Biostatistics Seminar Series
Tuesday, November 10th, 4:10pm, 1043 Gladys Valley Hall
Speaker: Daniel E Runcie, (Dept. Plant Sciences, UC Davis)
Title: Bayesian methods for studying the genetics of high dimensional traits
Abstract: Quantitative genetics extends the study of inheritance to continuous traits that are controlled by both genetic and environmental factors, and is the foundation for much of evolutionary theory, complex trait genetics, and artificial selection in plant and animal breeding programs. The field of quantitative genetics has been revolutionized by modern genotyping technologies, and recent developments have focused on developing efficient models for associating traits with thousands or millions of genetic markers. However, similar progress has not followed the parallel developments of modern phenotyping technologies that can rapidly measure high dimensional traits such as from image data, or gene expression. I will show a Bayesian method for studying the genetics of high dimensional traits that exploits the observation that biological systems tend to be modular – that suites of traits may be highly correlated among individuals, but un-correlated with other traits. Through informative, biologically motivated priors, this method concentrates the inference on identifying the strongest signals in the data and provides results that robust and are interpretable. I will describe this modeling approach and show an example of it applied to gene expression data from an artificial population of Brassica rapa.