Best Linear Unbiased Prediction and Family Selection in Crop Species

Abstract

ABSTRACTBest linear unbiased prediction (BLUP) is the method primarily used for genetic evaluation in animal and forest species breeding. The objective was to present theory and application of BLUP in family selection. Statistical models aim to predict the breeding values of parents of half‐sib and inbred families and the individual or average additive values of parents of full‐sib families. We analyzed the expansion volume (EV) and grain yield of half‐ and full‐sib families and of inbred progeny from the Viçosa popcorn {Zea mays L. subsp. mays [syn. Zea mays L. subsp. everta (Sturtev.) Zhuk.]} population in two selection cycles. The noninbred families were evaluated in a 14 by 14 simple lattice, and the inbred progeny were assessed in the incomplete block design. The additive‐dominant model was fitted for yield of full‐sib families of the second cycle and across generations for yield of full‐sib families and EV of inbred progeny. Additive × additive epistasis was not significant for EV and yield of half‐sib families. Although the predictions of breeding values in the additive and additive‐dominant models are positively correlated, different sets of parents were included in the top 10% of breeding values from the two models. Therefore, the additive‐dominant model should be fitted wherever possible.