Predicting phenotypic traits from genotypes is a key focus in agrigenomics, as researchers and commercial farming operations work to increase crop yields and meat production to satisfy the needs of a growing global population. Genomic prediction allows these scientists to identify the plants or animals with the best breeding potential for desirable traits without having to go through lengthy and expensive field trials.
The Golden Helix SNP and Variation Suite (SVS) offers three methods for genomic prediction: Bayes C, Bayes C-pi and Genomic Best Linear Unbiased Predictors (GBLUP). This webcast will discuss the principles of genomic prediction. It describes how these methods are applied within SVS predicting phenotypes for both plant and animal species. In addition, we show how k-fold cross-validation can be utilized optimizing predictive models.
About the Presenter
Darby Kammeraad is a Field Application Scientist at Golden Helix, joining the team in April of 2017. Darby graduated in 2016 with a masters degree in Plant Sciences from Montana State University, where he also received his bachelors degree in Plant Biotechnology. Darby works on customer support and training. When not in the office, Darby is learning how to play guitar, hunting, fishing, snowboarding, traveling or working on a new recipe in the kitchen.