Fine-Mapping the Genetic Association of the Major Histocompatibility Complex in Multiple Sclerosis: HLA and Non-HLA Effects
et al. (2013) Fine-Mapping the Genetic Association of the Major Histocompatibility
Complex in Multiple Sclerosis: HLA and Non-HLA Effects. PLoS Genet 9(11): e1003926. doi:10.1371/journal.pgen.1003926
Fine-Mapping the Genetic Association of the Major Histocompatibility Complex in Multiple Sclerosis: HLA and Non-HLA Effects
Nikolaos A. Patsopoulos 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Lisa F. Barcellos 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Rogier Q. Hintzen 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Catherine Schaefer 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Cornelia M. van Duijn 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Janelle A. Noble 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Towfique Raj 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
IMSGC" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
ANZgene" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Pierre-Antoine Gourraud 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Barbara E. Stranger 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Jorge Oksenberg 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tomas Olsson 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Bruce V. Taylor 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Stephen Sawcer 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
David A. Hafler 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Mary Carrington 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Philip L. De Jager 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
P 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
ul I. W. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
B 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
kk 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
r 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Greg Gibson, Georgia Institute of Technology, United States of America
0 America, 3 Harvard Medical School , Boston , Massachusetts, United States of America, 4 Broad Institute of Harvard and Massachusetts Institute of Technology , Cambridge
1 Department of Immunobiology, Yale University, School of Medicine, New Haven, Connecticut, United States of America, 17 Cancer and Inflammation Program , Laboratory
2 United States of America, 2 Division of Genetics, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School , Boston, Massachusetts , United States of
3 Australia, 15 University of Cambridge, Department of Clinical Neuroscience, Addenbrooke's Hospital , Cambridge , United Kingdom , 16 Department of Neurology
4 MGH, MIT, and Harvard, Charlestown, Massachusetts, United States of America, 19 Department of Medical Genetics, Division of Biomedical Genetics, University Medical
5 1 Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham & Women's Hospital , Boston, Massachusetts
6 of Experimental Immunology , SAIC Frederick , Frederick National Laboratory for Cancer Research , Frederick , Maryland, United States of America, 18 Ragon Institute of
7 America, 13 Department of Clinical Neuroscience CMM, Karolinska Institutet, Stockholm, Sweden, 14 Menzies Research Institute Tasmania, University of Tasmania , Hobart
8 University of Chicago , Chicago , Illinois, United States of America, 12 Institute for Genomics and Systems Biology, University of Chicago , Chicago, Illinois , United States of
9 Neurology, University, of California at San Francisco , San Francisco , California, United States of America, 11 Section of Genetic Medicine, Department of Medicine
10 Genetics, Erasmus MC , Rotterdam , The Netherlands , 9 Children's Hospital Oakland Research Institute, Oakland, California, United States of America, 10 Department of
11 Center , Utrecht , The Netherlands , 20 Julius Center for Health Sciences and Primary Care, University Medical Center , Utrecht , The Netherlands
12 Neurology, MS Centre ErasMS, Erasmus MC , Rotterdam , The Netherlands , 8 Genetic Epidemiology Unit, Department of Epidemiology and Biostatistics and Clinical
13 Berkeley, Berkeley, California, United States of America, 6 Kaiser Permanente Division of Research, Oakland, California, United States of America, 7 Department of
14 Massachusetts, United States of America, 5 Division of Epidemiology, Genetic Epidemiology and Genomics Laboratory, School of Public Health, University of California
The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLADRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data from genome-wide studies, we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen (HLA) genes in 5,091 cases and 9,595 controls. We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II, one HLA-A and two B alleles in class I, and one signal in a region spanning from MICB to LST1. This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC. Interestingly, this region contains the TNF gene, the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene. The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves. This study dissects the independent effects in the MHC, a critical region for MS susceptibility that harbors multiple risk alleles.
Funding: This investigation was supported (in part) by a Postdoctoral Fellowship from the National Multiple Sclerosis Society to NAP, and by R01NS049477,
R01NS026799, NIH/NINDS R01NS049510, R01NS0495103, NIH/NIAID R01AI076544, Dutch MS Research foundation, Bibbi and Niels Jensens Foundation, The
Swedish Brain Foundation and Swedish research council, Stockholm County Council (562183), Swedish Council for Working life and Social Research, Knut and
Alice Wallenbergs foundation, MS research Australia (MSRA), John T. Reid Charitable Trusts, Trish MS Research Foundation and the Australian Research Council,
under the Linkage Projects Scheme (LP0776744), the Cambridge NIHR Biomedical Research Centre. This project has been funded in whole or in part with federal
funds from the Frederick National Laboratory for Cancer Research, under Contract No. HHSN261200800001E. The content of this publication does not necessarily
reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply
endorsement by the U.S. Government. This Research was supported in part by the Intramural Research Program of the NIH, Frederick National Lab, Center for
Cancer Research. PLDJ is a Harry Weaver neuroscience scholar of the National MS Society (JF2138A1). PIWdB is the recipient of a VIDI Award from the Netherlands
Organization for Scientific Research (NWO project 016.126.354). 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.
. These authors contributed equally to this work.
" Membership of IMSGC and ANZgene is provided in the Acknowledgments.
