Prof. Daniel Gianola University of Wisconsin-Madison, USA

Daniel Gianola is a geneticist based at the University of Wisconsin-Madison (US), reputed for his contributions in quantitative genetics to the fields of animal and plant breeding. In the early 1980s, Gianola extended best linear unbiased prediction to the non-linear domain for analysis of categorical traits (fertility, survival, resistance to... read morediseases), using the classical threshold model of Sewall Wright. Subsequently, he pioneered the use of Bayesian methodologies and Monte Carlo Markov chain methods in quantitative genetics. He also revived early work by Sewall Wright on structural equation models and cast their application in the context of modern quantitative genetics and statistical methodology. His group in Wisconsin was the first in the world applying non-parametric methods, such as reproducing Kernel Hilbert spaces regression and Bayesian neural networks, to genome-enabled selection in animal breeding, agriculture and whole-genome (i.e., using a massive number of DNA markers) prediction of complex traits or diseases. Gianola published extensively on thresholds models, Bayesian theory, prediction of complex traits using mixed model methodology, hierarchical Bayesian regression procedures and machine learning techniques. Gianola has been also involved in whole-genome prediction of skin and bladder cancer in humans. He has taught extensively in more than twenty countries including recurrent visiting professorships at the Universidad Politecnica de Valencia (Spain), the Norwegian University of Life Sciences (Norway), Aarhus University (Denmark), Georg-August University (Germany) and the Technical University of Munich (Germany).