Please use this identifier to cite or link to this item: https://une.intersearch.com.au/unejspui/handle/1959.11/21204
Title: BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data
Contributor(s): Boerner, Vinzent  (author); Tier, Bruce  (author)
Publication Date: 2016
Open Access: Yes
DOI: 10.1186/s12711-016-0241-xOpen Access Link
Handle Link: https://hdl.handle.net/1959.11/21204
Open Access Link: http://dx.doi.org/10.1186/s12711-016-0241-xOpen Access Link
Abstract: Background: The advent of genomic marker data has triggered the development of various Bayesian algorithms for estimation of marker effects, but software packages implementing these algorithms are not readily available, or are limited to a single algorithm, uni-variate analysis or a limited number of factors. Moreover, script based environments like R may not be able to handle large-scale genomic data or exploit model properties which save computing time or memory (RAM). Results: BESSiE is a software designed for best linear unbiased prediction (BLUP) and Bayesian Markov chain Monte Carlo analysis of linear mixed models allowing for continuous and/or categorical multivariate, repeated and missing observations, various random and fixed factors and large-scale genomic marker data. BESSiE covers the algorithms genomic BLUP, single nucleotide polymorphism (SNP)-BLUP, BayesA, BayesB, BayesCπ and BayesR for estimating marker effects and/or summarised genomic values. BESSiE is parameter file driven, command line operated and available for Linux environments. BESSiE executable, manual and a collection of examples can be downloaded http:// turing.une.edu.au/~agbu-admin/BESSiE/. Conclusion: BESSiE allows the user to compare several different Bayesian and BLUP algorithms for estimating marker effects from large data sets in complex models with the same software by small alterations in the parameter file. The program has no hard-coded limitations for number of factors, observations or genetic markers.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, v.48, p. 1-5
Publisher: BioMed Central Ltd
Place of Publication: United Kingdom
ISSN: 1297-9686
Field of Research (FOR): 060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
060408 Genomics
060411 Population, Ecological and Evolutionary Genetics
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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