www.elsevier.com/locate/jneuroim

Journal of Neuroimmunology 143 (2003) 53–59

Refining the analysis of a whole genome linkage disequilibrium

association map: the United Kingdom results

Tai Wai Yeoa, Richard Roxburgha, Mel Maraniana, Sara Singlehursta, Julia Graya,Anke Hensieka, Efrosini Setakisb, Alastair Compstona, Stephen Sawcera,*

aUniversity of Cambridge Neurology Unit, Addenbrook’s Hospital, Hills Road, Cambridge, CB2 2QQ, UKbMRC Biostatistics Unit, Institute of Public Health, University Forvie site, Robinson Way, Cambridge, CB10 1SB, UK

Abstract

Individual genotyping of the 10 most promising markers identified in our previously reported screen for linkage disequilibrium (LD) in

multiple sclerosis identified a number of effects which confound the analysis and are of general importance in the interpretation of results

obtained using microsatellite markers typed in pooled DNA. In order to identify and characterise these effects, we individually genotyped 529

promising markers in 16 trio families. We then devised adapting factors, which were designed to correct for these confounders. This more

extensive analysis of the previously published UK data set and the repeat analyses incorporating these adaptations led to the identification of

two novel markers that may be associated with multiple sclerosis in this population, providing a close correlation between the results of

pooled analysis and individual typing.

D 2003 Elsevier B.V. All rights reserved.

Keywords: Genome screen; Linkage disequilibrium; Multiple sclerosis; Microsatellites; DNA pooling

1. Introduction

In 1997, Barcellos et al. (1997) proposed an efficient

method for screening for linkage disequilibrium (LD) in

complex traits based on typing a dense map of micro-

satellite markers in pooled DNA. We have previously used

an adapted version of this method to perform two system-

atic screens for LD in multiple sclerosis. Preliminary

results were reported in 2002 (Sawcer et al., 2002). In

this screen, allele image patterns (AIPs) generated from

separately pooled cases and controls were normalised

according to the number of alleles in the corresponding

pools. Resulting allele counts were then compared statis-

tically using a chi squared test (alleles with a frequency of

less than 5% were grouped together). The first screen

employed unrelated cases (n = 216) and controls (n = 219),

while the second was based on 745 trio families (an affected

individual and their parents) and used the un-transmitted

parental alleles as controls, resulting in an analysis equiv-

alent to the AFBAC method proposed by Thomson (1995).

0165-5728/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.jneuroim.2003.08.011

* Corresponding author. Tel.: +44-1223-217091; fax: +44-1223-

336941.

E-mail address: sjs1016@mole.bio.cam.ac.uk (S. Sawcer).

Because pooling introduces additional sources of error,

above and beyond sampling variance, it is impossible to

establish absolute p-values for the calculated chi squared

statistics. However, by using the observed distribution of

these statistics, we established significance empirically and

thereby ranked markers in terms of their apparent evidence

for association—as reflected in the observed difference in

case-control AIPs (Setakis, 2003). In order to reduce the

effect of additional sources of variance on the final ranking

of markers, extra-AIPs were generated for those markers

(n = 520) showing the most extreme results.

This approach was adopted generically by the GAMES

collaborative and used to screen a variety of additional

populations. Results from the majority of these screens

are reported in this issue. This article describes our

efforts to refine results of the previously reported UK

screen.

2. Materials and methods

2.1. Samples and markers

The patients and controls employed in this study are as

previously described (Sawcer et al., 2002). All are Cauca-

Table 1

Number of markers by repeat class

Total

(6000)

N (%)

Most promising

(659)

N (%)a

Typed novel

(7)

N (%)

Dinucleotides 4723 (79) 463 (70) 1 (14)

Trinucleotides 258 (4) 45 (7) 3 (43)

Tetranucleotides 1019 (17) 151 (23) 3 (43)

a Markers showing empirical p-values of < 5% in either (or both)

cohorts (n= 659) in the original UK screen (Sawcer et al., 2002).

