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ABSTRACT: The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

ABSTRACT: The Fisher’s infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation.

ABSTRACT: The development of efficient methods for genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single–marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non–overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single–marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.

ABSTRACT: Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.

ABSTRACT Genomic selection (GS) has recently been proposed as a new selection strategy which represents an innovative paradigm in crop improvement, now widely adopted in animal breeding. Genomic selection relies on phenotyping and high-density genotyping of a sufficiently large and representative sample of the target breeding population, so that the majority of loci that regulate a quantitative trait are in linkage disequilibrium with one or more molecular markers and can thus be captured by selection. In this study we address genomic selection in a practical fruit breeding context applying it to a breeding population of table grape obtained from a cross between the hybrid genotype D8909-15 (Vitis rupestris × Vitis arizonica/girdiana), which is resistant to dagger nematode and Pierce’s disease (PD), and ‘B90-116’, a susceptible Vitis vinifera cultivar with desirable fruit characteristics. Our aim was to enhance the knowledge on the genomic variation of agronomical traits in table grape populations for future use in marker-assisted selection (MAS) and GS, by discovering a set of molecular markers associated with genomic regions involved in this variation. A number of Quantitative Trait Loci (QTL) were discovered but this method is inaccurate and the genetic architecture of the studied population was better captured by the BLasso method of genomic selection, which allowed for efficient inference about the genetic contribution of the various marker loci. The technology of genomic selection afforded greater efficiency than QTL analysis and can be very useful in speeding up the selection procedures for agronomic traits in table grapes.

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.

Sugarcane (Saccharum spp.) is one of the most important crops cultivated in the tropics and subtropics and plays a significant economic and environmental role in Brazil. Twentyfour new clones were evaluated in different locations as potential models for recommendation as new varieties. The mixed model methodology, using the harmonic mean of the relative performance of genetic values (MHPRVG), facilitated the analysis of genotypic stability and adaptability, culminating in the recommendation of clones for each location. MHPRVG ranked clones RB92579, RB867515, SP81-3250, RB947520 and RB931530 as the best five, and, additionally, clones with greater genotypic potential were identified for each test in the six localities.

The increasing demand for raw material for multiple uses of forest products and by-products has attracted the interest for fast growing species, such as the Australian Cedar (Toona ciliata), which presents high productive and economic potential. This study aimed at estimating genotypic parameters and values for the species through the use of the BLUP procedure, at individual level, with repeated measures, by means of the conventional evaluation procedures and the introduction of innovative digitalization of the measurements by digital camera with the images provided by the Imagej software system. The main objective is to subsidize the beginning of a breeding program for the species. The assays were carried out in private properties, in plantations located in the state of Rio de Janeiro, Brazil. The results generated by three evaluations revealed that the image digital analysis is adequate to quantify characteristics of Toona ciliata. It is also an effective and accurate alternative to minimize the costs of data collection in evaluations with the species. There was high accuracy for the characters plant height, diameter at breast height and cylindrical volume. Out of the 90 genotypes evaluated, 38 expressed genotypic values predicted for the diameter at breast height higher than the general average of this character, 33 for the cylindrical volume and 49 for height, allowing gains of up to 24.9 % in average for cylindrical volume. The method of mixed models (REML/BLUP) applied via the SELEGEN software system, using the BLUP procedure at individual level and repeated measures in each individual proved to be adequate to estimate the genetic parameters and predict genotypic values in situations of unbalanced data. Therefore, it is very useful and practical for Toona ciliata genetic breeding programs.

ABSTRACT: Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.

ABSTRACT: Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.