8-12 February 2021
Due to the COVID-19 outbreak, this course will be held online
This course will introduce students, researchers and professionals to the steps needed to acquire expertise in the genomic prediction area applied to animals, plants and humans. The course will describe all the necessary steps involved.
The course is structured in modules over five days. Each day will include introductory lectures with class discussions of key concepts. The remainder of each day will consist of practical hands-on sessions. These sessions will involve a combination of both mirroring exercises with the instructor to demonstrate a skill as well as applying these skills on your own to complete individual exercises. After and during each exercise, results will be interpreted and discussed in group.Time schedule will be adjusted according to the timezone of the participants. Discussions among participants and with the instructors on concepts and data analysis will be possible through video conferencing and a dedicated Slack channel.
The course is aimed at students, researchers and professionals interested in learning the different steps and approaches to perform a genomic prediction study. It will include information useful for both beginners and more advanced users. We will start by introducing general concepts of Quantitative Genetics and mixed model theory, progressively describing all steps and putting there seamlessly together in a general workflow. Attendees should have a background in biology, specifically genetics; previous exposure to statistical genetics would also be beneficial. There will be a mix of lectures and hands-on practical exercises using R, Linux command line and custom software. Some basic understanding of R programming and Unix will be advantageous. Attendees should also have some basic familiarity with genomic data such as those arising from NGS experiments.
Introduction to Genome-wide Prediction in Human genetics and Animal and Plant breeding.
Review of Quantitative genetics. Linear mixed models. Resemblance among relatives:
Pedigree vs Genomic-based. Factors affecting Genomic Prediction.
The‘Curse’ of Dimensionality in large p small n problems. Shrinkage estimation. Genotype imputation procedures. GBLUP and Kernel-based regression models.
Bayesian alphabet (Methods on SNP regression). Review on post-Gibbs convergence and McMC chains inspection analysis.
Bayesian alphabet. Machine learning approaches.
Predictive ability metrics: MSE, Pearson and Spearman correlations, AUC-ROC curves, cross validation strategies
> 30 days before the start date = 30% cancellation fee
< 30 days before the start date= No Refund.
Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.