A high-density screen for linkage in multiple sclerosis.

Abstract

To provide a definitive linkage map for multiple sclerosis, we have genotyped the Illumina BeadArray linkage mapping panel (version 4) in a data set of 730 multiplex families of Northern European descent. After the application of stringent quality thresholds, data from 4,506 markers in 2,692 individuals were included in the analysis. Multipoint nonparametric linkage analysis revealed highly significant linkage in the major histocompatibility complex (MHC) on chromosome 6p21 (maximum LOD score [MLS] 11.66) and suggestive linkage on chromosomes 17q23 (MLS 2.45) and 5q33 (MLS 2.18). This set of markers achieved a mean information extraction of 79.3% across the genome, with a Mendelian inconsistency rate of only 0.002%. Stratification based on carriage of the multiple sclerosis-associated DRB1*1501 allele failed to identify any other region of linkage with genomewide significance. However, ordered-subset analysis suggested that there may be an additional locus on chromosome 19p13 that acts independent of the main MHC locus. These data illustrate the substantial increase in power that can be achieved with use of the latest tools emerging from the Human Genome Project and indicate that future attempts to systematically identify susceptibility genes for multiple sclerosis will have to involve large sample sizes and an association-based methodology.

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Am. J. Hum. Genet. 77:454–467, 2005
454
A High-Density Screen for Linkage in Multiple Sclerosis
International Multiple Sclerosis Genetics Consortium*
To provide a definitive linkage map for multiple sclerosis, we have genotyped the Illumina BeadArray linkage
mapping panel (version 4) in a data set of 730 multiplex families of Northern Europeandescent.Aftertheapplication
of stringent quality thresholds, data from 4,506 markers in 2,692 individuals were included in the analysis. Mul-
tipoint nonparametric linkage analysis revealed highly significant linkage in the major histocompatibility complex
(MHC) on chromosome 6p21 (maximum LOD score [MLS] 11.66) and suggestive linkage on chromosomes 17q23
(MLS 2.45) and 5q33 (MLS 2.18). This set of markers achieved a mean information extraction of 79.3% across
the genome, with a Mendelian inconsistency rate of only 0.002%. Stratification based on carriage of the multiple
sclerosis–associated DRB1*1501 allele failed to identify any other region of linkage with genomewide significance.
However, ordered-subset analysis suggested that there may be an additional locus on chromosome 19p13 that acts
independent of the main MHC locus. These data illustrate the substantial increase in power that can be achieved
with use of the latest tools emerging from the Human Genome Project and indicate that future attempts to sys-
tematically identify susceptibility genes for multiple sclerosis will have to involve large sample sizes and an asso-
ciation-based methodology.
Introduction
Familial clustering of multiple sclerosis (MIM 126200)
has been recognized for more than a century (Eichorst
1896) and has been carefully measured in several large
population-basedstudies(Sadovnicketal.1988;Robert-
son et al. 1996; Carton et al. 1997). The increased risk
seen in thesiblings of affectedindividualscomparedwith
the general population is a useful measure of the degree
of familial clustering, known as “ls” (Risch 1990). In
multiple sclerosis, this risk ratio has a value of ∼20,
which indicates that the lifetime risk of developing the
disease is 20 times greater for a sibling of an affected
individual than for an individual from the general popu-
lation. Supplementary epidemiological studies of twins
(Mumfordetal. 1994;Willeretal.2003),adoptees(Ebers
et al. 1995), conjugal pairs (Robertson et al. 1997), and
half siblings (Ebers et al. 2004) indicate that shared ge-
netic factors are at least partly responsible for the ob-
served familial clustering, whereas studies of migrants
(Dean et al. 1976) and concerning the month of birth
(Willer et al. 2005) confirm that environmental factors
are also important in the pathogenesis of the disease
(Hogancamp et al. 1997).
Received May 17, 2005; accepted for publication July 6, 2005;
electronically published July 29, 2005.
Address for correspondence and reprints: Dr. Stephen Sawcer, Uni-
versity of Cambridge, Department of Clinical Neuroscience, Adden-
brooke’s Hospital, Hills Road, Cambridge, CB2 2QQ, United King-
dom. E-mail: sjs1016@mole.bio.cam.ac.uk
* The authors—those members of the Consortium who made a
substantial contribution to this work—are listed at the end of the text.
