Improving estimates of genetic maps: a maximum likelihood approach.

Improving estimates of genetic maps: a maximum likelihood approach. Research Abstract Details 

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Improving estimates of genetic maps: a maximum likelihood approach. Abstract Text:

William C L Stewart,Elizabeth A Thompson,

As a result of previous large, multipoint linkage studies there is a substantial amount of existing marker data. Due to the increased sample size, genetic maps estimated from these data could be more accurate than publicly available maps. However, current methods for map estimation are restricted to data sets containing pedigrees with a small number of individuals, or cannot make full use of marker data that are observed at several loci on members of large, extended pedigrees. In this article, a maximum likelihood (ML) method for map estimation that can make full use of the marker data in a large, multipoint linkage study is described. The method is applied to replicate sets of simulated marker data involving seven linked loci, and pedigree structures based on the real multipoint linkage study of Abkevich et al. (2003, American Journal of Human Genetics 73, 1271-1281). The variance of the ML estimate is accurately estimated, and tests of both simple and composite null hypotheses are performed. An efficient procedure for combining map estimates over data sets is also suggested.

Improving estimates of genetic maps: a maximum likelihood approach. Publishing Authors By Initials

WC Stewart,EA Thompson,

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Improving estimates of genetic maps: a maximum likelihood approach. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Biometrics

VOLUME: 62

Page Numbers: 728-34

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ISSN: 0006-341X

DAY: 3

MONTH: Sep

YEAR: 2006

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LANGUAGE: eng

NlmUniqueID: 370625

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Grant and Affiliation Information for Improving estimates of genetic maps: a maximum likelihood approach.

AFFILIATION: Department of Statistics, University of Washington, Seattle, Washington 98195, USA. wstew@umich.edu

Country: United States

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MEDLINETA: Biometrics

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