LW, Wang S,
Champy MF, Zhang B, Emilsson V, Doss S, Ghazalpour A, Horvath S,
Drake TA, Lusis AJ, Schadt EE: Variations in DNA elucidate
molecular networks that cause disease. Nature 2008,
6.Schadt EE, Lum PY: Thematic review series: systems biology
approaches to metabolic and cardiovascular disorders.
Reverse engineering gene networks to identify key drivers of
complex disease phenotypes. J Lipid Res 2006, 47:2601-2613.
7.Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, Orr N, Yu K,
Chatterjee N, Welch R, Hutchinson A, Crenshaw A, Cancel-Tassin G,
Staats BJ, Wang Z, Gonzalez-Bosquet J, Fang J, Deng X, Berndt SI,
Calle EE, Feigelson HS, Thun MJ, Rodriguez C, Albanes D, Virtamo J,
Weinstein S, Schumacher FR, Giovannucci E, Willett WC, Cussenot
O, Valeri A, et al.: Multiple loci identified in a genome-wide
association study of prostate cancer. Nat Genet 2008,
8.Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS: Speeding
disease gene discovery by sequence based candidate prioriti-
zation. BMC Bioinformatics 2005, 6:55.
9.Lopez-Bigas N, Ouzounis CA: Genome-wide identification of
genes likely to be involved in human genetic disease. Nucleic
Acids Res 2004, 32:3108-3114.
10.Oti M, Snel B, Huynen MA, Brunner HG: Predicting disease genes
using protein-protein interactions. J Med Genet 2006,
Wu X, Jiang R, Zhang MQ, Li S: Network-based global inference
of human disease genes. Mol Syst Biol 2008, 4:189.
Hristovski D, Peterlin B, Mitchell JA, Humphrey SM: Using litera-
ture-based discovery to identify disease candidate genes. Int
J Med Inform 2005, 74:289-298.
Perez-Iratxeta C, Wjst M, Bork P, Andrade MA: G2D: a tool for
mining genes associated with disease. BMC Genet 2005, 6:45.
Tranchevent LC, Barriot R, Yu S, Vooren SV, Loo PV, Coessens B,
Moor BD, Aerts S, Moreau Y: ENDEAVOUR update: a web
resource for gene prioritization in multiple species. Nucleic
Acids Res 2008, 36:W377-W384.
Oti M, Brunner HG: The modular nature of genetic diseases.
Clin Genet 2007, 71:1-11.
Zhu M, Zhao S: Candidate gene identification approach:
progress and challenges. Int J Biol Sci 2007, 3:420-427.
Gaulton KJ, Mohlke KL, Vision TJ: A computational system to
select candidate genes for complex human traits. Bioinformat-
ics 2007, 23:1132-1140.
Masseroli M, Galati O, Pinciroli F: GFINDer: genetic disease and
phenotype location statistical analysis and mining of dynam-
ically annotated gene lists. Nucleic Acids Res 2005,
Tiffin N, Kelso JF, Powell AR, Pan H, Bajic VB, Hide WA: Integration
of text- and data-mining using ontologies successfully selects
disease gene candidates. Nucleic Acids Res 2005, 33:1544-1552.
van Driel MA, Cuelenaere K, Kemmeren PP, Leunissen JA, Brunner
HG, Vriend G: GeneSeeker: extraction and integration of
human disease-related information from web-based genetic
databases. Nucleic Acids Res 2005, 33:W758-W761.
Ala U, Piro RM, Grassi E, Damasco C, Silengo L, Oti M, Provero P, Di
Cunto F: Prediction of human disease genes by human-mouse
conserved coexpression analysis. PLoS Comput Biol 2008,
Rossi S, Masotti D, Nardini C, Bonora E, Romeo G, Macii E, Benini L,
Volinia S: TOM: a web-based integrated approach for identifi-
cation of candidate disease genes. Nucleic Acids Res 2006,
Ma X, Lee H, Wang L, Sun F: CGI: a new approach for prioritiz-
ing genes by combining gene expression and protein-protein
interaction data. Bioinformatics 2007, 23:215-221.
Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C,
Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining
tens of millions of expression profiles: database and tools
update. Nucleic Acids Res 2007, 35:D760-D765.
Becker KG, Barnes KC, Bright TJ, Wang SA: The genetic associa-
tion database. Nat Genet 2004, 36:431-432.
Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS,
Abeysinghe S, Krawczak M, Cooper DN: Human Gene Mutation
Database (HGMD): 2003 update. Hum Mutat 2003, 21:577-581.
Chen R, Li L, Butte AJ: AILUN: reannotating gene expression
data automatically. Nat Methods 2007, 4:879.
Tusher VG, Tibshirani R, Chu G: Significance analysis of micro-
arrays applied to the ionizing radiation response. Proc Natl
Acad Sci USA 2001, 98:5116-5121.
Wellcome Trust Case Control Consortium: Genome-wide associ-
ation study of 14,000 cases of seven common diseases and
3,000 shared controls. Nature 2007, 447:661-678.
Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF, Stevens HE,
Reynolds P, Hardy M, King E, Masters J, Hulme J, Maier LM, Smyth D,
Bailey R, Cooper JD, Ribas G, Campbell RD, Clayton DG, Todd JA:
Localization of type 1 diabetes susceptibility to the MHC
class I genes HLA-B and HLA-A. Nature 2007, 450:887-892.
Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V,
Bailey R, Nejentsev S, Field SF, Payne F, Lowe CE, Szeszko JS, Hafler
JP, Zeitels L, Yang JH, Vella A, Nutland S, Stevens HE, Schuilenburg H,
Coleman G, Maisuria M, Meadows W, Smink LJ, Healy B, Burren OS,
Lam AA, Ovington NR, Allen J, Adlem E, Leung HT, et al.: Robust
associations of four new chromosome regions from genome-
wide analyses of type 1 diabetes. Nat Genet 2007, 39:857-864.
Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vin-
cent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hud-
son TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding
DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide associ-
ation study identifies novel risk loci for type 2 diabetes.
Nature 2007, 445:881-885.
Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H,
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.14
Genome Biology 2008, 9:R170
Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L,
Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson Bos-
trom K, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, New-
ton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK,
Taskinen MR, Tuomi T, et al.: Genome-wide association analysis
identifies loci for type 2 diabetes and triglyceride levels. Sci-
ence 2007, 316:1331-1336.
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL,
Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson
L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely
KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart
MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, et al.: A
genome-wide association study of type 2 diabetes in Finns
detects multiple susceptibility variants. Science 2007,
Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen
G, Ng DP, Holmkvist J, Borch-Johnsen K, Jorgensen T, Sandbaek A,
Lauritzen T, Hansen T, Nurbaya S, Tsunoda T, Kubo M, Babazono T,
Hirose H, Hayashi M, Iwamoto Y, Kashiwagi A, Kaku K, Kawamori R,
Tai ES, Pedersen O, Kamatani N, Kadowaki T, Kikkawa R, Nakamura
Y, Maeda S: SNPs in KCNQ1 are associated with susceptibility
to type 2 diabetes in East Asian and European populations.
Nat Genet 2008, 40:1098-1102.
Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, Hirota
Y, Mori H, Jonsson A, Sato Y, Yamagata K, Hinokio Y, Wang HY, Tan-
ahashi T, Nakamura N, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y,
Seino Y, Maegawa H, Kashiwagi A, Takeda J, Maeda E, Shin HD, Cho
YM, Park KS, Lee HK, Ng MC, Ma RC, et al.: Variants in KCNQ1
are associated with susceptibility to type 2 diabetes mellitus.
Nat Genet 2008, 40:1092-1097.
FitSNPs for GWAS [http://fitSNPs.stanford.edu/fitSNPs.php]
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,
Haussler D: The human genome browser at UCSC. Genome
Res 2002, 12:996-1006.