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–5954

sians of British descent and all gave informed consent for

genetic analysis. Each patient satisfied Poser criteria for the

diagnosis of multiple sclerosis (Poser et al., 1983). The

number and specificity of markers are as previously de-

scribed (Sawcer et al., 2002). Full details are available from

our web site (http://www-gene.cimr.cam.ac.uk/MSgenetics/

GAMES).

2.2. Genotyping

DNA extraction and pooling are as previously described

(Sawcer et al., 2002). Briefly, PCR amplification was

performed using TrueAllele PCR premix (Applied Biosys-

tems) according to the manufacturers recommended meth-

ods on 9700 thermal cyclers (Applied Biosystems).

Electrophoresis was performed on a 3700 DNA analyser

(Applied Biosystems). Both pooled and individual geno-

typing were performed according to the same conditions,

although limited multiplexing was employed for individual

genotyping of the best markers. Individual genotyping of

the 16 trio families was performed without multiplexing.

Analysis of electropherograms was performed using the

GENESCAN and GENOTYPER software packages (Ap-

plied Biosystems).

Where additional AIPs were required a total of four

PCR replicates was generated from case-control pools,

after which each PCR product was electrophoresed twice

in order to generate up to eight new AIP replicates from

each pool. Double this number of PCR replicates was

generated from the trio family pools, each being electro-

phoresed twice; the greater number of individuals making

up the trio family pools demands more replicate AIPs

(Barratt et al., 2002).

2.3. Statistical analysis

The adapting factors were incorporated into the statis-

tical analysis of AIP data, as follows. First, signal from

peaks determined to be erroneous were excluded from the

raw AIP data. Secondly, when required, correction for

length dependent amplification was achieved by multiply-

ing the signal from each retained peak in each AIP by an

appropriate factor. For the ith allele in each AIP, this

factor=(1 + f*li), where f= the calculated proportional re-

duction factor for that marker and li = the difference in

length (in base pairs) between the ith allele and the

shortest allele included in the AIP (allele 1). Finally, these

edited data were analysed using the method developed by

Setakis (2003) but with each marker normalised according

to the ‘‘effective’’ number of alleles in the corresponding

pools. This is the actual number reduced by the estimated

proportion of alleles not contributing to the AIP. For

example, in the analysis of a marker where the AIP

captures only 90% of the observed alleles, peak heights

would be normalised to 90% of the allele counts in the

respective pools.

3. Results

3.1. Analysis by repeat class

Inspection of our previously published analysis (Sawcer

et al., 2002) reveals a statistically significant under-repre-

sentation of dinucleotide markers amongst those at the

extreme end of the ranking (see Table 1). This distortion

in the expected proportions suggests that markers behave

differently in a pooling experiment according to repeat

class. It therefore follows that estimating the empirical

significance from a distribution including all classes of

repeats is likely to favour those classes of repeats showing

the greatest variance. This problem is further compounded

by the fact that the proportion of dinucleotides varies

according to the number of degrees of freedom; that is,

the number of common alleles identified for each micro-

satellite. Amongst markers with low degrees of freedom the

proportion of dinucleotides was 40–50%, compared with

80–90% amongst those showing high degrees of freedom.

The dinucleotides with low degrees of freedom are thus

particularly under represented in the list of extreme results

identified in our previous analysis.

Separate analysis of the data from each class of repeat

confirms the marked difference in error structure between

the repeat classes and substantially changes the overall

marker ranking. A total of 104 markers not previously

included amongst those for which we have already gener-

ated refining AIPs emerged as interesting in this repeat class

specific analysis. Additional AIPs were therefore generated

for these markers and added to the existing UK data.