Multiple sclerosis (MS) is an inflammatory and
neurodegenerative disease with a heritable component. Although
it has been known for a long time that the strongest MS
risk factor maps to the major histocompatibility complex
(MHC) on chromosome 6, there are still many unresolved
questions as to the identity and the nature of the risk
variants within the MHC. Because the MHC has a complex
structure, systematic investigation across this region has
been challenging. In this study, we used state-of-the-art
imputation methods coupled to statistical regression to
query variants in the human leukocyte antigen (HLA) class I
and II genes for a role in MS risk. Starting from available
SNP genotype data, we replicated the strongest risk factor,
the HLA-DRB1*15:01 allele, and were able to identify 11
independent effects in total. Functional studies are now
needed to understand their mechanism in MS etiology.
Across the entire human genome, the major histocompatibility
complex (MHC) on chromosome 6 makes the single largest
contribution to multiple sclerosis (MS) susceptibility. The classical
HLA-DRB1*15:01 allele has been documented as the strongest
association to MS risk, and its role has been studied and replicated
extensively . Numerous other HLA alleles have been suggested
to be associated with MS susceptibility, but the complex structure
of the MHC has made it challenging to unequivocally pinpoint
variants that play a causal role in MS [1,2]. For example, it has
been suggested that DQB1*06:02, an MHC class II allele in strong
linkage disequilibrium (LD) with DRB1*15:01, either has no
independent effect  or acts in an extended haplotype with
DRB1*15:01, the DRB1*15:01DQB1*06:02 haplotype, or the
DRB1*15:01DQA1*0102DQB1*06:02 haplotype [4,5]. The
ambiguity and the lack of replication for many of the MHC
associations can be attributed to the extended LD structure of the
MHC , the limited number of HLA loci analyzed, and the
relatively small sample size of previous studies.
Thanks to a large sample size and a novel procedure to impute
classical HLA alleles from SNP data, a recent study described
independent MHC effects for DRB1*15:01, *03:01 and *13:03 as
well as HLA-A*02:01 and rs9277535 . In the present study we
sought to test not only the role of classical HLA alleles but also of
IMSGC: International Multiple Sclerosis Genetics Consortium; BWH: Brigham &
Womens Hospital; ANZ: Australia and New Zealand genetic study; DU:
Netherlands; US: United States; SW: Switzerland.
potentially functional variation within the HLA genes. To this end,
we imputed classical alleles as well as their corresponding amino
acid sequences in 8 HLA genes in a large population of 5,091 MS
cases and 9,595 healthy controls, with genome-wide data (GWAS),
following a recently described imputation protocol . Both the
samples and the imputation method used were independent of
recent efforts exploring MHC associations to MS susceptibility .
We have successfully imputed 3,613 SNPs, 202 amino acid
positions, 78 classical HLA alleles at two-digit resolution, and 99
classical HLA alleles at four-digit resolution (Figure S1). Given the
number of hypotheses that are tested in this analysis, we set, a
priori, p,161025 as the threshold for statistical significance. This
threshold accounts for 5,000 independent tests, assuming a
studywide type 1 error rate (a) of 5%. Overall, we analyzed 5,091 MS
cases and 9,595 healthy controls from eight different GWAS data
sets (Table 1).
Multi-allelic nature of association at HLA-DRB1
The most statistically significant variant in the univariate
analysis (see Material and Methods for details) was
HLADRB1*15:01 (odds ratio [OR] = 2.92, p = 1.46102234,
Figure 1A). Looking at each category of variants (SNPs, two-digit
HLA alleles, four-digit HLA alleles and amino acid positions), the
amino acid position with the smallest p-value was position 25 in
the leader peptide of DQb1 (p = 7.66102231), and the most
statistically significant SNP was at position 32,742,280 (OR for the
A allele = 2.96, p = 5.16102229). An equivalent effect was
observed for HLA-DQB1*06:02 (OR = 2.96, p = 5.46102229). We
first tested whether the DRB1*15:01 effect could be explained by
DQB1. Adjusting for DQB1 variants, we observed that
DRB1*15:01 always had a residual effect (p,1026). Conversely,
adjusting for DRB1*15:01, the effect of DQB1 variants were
accounted for (p.0.8), suggesting that DRB1*15:01 had a
nonequivalent and more statistically significant effect than the DQB1
variants. Furthermore, the extended DRB1*15:01DQB1*06:02
haplotype (p = 7.56102231) did not improve upon the association
of DRB1*15:01 alone. Similarly, the classical DQA1*01:02 allele
that was suggested to contribute to the effect of the haplotype
was strongly associated (p = 4.86102178), but its effect could be
entirely explained by DRB1*15:01. These observations strengthen
the hypothesis that the primary MHC effect in MS is mediated by
DRB1*15:01 and not by variants in the DQB1 or DQA1 loci.