? 2005 by The American Society of Human Genetics. All rights reserved.
0002-9297/2005/7703-0012$15.00
Many candidate genes have been investigated, but, to
date, the only region of the genome that has clearly and
consistently shown evidence of association with the dis-
ease is the major histocompatibility complex (MHC) on
chromosome 6p21, where, in Northern Europeans, as-
sociationwiththeDR15humanleukocyteantigen(HLA)
haplotype (DRB1*1501-DQB1*0602) is a constant
finding(Jersildetal.1972;Winchesteretal.1975;Comp-
ston et al. 1976; Olerup and Hillert 1991; Stewart et al.
1997; Haines et al. 1998). In the mid-1990s, it became
possible to complete systematic whole-genome screens
for linkage; to date, 11 such studies have been published
about multiple sclerosis (Ebers et al. 1996; Haines et al.
1996; Sawcer et al. 1996; Kuokkanen et al. 1997;Broad-
ley et al. 2001; Coraddu et al. 2001; A˚kesson et al. 2002;
Ban et al. 2002; Eraksoy et al. 2003; Hensiek et al. 2003;
Kenealy et al. 2004). These screens have all been based
on microsatellite marker sets with a density of approxi-
mately one marker every 10 cM. This design has long
been accepted as the optimal approach (Hauser et al.
1996). Although all of these screens have identified
regions of interest, none has demonstrated linkage with
genomewide significance, although each has shown
more allele sharing among affected individuals than
would have been expected by chance alone. A recent
meta-analysis of the available raw genotypes demon-
strated linkage that just reached genomewide signifi-
cance in the MHC region but found no significant link-
age outside the region, despite the inclusion of 1700
multiplex families (GAMES and Transatlantic Multiple
Sclerosis Genetics Cooperative 2003). Close inspection
of the quality of the data included in this meta-analysis
revealed worrying inadequacies: the average genotyping

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Table 1
Number of Markers per Chromosome
CHROMOSOME
NUMBER OF MARKERS
PER CHROMOSOME BY SAMPLE
Fulla
Datab
Qualityc
Finald
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Pseudo-Xp
X
Pseudo-Xq
Y
Total
478
475
398
296
312
410
285
260
205
248
229
285
187
210
201
197
159
170
149
130
103
121
20
304
472
471
395
291
309
408
284
259
204
246
223
281
184
206
199
195
154
168
143
127
103
121
20
290
444
454
377
273
296
390
272
246
195
228
203
261
163
189
190
175
134
161
129
121
97
106
17
154
396
377
319
246
252
334
230
212
176
196
177
222
145
158
150
145
113
142
102
102
73
96
8
132
887
0
3
0
18
12
5,8585,7735,2824,506
aThe complete Illumina linkage panel (version 4).
bMarkers that generated data in samples.
cMarkers that generated quality data (
198% of samples.
dFinal set of markers (all pairs having
) in GQ 1 0.7
).
2r ! 0.16
success rate was just 80%, and the average information
extraction was only 44%. Although the accuracy of
genotyping was not reported in the majority of these
studies, others have observed that error rates as low as
0.5%–1% are likely to hold only for good-quality mi-
crosatellites (Lathrop et al. 1983) and that higher rates
would be expected for many markers (Brzustowicz et
al. 1993). This level of inaccuracy is predicted to pro-
foundly limit the expected LOD score and thereby se-
riously reduce the power to detect linkage (Abecasis et
al. 2001a). In light of these inadequacies, we compared
existing data from five affected-sib-pair families in-
cluded in the original 1996 British genome screen with
those generated using a more extensively engineered
higher density set of microsatellite markers (Applied
Biosystems High Density Linkage Mapping Set) and
two high-density SNP-based mapping sets (Illumina
BeadArray linkage mapping panel [version 3] and the
Affymetrix GeneChip Human Mapping 10K array).
