Rice cultivation has great national and global importance, being one of the most produced and consumed cereals in the world and the primary food for more than half of the world’s population. Because of its importance as food, developing efficient methods to select and predict genetically superior individuals in reference to plant traits is of extreme importance for breeding programs. The objective of this research was to evaluate and compare the efficiency of the Delta-p, G-BLUP (Genomic Best Linear Unbiased Predictor), BayesCpi, BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator), Delta-p/G-BLUP index, Delta-p/BayesCpi index, and Delta-p/BLASSO index in the estimation of genomic values and the effects of single nucleotide polymorphisms on phenotypic data associated with rice traits. Use of molecular markers allowed high selective efficiency and increased genetic gain per unit time. The Delta-p method uses the concept of change in allelic frequency caused by selection and the theoretical concept of genetic gain. The Index is based on the principle of combined selection, using the information regarding the additive genomic values predicted via G-BLUP, BayesCpi, BLASSO, or Delta-p. These methods were applied and compared for genomic prediction using nine rice traits: flag leaf length, flag leaf width, panicles number per plant, primary panicle branch number, seed length, seed width, amylose content, protein content, and blast resistance. Delta-p/G-BLUP index had higher predictive abilities for the traits studied, except for amylose content trait in which the method with the highest predictive ability was BayesCpi, being approximately 3% greater than that of the Delta-p/G-BLUP index.
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.
Although genome‐wide association studies (GWAS) based on single‐marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false‐positive rates and the limited power to detect associations. Bayesian methods, which estimate marker effects simultaneously, proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM), on the other hand, determines the genome region (group of markers) associated with the phenotype, considers population structure and familial relatedness, and is more powerful to detect quantitative trait loci (QTL) and reduced false‐positive rates than single‐marker methodologies. A single‐marker mixed model (SM‐MM) Bayesian approach and RHM were used for 11 traits in 413 rice (Oryza sativa L.) accessions genotyped for 44,100 single‐nucleotide polymorphism (SNP) markers. Using RHM in regions of 0.21 and 0.69 Mb, respectively, detected five and seven associated regions with 163 and 569 SNPs. Bayesian method with regions of 0.21 and 0.69 Mb detected regions for all traits, whereas SM‐MM detected four single SNP–trait associations. For the 11 traits, RHM explained approximately 25–40 and 25–76% using genome regions of 0.21 and 0.69 Mb, respectively, and SM‐MM using single markers explained 1–7% of the genomic heritability. Regional heritability mapping was more effective than SM‐MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q‐TARO and Gramene QTL databases.
Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.
Coffee breeding techniques were based on classical techniques for a long time, however, with the advent of genomic techniques and precision phenotyping, breeding programs have been showing best and faster results, even with the programs becoming more complex, in terms of quantities and types of characteristics studied. Thus, the existence of interrelationships between characters can generate important impacts in a breeding program, such as the discovery of genomic regions that contribute to certain characteristics, these can act directly, or indirectly. Knowing this, understanding the direct and indirect effects that one character has on another is of great importance for the selection phase. Traditionally, multivariate techniques are applied, but phenotypic interrelationships are neglected. Thus, the use of the Bayesian Network (BN) in conjunction with the Structured Equation Model (SEM) under the focus of the genomic wide association study (GWAS), allows quantifying genetic parameters, partitioning such values into direct and indirect effects for the traits. present in the formed network. In order to explore these interrelationships, they were able to phenotypes related to morphological (fruit size and number of reproductive nodes), physiological (vegetative vigor) and productive (production) characteristics in 195 Coffea arabica genotypes from a partnership between Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG), Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) and Federal University of Viçosa (UFV). The phenotypic network inferred by means of the Hill Climbing algorithm was used to estimate the appropriate coefficients. By performing an integration between multivariate models - GWAS and SEM- GWAS it was possible to identify a positive interrelationship between vegetative vigor in yield and vegetative vigor for the number of reproductive nodes and negative for the number of reproductive nodes and fruit size for yield. It was also possible to detect significant genomic regions, and thus identify three genes that act directly on yield. Keywords: Coffea arabica. Bayesian Network. Structural Equation Models. GWAS.
Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.
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