The DRB1 locus (all four-digit alleles in one model) had a
pvalue of 4.06102263 in the initial analysis (Figure 1B). After
adjusting for DRB1*15:01, the residual DRB1 locus effect (due to
all remaining DRB1 four-digit alleles) was still statistically
significant (p = 3.1610237), indicating the presence of multiple
independent DRB1 effects. Applying a forward stepwise strategy
(see Materials and Methods for details), we established statistical
independence for 5 additional DRB1 alleles: *03:01, *13:03,
*04:04, *04:01, and *14:01 (Table S1). After controlling for the
effects of all 6 significant DRB1 alleles (including *15:01), there was
no evidence for a residual signal (p = 1.5610205). We also applied
several other variant selection approaches to test the robustness of
these findings; all approaches identified the same six alleles (Table
HLA-A*02:01 has an independent protective effect
Having analyzed the effects at HLA-DRB1, we tested all other
variation across the MHC while correcting for the six statistically
independent DRB1 alleles, namely DRB1*15:01, DRB1*03:01,
DRB1*13:03, DRB1*04:04, DRB1*04:01, and DRB1*14:01. The
most statistically significant variant was SNP rs2844821 near
HLAA (OR for G allele = 0.70, p = 3.2610229, Figure 1C). Due to LD,
this SNP effect is statistically equivalent to the effect of
HLAA*02:01 (OR = 0.70, p = 7.4610229) and amino acid Val at
position 95 in the peptide-binding groove of the HLA-A protein
(OR = 0.70, p = 9.6610229, Figure 2). Controlling for this effect,
there were no other HLA-A associations.
The DPB1 association with MS susceptibility
Controlling for the 6 DRB1 alleles and the HLA-A effect, the
next most statistically significant variant was rs9277489 (OR for
C = 1.31, p = 2.6610218). This SNP is in the intronic region of
HLA-DPB1 gene and in perfect LD (r2 = 1, based on HapMap
Phase II) with rs9277535 that was previously associated with MS
susceptibility [7,9]. The most statistically significant HLA allele
was DPB1*03:01 (p = 3.6610215), but the effect of rs9277489
cannot be explained by DPB1*03:01 alone (p = 1.7610206 for
rs9277489 in the presence of DPB1*03:01). The most statistically
significant amino acid mapped to position 65 of HLA-DPb1 (OR
for Leu vs. Ile = 1.37, p = 3.7610218), which explained the effect
of rs9277489 (p = 0.003 for rs9277489 in the presence of Leu65 in
HLA-DPb1). This amino acid is also located in the
peptidebinding groove of HLA-DPb1 (Figure 2). After controlling for
rs9277489, there was no residual effect at the DPB1 locus
A non-classical MHC association in MICB-LST1
Adjusting also for the DPB1 effect, we identified rs2516489 as
the next most statistically significant variant (OR for T = 1.31,
Figure 2. 3D ribbon models for HLA DR, HLA A, HLA DP and HLA B. All structures are positioned to accommodate the view of the
peptidebinding groove and the associated amino acid residues. The Protein Data Bank entries 3pdo, 1a1m, 3lqz, and 2bvp were used to produce the 3D
structures, respectively, using UCSF Chimera .
p = 6.7610213, Figure 1G, Figure S2B). This SNP tags a region of
extended LD containing several non-classical MHC class I, class
III and cytokine genes, i.e. MICB, DDX39B (BAT1), NFKBIL1,
TNF, LTA, LTB, and LST1 (Figure 3). We note that this region
had no substantial effect in the univariate analysis (Figure 1A,
Figure S2A), but it became genome-wide significant once the
DRB1*15:01 effect was accounted for (Table S2). There was no
evidence of interaction either with DRB1*1501 (Table S2) or any
other of the identified effects. To explore this phenomenon
further, we stratified the samples according to the presence of
DRB1*15:01 into carriers (heterozygous and homozygous) and
non-carriers. Univariate analysis in these two strata revealed a
consistent but modest effect (OR ,1.2) for the associated SNP in
both DRB1*15:01 carriers and non-carriers (Table S2, Figure S3).
This phenomenon can likely be explained by Simpsons paradox,
where two subgroups share the same association but the overall
population shows no association (or even a reversed one) .
This analysis therefore returns, for the first time, robust evidence
supporting the role of non-HLA genes within the MHC.
To explore any functional consequences of the SNPs in the
MICB-LST1 region we tested these SNPs for cis-eQTL (expression
quantitative trait loci) effects in peripheral blood mononuclear cells
(PBMCs) of 213 MS subjects  as well as CD4+ T cells and
CD14+ monocytes of 211 healthy controls (Table S3). None of the
associated SNPs had a strong cis-eQTL effect (p.161025): the
strongest effect in this region is the relation of rs2516489 to LST1
expression (p = 1.9161025) in the CD4+ T cells of healthy
individuals. The next strongest effect also involved rs2516489 but
was seen in relation to HCG18 (p = 3.1961025) in the PBMCs of
MS subjects. None of the SNPs had a statistically significant
ciseQTL effect on any of the class I or II classical HLA genes (Table
S3). Leveraging the publicly available Encyclopedia of DNA
Elements (ENCODE)  and NIH Epigenomics Roadmap 
for immune cells and cell lines it is evident that the region has an
abundance of functional elements (Figure 3). Of specific interest is
the non-coding naturally occurring read-through transcription
between the neighboring ATP6V1G2 (ATPase, H+ transporting,
lysosomal 13 kDa, V1 subunit G2) and DDX39B (DEAD box
polypeptide 39B) genes. Two SNPs, rs2523512 and rs2251824, tag
this element that has a strong signal in the DNase hypersensitivity
assay in all immune cell types, suggesting that it is an active
cisregulatory region. The histone markers for promoters, enhancers
and active elongation also support these data, while this region is
identified as an active transcription start site using chromatin states
. Other candidates are the TNF and LTB genes. Rs2516489,
the SNP with the best (but not statistically significant) cis-eQTL
effects, lies within a region of heterochromatin, with no indication
of regulatory potential in the available data.
