Course "Statistical methods for genome-enabled selection" by TUM-IAS Fellow Prof. Daniel Gianola & Dr. Gustavo de los Campos
Dr. Gustavo de los Campos, Prof. Daniel Gianola
Title: Statistical methods for genome-enabled selection
Date: September 8-12, 2012
Course held by:
Prof. Daniel Gianola, TUM-IAS Hans Fischer Senior Fellow; Department of Animal Sciences, University of Wisconsin-Madison &
Dr. Gustavo de los Campos, Department of Biostatistics, University of Alabama at Birmingham
Venue:
TUM Institute for Advanced Study (TUM IAS), TUM Campus Garching, Lichtenbergstr. 2a
85748 Garching, Germany
Directions
Hosts: TUM-IAS and Synbreed
Language: English
Participants: max. 23
Summary:
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.
Requirements:
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 (http://www.r-project.org/). Basic exposure to the R
environment is required.
