Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better

Daw, E Warwick

Heath, Simon C

Lu, Yue

BMC Genetics

English

PubMed

E Warwick Daw

Simon C Heath

Yue Lu

Springer - Publisher Connector

BioMed CentralBMC Genetics
ssOpen AcceProceedings
Single-nucleotide polymorphism versus microsatellite markers in a 
combined linkage and segregation analysis of a quantitative trait
E Warwick Daw*1,2, Simon C Heath3 and Yue Lu1
Address: 1Department of Epidemiology, unit 1340, University of Texas M.D. Anderson Cancer Center, 1155 Pressler Street, 4th Floor, Houston, 
Texas 77030 USA, 2Program in Human and Molecular Genetics, University of Texas Graduate School of Biomedical Sciences, Houston, Texas USA 
and 3Centre National de Génotypage, 2 rue Gaston Grémieux CP 5721, 91057 Evry Cedex, France
Email: E Warwick Daw* - warwick@request.mdacc.tmc.edu; Simon C Heath - heath@cng.fr; Yue Lu - elu@mdanderson.org
* Corresponding author    
Abstract
Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to
microsatellite markers. However, methods developed for microsatellites may be problematic when
applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo
Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These
methods were developed with microsatellite markers in mind. We selected chromosome 7 from
the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an
electrophysiological trait had been reported there. We found linkage in the same region of
chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker
data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this
MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as
microsatellite data and it is possible that the localizations with the SNP data are better.
Background
Monte Carlo Markov chain (MCMC) oligogenic com-
bined segregation and linkage analysis has been imple-
mented in the program Loki [1]. These methods use
linkage data on pedigrees and estimate the number, loca-
tion, and effects of loci that contribute to a quantitative
trait. These methods were designed for microsatellite
marker maps. Microsatellite markers differ from single-
nucleotide polymorphisms (SNPs) in two important
respects. First, individual microsatellites tend to be more
polymorphic, and thus more informative, than individual
SNPs. Consequently, it is easier to detect genotyping
errors in microsatellites and fewer microsatellite markers
provide can provide the same information. Second, SNPs
are far more common than microsatellites, which means
that a SNP map can be far denser and potentially more
informative than a microsatellite map. The density of a
SNP map can also be problematic for analysis methods.
Previously, we had found that in some situations, the
MCMC sampler in Loki could perform poorly with tightly
linked markers. A number of improvements have been
made in Loki, so we decided to analyze the Collaborative
Study on the Genetics of Alcoholism (COGA) data for
chromosome 7 made available for Genetic Analysis Work-
shop 14 (GAW14). Specifically, we compared the per-
formance of the two SNP marker sets available with the
performance of the microsatellite marker set in a com-
bined segregation and linkage analysis of the response at
the FP1 electrode to the target case of the visual oddball
experiment. For these analyses, we used a pre-release ver-
sion of Loki 2.4.8.
from Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism
Noordwijkerhout, The Netherlands, 7-10 September 2004
Published: 30 December 2005
BMC Genetics 2005, 6(Suppl 1):S32 doi:10.1186/1471-2156-6-S1-S32
<supplement> <title> <p>Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism</p> </title> <editor>Joan E Bailey-Wilson, Laura Almasy, Mariza de Andrade, Julia Bailey, Heike Bickeböller, Heather J Cordell, E Warwick Daw, Lynn Goldin, Ellen L Goode, Courtney Gray-McGuire, Wayne H ning, ail Jarvik, Brion S Maher, Nancy Mendell, Andrew D Paterson, John Rice, Glen Satten, Brian Suar z, Veronica Vieland, Marsha Wilcox, Heping Zhang, Andre s Ziegler and Jean W MacCluer</editor> <note>Proceedings</note> </suppleme t>Page 1 of 3
(page number not for citation purposes)
BMC Genetics 2005, 6:S32Methods
Trait and marker selection
We wanted to compare linkage results with SNP vs. mic-
rosatellite markers and so we selected the response at the
FP1 electrode to the target case of the visual oddball exper-