This pilot experiment indicated that the SNP-based sys-
tems offer considerable advantages for genotyping suc-
cess rate, information extraction, and—most impor-
tantly—genotyping accuracy (International Multiple
Sclerosis Genetics Consortium 2004). The value of an
accurate linkage map cannot be overstated. Demon-
stration of robust linkage has enabled the identification
of susceptibility genesin severalcomplexdiseases(Hori-
kawa et al. 2000; Hugot et al. 2001; Haines et al. 2005)
and thus makes a definitive linkage study desirable.
On the basis of concerns about the quality of the geno-
typing data included in previous linkage screens (which
are no better or worse than those performed for other
complex diseases) and the encouraging results from the
pilot study, we rescreened available families from four
ofthepopulations(Australian,Scandinavian,British,and
American) with the Illumina BeadArray linkagemapping
panel (version 4).
Material and Methods
Markers and Maps
The Illumina BeadArray linkage mapping panel (ver-
sion 4) includes 5,858 markers (table 1). In our samples,
5,773 (98.5%) of these markers generated potentially
usabledata that satisfiedminimumIlluminaqualitystan-
dards. Of these markers, 17 were uninformativeforlink-
age: 5 were monomorphic in our samples and 12 were
Y-linked (included in the panel to confirm the sex of
typed samples). A further 474 markers were excluded,
since !98% of genotypes generated by these markers had
GenCall (as defined in the “Genotyping” section) scores
10.7 among those samplesconsideredtobeofacceptable
quality (see the “Samples and Families” section). None
of the remaining 5,282 quality markers showeddeviation
from Hardy-Weinberg equilibrium (HWE) (the genotyp-
ing success rateandheterozygosityforeachofthese5,282
markers are provided in data file 1 [online only]). Among
these 5,282 markers, there are 5,812 pairs of markers
separated by !500 kb. Among the 5,812 pairs, therewere
1,168inwhichthelinkagedisequilibrium(LD)wasfound
to have . Since simulation studies (Boyles et al.,
r ? 0.16
in press) have shown that there is little if any inflation of
linkage evidence when the LD between markers had
, this threshold was applied to our data, and
r ! 0.16
selected markers were removed until all pairs of markers
separated by !500 kb, consecutive and nonconsecutive,
had. In this exclusion process, we aimed to re-
r ! 0.16
move the minimum number of markers possible. In
regions of high LD, this invariably meant that all but one
of a cluster of markers showing LD with each other
needed to be excluded. In each instance,markersshowing
thelowestheterozygositywereexcludedfirst,exceptwhen
the difference in heterozygosity betweena pairofmarkers
was !10% and the more heterozygous marker included
2
2
2

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Am. J. Hum. Genet. 77:454–467, 2005
Table 2
Population-Specific Breakdown of Families and Individuals
Population
No. of
Families
No. of
Individualsa
Affected
Individuals
(M:F)
Unaffected
Founders
(M:F)
Unaffected
Nonfounders
(M:F)
Australian
Scandinavian
British
American
97
165
298
170
330
475
1,023
864
59:142
140:215
186:428
128:297
33:37
26:54
107:159
92:110
26:33
18:22
65:78
121:116
aNumbers of individuals for whom DNA was available and typed
Table 3
Demographic Features of Affected Individuals
POPULATION
AVERAGEa
RR
(%)
SP
(%)
PP
(%)
Age
(years)
Disease Duration
(years)EDSS
Australian
Scandinavian
British
American
53.6
49.5
45.0
45.3
15.8
19.1
15.7
15.4
3.5b
4.7
4.5
5.5
64
49
57
62
24
32
28
34
12
19
15
4
aEDSS p Expanded Disability Status Scale; RR p Relapse Re-
mitting; SP p Secondary Progressive; PP p Primary Progressive.
bThe EDSS was not determined for these patients; instead, the av-
erage “disease steps” measure is shown (Hohol et al. 1999).
more than three Mendelian inconsistencies,inwhichcase,
the genotyping error rate was considered to “trump” the
heterozygosity, and the more error-prone marker was re-
moved. In total, 776 markers were excluded from the
5,282 quality typed markers. Multipoint linkage analysis
was thus based on a total of 4,506 markers; all these
markers had an average genotyping successrate(forqual-
ity genotypes with GenCall scores 10.7) of 99.7% and
an average heterozygosity of 44.6% (only 23 markers
have a heterozygosity of !5%). When consecutivemarker
pairs separated by !2 Mb were considered, only six ad-
ditional pairs had, and all these had
r 1 0.16
It is therefore unlikely that residual LD has contributed
significantly to the background-allele sharing.