3.2. Individual typing of markers emerging from the

original UK screens

We individually genotyped the 10 most promising

markers identified in our previous report (Sawcer et al.,

2002) in all samples used to create the original pools still

available: 207 cases (96%), 160 controls (73%) and all 745

trio families. The results are shown in Table 2. In line with the

statistical analysis performed in our original study alleles

with a frequency of < 5% were combined and global chi-

squared statistics calculated for each marker in both cohorts.

In the trio families, index cases were compared with the un-

transmitted parental alleles using the AFBAC program

Table 2

p-values from individual typing of top UK markers

Marker Case-controls Trio families

D1S1590 0.81 0.13

GGAA30B06 0.002 0.47

D2S2739 0.01 0.79

D4S416 0.25 0.10

D6S1615a 0.03 6.12E� 12

D6S2444a 6.15E� 05 6.59E� 14

TNFaa 0.02 3.87E� 09

D17S1535 0.60 0.39

GCT6E11 0.56 0.34

D19S585 0.12 0.77

a These three markers are all from the HLA region on 6p21.

Table 3

Mean and standard deviation (S.D.) for adapting factors estimated from

consecutive 16 family subsets of the 745 UK trio families

Missed allele

proportion

Relative signal reduction per

base pair

Mean S.D.a Mean S.D.

D1S1590 0.082 0.035 0.051 0.004

GGAA30B06 0.001 0.003 0.015 0.003

D2S2739 0.196 0.053 0.019 0.003

D4S416 0.001 0.004 0.039 0.006

D6S1615 0.106 0.044 0.037 0.002

D6S2444 0.001 0.003 0.059 0.004

TNFa 0.027 0.018 0.028 0.009

D17S1535 0 NAb 0.025 0.005

GCT6E11 0.416 0.12 0.053 0.017

D19S585 0.043 0.025 0.04 0.006

a As expected, these SD estimates are in close agreement with those

predicted for a binomial distribution.b As no missed alleles were seen in any of the replicate data sets for this

marker, no deviation was observed and therefore no S.D. estimate can be

calculated.

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–59 55

(Thomson, 1995). As expected, association with the three

HLA makers was confirmed. However, after correction for

multiple testing, none of the seven novel markers retain

evidence for association. A number of un-anticipated effects

were observed, providing an explanation for these reduction-

ist results.

Two of the seven novel markers were found to have

genotype frequencies, which deviated significantly from

Hardy Weinberg equilibrium (D19S585 and GGAA30B06).

In the trio family analysis, the presence of null or very

poorly amplified alleles could be inferred for these markers,

indicating that a proportion of alleles in each pool failed to

contribute to the observed AIPs. Since peak heights in an

AIP are normalised on the assumption that they reflect all

alleles in a pool, allele count differences will be over

estimated when a substantial proportion do not contribute

to the measured AIPs. Other effects also result in alleles

failing to contribute to AIPs. For marker GCT6E11, length

dependant amplification resulted in the longest common

allele being missed from the AIP. This allele did generate a

peak in the electropherogram but so small that it was only

measurable in a proportion of replicates and therefore was

not included amongst peaks considered to make up the AIP

for this marker. Similarly, for marker D2S2739, a total of 16

peaks were included in the AIP although various smaller

peaks could be seen in some electropherograms. Individual

typing, however, revealed a total of 41 different alleles

indicating that some 61% of the possible alleles had not

been included in the AIP.

As well as missed alleles, we also observed that some of

the peaks included in AIPs had no corresponding allele. For

marker D1S1590, the majority of individual genotypes

included an additional fragment, presumably arising from

non-specific amplification. Because of its position, this

fragment had mistakenly been labelled as an allele in the

AIPs. For marker D4S416, a number of peaks included in

the AIP had no corresponding allele being purely stutter

bands from other alleles. The erroneous inclusion of these

additional peaks as alleles increases the number of degrees

of freedom in testing these markers. Since significance is

judged against the distribution of markers with the same

number of degrees of freedom, this effect distorts the result

for that marker and all others in the class. Finally, re-

inspection of the AIP data for marker D17S1535 revealed

a genotyping error which, when corrected, left this marker

showing no statistically significant difference in the AIPs

from either the case-control or trio family cohorts, in

agreement with individual typing results.