Independent HLA-B effects
Adjusting for 6 classical DRB1 alleles, HLA-A*02:01, rs9277489
(HLA-DPB1 effect) and rs2516489, we observed another novel signal
emerging from the HLA-B locus (p = 7.9610211). The most
statistically significant variants were HLA-B*37, HLA-B*37:01, amino
acid Ser at position 99 in HLA-B (Figure 2) and a SNP in position
31,431,006 (hg18) (Figure 1I, J). All of these variants had statistically
equivalent effects (OR = 1.75, p = 2.2610208). Accounting for the
effect of HLA-B*37:01, no other variant in HLA-B exceeded our a
priori defined threshold, although the residual effect at the HLA-B
locus due to all remaining classical HLA-B alleles was still statistically
significant in our analysis (p = 6.5610206, Figure 1L). This residual
association could be accounted for by HLA-B*38:01 (OR = 0.55;
p = 4.1610205). After adding HLA-B*38:01 to the model, there was
no longer evidence for a residual effect of classical HLA-B alleles
(p.0.002) or elsewhere across the MHC. No amino acid position in
HLA-B could explain the HLA-B*38:01 effect.
Amino acid residues in DRb1
Next, we set out to assess whether a specific set of amino acids
within the HLA-DR molecule could explain the collective effect of
the six classical DRB1 alleles identified above. To this end, we tested
each polymorphic amino acid position using an omnibus test (a
regression model with all but one amino acids carried by a given
position), adding all amino acids (but one) of the most statistically
significant position to the model in a forward stepwise fashion. The
most significant amino acid position in DRb1 mapped to position
71 (p = 1.26102227, Figure S4A), which carries 4 possible alleles:
Ala, Arg, Glu, and Lys. Controlling for the alleles at position 71
(df = 3), there was still a strong residual signal for DRB1*15:01
(p = 5.8610213), indicating that amino acid position 71 alone does
not explain the DRB1*15:01 effect. Adjusting for the alleles at
position 71, position 74 was the next most statistically significant
(p = 1.2610216, Figure S4B). This position harbors five possible
alleles: Arg, Leu, Glu, Ala and Gln. Controlling for positions 71 and
74, position 57 (with four alleles: Asp, Ser, Val or Ala) was the next
most statistically significant association (p = 4.9610211, Figure
S4C). Controlling for positions 71, 74 and 57, we found position
86 as the most statistically significant association (OR = 1.35 for Val
vs. Gly, p = 1.0610206, Figure S4D). After controlling for these four
positions, no other amino acid position exceeded our significance
threshold (Figure S4E), although HLA-DRB1*15:01 still showed a
residual association signal (p = 10205). The model with the four
DRb1 amino acid positions could explain the data better than a
model with only DRB1*15:01 (p = 2.6610226 in favor of the DRb1
amino acid positions), but it was slightly worse than the model with
the six DRB1 alleles (p = 0.001 in favor of the 6 DRB1 alleles). All
four amino acid positions reside in the peptide-binding groove of the
HLA-DR molecule (Figure 2; Table S4 lists the correspondence
between the amino acids at these positions and the six associated
classical DRB1 alleles).
Integrating all of the results, HLA-DRB1*15:01 accounted for
10% of the phenotypic variance in the data, whereas all 6
independent HLA-DRB1 alleles explained 11.6%. A model with all
identified statistically independent effects (HLA-DRB1*15:01,
HLA-DRB1*03:01, HLA-DRB1*13:03, HLA-DRB1*04:04,
HLADRB1*04:01, HLA-DRB1*14:01, HLA-A*02:01, rs9277489/Leu65
in HLA-DPb1, rs2516489, HLA-B*37:01, and HLA-B*38:01)
accounted for 14.2% of the total variance in MS susceptibility.
We have imputed classical alleles of HLA genes, their
corresponding amino acids and SNPs across the MHC, and
tested all variants for association in a large case-control collection.
Our analysis corroborates the effects of DRB1 alleles other than
the well-known DRB1*15:01 association. Classical alleles
DRB1*03:01, *13:03, *04:04, *04:01, and *14:01 display robust,
independent associations in our data. The DQB1 and DQA1 genes
have been suggested to form extended haplotypes with DRB1
alleles, mostly *15:01 . In our hands, the effect of DQB1*06:02
does not explain the effect of DRB1*15:01. Furthermore, the
DRB1*15:01DQB1*06:02 haplotype does not appear to explain
the data as well as the effect of DRB1*15:01 alone. Based on these
results, DRB1*15:01 and the remaining DRB1 alleles are better
candidates than DQB1 variants for a causal role in MS
susceptibility, a hypothesis that agrees with the MHC analysis of
MS subjects with African origin . We note that this
interpretation counters evidence in favor of DQB1 from certain
murine models that capture elements of human inflammatory
demyelination by triggering experimental autoimmune
encephalomyelitis induced with myelin-associated oligodendrocytic basic
protein  or proteolipid protein .