iment (ttth1) because the data description distributed
with the COGA data indicated that there was a "strong
linkage signal" for ttth1 on chromosome 7. In addition,
linkage to chromosome 7 has been reported for alcohol-
ism susceptibility [2]. We analyzed the ttth1 phenotype
data with each of the chromosome 7 marker sets sepa-
rately. These marker sets included 1) the microsatellite
markers, 2) all the Affymetrix SNPs, 3) all the Illumina
SNPs, 4) select Affymetrix SNPs, and 5) select Illumina
SNPs. In all cases, the "clean" SNP data was used. For the
select SNP sets, we used the first SNP at each meiotic map
position. Because there were more SNPs with duplicate
meiotic map positions in the Illumina set, the reduction
in number of markers for the "select" set was greater for
the Illumina set (147 out of 271 SNPs) than for the
Affymetrix set (515 out of 578 SNPs). There are two rea-
sons we were interested in using a subset of the SNPs.
First, because the computation time for the MCMC meth-
ods increases linearly with the number of markers, reduc-
ing the number of markers reduces the computation time.
We wished to examine whether a reduced set of the SNPs
could provide as good a localization of the linkage signal
as the full set. Second, there was the practical matter that,
as currently implemented, these methods cannot deal
with two markers at the same meiotic map position. For
the "all" SNP sets, we used the physical map information
provided to displace the markers very slightly so that no
two markers would have the same meiotic map position.
Age and sex were included in our analyses as covariates.
MCMC segregation and linkage analysis
To estimate the number, effects, and location of loci con-
tributing to ttth1, we applied the MCMC segregation and
linkage analysis methods described by Heath [1]. These
methods also estimate covariate effects, and the trait
model is given by , where µ is the
"reference" trait value, X is the incidence matrix for covari-
ate effects, β is the vector of covariate effects, Qi is the inci-
dence matrix for the effects of quantitative trait locus
(QTL) i, αi is the vector of effects for QTL i, e is the nor-
mally distributed residual effect, and k is the number of
QTLs currently estimated (k ≥ 0). The MCMC process sam-
ples µ, β, αi, i, and e as well as parameters such as unob-
served marker genotypes. All these parameters are
sampled from the space of model values consistent with
the data observed. Values are sampled proportional to
their posterior probability. After the number of sampling
iterations is sufficiently large, the sampled values provide
an estimate of the posterior probability distribution over
the space of possible parameter configurations.
We carried out analyses of ttth1 on chromosome 7 using
500,000 iterations, while saving every fifth iteration. A
total of 10 analysis runs were done: each of the five marker
sets was run twice, once with an LM ratio of 0 and once
with an LM ratio of 0.2. The LM ratio is a parameter in the
Loki program that sets the proportion of "meiosis"
updates vs. "locus" updates. L updates are required to
guarantee irreducibility of the sampler, while M steps can
improve mixing. Graphical analysis was used to assess
MCMC mixing.
Bayesian "L-score" and LOP
To evaluate evidence for linkage, we considered two
scores. First we considered "L-scores" estimated over 1-cM
wide bins along the chromosome. An L-score is simply the
posterior probability divided by the prior probability. In
the absence of any data, a Bayesian analysis should have
posterior probability equal to the prior probability. Thus,
an L-score of 1 indicates that the data contains no infor-
mation for or against linkage. An L-score <1 indicates evi-
dence against linkage, while an L-score >1 indicates
y X Q ei i
i
k
= + + +
=
∑µ β α
1
Table 1: Peak positions on chromosome 7 with different marker sets
Marker set LM ratio L-score peak position L-score LOP peak position LOP
microsatellite 0 156 cM 60.24 157 cM 4.11
microsatellite 0.2 155 cM 54.47 155 cM 3.99
Affymetrix – all 0 138 cM 50.07 137 cM 3.74
Affymetrix – all 0.2 138 cM 24.47 137 cM 3.71
Affymetrix – 1st 0 142 cM 33.02 141 cM 3.66
Affymetrix – 1st 0.2 138 cM 35.37 137 cM 3.57
Illumina – all 0 143 cM 33.56 137 cM 3.74
Illumina – all 0.2 139 cM 36.79 139 cM 3.48