3.3. Estimating adapting factors to correct for these

phenomena

The phenomena described above can be expected vari-

ably to affect the remaining 5990 markers employed in our

original LD screen. In an effort to estimate the scale of these

effects, and thereby appropriately correct for them, we

individually genotyped a selection of markers in 16 trio

families. This number of families provides a total of 64

independent chromosomes (from 32 unrelated individuals)

and thereby a >80% power to observe alleles with a

frequency of >2.5% (and at least 50% power to detect

alleles with a >1% frequency). Trio families were typed

rather than unrelated individuals in order more easily to

identify null alleles and contaminating artefacts. Compari-

son of alleles seen in the individual genotyping with the

pattern suggested by the AIP allowed easy identification of

peaks erroneously included in the AIP, and provided an

estimate of the proportion of alleles not contributing to the

measured AIP. Knowledge of this proportion enables an

appropriate reduction in the normalisation of AIPs, restrict-

ing allele counts to just that faction influencing the AIP. As

well as excluding peaks that do not correspond to observed

alleles, we also excluded peaks where a corresponding allele

was identified but only accounted for less than a quarter of

the observed peak height. We reasoned that since such peaks

principally reflect stutter artefact from longer alleles, in-

cluding such data was more harmful than dropping the peak

and thereby failing to measure a rare allele.

Table 4

Marker Case-controls Trio families

(a) Markers with final empirical p-values < 10% in both case-control and trio family cohorts. Analysis was performed without correction for length dependant

amplification

D5S112 0.0281 0.0930

D6S1615a 0.0001 0.0395

D6S2444a 0.0310 0.0777

TNFaa 0.0180 0.0259

(b) Markers with final empirical p-values < 10% in both case-control and trio family cohorts. Analysis was performed with correction for length dependant

amplification

D5S112 0.0255 0.0926

D6S1615a 0.0001 0.0404

TNFaa 0.0148 0.0307

D10S1769 0.0664 0.0780

a These three markers are from the HLA region on 6p21.

Table 5

p-values from individual typing of the two new novel markers

Marker Case-controls Trio families

D5S112 0.1080 0.0535

D10S1769 0.0956 0.0903

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–5956

Assuming a linear relationship between allele length and

reduction in allele signal, we were also able to estimate the

extent of length dependant amplification for the typed

markers. Expressing this as the proportionate reduction in

peak height per base pair of extra length, we could then use

this value as a crude correction for length dependant

amplification by inflating the signal for longer alleles

accordingly (prior to normalisation).

A total of 529 markers were typed in the 16 trio families.

These markers were mostly chosen from the extreme (most

promising) end of the original UK study, the remainder

having given extreme results in other GAMES screens. The

mean missing allele proportion was found to be 9.2% and

the mean length-dependent amplification factor (proportion-

ate signal reduction per base pair) was 2.9%. Interestingly,

this factor was almost double for dinucleotides (3.5%)

compared to longer repeats (2.0%), suggesting that length

dependent amplification is principally determined by repeat

number rather than absolute length. Peaks not corresponding

to observed alleles were seen in 72% of markers, 22%

having just one aberrant peak, 21% two such peaks and 29%

three or more such peaks. As expected, the first peak in the

AIP was the most often considered aberrant (corresponding

to a stutter band).

In order to test the stability of the adapting factors

estimated by typing just 16 trio families, we used the data

from individual typing of the 10 markers listed in Table 1,

each of which had been genotyped in the 745 UK trio family

cohort. By considering consecutive 16 family subsets, we

were able to generate multiple estimates for the adapting

factors and thereby calculate their mean and standard

deviation (see Table 3). We were also able to test how often

peaks corresponding to relatively common alleles (those

with a frequency of >10% in the 745 trio families) are

inadvertently excluded when just 16 trio families are typed.

