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Phosphorus is a non-renewable natural resource that will run out of reserves in the upcoming decades, making it essential to understanding the inheritance of nutrient use efficiency for selecting superior genotypes. This study investigated the additive and non-additive effects of commercially relevant traits for the popcorn crop (grain yield-GY, popping expansion-PE, and expanded popcorn volume per hectare-PV) in different conditions of phosphorus (P) availability in two locations in Rio de Janeiro State, Brazil. Six S 7 lines previously selected for P use-L59, L70, and P7, efficient and responsive; and L54, L75, and L80, inefficient and non-responsive-were used as testers in crosses with 15 progenies from the fifth cycle of intrapopulation recurrent selection of UENF-14, with adaptation to the North and Northwest regions of Rio de Janeiro State. Using the Griffing diallel analysis, P use efficiency was predominantly additive in the expression of PE, and non-additive effects were prominent for GY and PV. For obtaining genotypes that are efficient for phosphorus use, it is recommended that heterosis with parents that provide additive gene accumulation for PE be explored.

Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

The method of regional heritability mapping (RHM) has become an important tool in the identification of quantitative trait loci (QTLs) controlling traits of interest in plants. Here, RHM was first applied in a breeding population of popcorn, to identify the QTLs and candidate genes involved in grain yield, plant height, kernel popping expansion, and first ear height, as well as determining the heritability of each significant genomic region. The study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14, which were genetically evaluated in two environments (ENV1 and ENV2). Seventeen and five genomic regions were mapped by the RHM method in ENV1 and ENV2, respectively. Subsequent genome-wide analysis based on the reference genome B73 revealed associations with forty-six candidate genes within these genomic regions, some of them are considered to be biologically important due to the proteins that they encode. The results obtained by the RHM method have the potential to contribute to knowledge on the genetic architecture of the growth and yield traits of popcorn, which might be used for marker-assisted selection in breeding programs.

Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans' yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness ( r 2 sv ), was 98.63 and 124.18 kb for landraces and breeding lines, respectively. Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information (∼221 QTLs; 650 genes; r 2 from 0.01% to 32%). Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean., (Copyright © 2020 Valdisser, Müller, de Almeida Filho, Morais Júnior, Guimarães, Borba, de Souza, Zucchi, Neves, Coelho, Brondani and Vianello.)

The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits., (Copyright © 2019 de Almeida Filho et al.)

Genome-wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta-analysis using information from independent studies. We carried out GWAS for growth traits with six single-marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by Joint-GWAS, using gene and segment-based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. While single-single nucleotide polymorphism (SNP) GWAS hardly detected significant associations at high-stringency in each population, gene-based Joint-GWAS revealed nine genes significantly associated with tree height. Associations detected using single-SNP GWAS, RHM and Joint-GWAS set-based models explained on average 3-20% of the phenotypic variance. Whole-genome regression, conversely, captured 64-89% of the pedigree-based heritability in all populations. Several associations independently detected for the same SNPs in different populations provided unprecedented GWAS validation results in forest trees. Rare and common associations were discovered in eight genes involved in cell wall biosynthesis and lignification. With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, Joint-GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications., (© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.)

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.

The successful development of hybrid cultivars depends on the reliability of estimated combining ability of the parent lines. The objectives of this study were to assess the combining ability of partially inbred S 3 families of popcorn derived from the open-pollinated variety UENF 14, via top-crosses with four testers, and to compare the testers for their ability to discriminate the S 3 progenies. The experiment was conducted in the 2015/2016 crop season, in an incomplete-block (Lattice) design with three replications. The following agronomic traits were evaluated: average plant height, grain yield (GY), popping expansion (PE), and expanded popcorn volume per hectare. The top-cross hybrid, originating from the BRS-Angela vs S 3 progeny 10 combination, was indicated as promising, showing high values for specific combining ability for GY and PE. For the S 3 progenies that showed high and positive GCA values for GY and PE, the continuity of the breeding program is recommended, with the advance of self-pollination generations. Fasoulas' differentiation index discriminated the BRS-Angela tester as the most suitable for identifying the superior progenies.

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.