Dr. Robert J. Tempelman

Bayesian Inference in Genetics and Genomics
Quantitative Genetics
Mixed Model Inference

Dr. Tempelman’s research program is centered around the development and application of hierarchical statistical models to inferential problems in animal breeding and genetics. Hierarchical Bayesian models are particularly useful for modeling multiple layers of variability and heterogeneity as characteristic of field data and genomics data. Our two broad areas of application are currently in (1) livestock genetic evaluation and (2) functional genomics. One recent specific application from our group hasinvolved the genetic evaluation of calving ease, where models allowing for outlier-robustness and heterogeneous variability across environments (for example, herds) were found to fit field data much better than conventional genetic evaluation models. Other recent applications include the development of genetic evaluation models when uncertain paternity is common (as in extensive cattle production systems), and multi-breed genetic evaluations. Our current efforts are directed towards the construction ofhierarchical models for the analysis of cDNA microarray data in the anticipation of better control over false negative and false positive rates on conclusions regarding differential gene expression. Dr. Tempelman also collaborates extensively with many other researchers on campus, particularly in the application of mixed effects and/or Bayesian model analyses to experimental and observational data.