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.

BackgroundThe 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.ResultsPredictive 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.ConclusionsThis 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3920-2) contains supplementary material, which is available to authorized users.

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 (r2sv), 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; r2 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.

Summary 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.

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.

RESUMO-Diante da importância de cultivo da cultura do sorgo em ambientes passíveis de estresse hídrico, objetivou-se neste trabalho a avaliação de 49 híbridos simples de sorgo granífero em dois ambientes contrastantes, um deles com irrigação plena e outro com estresse hídrico induzido após o florescimento das plantas. Em geral, não foi observado efeito da interação genótipo por ambiente para a maioria das características e o estresse hídrico proporcionou decréscimo nas médias das variáveis avaliadas: rendimento de grãos, altura, florescimento e índice de trilha, que é a relação, em porcentagem, da massa de grãos em relação à massa total da panícula. A alta produtividade de grãos no ambiente com estresse hídrico está associada, possivelmente, ao rápido desenvolvimento das plantas, enquanto no ambiente irrigado está associada ao maior período vegetativo e, em todos os casos, indivíduos com alto índice de trilha tendem a ser mais produtivos. A altura não foi fator limitante para a grande maioria dos híbridos de sorgo avaliados. Diversos híbridos experimentais foram mais precoces do que as cultivares comerciais utilizadas como testemunhas, em ambos os ambientes. Os híbridos 0306037 e 0009035 se destacaram por apresentar altas produtividades de grãos e serem precoces. As cultivares DKB 599 e 1G150 também apresentaram alta produtividade de grãos.

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 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 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 progeny 10 combination, was indicated as promising, showing high values for specific combining ability for GY and PE. For the S 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.

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