A cluster-based technique to group random effects from empirical BLUPs: An application to genotype selection

DI RIENZO J; GONZALEZ L; TABLADA M; BRUNO C; BALZARINI M

JOURNAL OF CROP IMPROVEMENT

The Haworth Press

Lugar: Binghamton, NY; Año: 2005 vol. 15 p. 133 - 133

1542-7528

p class="MsoNormal" style="MARGIN: 0pt 47.2pt 0pt 0pt; TEXT-ALIGN: justify; tab-stops: 315.0pt">Prediction of random effects arises in many applications, including plant breeding. Many times, plant breeders strive to select genotypes according to predictions of unobserved genetic merits derived from phenotypic data. There are different approaches to predict random effects, but a popular one is to use empirical BLUPs of genetic effects to rank genotypes. This leads to the problem of how best to use empirical BLUP values to select a set of truly superior genotypes with high probability. In this paper, we propose an algorithm based on hierarchical cluster analysis to group genotypes according to their empirical BLUPs. Hierarchical cluster algorithms produce, in general, no analytical expression to solve the problem of detecting the number of underlying groups. Via simulation, we obtain decision curves to group empirical BLUPs from the resu