Prediction Of Genotypic Values Research Articles

Prediction of genotypic values via REML/BLUP and path analysis in maize half-sib families

Maize has significant relevance to food and economic security. The study aimed to evaluate the genetic gain between maize half-sib families, in the first cycle of recurrent selection, using the REML/BLUP methodology. Path analysis was also performed in order to understand the relationship between the agronomic traits. The experiment was conducted at the Federal Institute of Education, Science and Technology of Triangulo Mineiro – Campus Uberlandia. The agronomic traits evaluated, were: plant height (PH), ear height (EH), stem diameter (SD), ear length (EL), ear diameter (ED) and grain weight (GW). The results indicated significant differences only for the traits EL, GW and ED. A high coefficient of genetic variation (CVg) was observed for the traits EL, GW and ED, with only ED presenting a coefficient of relative variation (CVr) above one. The REML/BLUP methodology demonstrated high accuracy values and the estimates of gains were promising, with values of 24.07%, 21.88% e 18.23% for EL, GW and ED, respectively. Path analysis revealed the possibility of indirect gains in GW through EL.

Genetic divergence and truncation and simultaneous selection in inbred families (S1) of elephant grass for bioenergetic purposes via mixed models

The State University of North Fluminense Darcy Ribeiro (UENF) has been developing for fifteen years a breeding program that aims at the development of new cultivars of elephant grass due to its high potential and the low availability of cultivars developed by genetic breeding programs that meet the needs of producers in the State of Rio de Janeiro. In this sense, inbred families were also obtained as a way of fixing potential alleles for traits related to production, as the inbreeding process apparently does not strongly affect elephant grass in aspects related to inbreeding depression. This study aimed to estimate genetic diversity, variance components and prediction of genotypic values in 11 (S1) elephant grass families, and perform the truncation and simultaneous selection of traits using the selection index, by mixed models. The experimental design consisted of randomized blocks with 11 (S1) families, three replications, and six plants per plot. For variables dry matter production, percentage of dry matter, plant height, stem diameter, number of tillers and leaf blade width, was performed the estimation of genetic parameters and selection of the best genotypes based selection index using mixed model. The descriptors were subjected to correlation analysis, distance matrices were generated by the Mahalanobis method, and individuals were grouped by the UPGMA method. In the selection via mixed models (REML/BLUP), families 6, 11, 8, 1, 3, 7, and 9 contributed most of the genotypes selected for the evaluated traits, indicating their high potential to generate superior genotype. The selection indices via mixed models indicated that the multiplicative index presented a greater selection gain.The phenotypic correlations showed the possibility of performing an indirect selection from six evaluated traits.The genotypes were separated into 18 groups by the Mahalanobis distance, allowing the observation of a wide genetic diversity. The most divergent and productive genotypes were self-fertilized to obtain the second generation (S2), continuing the development program.

Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

• General plot-level model for repeated high-throughput field phenotyping measurements. • Extraction of three main intermediate trait categories for dynamic modelling. • Seamless processing approach that integrates temporal and spatial modelling. • Phenomics data processing cheatsheet. Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.

Prediction of Genotypic Values and Selection Gains in Peanut Lines Derived from a Synthetic Amphidiploid

Genotypic values and selection gains were estimated in peanut lines derived from backcrossing with synthetic amphidiploid (A. batizocoi x A. duranensis)4x and a commercial cultivar, based on agronomical traits. Thirteen BC1F3 lines were grown in the field and submitted to 21 d of water suppression. Two earliness and drought tolerant cultivars (BR 1 and Senegal 55 437) were used as controls. Eight agronomic traits were adopted to characterize the lines. The estimates were based on mixed models (REML/BLUP) and Sum of Ranks Selection Index. We found that residual variance was greater than genotypic one, indicating the existence of variability within the families. Most traits showed high accuracy values. Three families showed an increased number of pods/plant, pod length and several seeds/pod. Negative gains were achieved for time for blooming and full pod maturation. As these traits are used as criteria for selection for earliness, these results favour the identification of short-cycle plants and prone to tolerate dry environments.

Genetic progress with selection of Coffea canephora clones of superior processed coffee yield

ABSTRACT: The aim of this study was to select Coffea canephora clones of superior processed coffee productivity based on the estimates of the genetic parameters and of genetic progress with the plant selection. For this, the production of processed coffee from 130 clones was evaluated from 2014 to 2016 in a clonal test, installed in randomized complete blocks with four plants per plot and six replications, spaced 3 × 2m at the experiment station Embrapa Rondônia in the municipality of Ouro Preto do Oeste - RO. The estimation of genetic parameters and prediction of genotypic values were performed using REML/BLUP procedure (Restricted Maximum Likelihood/Best Unbiased Linear Prediction). Estimates of the genetic parameters confirmed the predominance of the genetic component in the expression of this trait, indicating the possibility of obtaining gains with the plant selection. Genetic progress of processed coffee productivity from the selection of 10% of the best clones was 49.88%, which is equivalent to an increase in average productivity from 42.57 bags.ha-1 to 66.95 bags.ha-1. The use of the harmonic mean of the genetic values helped to identify the clones of superior performance, with higher adaptability and stability for the northern region of Brazil.