A number of studies have highlighted the importance of class I
HLA alleles in MS susceptibility, with HLA-A*02:01 being the
most prominent allele . Here, we replicated the
HLAA*02:01 association and attributed it to an amino acid
polymorphism at position 95 in the peptide-binding groove of the HLA-A
molecule. We also replicated the recently proposed DPB1*03:01
association, and identified a more statistically significant effect at
amino acid position 65 in the peptide binding groove of
HLADPb1 [7,9]. Although our study has overlapping samples with the
first study to identify an independent HLA-DPB1 effect , these
account for only 24% of the present sample set. The evidence of
an HLA-DPB1 effect is strengthened by the fact that the second
study reporting such a signal  has no overlapping samples with
our study. Furthermore, we confirmed the presence of statistically
independent HLA-B effects [21,22]. Our analysis fine-mapped
these to B*37:01 and B*38:01. Of these, B*37:01 can be explained
by amino acid Ser99 of the HLA-B protein, which is also in this
molecules peptide-binding groove. The HLA-C locus
demonstrated no convincing evidence for a statistically independent effect,
suggesting that previous results may have tagged untested HLA-A
or HLA-B effects across the class I region . Although some of
the above associations could be explained by specific amino acid
polymorphisms in the corresponding HLA proteins, the picture at
HLA-DRB1 however appears to be more complex as there was no
single model based on amino acids that could explain the entire
locus effect (including the specific effect due to DRB1*15:01). At
this stage, our conservative interpretation of these results is that the
implicated amino acids allow new hypotheses to be formulated for
future functional studies.
An interesting finding in our analysis was the association of the
region spanning from MICB to LST1, which contains several
important class I, class III and cytokine-related genes. Although the
identified SNPs were not significant in the initial (univariate)
analysis, we established that these reached significance after
adjusting for the strong DRB1*15:01 effect. One small study
previously examined MICB along with DRB1*15 and had found
evidence for an independent association . Another study
reported that variation in TNF can modify the effect of
DRB1*15:01 . We did not obtain evidence for statistical
interaction between this locus and the other MHC variants,
indicating that the MHC susceptibility variants we have catalogued
likely act independently and additively in terms of MS susceptibility.
Overall, we offer robust evidence for the role of a specific MS
susceptibility haplotype in this region of the MHC. This region
harbors evidence for association with several other diseases, e.g.
Crohns disease and ulcerative colitis , rheumatoid arthritis
, Sjogrens syndrome , and hepatitis C virus-associated
dilated cardiomyopathy . However, the identity of the causal
gene(s) within this associated region remains unclear at this time, but
it is intriguing that three of the genes (TNF, LTA and LTB) are
ligands for one of the validated MS susceptibility genes, TNFRSF1A
. We did not observe any evidence of statistical interaction
(p.0.5) with this non-MHC locus in our data. Our preliminary
analysis using cis-eQTL data in healthy individuals and MS subjects
as well as the publicly available genomic data from the ENCODE
and NIH Epigenomics Roadmap did not identify a single variant/
gene as the likely causal one. From this information it seems that
several genes have functional potential, but more detailed functional
studies will be needed to unravel the causal variants and genes.
Leveraging genome-wide genotype data, the collection of
analyses presented here provides a well-powered investigation of
thousands of genotyped and imputed SNPs, classical alleles of 8
class I and II HLA genes and amino acid sequence variation of
these HLA proteins. The combination of the large sample size
with additional variation types allowed us to present an enhanced
dissection of the critical role of the MHC in MS susceptibility. Our
results highlight a possible role for certain residues in the
peptidebinding groove of HLA molecules associated with peptide antigen
recognition. In HLA-DRb1 we identified a set of four amino acids
in positions 71, 74, 57 and 86 that capture most (but not all) of the
DRB1 association. Of these, Val86 has been associated previously
with MS , and this residue appears to be important for the
presentation of peptides from a putative target antigen in MS,
myelin basic protein , and for the stability of the DRab dimer
. Another study suggested an association at position 60 
and another one at position 13 , although these were not
replicated in the present study. Interestingly, the HLA-DRb1
amino acids in positions 71 and 74 were recently also associated
with susceptibility to rheumatoid arthritis . Overall, consistent
with the known biology of MS, it appears that disease-associated
variants in HLA-DRB1 primarily influence the structural
characteristics of the peptide-binding groove and presumably lead to
alterations of the T cell repertoire that enhance the likelihood of
an inflammatory demyelinating process. However, the MHC also
harbors at least one other risk allele that does not directly affect an
antigen-presenting molecule: the robust evidence supporting a risk
haplotype in the vicinity of MICB will have a different mechanism,
one that is likely to affect the function of one or perhaps several
This study displays an effective strategy for in-depth
characterization of this complex region of the human genome. Increasing
study sample sizes and more complete reference panels are likely
to continue to provide a more detailed perspective on the
architecture of genetic susceptibility in this region. The identified
amino acid residues may help prioritize the identification of
binding peptides and investigations of other potential roles that
these susceptibility alleles might have in the biology of MS
susceptibility aside from antigen presentation.
