About this webinar
Recorded On: Wednesday, December 13, 2017
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.
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