Bayesian and maximum likelihood estimation of genetic maps

Thomas L. York; Richard T. Durrett; Steven Tanksley; Rasmus Nielsen

doi:10.1017/S0016672305007494

Bayesian and maximum likelihood estimation of genetic maps

Thomas L. York, Richard T. Durrett, Steven Tanksley, Rasmus Nielsen

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

4 Citationer (Scopus)

Abstract

Udgivelsesdato: April 2005

Originalsprog	Engelsk
Tidsskrift	Genetics Research
Vol/bind	85
Udgave nummer	2
Sider (fra-til)	159-168
ISSN	0016-6723
DOI	https://doi.org/10.1017/S0016672305007494
Status	Udgivet - 2005

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@article{23a9fff074c311dbbee902004c4f4f50,

title = "Bayesian and maximum likelihood estimation of genetic maps",

abstract = "There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.",

author = "York, {Thomas L.} and Durrett, {Richard T.} and Steven Tanksley and Rasmus Nielsen",

year = "2005",

doi = "10.1017/S0016672305007494",

language = "English",

volume = "85",

pages = "159--168",

journal = "Genetics Research",

issn = "0016-6723",

publisher = "Cambridge University Press",

number = "2",

}

TY - JOUR

T1 - Bayesian and maximum likelihood estimation of genetic maps

AU - York, Thomas L.

AU - Durrett, Richard T.

AU - Tanksley, Steven

AU - Nielsen, Rasmus

PY - 2005

Y1 - 2005

N2 - There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.

AB - There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.

U2 - 10.1017/S0016672305007494

DO - 10.1017/S0016672305007494

M3 - Journal article

C2 - 16174334

SN - 0016-6723

VL - 85

SP - 159

EP - 168

JO - Genetics Research

JF - Genetics Research

IS - 2

ER -