FitSNPs Gene [http://fitSNPs.stanford.edu/gene.php]
Suzuki Y, Matsuura N, Suzuki S, Muramatsu T, Taniyama M, Ohta S,
Higuchi S, Tsukahara M, Atusmi Y, Matsuoka K: Aldehyde dehydro-
genase 2 genotype in type 1 diabetes mellitus. Diabetes Res Clin
Pract 2003, 60:139-141.
van Heel DA, Franke L, Hunt KA, Gwilliam R, Zhernakova A, Inouye
M, Wapenaar MC, Barnardo MC, Bethel G, Holmes GK, Feighery C,
Jewell D, Kelleher D, Kumar P, Travis S, Walters JR, Sanders DS,
Howdle P, Swift J, Playford RJ, McLaren WM, Mearin ML, Mulder CJ,
McManus R, McGinnis R, Cardon LR, Deloukas P, Wijmenga C: A
genome-wide association study for celiac disease identifies
risk variants in the region harboring IL2 and IL21. Nat Genet
Zhernakova A, Alizadeh BZ, Bevova M, van Leeuwen MA, Coenen MJ,
Franke B, Franke L, Posthumus MD, van Heel DA, Steege G van der,
Radstake TR, Barrera P, Roep BO, Koeleman BP, Wijmenga C: Novel
association in chromosome 4q27 region with rheumatoid
arthritis and confirmation of type 1 diabetes point to a gen-
eral risk locus for autoimmune diseases. Am J Hum Genet 2007,
Kodama K, Butte AJ, Creusot RJ, Su L, Sheng D, Hartnett M, Iwai H,
Soares LR, Fathman CG: Tissue- and age-specific changes in
gene expression during disease induction and progression in
NOD mice. Clin Immunol 2008, 129:195-201.
Barnes MG, Aronow BJ, Luyrink LK, Moroldo MB, Pavlidis P, Passo
MH, Grom AA, Hirsch R, Giannini EH, Colbert RA, Glass DN,
Thompson SD: Gene expression in juvenile arthritis and
spondyloarthropathy: pro-angiogenic ELR+ chemokine
genes relate to course of arthritis. Rheumatology (Oxford) 2004,
Fall N, Barnes M, Thornton S, Luyrink L, Olson J, Ilowite NT, Gottlieb
BS, Griffin T, Sherry DD, Thompson S, Glass DN, Colbert RA, Grom
AA: Gene expression profiling of peripheral blood from
patients with untreated new-onset systemic juvenile idio-
pathic arthritis reveals molecular heterogeneity that may
predict macrophage activation syndrome. Arthritis Rheum
Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J,
Soden R, Hayakawa M, Kreiman G, Cooke MP, Walker JR, Hogenesch
JB: A gene atlas of the mouse and human protein-encoding
transcriptomes. Proc Natl Acad Sci USA 2004, 101:6062-6067.
Blekhman R, Man O, Herrmann L, Boyko AR, Indap A, Kosiol C, Bus-
tamante CD, Teshima KM, Przeworski M: Natural selection on
genes that underlie human disease susceptibility. Curr Biol
FitSNPs prediction. [http://fitsnps.stanford.edu/prediction.php]
Wong KK, deLeeuw RJ, Dosanjh NS, Kimm LR, Cheng Z, Horsman
DE, MacAulay C, Ng RT, Brown CJ, Eichler EE, Lam WL: A compre-
hensive analysis of common copy-number variations in the
human genome. Am J Hum Genet 2007, 80:91-104.
International HapMap Consortium: A haplotype map of the
human genome. Nature 2005, 437:1299-1320.
Butte AJ: Medicine. The ultimate model organism. Science
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM,
Sirotkin K: dbSNP: the NCBI database of genetic variation.
Nucleic Acids Res 2001, 29:308-311.
UCSC genome browser [http://genome.ucsc.edu/cgi-bin/hgGate
Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M,
Hinokio Y, Lindner TH, Mashima H, Schwarz PE, del Bosque-Plata L,
Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Con-
cannon P, Iwasaki N, Schulze J, Baier LJ, Bogardus C, Groop L, Boer-
winkle E, Hanis CL, Bell GI: Genetic variation in the gene
encoding calpain-10 is associated with type 2 diabetes melli-
tus. Nat Genet 2000, 26:163-175.
Kang ES, Kim HJ, Nam M, Nam CM, Ahn CW, Cha BS, Lee HC: A
novel 111/121 diplotype in the Calpain-10 gene is associated
with type 2 diabetes. J Hum Genet 2006, 51:629-633.