No such alleles were excluded when only those strictly

absent from the 16 trio family genotyping were considered.

However, such alleles were occasionally dropped when their

frequency was under estimated such that the estimated

frequency contributed less than a quarter of the corres-

ponding peak height. This was observed once for an allele

with a frequency of 16%, once for an allele with a frequency

of 11% and twice for an allele with a frequency of 10%. No

other high frequency alleles were excluded through adop-

tion of this rule. Thus, it is clear that our policy of excluding

peaks when the estimated allele frequency contributes less

than one quarter of the peak height results in only a very low

probability ( < 0.1%) that data from important alleles will be

inadvertently ignored. We feel that the advantage of exclud-

ing peaks that are essentially (>75%) stutter bands far

outweighs this small risk.

3.4. Refined analysis

Employing these adapting factors, we reanalysed all

available UK data. This reanalysis was performed both with

and without crude correction for length dependant amplifi-

cation. In each case the di-, tri- and tetra-nucleotides were

analysed independently. In the analysis with correction for

length dependant amplification, the mean value of 2.9% (per

base pair) was applied to all markers not included amongst

the 529 specifically typed. In the analysis without correction

for length-dependent amplification, we identified a total of 34

markers with empirical p-values < 10% in both screens.

Thirty-five such markers were identified in the analysis

employing the correction for length-dependent amplification.

Results from these two analyses are very similar and sub-

stantially overlap, such that in total just 48 extreme markers

were identified. This list of extreme markers includes 35 for

which no extra AIPs had previously been generated, as well

as 28 that were not amongst the 529 for which adapting

Table 6

Ranked position of HLA markers in combined analysis of case-control and

family data

Marker Original Refined

D6S1615 31 1

TNFa 195 7

D6S2444 132 11

D6S265 95 14

D6S273 1015 166

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–59 57

factors had been calculated (25 overlapped, that is had neither

extra AIPs or appropriate adapting factors).

3.5. Final analysis

In order to refine further the new list of most extreme

markers, additional replicate AIPs were generated for the 35

markers not previously treated in this way. We also estab-

lished appropriate adapting factors for the 28 extreme

markers not included amongst the 529 previously treated in

this manner. As before, these adapting factors were deter-

mined by individually genotyping the markers in 16 trio

families. A final analysis was performed combining all the

available data.

In this final analysis, only 5 of these 48 promising

markers retained empirical p-values of better than 10% in

both case-control and trio family studies (the empirical p-

values for these markers are shown in Table 4a and b).

Three markers come from the HLA region (D6S1615,

D6S2444 and TNFa). Each is in LD with the class II

susceptibility allele DRB1*1501. These are the same three

HLA markers identified in our original publication. Results

from individual typing of the two novel markers (D5S112

Fig. 1. Multipoint p-values along chromosome 6 calculated using a 2cM sliding wi

used in this analysis were those calculated in the combined analysis of data from

and D10S1769) are shown in Table 5. Neither of these novel

markers is confirmed as associated. However, there is

concordance between the pooled analysis and the results

from individual genotyping.

The HLA markers listed in Table 4a and b are securely

and robustly identified, making it unlikely that other markers

showing similar degrees of LD (DV = 52% for TNFa; Cor-

addu et al., 1998) have been missed. Given that other HLA

markers, such as D6S265 and D6S273 (DV = 39% for this

marker; Coraddu et al., 1998), show lower degrees of LD

with DRB1*1501, it is perhaps not surprising that they are

absent from the final list of promising markers. Performing a

combined analysis of both the case control and trio family

data, in order to compare with the results from our previously

published analysis, shows that this refined analysis has

improved the ranked position of these less strongly associ-

ated markers (see Table 6). This observation suggests that

novel markers showing similarly modest degrees of LD (if

included in the 6000 considered) are likely also to have

moved up the marker ranking—although, with this method-

ology and sample size, these cannot be disentangled from the

background un-associated markers.

