• USDA, 2009-2011, Whole Genome Selection Realized: Machine Learning and Non-Parametric Regression Tools for Maximizing its Impace on US Dairy Farms
• Hatch Act Formula Fund, 2008-2012, Statistical Methods for Genomic Selection in Livestock
• University of Wisconsin Foundation, 2004-2099, AB Chapman Lecture Fund

• "Radial basis function regression methods for predicting quantitative traits using SNP markers"

Long NY, Gianola D, Rosa GJM, et al., Genetics Research 92 (3): 209-225 Jun 2010.  The simulation included a toy example in which an arbitrary non-linear genotype phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0.1, 0.25, 0.5, 0.75 and 0.9) were created.

• "L-2-Boosting algorithm applied to high-dimensional problems in genomic selection"

Gonzalez-Recio O, Weigel KA, Gianola D, et al., Genetics Research 92 (3): 227-237 Jun 2010.  L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time.

• "Radial basis function regression methods for predicting quantitative traits using SNP markers"

Long NY, Gianola D, Rosa GJM, et al., Genetics Research 92 (3): 209-225 Jun 2010.  Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers.

• "A two-step method for detecting selection signatures using genetic markers"

Gianola D, Simianer H, Qanbari S, Genetics Research 92(2): 141-155 Apr 2010.  A two-step procedure is presented for analysis of theta statistics obtained for a battery of loci, which eventually leads to a clustered structure of values.

• Intra-University Collaboration: Computer Science, Dairy Science