Genetic evaluation of popcorn families using a Bayesian approach via the independence chain algorithm
Marcos Rodovalho, Freddy Mora, Osvin Arriagada, Carlos Maldonado, Emmanuel Arnhold and Carlos Alberto Scapim
Abstract – The objective of this study was to examine genetic parameters of popping expansion and grain yield in a trial of 169 halfsib families using a Bayesian approach. The independence chain algorithm with informative priors for the components of residual and family variance (inverse-gamma prior distribution) was used. Popping expansion was found to be moderately heritable, with a posterior mode of h2 of 0.34, and 90% Bayesian confidence interval of 0.22 to 0.44. The heritability of grain yield (family level) was moderate (h2 = 0.4) with Bayesian confidence interval of 0.28 to 0.49. The target population contains sufficient genetic variability for subsequent breeding cycles, and the Bayesian approach is a useful alternative for scientific inference in the genetic evaluation of popcorn.