The goal of animal breeding and genetics is to optimize the genetic improvement of economically and socially important traits in livestock and companion animals. We investigate the genetic and epigenetic mechanisms underlying phenotypic traits using gene mapping and functional genomics tools, and we develop statistical and computational methods to integrate multiple sources of information, including pedigree, genomic and phenotypic data. In addition, we develop tools for recording novel phenotypes such as feed efficiency and animal behavior and the use of genomic information to optimize management decisions. Some specific topics of interest include genomic selection, i.e. the prediction of genetic merit using single nucleotide polymorphism (SNP) markers, the relationship between genomic predictions and future progeny performance, the prediction of future phenotypes using both genetic and non-genetic data, the control of inbreeding in modern breeding programs, the development of cost-effective genotyping strategies, the use of statistical models and machine learning algorithms to identify superior breeding stock, and the discovery and characterization of specific genes with large effects using genome-wide association studies.
A major focus of our group is on dairy cattle, but other species of interest include beef cattle, pigs, and poultry, as well as small ruminants and aquaculture species. Depending on the species and the production system, target phenotypic traits include fertility, calving ability, early postpartum health, carcass traits, disease susceptibility, and feed efficiency. Some of the current research projects in molecular and functional genomics include the genetics of twining and ovulation rate in cattle, the identification of non-invasive biomarkers for early embryonic development and fertility in cattle, the effects of paternal nutrition of the epigenome and the phenotypes of the offspring, and the paternal contribution to embryo development. On the quantitative and breeding side, projects include efficient methods for improving feed efficiency, farm-recorded data and sensor technology for the selection of novel traits, efficient data analytics, and machine learning tools for prediction of animal performance.
Students will take courses in molecular, quantitative and population genetics, statistical methods, experimental design, computer science, programming, and bioinformatics.