Genome-enabled Prediction of Complex Traits with Kernel Methods: What Have We Learned?

Tuesday, August 19, 2014: 4:30 PM
Bayshore Grand Ballroom E-F (The Westin Bayshore)
Daniel Gianola , University of Wisconsin - Madison, Madison, WI
Gota Morota , University of Wisconsin - Madison, Madison, WI
Jose Crossa , CIMMYT, El Batan, Mexico
Abstract Text:

Complex traits are presumably affected by several genomic regions acting in some concerted manner (epistasis), by non-linearity between genome and phenotypes stemming from enzyme kinetics, and by interactions with environmental forces. Prompted by these considerations, non-parametric approaches entered into quantitative genetics early in the 21st century, and a decade of experience has been accumulated, mostly in animals and plants. Some developments are reviewed in this paper, and areas for additional investigation are discussed.


Complex Traits

Genome-enabled Prediction

Non-parametric Regression