Materials and Methods
We used data from 8 genome-wide association studies (GWAS)
of European ancestry (Table 1): (a) three GWAS of the GeneMSA
[30,39] with samples from the Netherlands (GeneMSA DU),
Switzerland (GeneMSA SW), and the United States (GeneMSA
US); (b) an early GWAS from the IMSGC [30,39,40] with samples
from the United States (ISMGC US) and the United Kingdom
(IMSGC UK), that was collapsed in one stratum removing the UK
cases; (c) a GWAS with cases from the Brigham and Womens
using a log-likelihood ratio test (2) comparing the likelihood L0 of
the null model (3) against the likelihood L1 of the fitted model:
Log-likelihood ratio test
where D is the log-likelihood test value, also known as deviance.
D follows an approximate chi-square distribution with k degrees of
freedom, where k is the difference of the regressed parameters
between the two models. Representation of the null model:
Hospital and controls from the MIGEN study (BWH) [30,39]; (d)
the Australia and New Zealand Multiple Sclerosis Genetics
Consortium (ANZgene) ; (e) an unpublished GWAS set from
Erasmus Medical Center in Rotterdam, the Netherlands; and (f)
an unpublished GWAS collection from the Kaiser Permanente
MS Research Program (Kaiser Permanente). All the above GWAS
data sets were filtered with the same quality control criteria as part
of an ongoing meta-analysis of Multiple Sclerosis GWAS. In each
of these data sets we performed principal components analysis
(PCA) to identify population outliers and to calculate covariates to
control for population stratification between cases and controls.
Imputation of classical HLA type I and II alleles and
respective amino acid sequences
From each GWAS we extracted SNPs within the extended MHC
region (chr6:29,299,390 to 33,883,424; hg18) to impute classical
alleles for class I HLA genes (HLA-A, HLA-B, and HLA-C) and class
II HLA genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1,
and HLA-DRB1), their corresponding amino acid sequences and
SNPs not captured in the genotypic platforms used. The imputation
was performed with the software BEAGLE  using a collection of
2,767 individuals of the Type 1 Diabetes Genetics Consortium
(T1DGC) with 4-digit classical allele genotyping for the above HLA
genes as the reference panel. This method and reference panel have
been used for fine-mapping MHC associations in HIV control 
and seropositive rheumatoid arthritis . Cases and controls from
each GWAS dataset were imputed together. All variants in the
reference panel were coded as biallelic markers (presence vs.
absence), allowing us to use BEAGLE for the imputation.
Postimputation we excluded variants with minor allele frequency less
than 1% from the analysis. Table S5 lists the imputation quality for
the identified variants.
We analyzed each variant using a logistic regression model,
assuming alleles have an additive effect on the log-odds scale. We
also assumed the genetic effects were fixed across all eight GWAS.
In each model we included the top 5 principal components to
control for within-GWAS population stratification and 7 dummy
variables to account for between-GWAS specific effects.
Throughout the text we refer to such a model as univariate (Mu), even if
several covariates were included in the model, reflecting the fact
that only one MHC-specific variant is included in the model. This
is the representation of the univariate model:
Mu, Univariate logistic regression model
where y is the log(odds) for the case-control status, b0 is the
logistic regression intercept and bi,j is the log-additive effect for the
allele j of the variant i with p alleles. In this paper, the term variant
is used for any type of SNP (biallelic, triallelic, etc), two-digit HLA
allele, four-digit HLA allele and amino acid position. In any case
we included p-1 alleles, with the one excluded being the reference
allele. Where possible we tried to select the most frequent variant
in the controls as the reference allele. The five included principal
components are represented in the model as l and the last block in
the model represents the dummy variables included for the n
studies (n-1 parameters added in the model).
To calculate an omnibus p-value for the variant, regardless of
the number of alleles included in the univariate model, we used
M0, Null logistic regression model
Besides testing variants for association, i.e. SNPs, HLA alleles and
amino acids, we also fitted models that estimated the overall effect of
the each of the eight HLA genes. We did so, by fitting all respective
four-digit alleles of a given HLA gene in the same model. The
respective p-values reflect the overall significance of the gene.
Framework for identification of statistically independent
In order to identify the statistically independent effects, we first
tested all variants under a univariate logistic regression model and
ranked them based on the p-value of the log-likelihood test. Next, in
a forward stepwise fashion, we included in the logistic regression
model the most statistically significant variant as a covariate,
analyzed all remaining variants and ranked them based on the new
p-value of the respective log-likelihood test. The models that
included at least one variant as covariate are referred to as conditional
throughout the text. In each iteration the null model used in the
loglikelihood test was the original null model (3) with the variants that
were used as covariates. We repeated the same steps until no variant
or no HLA gene reached the level of statistical significance, which
we a priori set to be 1025. This statistical significance threshold
accounts of 5,000 independent tests using Bonferroni correction.
Although most of the variants analyzed are correlated, we chose this
threshold to account also for the multiple stepwise fitted models. If
no variant reached the level of significance but an HLA gene did, we
kept adding variants in the overall model until the HLA gene
pvalue was larger than 1025.
To compare the effects of two (or more variants), e.g. A and B,
we fitted the following models: MA model with variant A, MB
model with variant B, and MAB model with both variants A and B.
All three model included the same other covariates. Then we used
the log-likelihood test to compare MAB vs. MB and MAB vs. MA.
