Genomic prediction with Bayesian methods

In genomic prediction a formula that predicts phenotype based on an individual’s genotype at genetic markers is derived. This prediction equation estimates the effect of each genetic marker on the phenotype. These effects are usually assumed to come from a prior distribution making this a Bayesian method.

Bio

Professor Mike Goddard has a joint appointment between Agriculture Victoria and University of Melbourne as Professorial Fellow in Animal Genetics. His research is in the genetics of quantitative or complex traits in livestock and in humans. In 2001, he published, with others, the first paper describing genomic selection which uses DNA polymorphisms to estimate the genetic merit of individuals. This method is now widely used in livestock and crops.

Bio

Ruidong Xiang obtained his PhD in animal genetics at the University of Adelaide, South Australia. His research focuses on understanding the mechanisms via which genetics shape phenotype in agricultural species. Currently, he works as a Research Fellow at the University of Melbourne, on how to use multi-trait and multi-omics data to improve genomic selection of cattle complex traits. He works closely with scientists from Agriculture Victoria to integrate biological and functional information into breeding programs to more accurately select productive and healthy animals.

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