GENOMIC SELECTION WORKSHOP: Hands on Practical Sessions Paulino Prez - - PowerPoint PPT Presentation

GENOMIC SELECTION WORKSHOP: Hands on Practical Sessions

Paulino Pérez 1 José Crossa 1

1ColPos-México 2CIMMyT-México

September, 2014.

SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 1/11

Contents

1

General comments

2

Software

3

Data and models

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

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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

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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

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BLR and rrBLUP are explained in these documents,

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Software

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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

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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

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SLU, Sweden GENOMIC SELECTION WORKSHOP:Hands on Practical Sessions 11/11