These two comparisons represent the effects of variants A and B,
respectively, in the presence of the other variant, i.e. B and A. For
these comparisons we used the nominal (a = 0.05) level of
Statistically independent effects in the DRB1 locus
After adjusting for the most statistically significant variant,
DRB1*15:01, the residual effect of the DRB1 locus, i.e. the effect
of all alleles besides *15:01, was still the most statistically significant
of any of the remaining variants. This led us to the hypothesis that
several other DRB1 alleles could explain the overall DRB1 locus
effect, already conditioning on DRB1*15:01. To identify such
effects inside the DRB1 locus, we applied the above forward
stepwise logistic regression approach to the four-digit DRB1
alleles. , To test the robustness of the results from the forward
stepwise regression, we also applied four other statistical methods
for variant selection: i) lasso,  ii) elastic net,  iii) least angle
regression,  and iv) forward Stagewise regression.  For the
lasso and elastic net we selected the largest value of lambda (l1)
after 10-fold cross-validation, such that error was within 1
standard error of the minimum mean cross-validated error. In
the respective results section, we illustrate that all methods reached
the same conclusion independently.
DRB1*15:01:DQB1*06:02 extended haplotypes
It has been proposed that extended DRB1*15:01DQB1*06:02
haplotypes confer the risk for MS rather than individual HLA
alleles. To test this hypothesis, we used the post-imputation phased
data to estimate the DRB1*15:01DQB1*06:02 diplotypes. Then
we fitted a logistic regression that estimated the effect of the
diplotype under a per-allelic model. Since this approach used
phased data, rather than post-imputation probabilities, the
imputation uncertainty is not properly accounted for. Thus, we
expect the respective p-values to be slightly inflated.
Functional analysis of the MICB-LST1 region
To investigate the functional potential of the MICB-LST1 region
in-house cis-eQTL (expression quantitative trait loci) in
PBMCs of 213 MS subjects  and CD4+ T cells and
CD14+ monocytes of 211 healthy individuals. The PBMCs
gene expression levels were quantified with mRNA derived
from of 213 subjects of European ancestry with relapsing
remitting (RR) multiple sclerosis (MS) via an Affymetrix
Human Genome U133 Plus 2.0 Array. The expression levels
were adjusted for confounding factors, such as subjects use of
immunomodulatory drugs, age, gender, and batch effects via
principle components analysis. Associations between SNP
genotypes and adjusted expression residual traits were
conducted by Spearman rank correlation (SRC). For the cis
analysis, we considered only SNPs within a 2 Mbps window
from the transcript start site (TSS) of genes. Furthermore, we
also explored any possible effect to the expression of class I
and II HLA classical genes, regardless of their physical
distance. Significance of the nominal p-values was
determined by comparing the distribution of the most significant p
values generated by permuting expression phenotypes 10,000
times independently for each gene. We call a cis-eQTL
significant if the nominal association p-value is greater than
the 0.05 tail of the minimal p-value distribution resulting
from the permuted associations, which corresponds to a
pvalue of 1610205. Similar methods were used to evaluate the
cis-regulatory effects in CD4+ T lymphocytes and CD14+
monocytes data sets consisting of 211 healthy individuals of
European ancestry. These analyses were conducted under the
auspices of a protocol approved by the institutional review
board of Partners Healthcare.
publicly available data from the ENOCDE  and NIH
Epigenomics Roadmap . Specifically we perused data for
functional potential from CD4 memory primary cells
(H3K4me3, H3K27ac, H3K36me3, chromatin states), CD4
nave primary cells (H3K4me3, H3K27ac, H3K36me3,
chromatin states), CD8 memory primary cells (H3K4me3,
H3K27ac, H3K36me3, chromatin states), CD8 nave
primary cells (H3K4me3, H3K27ac, H3K36me3, chromatin
states), CD4 primary cells (DNase hypersensitivity sites), CD8
primary cells (DNase hypersensitivity sites), Treg primary
cells (H3K4me3, H3K36me3, DNase hypersensitivity sites),
Th1 (DNase hypersensitivity sites), Th2 (DNase
hypersensitivity sites), Th17 (DNase hypersensitivity sites) and
GM12878 cell line (B-lymphocyte, lymphoblastoid,
International HapMap Project - CEPH/Utah - European
Caucasion, Epstein-Barr Virus; H3K4me3, H3K27ac, H3K36me3,
chromatin states, DNase hypersensitivity sites).
where L0 and L1 are the likelihoods of the null model and fitted
model respectively, and N is the number of individuals.
We used PLINK for the initial analysis of the data and to
estimate minor allele frequencies and imputation quality metrics,
i.e. INFO score.  We fitted all models in R using the glm
function and package lars and glmnet.
Figure S1 Scatter plots of minor allele frequency (MAF) and
INFO score for the imputed variants. INFO score is an imputation
quality metric and is defined as the ratio of the variance observed
over the variance expected. On average genotyped SNPs have a
Figure S2 Regional associational plots for the non-classical HLA
region spanning MICB-LST1. The figure displays the minus
logarithmic p-values (2log10P) for the SNPs in the MHC region
that includes class I and class III non-classical MHC genes. Panel
A has the 2log10P for the univariate analysis, and panel B the
2log10P adjusting for DRB1*15:01 in the model. Shades of red
represent the r2 between the SNPs and the best marker, i.e.
