BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Boerner, Vinzent; Tier, Bruce
• Is Part Of: Genetics, selection, evolution. Volume 48:Issue 1(2016)
• Page Start: 1
• Page End: 5
• Publication Date: 2016-12
• Subjects: Livestock -- Breeding -- Periodicals; Animal genetics -- Periodicals; Livestock -- Genetics -- Periodicals; Evolution -- Periodicals; 576.505
• DOI: 10.1186/s12711-016-0241-x
• Languages: English
• ISSNs: 1297-9686

Abstract

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, BayesC $$\pi$$ π 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 downloadedhttp://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.