Genetic evaluation of grain sorghum hybrids in Brazilian environments using the REML/BLUP procedure

When it comes to recommending sorghum (Sorghum bicolor) cultivars, it is essential to carry out a genetic evaluation of the agronomic traits of promising genotypes from several common environments where the crop is cultivated. This study consisted of a genetic evaluation of 52 experimental grain sorghum hybrids and eight commercial cultivars. Hybrids were evaluated in 19 experiments representing the most varied cultivation conditions in Brazil. Traits of agronomic interest such as grain yield, flowering and plant height were analysed. Genotypic evaluation was performed following the REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Predictor) procedure; the MHPRVG (Harmonic Mean of Relative Performance of Genotypic Values) method was also employed to study stability and adaptability. Hybrids which stood out in terms of highest grain yield based on genotypic values, stability and adaptability were 0306037, 1G150, DKB 599, 0306039, 1G282 and 0307671. Of these, only 1G282 showed restrictions as to plant height. For flowering, experimental hybrids showed shorter cycles than commercial cultivars, confirming the efficiency of genetic improvement for this trait. With the analysis of grain yield considering days to flowering and plant height as covariates, it was observed that most of the hybrids of greater performance, showed grain yield to be positively influenced by plant height and days to flowering

Data from: The contribution of dominance to phenotype prediction in a pine breeding and simulated population

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait

Genetic evaluation of grain sorghum hybrids in Brazilian environments using the REML/BLUP procedure

When it comes to recommending sorghum (Sorghum bicolor) cultivars, it is essential to carry out a genetic evaluation of the agronomic traits of promising genotypes from several common environments where the crop is cultivated. This study consisted of a genetic evaluation of 52 experimental grain sorghum hybrids and eight commercial cultivars. Hybrids were evaluated in 19 experiments representing the most varied cultivation conditions in Brazil. Traits of agronomic interest such as grain yield, flowering and plant height were analysed. Genotypic evaluation was performed following the REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Predictor) procedure; the MHPRVG (Harmonic Mean of Relative Performance of Genotypic Values) method was also employed to study stability and adaptability. Hybrids which stood out in terms of highest grain yield based on genotypic values, stability and adaptability were 0306037, 1G150, DKB 599, 0306039, 1G282 and 0307671. Of these, only 1G282 showed restrictions as to plant height. For flowering, experimental hybrids showed shorter cycles than commercial cultivars, confirming the efficiency of genetic improvement for this trait. With the analysis of grain yield considering days to flowering and plant height as covariates, it was observed that most of the hybrids of greater performance, showed grain yield to be positively influenced by plant height and days to flowering

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

Background: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Results: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000?10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. Conclusions: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.Made available in DSpace on 2018-08-08T00:50:51Z (GMT). No. of bitstreams: 1 s1286401739202.pdf: 1780380 bytes, checksum: 2f14911487425d0ea5a38143f03ae05b (MD5) Previous issue date: 2017-11-20bitstream/item/181027/1/s12864-017-3920-2.pd