GENOMIC SELECTION WORKSHOP: Hands on Practical Sessions Paulino Pérez 1 José Crossa 1 1 ColPos-México 2 CIMMyT-México September, 2014. SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 1/11
Contents General comments 1 Software 2 Data and models 3 SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 2/11
General comments General comments In this section we briefly review some of the statistical models commonly used in Genomic Selection. As you already know, there are a lot of models that have been developed to that end. Two papers from last years, contains an excellent review of the models and practical applications in GS, both in plants and animals. SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 3/11
General comments Continue... SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 4/11
Software Software The software to be used in the practical sessions is R, you can get it freely from http://www.r-project.org SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 5/11
Software Continue... We will concentrate on a few pieces of software (some of them created by us) that were developed to deal with the models that we will cover, BGLR: Bayesian Generalized Linear Regression package, r-forge.r-project.org/R/?group_id=1525 BLR: Bayesian Linear Regression package, cran.r-project.org/web/packages/BLR/index.html brnn: Bayesian Regularized Neural Networks, cran.r-project.org/web/packages/brnn/index.html rrBLUP: Ridge regression and other kernels for genomic selection cran.r-project.org/web/packages/rrBLUP/index.html SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 6/11
Software Continue... BLR and rrBLUP are explained in these documents, SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 7/11
Software Continue... For BGLR you can use the Vignette included in the package, The document is also available at http://genomics.cimmyt.org/SLU SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 8/11
Data and models Data and models We will assume that we have a set of Markers (Genotypic information) and a set of phenotypes and we are interested in predicting the phenotypes given those markers. We first show how to deal with Markers in the R environment and then we show how to fit several models. We will review the following models: Ridge Regression-BLUP Bayesian LASSO BayesA BayesB RKHS and GBLUP Modelling multi-environment data SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 9/11
Data and models Continue... If the time permits, we will review also Bayesian Regularized Neural networks, G3/Genetics, SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 10/11
Data and models Continue... SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 11/11
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