All the markers included in the linkage-mappingpanel
(version 4) have been ordered and placed on the physical
map (build 34) and have had genetic coordinatesinterpo-
lated from the deCODE map (Kong et al. 2002). Markers
unresolved on the genetic map were given an arbitrary
separation of 0.01 cM. Among the 4,506 markers in-
cluded in the multipoint linkage analysis, the average
interval between consecutive markers is 0.82 cM, there
are no intervals of 110 cM, and 71% of intervals are
!1 cM.
.
22
r ! 0.44
Samples and Families
At the inception of thisproject,2,923individualsfrom
780 families were identified and considered. To monitor
and assure experimental variables, such as plate orien-
tation, and to provide the opportunity to test genotyping
reproducibility, inter- and intraplate duplicate genotyp-
ing was performed. Further duplicate genotyping was
also performed to obtain data from as many samples as
possible when the amount of available DNA was mini-
mal (see below). In total, genotyping of the linkagepanel
markers was attempted for 3,417 samples. Of thesesam-
ples, 13% (446) failed to genotype, with most (404) of
these failures occurring in whole-genome amplification
(WGA)–derived DNA (see the “WGA” section). The in-
cluded duplicate genotyping ensured that only 83 indi-
viduals had no sample that yielded genotypes. However,
since some of the individuals who failed to genotypewere
affected, 41 families became uninformative for linkage
(because they had fewer than two successfully typed
affected individuals). Among the remaining 739 fami-
lies, comprising 2,754 typed individuals, 21 individuals
yielded !98% quality genotypes among the 5,282 good-
quality markers and were therefore also excluded. After
the exclusion of these lower-quality samples, a further
nine families became uninformative for linkage, and eight
families were reduced in size but were still informative.
From the five largest multigenerational families, 24 typed
individuals had to be excluded to ensure that the “bit”
parameter for these families did not exceed 19, the larg-
est value we were able to successfully analyze with this
versionofMERLIN(CenterforStatisticalGenetics)(Abe-
casis et al. 2002).
In conclusion, 2,692 individuals (from 730 multi-
plex families) were ultimately included in the analysis
reported here—1,595 affected individuals (513 males;
1,082 females), 618unaffectedfounders(258males;360
females), and 479 other unaffected relatives (230 males;
249 females). Familiesweredrawnfromfourwhitepopu-
lations (Australian, Scandinavian, British, and Ameri-
can), each of which had been previously screened for
linkageandeitherisNorthernEuropeanorhasNorthern
European ancestry (table 2). The included families over-
lap substantially (70%) with those previously studied,
but 216 new families not previously screened are also
included. A breakdown of the demographic features of
the affected individuals is shown in table 3. Together,
the 730 families provide 1,002 affected-relative pairs,

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Table 4
Breakdown of Affected Relative Pairs by Population
POPULATION
NO. OF AFFECTED-RELATIVE PAIRS
Sibling
Half
Sibling CousinParent-ChildAvuncular
Australian
Scandinavian
British
American
68
193
307
262
5
1
6
2
16
4
6
28
28
5
3
24
8
23 31
including 830sibpairs, 14half-sibpairs,54cousinpairs,
57 parent-child pairs, and 47 avuncular pairs; a break-
down by population is shown in table 4.
Ten individuals (all with DNA derived from cell lines)
showed significant loss of heterozygosity on one or, at
most, two chromosomes. These individuals were not in-
cluded in the analysis of the correspondingchromosomes.
Informed consent was provided by all individuals in-
volved in this study, and all affected individuals satisfy
standardized diagnostic criteria (Poser et al. 1983; Good-
kin et al. 1991; McDonald et al. 2001).
Allele Frequencies
By analyzing families from four populations together
as a single data set, we have tacitly assumed that these
are all drawn from the same homogenous population
(Northern European). To test this assumption, we cal-
culated Wright’s F statistic (
FST
tent to which observed heterozygosity falls below that
which would be expected if all studied families had the
same genetic background. Employing heterozygosity and
allelefrequencyestimatesderivedfromthefull730-fami-
ly data set using the PEDSTATS and MERLIN programs
(Center for Statistical Genetics) and averaging across all
4,506 markers, we found an
indicates a modest degree of population substructure(dif-
ference in the genetic background of the included fami-
lies), equivalent to that seen in older cohorts of the ho-
mogeneous Icelandic population (Helgason et al. 2005).