Figure S3 DRB1*15:01-stratified analysis for the SNPs tagging
the non-classical HLA LD haplotype. The y-axis lists the SNPs in
positional order. The x-axis displays the respective Odds Ratios
and 95% confidence intervals of these SNPs while analyzing with a
univariate model: a) all individuals [cases: 5,091; controls: 9,595]
(black color), b) DRB1*15:01 carriers [cases: 2,794; controls:
2,392] (green color), and c) non-carriers of DRB1*15:01 [cases:
2,367; controls: 7,204] (red color). Asterisks indicate associations
with a p-value less than 1610205.
Figure S4 Analysis of amino acid residues in DRb1. The
univariate analysis results are in the first row. Each next one plots
the 2log10(p-value) of the amino acid positions conditioning on
the amino acid residues if the previous rows. The solid black line
marks the threshold of statistical significance in the study. The
rows represent univariate analysis (A), conditioning on position 71
(B), conditioning on the above and position 74 (C), conditioning on
the above and position 57 (D), conditioning on the above and
position 86 (E).
Table S1 Statistically independent effects of the DRB1 locus
considering four digit resolution alleles. In all cells Odds Ratios are
listed. P-values are listed in parentheses for the forward stepwise
regression. The order of the DRB1 alleles is according to the
forward stepwise regression (primary analysis). The stopping rule
was the residual DRB1 locus effect to have a p-value.1.0e205. * For
the regression-based methods the step number in which the allele
was included in the model is displayed. Effect sizes (and p-values)
per variants are for the respective step in the forward stepwise
regression and for the final model in the least angle and forward
stagewise regressions. $ For the lasso and elastic net the Odds Ratios
are displayed, since both methods provide the best solution. The
largest value of l1 regularization parameter such that error is within
1 standard error of the minimum l1 regularization parameter was
used to identify the best solution. Both the lasso and elastic net also
identified *01:01 in their best solution, with OR of 0.99 and 0.97,
respectively. This allele comes up in step 7 in all regression methods.
Table S2 Proof of statistical independence of rs2516489 and
HLA-DRB1*15:01. The interaction term of the two variants
cannot explain either of the two effects. Especially in the saturated
model (both variants and the interaction term) the interaction term
is not statistically significant, even at the nominal level. The
stratification of the samples, based on HLA-DRB1*15:01 carrier
status, reveals the effect of rs2516489 in both strata. The numbers
listed are the Odds Ratio (OR), followed by the p-value. In all
models principal components and dummy variable for studies
were used as covariates.
Table S3 Cis-eQTL effects (p-value,0.05) of the SNPs in
MICBLST1 in PBMCs of MS subjects, CD4+ T cells of healthy individuals
and CD14+ monocytes of healthy individuals. This table is included
in the accompanied xls entitled Supplementary_Table_3.xls. None
of the cis-eQTLs reached statistical significance (p-value,161025).
Table S4 Association of the six identified DRB1 alleles and the
amino acid changes in the four associated DRb1 positions. Amino
acids that predispose to MS susceptibility are indicated with bold.
The rest are either protective or neutral. DRB1 alleles in bold
predispose to MS and the rest are protective.
Imputation quality scores for the identified variants.
Members of the International Multiple Sclerosis Genetics
Consortium (IMSGC) strategy group:
Lisa Barcellos, Luisa Bernardinelli, David Booth, Manuel Comabella,
Alastair Compston, Sandra DAlfonso, Philip De Jager, Bertrand Fontaine,
An Goris, David Hafler, Jonathan Haines, Hanne Harbo, Steve Hauser,
Clive Hawkins, Bernhard Hemmer, Rogier Hintzen, Adrian Ivinson,
Christina Lill, Roland Martin, Filippo Martinelli-Boneschi, Jorge
Oksenberg, Tomas Olsson, Annette Oturai, Aarno Palotie, Margaret
PericakVance, Janna Saarela, Stephen Sawcer, Graeme Stewart, Bruce Taylor,
Members of the ANZgene consortium:
Rodney J Scott, Jeannette Lechner-Scott, Pablo Moscato1 David R
Booth, Graeme J Stewart, Robert N Heard, Deborah Mason, Lyn
Griffiths, Simon Broadley, Matthew A Brown, Mark Slee, Simon J Foote,
Jim Stankovich, Bruce V Taylor, James Wiley, Melanie Bahlo, Victoria
Perreau, Judith Field, Helmut Butzkueven, Trevor J Kilpatrick, Justin
Rubio, Mark Marriott, William M Carroll1, Allan G Kermode.
Conceived and designed the experiments: NAP PLDJ PIWdB. Performed
the experiments: LFB RQH CS CMvD JAN PAG JO TO BVT SS DAH
PLDJ. Analyzed the data: NAP. Contributed reagents/materials/analysis
tools: LFB RQH CS CMvD JAN PAG BES TR JO TO BVT SS DAH
PLDJ PIWdB. Wrote the paper: NAP MC PLDJ PIWdB. Interpretation of
the results: NAP LFB RQH CS CMvD JAN PAG JO TO BVT SS DAH
MC PLDJ PIWdB. Revised the manuscript critically for important
intellectual content: LFB RQH CS CMvD JAN PAG JO TO BVT SS
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