Using Genomic Prediction for Trait Optimization
Genomic estimated breeding values (GEBVs) can help a researcher determine which animals or plants to continue breeding due to high estimated breeding values for desired traits such as milk production, weight gain, and marbling or increased yield. In addition to obtaining GEBV's for known phenotypic traits, it is important to be able to use genetic information for predicting phenotypes or GEBV's and for training and validating models for phenotypic prediction. One method available to compute GEBVs and for genomic prediction is Genomic Best Linear Unbiased Predictors (GBLUP).
This webcast will discuss the benefits of genomic prediction for trait optimization, how to set up training and validation datasets, cover the highlights of the GBLUP method, and demonstrate genomic prediction and training/validation using GBLUP in Golden Helix's SNP and Variation Suite (SVS) software. We will use two datasets for the demonstration portion of the webcast, one plant dataset and one animal dataset. These methods can also be extended to human data analysis using the same techniques presented.
About the Presenter
Greta Linse Peterson is Golden Helix's Director of Services. Her main duty is managing the Field Application Scientist and Customer Support teams. Greta and her team are also responsible for software quality control, ensuring that the software releases are subject to the most rigorous testing protocols and for all the technical documentation and tutorials. Greta joined Golden Helix in 2008 when she completed her Masters degree in both Mathematics and Statistics at Montana State University in Bozeman. When Greta is not working, she enjoys spending time with her family and hiking the surrounding areas of Bozeman.