Illumina – 1st 0 144 cM 67.29 143 cM 4.14
Illumina – 1st 0.2 140 cM 40.24 139 cM 3.88Page 2 of 3
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evidence for linkage. Second, we used the log of the pos-
terior placement probability ratio (LOP) described by
Daw et al. [3], which compares evidence for linkage on the
real chromosome with information on a simulated
pseudo-chromosome.
Traditional LOD score linkage
For purposes of comparison, we carried out traditional
two-point linkage analysis using the segregation parame-
ters obtained from Loki. These analyses were conducted
both on the raw ttth1 data and after a sex-specific regres-
sion by age was carried out.
Results
In all analysis runs, we found evidence for a trait locus
contributing to variation in ttth1 on chromosome 7
(Table 1). There was a difference in the location of the
peak L-score between the microsatellite marker set and the
two SNP marker sets, but there was overlap in the plausi-
ble intervals for linkage in all analysis runs. There was
strong agreement in the plausible interval between all four
of the SNP sets. Because the SNP runs represent two inde-
pendent marker sets, these results could indicate that the
localization was better with the denser SNP marker data
than with the sparser microsatellite data.
To examine mixing, we plotted several parameter values
vs. sampler iteration. Most parameter values produced
similar plots whether the LM ratio was 0 (all L updates) or
0.2 (20% M updates). In the plots of linked QTL position
vs. iteration, mixing appeared slightly better when the LM
ratio was >0, but it appeared acceptable in both cases.
In the two-point LOD score linkage analyses, no appreci-
able LOD score was obtained on chromosome 7 for the
raw ttth1 trait. The maximum across all three marker sets
was a LOD of 1.25 at marker tsc0309170, which was
mapped to ~28 cM. We also examined two-point LOD
scores for ttth1 after sex-specific regression by age. These
analyses of the regressed ttth1 resulted in modest
increases in the LOD scores for markers in the region iden-
tified by the Loki analyses. Also, the LOD scores for some
SNPs around 65 cM in both SNP sets were increased in the
analysis of the regressed ttth1.
Conclusion
It appears that the sampler implemented in Loki can han-
dle SNP data as well as microsatellite data. In all the Loki
runs, we found evidence for linkage of the ttth1 trait to
chromosome 7. It is possible that the localizations we
obtained with the SNP data are better because the peaks
found with the two SNP sets agree more closely with each
other than with the microsatellite set. Setting the LM ratio
> 0 improved mixing slightly. The localizations for all the
SNP sets were similar, suggesting that information about
linkage was not increased when going from a fairly dense
SNP screen (~ 1 SNP per cM) to a more dense SNP screen.
The computational burden was increased substantially
with the very dense maps: the analysis runs with all 578
Affymetrix SNPs took about 3 weeks on a 1.4 Ghz G4
Macintosh, while the runs with the 147 selected Illumina
SNPs ran about four times faster. The microsatellite runs
were faster still, but if one is to use SNPs in oligogenic
combined segregation and linkage analysis, it seems pru-
dent to select ~1 SNP per cM rather than using all availa-
ble SNPs in the analysis.
Abbreviations
COGA: Collaborative Study on the Genetics of Alcohol-
ism
LOP: Log of the posterior placement probability ratio
MCMC: Monte Carlo Markov chain
QTL: Quantitative trait locus
SNP: Single-nucleotide polymorphism
Authors' contributions
EWD designed the study, participated in the statistical
analysis, and drafted the manuscript. SCH modified his
Loki analysis program to provide LOP scores and pro-
vided input on the analyses. YL carried out the statistical
analyses.
References
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Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

EA: A score for Bayesian genome screening. Genet Epidemiol

Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

Reich T: Alcoholism susceptibility loci: confirmation studies in a replicate sample and further mapping. Alcohol Clin Exp Res

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Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

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