Prediction of hybrid means from a partial circulant diallel table using the ordinary least square and the mixed model methods

Genetics and Molecular Biology

Américo José Dos Santos Reis¹  Lázaro José Chaves¹  João Batista Duarte¹  Edward Madureira Brasil¹ 

[1] ,Universidade Federal de Goiás Escola de Agronomia e Engenharia de Alimentos Goiânia GO ,Brazil

关键词: diallel analysis;    BLUP;    prediction;    cross-validation;   

DOI  :  10.1590/S1415-47572005000200023

来源: SciELO

【 摘 要 】

By definition, the genetic effects obtained from a circulant diallel table are random. However, because of the methods of analysis, those effects have been considered as fixed. Two different statistical approaches were applied. One assumed the model to be fixed and obtained solutions through the ordinary least square (OLS) method. The other assumed a mixed model and estimated the fixed effects (BLUE) by generalized least squares (GLS) and the best linear unbiased predictor (BLUP) of the random effects. The goal of this study was to evaluate the consequences when considering these effects as fixed or random, using the coefficient of correlation between the responses of observed and non-observed hybrids. Crossings were made between S1 inbred lines from two maize populations developed at Universidade Federal de Goiás, the UFG-Samambaia "Dent" and UFG-Samambaia "Flint". A circulant inter-group design was applied, and there were five (s = 5) crossings for each parent. The predictions were made using a reduced model. Diallels with different sizes of s (from 2 to 5) were simulated, and the coefficients of correlation were obtained using two different approaches for each size of s. In the first approach, the observed hybrids were included in both the estimation of the genetic parameters and the coefficient of correlation, while in the second a cross-validation process was employed. In this process, the set of hybrids was divided in two groups: one group, comprising 75% of the original group, to estimate the genetic parameters, and a second one, consisting of the remaining 25%, to validate the predictions. In all cases, a bootstrap process with 200 resamplings was used to generate the empirical distribution of the correlation coefficient. This coefficient showed a decrease as the value of s decreased. The cross-validation method allowed to estimate the bias magnitude in evaluating the correlation coefficient using the same hybrids, to predict the genetic parameters and the correlation evaluation. The bias was shown to be greater when the OLS method was used. When the correlation coefficients of the observed and estimated hybrid means were obtained through the mixed instead of the fixed model, this decrease was less marked. The selection of hybrids superior to the checks, in terms of grain weight, also differed in the two different approaches. Nineteen percent of the hybrids were shown to be superior to the checks in the fixed models, while only 1.8% of them were superior in the mixed model.

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