The Effect of Single Nucleotide Polymorphisms from
Genome Wide Association Studies in Multiple Sclerosis
on Gene Expression
Adam E. Handel1,2, Lahiru Handunnetthi1,2, Antonio J. Berlanga1,2, Corey T. Watson3, Julia M.
Morahan1,2, Sreeram V. Ramagopalan1,2,4*
1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 2Department of Clinical Neurology, University of Oxford, Oxford, United
Kingdom, 3Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada, 4Blizard Institute of Cell and Molecular Science, Barts and The
London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
Background: Multiple sclerosis (MS) is a complex neurological disorder. Its aetiology involves both environmental and
genetic factors. Recent genome-wide association studies have identified a number of single nucleotide polymorphisms
(SNPs) associated with susceptibility to (MS). We investigated whether these genetic variations were associated with
alteration in gene expression.
Methods/Principal Findings: We used a database of mRNA expression and genetic variation derived from immortalised
peripheral lymphocytes to investigate polymorphisms associated with MS for correlation with gene expression. Several SNPs
were found to be associated with changes in expression: in particular two with HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB1,
HLA-DRB4 and HLA-DRB5, one with ZFP57, one with CD58, two with IL7 and FAM164A, and one with FAM119B, TSFM and
KUB3. We found minimal cross-over with a recent whole genome expression study in MS patients.
Discussion: We have shown that many susceptibility loci in MS are associated with changes in gene expression using an
unbiased expression database. Several of these findings suggest novel gene candidates underlying the effects of MS-
associated genetic variation.
Citation: Handel AE, Handunnetthi L, Berlanga AJ, Watson CT, Morahan JM, et al. (2010) The Effect of Single Nucleotide Polymorphisms from Genome Wide
Association Studies in Multiple Sclerosis on Gene Expression. PLoS ONE 5(4): e10142. doi:10.1371/journal.pone.0010142
Editor: Syed A. Aziz, Health Canada, Canada
Received March 14, 2010; Accepted March 23, 2010; Published April 13, 2010
Copyright: ? 2010 Handel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by the Wellcome Trust (Grant Number 075491/Z/04). SVR is a Goodger Scholar at the University of Oxford. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Multiple sclerosis (MS) is an inflammatory disease of the central
nervous system characterised by demyelination and axonal loss.
Studies conducted in mono- and dizygotic twin pairs and siblings
have shown that genetics plays a role in MS susceptibility.
Linkage was effective in identifying the locus exerting the single
strongest genetic effect in MS, namely, the human leukocyte
antigen (HLA) class II region. The risk associated with this
region has since been shown to be determined by epistatic
interactions between different HLA alleles, and is thought to be
responsible for approximately 50% of the genetic risk of MS.
Beyond this powerful determinant of MS genetic susceptibility,
research has taken considerably longer to bear useful fruit. Finally,
after the genotyping of hundreds of thousands of single nucleotide
polymorphisms (SNPs) in many thousands of MS patients and
controls, we are beginning to establish a network of loci outside of
the HLA region involved in determining MS susceptibili-
ty.[6,7,8,9,10,11,12,13,14,15,16,17] It is worth considering that
even the most strongly associated of these with MS is still a
significantly weaker determinant of MS susceptibility than HLA
alleles. For some of these loci, functional studies have been
undertaken.[11,18,19] However, such studies are rarely carried
out in an unbiased manner since these generally correlate genetic
variations with the expression of a candidate gene. A recent study
of mRNA levels in MS patients and healthy controls showed a
great multitude of differentially expressed genes however it is
uncertain to what extent this reflects the aetiology of disease as
opposed to the disease process or adaptive biological path-
A recent investigation has performed whole genome expression
analysis in lymphoblastoid cell lines (LCLs) from healthy
volunteers who were also genotyped for a large number of
SNPs. We used the data from this study to examine the effects
of current susceptibility loci in MS on gene expression.
Gene expression analysis
This was carried out as described in Dixon et al. Briefly,
peripheral lymphocytes were transformed using Epstein-Barr virus
before being cultured, pelleted and frozen for storage. cDNA
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templates were created using the One-Cycle cDNA Synthesis Kit
(Affymetrix). In vitro transcription of cDNA was performed using
the IVT Labeling Kit (Affymetrix) and, after hybridisation on
U133 Plus 2.0 GeneChips (Affymetrix), this was scanned using a
high-resolution scanner (Affymetrix). Whole-genome genotyping
was carried out according to manufacturers’ instructions using the
Sentrix Human-1 Genotyping BeadChip and the HumanHap300
Genotyping BeadChip. The analysis of expression was carried out
on the publically available database of mRNA by SNP Browser
1.0 as described.
mRNA by SNP analysis
We investigated the mRNAs significantly altered in expression
by the SNPs reported in the literature to be at or close to genome-
wide significance.[6,7,8,9,10,11,12,13,14,15,16,17] If the suscep-
tibility SNP was not available on the database, we used the SNP
with the strongest linkage disequilibrium (LD) with the suscepti-
bility SNP as provided by SNP Browser 1.0 based on r2. For SNPs
where no proxy was provided, we investigated all genotyped SNPs
within 500 kb for LD with r2$0.4 for a suitable proxy SNP. We
also assessed the degree of LD with potentially interesting SNPs
within 500 kb of the original susceptibility SNP. Finally we
assessed the SNPs associated with expression of putative candidate
genes to ensure that we did not miss any important associations
We chose to look at a set of 38 SNPs which were the top loci to
reach genome-wide significance selected from currently reported
genome wide association studies (GWAS) of which 14 had been
independently replicated in 2 studies. 17 of these were not present
in the genome-wide association mRNA expression library and
so when possible proxy SNPs in strong-to-moderate LD were
used instead. The SNPs and proxy SNPs used are detailed in
13 of the MS susceptibility SNPs or proxy SNPs were associated
with changes in mRNA expression (Table S1). Two SNPs in
strong LD with multiple MS-associated SNPs in the HLA region
were related to expression of various HLA alleles, including HLA-
DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB1, HLA-DRB4 and
HLA-DRB5. One SNP in the HLA class I region was associated
with altered expression of ZFP57. Both SNPs in CD58 were
associated with expression of CD58. A SNP in the IL7 region was
associated with expression of mRNA encoding IL7 and FAM164A.