We then used the MERLIN program (Center for Statis-
tical Genetics) (Abecasis et al. 2002) to estimate sepa-
rately the allele frequencies among the founders in each
of the four populations for each of the 4,506 indepen-
dent markers. After Bonferroni correction, no marker
showed statistically significant difference in allele fre-
quency across the four populations. The average differ-
ence in the frequency of an allele between any two popu-
lations was only 3.6%, an amount that would not be
expected to significantly influence the results of linkage
analysis. In keeping with the four populations included
in the screen having a common genetic background, the
extent of difference in the estimated allele frequencywas
found to be highly dependent on the number of samples
typed from each compared population. The smaller the
sample size used to estimate an allele frequency, the
greater the variance in the resultant estimate. Thus, be-
tween the American and British populations, there are
only 15 markers (0.3%) that showed a difference of
110% in allele frequency, whereas, between the Austra-
lian and Scandinavian populations, 361 markers (8.0%)
showed such a difference. Similarly, the frequency of
DR15 carriage among the typed founders from the four
populations did not show any statistically significant
difference.
), which measures the ex-
value of 0.0019, which
FST
For each marker in the linkage panel, Illumina pro-
vided ethnic subgroup–specific allele frequencies. For
whites, these frequencies were based on the genotyping
of 82 unrelated CEPH individuals. When we compared
these CEPH frequencies with those estimated from the
founders in our total set of 730 families, we found no
statistically significant differences after Bonferroni cor-
rection. Repeating the linkage analysis with use of the
CEPH allele frequencies rather than those derived from
the data itself did not produce any substantial change
in the results (the average change in the multipoint LOD
score was just 0.01, the greatest increase was just 0.21,
and thegreatestreductionwasjust0.30),whichconfirms
that our analysis is robust to modest misspecification of
allele frequencies.
Genotyping
Each individual provided a venous blood sample. Cell
lines were established from 390 individuals, andtheDNA
used in this screen was extracted from a growth of these
lines. In the remaining individuals, DNA was extracted
directly from blood by use of standard methods. In some
cases, additional DNA was generated by WGA (see the
“WGA” section).
Each sample included in the analysis was genotyped
in four highly multiplexed assays containing up to 1,536
markers (SNPs) per tube. The assay products from these
were hybridized to high-density, bead-basedmicroarrays
and were imaged on a submicron-resolutionscanner.This
work was performed by the Illumina BeadLab service
facility in San Diego. The Illumina genotyping system is
fluorescence based, with one color specifying the first
allele (A) and a second color specifying the second allele
(B). Homozygous samples (AA or BB) therefore generate
a signal exclusively/predominantly in the corresponding
singlecoloronly,whereasheterozygoussamples(AB)gen-
erate a signal in both colors. Since samples vary in their
performance, each marker produces three clusters of re-
sults within color-signal space, one for each genotype.
The tighter and more distinct the three different clusters
are, the more accurate the genotyping. As part of the
Illumina genotyping process, a quality measure known
as a “GenCall score” is determined for each genotype.

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Am. J. Hum. Genet. 77:454–467, 2005
This metric measures how close a genotype is to the
centre of the cluster of other samples assigned the same
genotype, as compared with the centers of the clusters
for the two other genotypes. This metric varies from 0
to 1, such that the higher the value, the more reliable
the genotype. A sample giving a signal close to the center
of a cluster that is tight and distinct from the other two
clusters will have a high GenCall score.
Genotyping data from the class II HLA gene DRB1
was generated using several differenttypingmethodswith
varying resolution. To maintain consistency across data
sets, we thus coded the HLA-DRB1 locus in terms of
the presence or absence of the DRB1*1501 allele (the
multiple sclerosis–associated allele). These data were
available for 2,448 (91%) of the individuals.