Kifagi C, Makni K, Mnif F, Boudawara M, Hamza N, Rekik N, Abid M,
Rebai A, Granier C, Jarraya F, Ayadi H: Association of calpain-10
polymorphisms with type 2 diabetes in the Tunisian popula-
tion. Diabetes Metab 2008, 34:273-278.
Beamer BA, Yen CJ, Andersen RE, Muller D, Elahi D, Cheskin LJ,
Andres R, Roth J, Shuldiner AR: Association of the Pro12Ala var-
iant in the peroxisome proliferator-activated receptor-
gamma2 gene with obesity in two Caucasian populations.
Diabetes 1998, 47:1806-1808.
Lindi VI, Uusitupa MI, Lindstrom J, Louheranta A, Eriksson JG, Valle
TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso
M, Tuomilehto J: Association of the Pro12Ala polymorphism in
the PPAR-gamma2 gene with 3-year incidence of type 2 dia-
betes and body weight change in the Finnish Diabetes Pre-
vention Study. Diabetes 2002, 51:2581-2586.
Herder C, Rathmann W, Strassburger K, Finner H, Grallert H, Huth
C, Meisinger C, Gieger C, Martin S, Giani G, Scherbaum WA, Wich-
mann HE, Illig T: Variants of the PPARG, IGF2BP2, CDKAL1,
HHEX, and TCF7L2 genes confer risk of type 2 diabetes
independently of BMI in the German KORA studies. Horm
Metab Res 2008, 40:722-726.
Sanghera DK, Ortega L, Han S, Singh J, Ralhan SK, Wander GS, Mehra
NK, Mulvihill JJ, Ferrell RE, Nath SK, Kamboh MI: Impact of nine
common type 2 diabetes risk polymorphisms in Asian Indian
Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO var-
iants confer a significant risk. BMC Med Genet 2008, 9:59.
Horiki M, Ikegami H, Fujisawa T, Kawabata Y, Ono M, Nishino M, Shi-
mamoto K, Ogihara T: Association of Pro12Ala polymorphism
of PPARgamma gene with insulin resistance and related dis-
eases. Diabetes Res Clin Pract 2004, 66(suppl 1):S63-S67.
Black MH, Fingerlin TE, Allayee H, Zhang W, Xiang AH, Trigo E, Har-
tiala J, Lehtinen AB, Haffner SM, Bergman RN, McEachin RC, Kjos SL,
Lawrence JM, Buchanan TA, Watanabe RM: Evidence of interac-
tion between PPARG2 and HNF4A contributing to variation
in insulin sensitivity in Mexican Americans. Diabetes 2008,
Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD, Lam VK, Ma RC,
So WY, Cho YS, Kim HL, Lee HK, Chan JC, Cho NH: Implication
of genetic variants near TCF7L2, SLC30A8, HHEX,
CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes
and obesity in 6,719 Asians. Diabetes 2008, 57:2226-2233.
Wu Y, Li H, Loos RJ, Yu Z, Ye X, Chen L, Pan A, Hu FB, Lin X: Com-
mon variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8
and HHEX/IDE genes are associated with type 2 diabetes
and impaired fasting glucose in a Chinese Han population.
Diabetes 2008, 57:2834-2842.
Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, Albre-
chtsen A, Clausen JO, Rasmussen SS, Jorgensen T, Sandbaek A, Lau-
ritzen T, Schmitz O, Hansen T, Pedersen O: Studies of association
of variants near the HHEX, CDKN2A/B, and IGF2BP2 genes
with type 2 diabetes and impaired insulin release in 10,705
Danish subjects: validation and extension of genome-wide
http://genomebiology.com/2008/9/12/R170 Download full-text
Genome Biology 2008, Volume 9, Issue 12, Article R170 Chen et al. R170.15
Genome Biology 2008, 9:R170
association studies. Diabetes 2007, 56:3105-3111.
Duesing K, Fatemifar G, Charpentier G, Marre M, Tichet J, Hercberg
S, Balkau B, Froguel P, Gibson F: Evaluation of the association of
IGF2BP2 variants with type 2 diabetes in French Caucasians.