Three SNPs in the region of METTL1-CYP27B1-CDK4 altered
the expression of several genes: FAM119B, TSFM and KUB3. The
common gene of altered expression for all three SNPs was TSFM.
Overlap with previous mRNA expression studies
We used the supplemental data supplied by Gandhi and
colleagues to examine cross-over between the results obtained in
that study and the genes we identified as being altered in
expression by susceptibility SNPs. Only three genes were in
common between the two sets: HLA-DQB1, HLA-DRB1 and
STAT3. HLA-DRB1 was upregulated in MS, relapsing-remitting
MS (RRMS) and secondary progressive MS compared with
healthy controls. HLA-DQB1 expression was reduced in MS and
RRMS compared with healthy controls. STAT3 was reduced in
primary progressive MS compared with healthy controls.
Our findings show that some, but by no means all, susceptibility
SNPs in MS are associated with changes in gene expression. Some
of these (CD58, had already been noted by previous investiga-
tors. We were unable to find supporting evidence in this
dataset for the previously reported allelic effect of the susceptibility
SNP in IL7R on expression of the gene. Similarly, SNPs in the
IL2RA gene did not correlate with expression of IL2RA mRNA,
despite previously finding altered levels of this in MS patients
relative to controls.
Table 1. SNPs and proxy SNPs analysed.
SNPputative gene association proxy SNPr2
rs10876994METTL1, CYP27B1, CDK4rs100831540.70067
rs12368653METTL1, CYP27B1, CDK4
rs17824933 CD6rs2237997 0.43718
rs2523393 HLA class Irs2394160 0.96552
rs3129860 HLA class II rs92713660.95557
rs3129934 C6orf10, BTNL2, NOTCH4rs92679920.95604
rs3135388HLA class II rs92713660.95699
rs34536443TYK2 No proxy available
rs4149584 TNFRSF1A No proxy available
rs703842 METTL1, CYP27B1, CDK4
rs8118449 TYK2No proxy available
rs9271366 HLA class II
MS SNPs and Gene Expression
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We also found several novel effects of susceptibility SNPs. Two
SNPs in tight LD with susceptibility SNPs in the HLA region
correlated with expression of several HLA class II mRNAs.
However, measuring gene expression in the HLA is a complex
task. There is haplotype specificity for some genes (HLA-DRB4 and
HLA-DRB5) and thus we are not sure whether differential
expression of HLA genes measured by microarray reflects different
probe affinity for different HLA class II alleles and thus further
work is needed to fully understand this association. Our
identification that a SNP in the HLA class I region was associated
with altered expression of ZFP57 is an interesting observation as
this gene has been linked with DNA methylation changes across
the genome resulting in transient neonatal diabetes. There is
some epidemiological evidence that MS may be partly determined
by epigenetic alterations and this would be an ideal candidate
functionally linking MS to the epigenome. A SNP in IL7
recently confirmed as associated with MS was shown to correlate
with the expression of several genes: IL7 and FAM164A. Naturally
the most compelling candidate of these is IL7 due to its probable
role in autoimmunity. However, the advantage of an unbiased
screen is that it raises the possibility of candidate genes that would
otherwise not be considered. This is especially so since the SNP is
far more strongly associated with FAM164A expression than with
IL7. FAM164A is a hypothetical protein encoded in the reverse
direction to IL7 and its functional importance is largely
unknown. The susceptibility region on chromosome 12 was
previously linked with the expression of FAM119B. We feel
that the relationship of all three major susceptibility SNPs with the
expression of TSFM suggests this as a strong candidate. This is a
plausible candidate in terms of function too as it is involved in the
translation of mitochondrial proteins, providing a potential link
with other susceptibility genes linked to mitochondrial function,
such as KIF21B.[8,25] Further functional work will be needed to
better assess these candidates.
The limited cross-over between known and suspected suscep-
tibility genes in the whole genome expression analysis of Gandhi
and colleagues is likely due to a number of differences including
the use of whole blood mRNA and individuals with established
disease in the Gandhi study. It is possible that future whole
genome analyses of expression conducted using RNA-seq in cell-
sorted samples of patients with very early disease may reveal
alterations in the level of susceptibility gene mRNA.
The advantage of an unbiased approach to linking the
expression of genes with genetic variation associated with disease
susceptibility is that there is no a priori hypothesis to blind
investigators to the presence of other genes. There are several
limitations to the approach we used. The mRNA screen was
conducted in transformed LCLs and so it would not be
informative about tissue-specific gene expression. Also, SNP
coverage across the genome was not complete and so the
functional effects of some SNPs for which no proxy was available
will be concealed. Furthermore, despite using expression data from
400 LCLs, we may have been underpowered to detect relevant
effects. However, our finding of several novel associations between
MS SNPs and gene expression is worthy of further investigation
and also raises the hypothesis that some disease associated SNPs
may not exert their effects on MS susceptibility through simple
effects on gene expression.
Found at: doi:10.1371/journal.pone.0010142.s001 (0.15 MB
Changes in mRNA expression associated with
Conceived and designed the experiments: AEH SVR. Analyzed the data:
AEH LH AJB CTW JMM SVR. Wrote the paper: AEH LH AJB CTW
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