Many traits of economic and biomedical importance, such as seed size, milk yield, cancer growth, HIV/AID progression and drug response, change their phenotypes with time or other variables in a certain pattern. Traditional statistical models for genetic studies of these traits by analyzing phenotypic data measured at a single point are too simple to take into account their developmental or dynamic trajectories. In this project, Wu and Casella will develop a collection of statistical models for detecting genetic loci that govern dynamic complex traits, in which mathematical aspects of trait formation and development are integrated into a statistical framework for genetic mapping.
Wu and Casella's models provide a useful platform to ask, disseminate and address biologically meaningful questions regarding the interplay between gene action and development across a range of physiological or ecological contexts. The depth and scope of scientific inference about the genetic architecture of complex traits can be greatly improved by using their models. With the availability of a complete reference sequence of the entire genome for humans and other species, Wu and Casella's models will be useful to extract genetic information and unravel genetic secrets hidden in increasingly accumulating data that are often high-dimensional and display complex dynamic structure.
