Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces
Crop Science Volume 51 Issue 5 (September/October 2011)
Dag Terje Filip Endresen, Kenneth Street, Michael Mackay, Abdallah Bari, and Eddy De Pauw (2011). Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces. Crop Science 51 (5): 2036-2055. doi: 10.2135/cropsci2010.12.0717
This FIGS study validates the FIGS approach designed to identify genebank accessions with a higher likelihood for having a useful trait of economic value for plant breeding or crop research. With this study we demonstrate how the FIGS approach can be used to more than double the likelihood of finding a target trait property compared to a random sampling of accessions. The Soft Independent Modeling of Class Analogy (SIMCA) and the k-Nearest Neighbor (kNN) data analysis methods proved superior to Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA). For this study we used a stem rust dataset for wheat landraces and a net blotch dataset with barley landraces, both kindly provided by the USDA GRIN.