Diabetes 2008, 57:1992-1996.
Palmer ND, Goodarzi MO, Langefeld CD, Ziegler J, Norris JM, Haff-
ner SM, Bryer-Ash M, Bergman RN, Wagenknecht LE, Taylor KD,
Rotter JI, Bowden DW: Quantitative trait analysis of type 2 dia-
betes susceptibility loci identified from whole genome asso-
ciation studies in the Insulin Resistance Atherosclerosis
Family Study. Diabetes 2008, 57:1093-1100.
Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, Kaku K,
Kawamori R, Nakamura Y, Maeda S: Association of CDKAL1,
IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with
susceptibility to type 2 diabetes in a Japanese population.
Diabetes 2008, 57:791-795.
Hertel JK, Johansson S, Raeder H, Midthjell K, Lyssenko V, Groop L,
Molven A, Njolstad PR: Genetic analysis of recently identified
type 2 diabetes loci in 1,638 unselected patients with type 2
diabetes and 1,858 control participants from a Norwegian
population-based cohort (the HUNT study). Diabetologia 2008,
Bronstein M, Pisante A, Yakir B, Darvasi A: Type 2 diabetes sus-
ceptibility loci in the Ashkenazi Jewish population. Hum Genet
Duesing K, Fatemifar G, Charpentier G, Marre M, Tichet J, Hercberg
S, Balkau B, Froguel P, Gibson F: Strong association of common
variants in the CDKN2A/CDKN2B region with type 2 diabe-
tes in French Europids. Diabetologia 2008, 51:821-826.
Florez JC, Wiltshire S, Agapakis CM, Burtt NP, de Bakker PI, Almgren
P, Bengtsson Bostrom K, Tuomi T, Gaudet D, Daly MJ, Hirschhorn
JN, McCarthy MI, Altshuler D, Groop L: High-density haplotype
structure and association testing of the insulin-degrading
enzyme (IDE) gene with type 2 diabetes in 4,206 people. Dia-
betes 2006, 55:128-135.
Furukawa Y, Shimada T, Furuta H, Matsuno S, Kusuyama A, Doi A,
Nishi M, Sasaki H, Sanke T, Nanjo K: Polymorphisms in the IDE-
KIF11-HHEX gene locus are reproducibly associated with
type 2 diabetes in a Japanese population. J Clin Endocrinol Metab
Kwak SH, Cho YM, Moon MK, Kim JH, Park BL, Cheong HS, Shin HD,
Jang HC, Kim SY, Lee HK, Park KS: Association of polymor-
phisms in the insulin-degrading enzyme gene with type 2 dia-
betes in the Korean population. Diabetes Res Clin Pract 2008,
Lewis JP, Palmer ND, Hicks PJ, Sale MM, Langefeld CD, Freedman BI,
Divers J, Bowden DW: Association analysis in african ameri-
cans of European-derived type 2 diabetes single nucleotide
polymorphisms from whole-genome association studies. Dia-
betes 2008, 57:2220-2225.
Ng MC, Tam CH, Lam VK, So WY, Ma RC, Chan JC: Replication
and identification of novel variants at TCF7L2 associated
with type 2 diabetes in Hong Kong Chinese. J Clin Endocrinol
Metab 2007, 92:3733-3737.
Palmer ND, Lehtinen AB, Langefeld CD, Campbell JK, Haffner SM,
Norris JM, Bergman RN, Goodarzi MO, Rotter JI, Bowden DW:
Association of TCF7L2 gene polymorphisms with reduced
acute insulin response in Hispanic Americans. J Clin Endocrinol
Metab 2008, 93:304-309.
Miyake K, Horikawa Y, Hara K, Yasuda K, Osawa H, Furuta H, Hirota
Y, Yamagata K, Hinokio Y, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y,
Seino Y, Maegawa H, Kashiwagi A, Yamamoto K, Tokunaga K, Takeda
J, Makino H, Nanjo K, Kadowaki T, Kasuga M: Association of
TCF7L2 polymorphisms with susceptibility to type 2 diabe-
tes in 4,087 Japanese subjects. J Hum Genet 2008, 53:174-180.
