Abstract
BackgroundThe 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).ResultsBESSiE 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, BayesCpi 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/.ConclusionBESSiE 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.
Highlights
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
In quantitative genetics, various software packages are available for the analysis of phenotypic observations with linear mixed models, which can be categorised by the algorithm used to infer dispersion and location parameters of the modelled factors: (a) restricted maximum likelihood (REML) based software, and (b) Bayesian inference based software using Markov chain Monte Carlo (MCMC) methods (e.g. Gibbs sampling)
While various REML software packages designed for quantitative genetics are widely used and well documented, (e.g. ASREML [1], WOMBAT [2], DMU [3], REMLF90 [4], VCE [5]), software packages that apply the MCMC methodology are less common
Summary
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. Background In quantitative genetics, various software packages are available for the analysis of phenotypic observations with linear mixed models, which can be categorised by the algorithm used to infer dispersion and location parameters of the modelled factors: (a) restricted maximum likelihood (REML) based software, and (b) Bayesian inference based software using Markov chain Monte Carlo (MCMC) methods (e.g. Gibbs sampling). Bayesian analysis of linear mixed models in quantitative genetics allowing for various factors, algorithms, largescale genomic data and both continuous as well as categorical observations.
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