3.6. Multipoint analysis

In the presence of extensive LD, a genuine susceptibility

locus might result in several adjacent markers showing

evidence for association. In order to search for such marker

clusters, we used the sliding window method suggested by

Setakis (2003). In this multipoint analysis, highly significant

results were seen in the HLA region on chromosome 6p21

(see Fig. 1). However, no other region of the genome

showed evidence for association. This failure is not unex-

ndow: p-values are plotted as � log( p). The raw p-values from each marker

both the case-control and trio family cohorts.

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–5958

pected. Average LD in the human genome is estimated to be

very much less than 100 kb (Gabriel et al., 2002) while the

markers employed in this screen have an average separation

of some 500 kb. These parameters make it very unlikely that

adjacent markers will all be in LD with a local susceptibility

locus. The HLA region is a notable exception, in that the

marker map employed in this region is denser than the

average while the LD is known to be more extensive.

4. Discussion

In order to refine the analysis of data generated in our

GAMES experiment, we have developed adapting factors

for 529 microsatellites that substantially improve the corre-

lation with results obtained from individual genotyping.

After individual genotyping, none of the novel (non-HLA)

markers identified in the UK screen have been confirmed as

associated with multiple sclerosis.

Microsatellites from the HLA region, which have high

levels of LD with DRB1*1501, are robustly identified

confirming that our experiment can detect markers in LD

with genuine susceptibility alleles. Conversely, our failure to

detect HLA markers with established but lesser degrees of

genuine LD (e.g. D6S265 and D6S273) indicates limitations

of the study. Three factors limit the study’s power.

First is the sample size. This is modest in the UK case-

control cohort, like most of the other GAMES studies

reported in this issue (c200 cases and c200 controls). Even

with full and accurate individual genotyping, such cohorts

have only modest power to detect effects as strong as TNFa,

and little or no power to detect weaker effects such as those

for markers like D6S265 and D6S273. This limitation is

well illustrated by results obtained after individually typing

the available UK case control samples for markers TNFa

and D6S1615. These are only just nominally significant,

and do not survive correction for multiple testing. It is

therefore expected that markers such as TNFa, D6S1615

and D6S2444 will be identified in many but not all GAMES

screens. On the other hand markers such as D6S265 and

D6S273 may be seen in a few but not the majority of

screens. Considering these positive control markers pro-

vides great insight into the expected behaviour of these

modestly powered screens.

The second factor limiting power is our use of pooling

methodology. This introduces additional sources of error

over and above sampling variance and thereby reduces the

effective sample size of the cohorts considered (Barratt et

al., 2002). Replicating pool construction, PCR and signal

detection improves the signal-to-noise ratio but reduces the

efficiency that pooling seeks to achieve. Barratt et al. (2002)

have shown that the need for replication increases with pool

size. The UK trio family cohort was analysed as two

particularly large pools (745 individuals in the index pool

and 1490 individuals in the parents pool). Compensating for

additional errors in the analysis of these pools would require

significantly more replication than we have performed. At

the level of replication employed in the study, the UK trio

cohort behaves with an effective sample size similar to that

seen for the UK case-control cohort (Sawcer et al., 2002).

The difference is that, in the case-control cohort, the

dominant source of variance is sampling variance while in

the trio family cohort the principal source of variance results

from the pooling process (the larger sample size having

essentially minimised the effects of sampling variance). This

difference in the contribution from the various sources of

variance explains why individual genotyping results (Tables

1 and 5) show a better correlation for the case-control than

for the trio family cohort. Promising but false positive

markers identified in the trio family analysis are more likely

to be the result of pooling induced errors than sampling

variance, which has a much smaller effect in a sample of this

size.