Humphries SE, Gable D, Cooper JA, Ireland H, Stephens JW, Hurel
SJ, Li KW, Palmen J, Miller MA, Cappuccio FP, Elkeles R, Godsland I,
Miller GJ, Talmud PJ: Common variants in the TCF7L2 gene
and predisposition to type 2 diabetes in UK European
Whites, Indian Asians and Afro-Caribbean men and women.
J Mol Med 2006, 84:1005-1014.
Alsmadi O, Al-Rubeaan K, Wakil SM, Imtiaz F, Mohamed G, Al-Saud
H, Al-Saud NA, Aldaghri N, Mohammad S, Meyer BF: Genetic study
of Saudi diabetes (GSSD): significant association of the
KCNJ11 E23K polymorphism with type 2 diabetes. Diabetes
Metab Res Rev 2008, 24:137-140.
Cejkova P, Novota P, Cerna M, Kolostova K, Novakova D, Kucera P,
Novak J, Andel M, Weber P, Zdarsky E: KCNJ11 E23K polymor-
phism and diabetes mellitus with adult onset in Czech
patients. Folia Biol (Praha) 2007, 53:173-175.
Horikoshi M, Hara K, Ito C, Shojima N, Nagai R, Ueki K, Froguel P,
Kadowaki T: Variations in the HHEX gene are associated with
increased risk of type 2 diabetes in the Japanese population.
Diabetologia 2007, 50:2461-2466.
Damcott CM, Hoppman N, Ott SH, Reinhart LJ, Wang J, Pollin TI,
O'Connell JR, Mitchell BD, Shuldiner AR: Polymorphisms in both
promoters of hepatocyte nuclear factor 4-alpha are associ-
ated with type 2 diabetes in the Amish. Diabetes 2004,
Barroso I, Luan J, Wheeler E, Whittaker P, Wasson J, Zeggini E,
Weedon MN, Hunt S, Venkatesh R, Frayling TM, Delgado M, Neuman
RJ, Zhao J, Sherva R, Glaser B, Walker M, Hitman G, McCarthy MI,
Hattersley AT, Permutt MA, Wareham NJ, Deloukas P: Population-
specific risk of type 2 diabetes (T2D) conferred by HNF4A
P2 promoter variants: a lesson for replication studies. Diabe-
tes 2008, 57:3161-3165.
Ek J, Rose CS, Jensen DP, Glumer C, Borch-Johnsen K, Jorgensen T,
Pedersen O, Hansen T: The functional Thr130Ile and
Val255Met polymorphisms of the hepatocyte nuclear factor-
4alpha (HNF4A): gene associations with type 2 diabetes or
altered beta-cell function among Danes. J Clin Endocrinol Metab
Beas-Zarate C, Morales-Villagran A, Tapia-Arizmendi G, Feria-
Velasco A: Effect of 3-acetylpyridine on serotonin uptake and
release from rat cerebellar slices. Eur J Pharmacol 1991,
Lehman DM, Richardson DK, Jenkinson CP, Hunt KJ, Dyer TD, Leach
RJ, Arya R, Abboud HE, Blangero J, Duggirala R, Stern MP: P2 pro-
moter variants of the hepatocyte nuclear factor 4alpha gene
are associated with type 2 diabetes in Mexican Americans.
Diabetes 2007, 56:513-517.
Johansson S, Raeder H, Eide SA, Midthjell K, Hveem K, Sovik O, Mol-
ven A, Njolstad PR: Studies in 3,523 Norwegians and meta-
analysis in 11,571 subjects indicate that variants in the hepa-
tocyte nuclear factor 4 alpha (HNF4A) P2 region are associ-
ated with type 2 diabetes in Scandinavians. Diabetes 2007,
Weedon MN, Owen KR, Shields B, Hitman G, Walker M, McCarthy
MI, Love-Gregory LD, Permutt MA, Hattersley AT, Frayling TM:
Common variants of the hepatocyte nuclear factor-4alpha
P2 promoter are associated with type 2 diabetes in the U.K.
population. Diabetes 2004, 53:3002-3006.