Lance F. Merrick

This study compares classification and regression models for predicting skewed phenotypes in winter wheat disease resistance, finding that transformed regression and classification models perform best.

This study compares single-trait and multi-trait GWAS models to dissect the genetic architecture of seedling emergence in wheat, finding that multi-trait models identify more large-effect markers.

This study compares 17 genomic selection models for predicting a complex trait in wheat breeding, finding that parametric models like cBLUP provide consistent moderate accuracy, making them suitable for practical implementation.

This paper evaluates genomic selection models for predicting quantitative disease resistance in wheat, finding higher accuracy than marker-assisted selection when combining major and minor gene markers.