GENETIC PARAMETERS IN RIPARIAN REFLORESTATION WITHOUT STRUCTURE OF PROGENIES BY MIXED MODELS (REML/BLUP)

ABSTRACT The objective of this work is to estimate genetic parameters for silvicultural traits in riparian reforestation at 20 years of age, without progeny structure by mixed models (REML / BLUP). The present work was carried out at the Fazenda de Ensino, Pesquisa e Extensão (FEPE) of the Faculdade de Engenharia de Ilha Solteira, Universidade Estadual “Julio de Mesquita Filho” (FEIS/UNESP), located in the municipality of Selvíria (MS). In February and March of 1986 began the planting of the riparian reforestation in modules, existing two Modules with different numbers of species. The spacing used in the planting of the species was 2 x 3 m. The riparian reforestation has an area of 9.5 ha in the total sum of the two Modules of planting. The silvicultural traita evaluated at 20 years were total plant height (ALT, m), diameter at breast height (DBH, cm) and survival of planted individuals, which were used to calculate the variance component components of genetic parameters, using the genetic-statistical program SELEGEN-REML/BLUP. High genetic variability was observed among the genotypes for the silvicultural traits studied in the riparian reforestation. The accuracy was of high magnitude, showing good reliability of the results. The mixed model method (REML/BLUP) was adequate for the estimation of genetic parameters and prediction of genotypic values in riparian reforestation, and it could be used routinely.

Indices estimated using REML/BLUP and introduction of a super-trait for the selection of progenies in popcorn.

Selection indices commonly utilize economic weights, which become arbitrary genetic gains. In popcorn, this is even more evident due to the negative correlation between the main characteristics of economic importance - grain yield and popping expansion. As an option in the use of classical biometrics as a selection index, the optimal procedure restricted maximum likelihood/best linear unbiased predictor (REML/BLUP) allows the simultaneous estimation of genetic parameters and the prediction of genotypic values. Based on the mixed model methodology, the objective of this study was to investigate the comparative efficiency of eight selection indices estimated by REML/BLUP for the effective selection of superior popcorn families in the eighth intrapopulation recurrent selection cycle. We also investigated the efficiency of the inclusion of the variable "expanded popcorn volume per hectare" in the most advantageous selection of superior progenies. In total, 200 full-sib families were evaluated in two different areas in the North and Northwest regions of the State of Rio de Janeiro, Brazil. The REML/BLUP procedure resulted in higher estimated gains than those obtained with classical biometric selection index methodologies and should be incorporated into the selection of progenies. The following indices resulted in higher gains in the characteristics of greatest economic importance: the classical selection index/values attributed by trial, via REML/BLUP, and the greatest genotypic values/expanded popcorn volume per hectare, via REML. The expanded popcorn volume per hectare characteristic enabled satisfactory gains in grain yield and popping expansion; this characteristic should be considered super-trait in popcorn breeding programs.

Prediction of genotypic values of maize for the agricultural frontier region in northeastern Maranhão, Brazil

Agricultural research, mainly focused on the evaluation of maize cultivars along agricultural frontiers, are scarce. This study assessed 128 maize genotypes in the agricultural frontier region of the State of Maranhão, between 2008 and 2009. The experiment was arranged in a lattice design with two replications. The genotypes were evaluated for grain yield in a mixed model, with fixed intercept and experimental effects. The random effects were genotype, genetic class (single-, double or triple-cross hybrid and variety), and incomplete block/replication/ environment. The genotype effect was significant by the likelihood ratio test in all models, highlighting the genotypic values of the single-cross hybrids DKB177, AG8088 and DKB390, the triple-cross PL6882, the double-cross BM502 and of the open-pollinated variety SHS3031.

Quantitative genetic analysis of methylxanthines and phenolic compounds in mate progenies

The objective of this work was to determine the contents of methylxanthines, caffeine and theobromine, and phenolic compounds, chlorogenic and caffeic acids, in 51 mate progenies (half-sib families) and estimate the heritability of genetic parameters. Mate progenies were from five Brazilian municipalities: Pinhão, Ivaí, Barão de Cotegipe, Quedas do Iguaçu, and Cascavel. The progenies were grown in the Ivaí locality. The contents of the compounds were obtained by high performance liquid chromatography (HPLC). The estimation of genetic parameters by the restricted maximum likelihood (REML) and the prediction of genotypic values via best linear unbiased prediction (BLUP) were obtained by the Selegen - REML/BLUP software. Caffeine (0.248-1.663%) and theobromine (0.106-0.807%) contents were significantly different (p<0.05) depending on the region of origin, with high individual heritability (ĥ²>0.5). The two different progeny groups determined for chlorogenic (1.365-2.281%) and caffeic (0.027-0.037%) acid contents were not significantly different (p<0.05) depending on the locality of origin. Individual heritability values were low to medium for chlorogenic (ĥ²<0.4) and caffeic acid (ĥ²<0.3). The content of the compounds and the values of genetic parameters could support breeding programs for mate.

Prediction of genotypic values and estimation of genetic parameters in common bean

Eighteen common bean (Phaseolus vulgaris L.) genotypes were evaluated in 25 environments of the state of São Paulo in 2001 and 2002. The estimation of genetic parameters by the Restricted Maximum Likelihood (REML) and the prediction of genotypic values via Best Linear Unbiased Prediction (BLUP) were obtained by software Selegen-REML/BLUP. The estimate of the broad-sense heritability was low for the grain yield (0.03), since it took individual plots into consideration and was free of the effects of interaction with years, cultivation periods and site. Nevertheless, the heritability at the level of line means across the various environments was high (0.75), allowing a high accuracy (0.87) in the selection of lines for planting in the environment mean. Among the 18 genotypes, the predicted genotypic values of nine were higher than the general mean. The genetic gain predicted with the selection of the best line, in this case line Gen 96A31 of the IAC, was 16.25%.