The third and final factor determining the power of our

study, and each of the other GAMES screens, is the number

of markers considered. The figure of 6000 proposed by

Barcellos et al. (1997) and utilised in GAMES, derived from

the most optimistic estimate of LD available at the time

(Jorde, 1995) and was commensurate with the number of

markers then available. It is now clear however that LD is

far less extensive. Current best estimates suggest that the

average length of so-called haplotype blocks is just 22 kb

(Gabriel et al., 2002). Assuming that our markers are

uniformly distributed (so that no one block includes more

than one marker), it is clear that we have markers in no more

than 4% of the haplotype blocks making up the human

genome. The extent to which a single microsatellite is able

to interrogate a block is unknown but it is clear that this is

limited by allele frequency mismatch and other confounding

effects (Muller-Myhsok and Abel, 1997). Assuming that

five tagging SNPs are able to extract most of the informa-

tion from a block (Johnson et al., 2001), it seems likely that,

on average, a single microsatellite will extract no more than

50% of the available information (the greater heterozygosity

of a microsatellite making it equivalent to approximately 2.5

SNPs (Kruglyak, 1997)). The greater mutation rate of

microsatellites increases the rate at which LD with suscep-

tibility variants is whittled down over time. But, in an

expanding and relatively young population such as Cauca-

sians Europeans, this is unlikely to have much effect

(Thompson and Neel, 1997). Allowing for these various

issues, it seems clear that no more than 1% of the genome

will have been tested in a typical GAMES screen (even

without allowing for incomplete marker typing). The num-

ber of markers employed is clearly the major limiting factor.

When comparing results obtained by individual genotyp-

ing with the pooled DNA analysis, it is important to

remember that not all samples included in the pools were

individually genotyped. Some samples used to construct

pools were no longer available when individual genotyping

was performed (27% for the controls pool) and genotyping

was also incomplete (success rate was 92% for D5S112 and

T.W. Yeo et al. / Journal of Neuroimmunology 143 (2003) 53–59 59

88% for D10S1769). As a result, there is only partial

overlap in samples contributing to the pooled and individual

typing results. Whilst the overlap is substantial, some

difference would be expected even if pooling was perfect

and true tests of significance rather than empirical p-values

obtained in the analysis. This comparison is further con-

founded when it is remembered that, although individual

genotyping is dogmatically considered as a gold standard, it

is not without error.

Under the null hypothesis of no associated markers, it

would be expected that approximately 60 markers would

have p-values of < 10% in both the case-control and trio

family cohorts (10% of 10% of 6000). However, in our final

analysis, there are only five such markers and three of these

are from the HLA region. This marked deficit in the

expected number of ‘‘positive in both’’ markers is a result

of our recursive concentration on this over-lapping group. In

each stage of our experiment, the refining methods have

only been applied to these overlapping markers—the most

interesting end of the distribution. Since our refining meth-

ods are conservative and, designed to reduce variance, they

have the tendency to reduce the number of markers falling

within the overlapped group. In effect, by concentrating on

just the extreme of the distribution, we have improved the

specificity of results for markers in this group but have had

very little effect on sensitivity of the experiment. Associated

markers, where the evidence for association was initially

under estimated, will not have been included amongst those

for which adapting factors and additional AIPs were gener-

ated. Thus, these have a low probability of being included in

the final analysis.

Although our UK GAMES screen has failed to find any

non-HLA markers showing evidence for association, it has

enabled us to substantially improve the quality of the analysis

and thereby enhanced the power of other GAMES screens.

Acknowledgements

We thank Aslaug Jonasdottir, Ragnheidur Fossdal and

Jeffrey Gulcher from deCODE for giving us access to their

genetic map, which was used in the multipoint analysis of

our data. We are grateful to the members of the Association

of British Neurologists for notifying patients participating in

this study.

This study represents one component of the GAMES

project designed and coordinated by DASC and SJS

(Cambridge, UK) with funding from the Wellcome Trust

(grant 057097) supplemented by the Multiple Sclerosis

Societies of the United States and Great Britain, and MS

International Federation.

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