Animal Breeding, Statistical Genetics & Molecular Genetics
- Coussens, Paul (molecular pathogenesis, immunobiology, functional genomics)
- Ernst, Cathy (swine and bovine molecular genetics, growth biology, meat science)
- Juan Steibel
- Templeman, Robert (biometry, livestock genetic evaluation, functional genomics)
Paul M. Coussens, Professor
Principal Investigator, Molecular Pathogenesis Laboratory
Depts. Animal Science and Microbiology and Molecular Genetics
The Molecular Pathogenesis Laboratory (MPL) focuses on the nature, cause, and host response to infectious diseases in livestock species. As many of the pathogens of importance to livestock species are zoonotics, our work often has biomedical implications, as well as relevance to animal health. Pathogens currently under study with a network of national and international collaborators include Mycobacterium paratuberculosis (Johne’s disease), M. bovis (bovine tuberculosis), bovine viral diarrhea virus, Brucella abortus, and several parasitic diseases in cattle. Work within the MPL is aided by an outstanding base of state-of-the-art equipment and tools for functional genomics. Although highly molecular in our approach to studying pathogenesis, all students in the MPL work with the appropriate host species, gaining experience in sampling and recognizing the effects of infectious disease. Students within the MPL also have ample opportunities for travel to multiple international laboratories of our collaborators during their course of study.
Catherine W. Ernst, Professor
Principal Investigator, Molecular Genetics Laboratory
Nancy Raney, Research Technician and Laboratory Coordinator
The overall goal of the Molecular Genetics Laboratory is to identify and evaluate molecular markers and genes for the genetic improvement of pigs and beef cattle with emphasis on performance traits, carcass composition and meat quality. Current projects include a genetical genomics project in pigs that involves integration of genetic marker and gene expression data for identifying genes controlling skeletal muscle and fat deposition and their relationship to growth, carcass merit and meat quality traits. Additional projects include identification of differentially expressed genes in developing pig skeletal muscle, transcriptional profiling of pigs infected with PRRS virus, and evaluation of behavior and temperament traits and their association with production traits in beef cattle and pigs. These projects are in collaboration with Drs. Juan Steibel, Ron Bates and Joan Lunney (USDA-ARS BARC, leader of the PRRS Host Genetics Consortium), as well as other colleagues at MSU, throughout the US, and in Brazil, Thailand, Korea and China..
Juan Steibel, Assistant Professor
Robert J. Tempelman, Professor
Quantitative Genetics Laboratory
Our 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 has involved 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 of hierarchical 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. We also collaborate extensively with many other researchers on campus, particularly in the application of mixed effects and/or Bayesian model analyses to experimental and observational data.