Bayesian estimation of marker dosage in sugarcane and other autopolyploids

Baker, Peter, Jackson, Phillip and Aitken, Karen (2010) Bayesian estimation of marker dosage in sugarcane and other autopolyploids. Theoretical and Applied Genetics, 120 8: 1653-1672. doi:10.1007/s00122-010-1283-z

Author Baker, Peter
Jackson, Phillip
Aitken, Karen
Title Bayesian estimation of marker dosage in sugarcane and other autopolyploids
Journal name Theoretical and Applied Genetics   Check publisher's open access policy
ISSN 0040-5752
Publication date 2010-05
Year available 2010
Sub-type Article (original research)
DOI 10.1007/s00122-010-1283-z
Volume 120
Issue 8
Start page 1653
End page 1672
Total pages 20
Place of publication Secaucus, NJ, U.S.A
Publisher Springer New York LLC
Collection year 2011
Language eng
Formatted abstract
In sugarcane or other autopolyploids, after generating the data, the first step in constructing molecular marker maps is to determine marker dosage. Improved methods for correctly allocating marker dosage will result in more accurate maps and increased efficiency of QTL linkage detection. When employing dominant markers like AFLPs, single-dose markers represent alleles present as one copy in one parent and null in the other parent, doubledose markers are those present as two copies in one parent and null in the other parent and so on. Observed segregation ratios in the offspring are employed to infer marker dosage in the parent from which the marker was inherited. Commonly, for each marker, a Xtest is used to assign dosage. Such an approach does not address important practical considerations such as multiple testing and departures from theoretical assumptions. In particular, extra-binomial variation or overdispersion has been observed in sugarcane studies and standard methods may result in fewer correct dosage allocations than the data warrant. To address these shortcomings, a Bayesian mixture model is proposed where all markers are considered simultaneously. Since analytic solutions are not available, Markov chain Monte Carlo methods are employed. Marker dosage allocation for each individual marker employs the estimated posterior probability of each dosage. For a sugarcane study these methods resulted in more markers being allocated a dosage than by standard approaches. Simulation studies demonstrated that, in general, not only are more markers classified but that more markers are also correctly classified, particularly if overdispersion is present. © 2010 Springer-Verlag.
Keyword Genetic linkage map
Quantitative trait loci
Polyploid model
Bayesian estimation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Public Health Publications
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Created: Sun, 09 May 2010, 00:00:36 EST