El análisis bayesiano y la precisión de los valores de la heredabilidad en especies perennes

Abstract

RESUMENLos objetivos del presente trabajo fueron evaluar la precisión del valor estimado de la heredabilidad determinada por medio de la desviación estándar, considerando un enfoque Bayesiano, y comparar tal estimativa con el procedimiento clásico. Se utilizaron datos de un ensayo de progenie con 39 familias de Eucalyptus cladocalyx. La variable dependiente usada fue el diámetro basal del fuste medido a los seis años de edad. El método Bayesiano fue implementado por medio del algoritmo de Cadenas Independientes, con informaciones a priori informativas, el cual entregó bajos valores de desviaciones estándar de la heredabilidad, si comparado con la estimación clásica de Robertson y distribución a priori de Jeffreys (la cual es una clase de distribución a priori no informativa y a menudo impropia). El método de análisis Bayesiano es una herramienta de inferencia valiosa para la evaluación genética de especies perennes, ya que considera la variabilidad de los parámetros por medio de las distribuciones a posteriori. Palabras-clave: inferencia Bayesiana; algoritmo de cadenas independientes; Eucalyptus; heredabilidad. ABSTRACTThe objectives of the present work were to evaluate the precision of the estimate of heritability, which was determined by standard error, considering a Bayesian approach, and to compare such estimate with the classic procedure. Data from progeny trial of 39 Eucalyptus cladocalyx families were used. Trunk basal diameter (measured at age 6) was the dependent variable analyzed. The Bayesian approach was implemented by using the Independence Chain algorithm with informative priors, which had a lower standard deviation of the heritability than both the classic method of Robertson and Jeffreys' prior distribution (Jeffreys' prior is in fact a class of uninformative and often improper prior distributions). The Bayesian approach is a valuable inference tool for the genetic evaluation of perennial species, because it considers the variability of the parameters by using posterior distributions.

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