WGA
For 456 individuals, the total amount of DNA avail-
able was less than the minimum recommended for the
Illumina system; therefore, for each of these, we pro-
duced additional DNA by WGA with use of the Mo-
lecularStagingREPLI-gkit,inaccordancewiththemanu-
facturer’s recommended conditions (Dean et al. 2002).
WGA was performed more than once for some individ-
uals, so that, in total, genotyping was attempted for 508
samples of WGA-derived DNA. Since initial results with
these samples were disappointing, we also attempted
genotyping in residual genomic DNA, when possible.
Among these 456 individuals, typing in residualgenomic
DNA was attempted for 401, so that just 55 individuals
had genotyping attempted only in WGA-derived DNA.
Only 104 of the 508 WGA-derived DNA samples
yielded usable genotypes. As expected, the likelihoodthat
genotypes could be generated from WGA-derived DNA
correlated with the starting concentration of genomic
DNA used in the production reaction: the mean starting
concentration was 5.9 ng/ml in the samples that failed
to genotype and 17.4 ng/ml in those that worked. The
104 successfully typed WGA-derived DNA samples pro-
vided genotypes for 100 individuals, 4 of whom were
successfully genotyped in duplicate.
Duplicate Genotyping
In total, 138 individuals were successfully genotyped
in duplicate, and 5 individuals were successfully geno-
typed in triplicate, providing 148 independent compari-
sons (1854,000 duplicate genotypes). Among thesecom-
parisons, there were only 688 genotypes that were dif-
ferent, and, in each instance, only one allele differed.
Since each genotype includes two alleles and was typed
twice, our results indicate a crude error rate of 0.02%
(i.e., 688 alleles in error of
called). When we considered the GenCall scores asso-
ciated with the erroneous genotypes, we observed that
alleles 854,000 # 2 # 2
67% of the 688 errors involved genotypes with a Gen-
Call score of !0.7, whereas only 5.7% of all genotypes
had GenCall scores below that threshold. Similarly, con-
sideration of the genotyping call rate (a crude surrogate
for quality) among the markers associated with errone-
ous genotypes revealed that 63% of the error genotypes
occurred in the 46 markers with genotyping call rates
!99%. To balance the amount of usable data with its
accuracy, we set the following stringent but arbitrary
thresholds. We chose to include only genotypes with a
GenCall score 10.7, among which we would expect the
crude error rate to be !0.01%. We also chose to exclude
all markers with a genotyping call rate of !98% for
quality genotypes (i.e., those with GenCall scores 10.7)
among the quality samples and to exclude all samples
with a call rate of !98% for quality genotypes (GenCall
10.7) among quality markers. These thresholds resulted
in the exclusion of 474 markers and 21 samples (as
described above).
Among the 148 comparisons, 52 involved only ge-
nomic DNA, 92 involved both WGA-derived and ge-
nomic DNA, and 4 involved only WGA-derived DNA.
The crude error rate in the 52 comparisons that were
based on only genomic DNA (0.012%) was less than
half that seen among the 96 comparisons involving at
least one WGA-derived sample (0.025%). In view of this
evidence, for greater reliability, genotypes derived from
genomic samples were included in preference to those
derived from WGA-derived samples when both were
available, except when the genotyping success rate in the
genomic sample was substantially less than that from
the WGA-derived DNA.
Across the 730 families typed for the 4,506 markers
finally included in the multipoint analysis (112 million
genotypes), only 254Mendelianinconsistencieswereob-
served, which indicates a final approximate error rate of
only 0.002%.
Statistical Analysis
The pedigree structure for each included family was
confirmed by comparing the expected and observed
identity-by-state (IBS) allele sharing for each pair of re-
lated individuals with use of the Graphical Relationship
Representation (GRR) program (Center for Statistical
Genetics) (Abecasis et al. 2001b). Pairs of individuals
were also compared across families to excludeany dupli-
cation. Marker heterozygosity, genotyping success rate,
and evidence of deviation from HWE were determined
using the PEDSTATS program (Center for Statistical Ge-
netics) (Wigginton et al. 2005).
LD between autosomal markers was tested using the
Haploview program (which considers both consecutive
and nonconsecutive pairs) (Barrett et al. 2005) and be-
tween X-linked markers, by use of the PDTPHASE pro-