PhD from Colorado State University
Graduate fields: Animal Science, Animal Breeding
Area(s) of interest: statistical and quantitative genetics, animal breeding
Teaching:
- American Society of Animal Science
Website(s): NBCEC.ORG
Current Research Interests:
Dr. Quaas sitting astride 'Red Littlebean' on a recent trip to Inner Mongolia.
My scientific interests are statistics and the genetic improvement by selection of domestic populations, particularly cattle populations, and more specifically beef cattle. These intersect because beef cattle selection programs are based on phenotypic data collected on literally thousands of farms and ranches from all over North America. It is a complicated statistical and computational analysis of these data that results in a single number representing the genetic merit for a particular trait of each animal in a population that may number in the millions.
Molecular techniques – DNA – have the promise of making selection much more efficient, but this promise was never realized because it was never available to those actually making selection decisions. With the advent of commercial DNA tests for favorable alleles/markers for quantitative traits in beef cattle, this promise is closer to practice. But with any new and expensive technology there is skepticism of its efficacy among potential adopters. I chair a committee of the National Beef Cattle Evaluation Consortium (NBCEC) charged with “validating” commercial markers. The purpose of the NBCEC commercial DNA test validation is to independently verify associations between genetic tests and traits as claimed by the commercial genotyping company using phenotypes and DNA from reference cattle populations. The validation process is a partnership of the owners of DNA and phenotypes (e.g., breed associations) and genomics companies, facilitated by the NBCEC. The most difficult aspect is finding reference populations with appropriate phenotypes. This has necessitated collaborations with scientists (and producers) from across the US as well as in Canada and Australia. Initially we would examine at 1 to 3 markers for a trait. Currently we are working with commercial panels with dozens of markers. These analyses are simple, but in the labs scientists now have tens of thousands of markers to study. Analyzing such data is again a complicated statistical and computational challenge.