Armidale Animal Breeding Summer Course 

Venue:    University of New England, Armidale, NSW Australia
Dates:     in early February each year               
Course Audience:
Postgraduate students and other professionals





Armidale Animal Breeding Summer Course 2012


Professor Daniel Gianola, University Of Wisconsin

Professor Gustavo de los Campos, University of Alabama at Birmingham


Statistical Methods for Genome-Enabled Selection                        Materials


                                    Photo of course participants


Venue:            University of New England, Armidale, NSW Australia
Monday 6 February - Friday 10 February 2012



In this course we focus on the problem of predicting complex traits using highly dimensional pedigrees, molecular markers (e.g., SNPs, sequences) and phenotypic records. After an overview of different paradigms that have dominated the field of quantitative genetics in the last century, we will discuss the opportunities and challenges posed by highly dimensional genomic data from a predictive perspective, and will introduce alternative statistical learning techniques to confront these challenges. The toolkit will include parametric (e.g., linear Bayesian regression models) and some semi-parametric procedures (e.g., Reproducing Kernel Hilbert Spaces and Neural Networks). Methods will be introduced and discussed in the morning lectures and practical applications using real data and publicly available software will be offered in the afternoon labs.



This course is designed for advanced PhD students and postdoctoral fellows with background in regression methods, statistical distributions, Bayesian Inference and quantitative genetics, although some review will be provided. Labs will be based on R ( Basic exposure to the R environment is required.



Linear models and Ordinary Least Squares (OLS: review and algorithms).

Performance of OLS estimates in ‘large p with small n’ regressions.

Some strategies to confront the limitations of OLS estimates:

Subset selection

Shrinkage estimation procedures

Ridge Regression

Penalized Regression

Computing Ridge Regression Estimates using various strategies in R

Effect of Regularization on Goodness of Fit and MSE

The Hat Matrix and the Linear Model as a “Local Smoother”

Bayesian view of Ridge Regression

Validation Methods

Training-Testing (TRN-TST)

Replicated TRN-TST


Choosing optimal shrinkage

-          Heritability-based rules

-          Bayesian Approach

-          Cross-validation methods

Other Bayesian Shrinkage Estimation Methods

The Bayesian Alphabet (BayesA, BayesB, Bayesian LASSO, Spike-Slab methods)

Methods Comparison:

Simulated Example

Real Data Example

Reproducing Kernel Hilbert Spaces (RKHS) methods

Reproducing Kernel Hilbert Spaces Methods

Choice of Kernel

Automatic Kernel Selection


Inquiries                      Julius van der Werf 

                                    Animal Genetics, UNE

                                    phone: 02 6773 2092

                                    fax       02 6773 3922

                                    jvanderw  (at) 


Armidale Animal Breeding Summer Course Home


Material of previous years:


Armidale Animal Breeding Summer Course 2011    Materials

Statistical methods and design in plant breeding and genomics: Ian Mackay

IBD inference in genome association studies: Elizabeth Thompson


Armidale Animal Breeding Summer Course 2010    Materials

Application of evolutionary algorithms to solve complex problems in quantitative genetics

and bioinformatics:   Brian Kinghorn, Cedric Gondro


Bayesian methods in genome association studies:  Dorian Garrick,  Rohan Fernando


Armidale Animal Breeding Summer Course 2009    Materials

Quantitative Genetic Theory and Analysis- Selection Theory:    Bruce Walsh

Quantitative Genetic Models for social interaction and inherited variability: Piter Bijma


Armidale Animal Breeding Summer Course 2008      Materials 

Genomic Selection:       Ben Hayes


Armidale Animal Breeding Summer Course 2007      Materials

Generalized Linear Mixed Models:        Steve Kachman


Armidale Animal Breeding Summer Course 2006    Materials

Gene Expression:          Toni Reverter

Breeding Program Design:         Graser, James, Van der Werf

Armidale Animal Breeding Summer Course 2005    Materials
Breeding Objectives: Gibson, Van der Werf, Kinghorn
Scientific Writing:          David Lindsay
ASReml:           Arthur Gilmour
Armidale Animal Breeding Summer Course 2004    Materials
Bayesian for Beginner               Kerrie Mengersen
Bayesian Models for QTL analysis        Michel Perez-Enciso
Bioinformatics   John McEwan

Armidale Animal Breeding Summer Course 2003    Materials

Scientific Writing:  David Lindsay
Linear Models for animal breeding:   Julius van der Werf, Mike Goddard
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