The power of genomic estimated breeding values for selection when using a finite population size in genetic improvement of tetraploid potato

Potato breeding relies heavily on visual phenotypic scoring for clonal selection. Obtaining robust phenotypic data can be labor intensive and expensive, especially in the early cycles of a potato breeding program where the number of genotypes is very large. We have investigated the power of genomic estimated breeding values (GEBVs) for selection from a limited population size in potato breeding. We collected genotypic data from 669 tetraploid potato clones from all cycles of a potato breeding program, as well as phenotypic data for eight important breeding traits. The genotypes were partitioned into a training and a test population distinguished by cycle of selection in the breeding program. GEBVs for seven traits were predicted for individuals from the first stage of the breeding program (T1) which had not undergone any selection, or individuals selected at least once in the field (T2). An additional approach in which GEBVs were predicted within and across full-sib families from unselected material (T1) was tested for four breeding traits. GEBVs were obtained by using a Bayesian Ridge Regression model estimating single marker effects and phenotypic data from individuals at later stages of selection of the breeding program. Our results suggest that, for most traits included in this study, information from individuals from later stages of selection cannot be utilized to make selections based on GEBVs in earlier clonal generations. Predictions of GEBVs across full-sib families yielded similarly low prediction accuracies as across generations. The most promising approach for selection using GEBVs was found to be making predictions within full-sib families

Regresión cuantil para predicción de caracteres complejos en bovinos Suizo Europeo usando marcadores SNP y pedigrí

Genomic prediction models generally assume that errors are distributed as normal, independent, and identically distributed random variables with zero mean and equal variance. This is not always true, in addition there may be phenotypes distant from the population mean, which are usually removed when making the prediction. Quantile regression (QR) deals with statistical aspects such as high dimensionality, multicollinearity and phenotypic distribution different from the normal one. The objective of this work was to compare QR using marker and pedigree information with alternative methods such as genomic best linear unbiased prediction (GBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) to analyze the birth (BW), weaning (WW) and yearling (YW) weights of Braunvieh cattle and simulated data with different degrees of asymmetry and proportion of outliers. The predictive capacity of the models was assessed by cross-validation. The predictive performance of QR both with marker information alone and with information of markers plus pedigree, with the actual dataset, was better than the GBLUP and ssGBLUP methodologies for WW and YW. For BW, GBLUP and ssGBLUP were better, however, only quantiles 0.25, 0.50 and 0.75 were used, and the BW distribution was not asymmetric. In the simulated data experiment, correlations between “true” marker effects and estimated effects, as well as “true” and estimated signal correlations were higher when QR was used compared to GBLUP. The advantages of QR were more noticeable with asymmetric distribution of phenotypes and with a higher proportion of outliers, as was the case with the simulated dataset.Los modelos de predicción genómica generalmente suponen que los errores se distribuyen como variables aleatorias normales, independientes e idénticamente distribuidas con media cero e igual varianza. Esto no siempre se cumple, además puede haber fenotipos distantes de la media poblacional, los que usualmente se eliminan al realizar la predicción. La regresión cuantil (QR) afronta aspectos estadísticos como alta dimensionalidad, multicolinealidad y distribución fenotípica diferente de la normal. El objetivo de este trabajo fue comparar QR utilizando información de marcadores y pedigrí con los métodos alternativos tales como mejor predicción lineal insesgada genómica (GBLUP) y mejor predicción lineal insesgada genómica en un solo paso (ssGBLUP) para analizar los pesos al nacimiento (PN), destete (PD) y al año (PA) de bovinos Suizo Europeo y datos simulados con diferente grado de asimetría y proporción de datos atípicos. La capacidad predictiva de los modelos se evaluó mediante validación cruzada. El desempeño predictivo de QR tanto sólo con información de marcadores como con marcadores más pedigrí, con el conjunto de datos reales, fue mejor que las metodologías GBLUP y ssGBLUP para PD y PA. Para PN GBLUP y ssGBLUP fueron mejores, sin embargo, solo se utilizaron los cuantiles 0.25, 0.50 y 0.75, y la distribución de PN no fue asimétrica. En el experimento de datos simulados, las correlaciones entre efectos de marcador “verdadero” y efectos estimados, así como las correlaciones de señales “verdaderas” y estimadas fueron más altas cuando se usó QR comparado con GBLUP. Las ventajas de QR fueron más notorias con distribución asimétrica de los fenotipos y con mayor proporción de datos atípicos, como fue el caso del conjunto de datos simulados

Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling

Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model

The Nutritional Dynamic is Key for Use Optimal Forage and Increase of Meat Production

The nutritional content grass could be considered a key tool to determine the optimal forage use, based on the requirements of the cattle to maximize production and achieve a highly productive and profitable livestock. The degradation protein complex associated with autophagy plant determines to a great extent the protein content of the grass over time, being priority found the value nutritional required for the livestock for intensification the animal production. The crude protein requirements (CP, 13.5%) to cover nutritional needs in cattle, was established between 28±1 y 30±1 for the dry period and wet period respectively in Camello® hybrid grass. The weight gains to level protein above mentioned were substantially high in both periods. In dry period was 0.9 kg d-1 animal-1 and wet period 1.1 kg d-1 animal-1. The little difference in weight gain between periods clarifies our hypothesis

Programa “Manos que comunican” para formar agentes inclusivos en los estudiantes del 5° de secundaria de una IE, 2019

La presente tesis tiene como objetivo, determinar la influencia del Programa “Manos que Comunican” para formar agentes inclusivos en los estudiantes del 5° de secundaria en una IE, 2019. Para el desarrollo del presente trabajo de investigación se ha considerado un tipo de estudio pre experimental, y un diseño pre experimental, además se ha tomado como población a los estudiantes de 5° de secundaria de la I.E “Carlos Wiesse” del distrito de Chao y con una muestra obtenida probabilísticamente de 29 estudiantes. Habiendo utilizado la técnica del Test y su instrumento denominado Pre Test y Post Test para efectos de la recolección de datos. Finalizada la investigación se arribó a la siguiente conclusión general: se ha logrado determinar la influencia del Programa “Manos que Comunican” para formar agentes inclusivos en los estudiantes del 5° de secundaria de la I.E “Carlos Wiesse” varió de un puntaje de 16,7931 en el pre test a un 19.8966 en el pos test, con el valor de T de Student, con n-1 grados de libertad de t28=9,725 y con una significancia de p=0,0000<0,05, lo que indica que existe diferencias significativas entre el pre y pos test, luego de haber aplicado el programa de Manos que Comunican a los estudiantes del 5° Secundaria de la IE “Carlos Wiesse”, Chao -2019 (tabla 7). Estos resultados confirman la hipótesis que se planteó: Programa “Manos que Comunican” influye significativamente en los estudiantes de secundaria de la I.E Carlos Wiesse de Chao.Tesis de segunda especialida

Genomic Prediction for Inbred and Hybrid Polysomic Tetraploid Potato Offspring

Potato genetic improvement begins with crossing cultivars or breeding clones which often have complementary characteristics for producing heritable variation in segregating offspring, in which phenotypic selection is used thereafter across various vegetative generations (Ti ). The aim of this research was to determine whether tetrasomic genomic best linear unbiased predictors (GBLUPs) may facilitate selecting for tuber yield across early Ti within and across breeding sites in inbred (S1 ) and hybrid (F1 ) tetraploid potato offspring. This research used 858 breeding clones for a T1 trial at Umeå (Norrland, 63◦4903000 N 20◦1505000 E) in 2021, as well as 829 and 671 clones from the breeding population for T2 trials during 2022 at Umeå and Helgegården (Skåne, 56◦0104600 N 14◦0902400 E), respectively, along with their parents (S0 ) and check cultivars. The S1 and F1 were derived from selfing and crossing four S0 . The experimental layout was an augmented design of four-plant plots across testing sites, where breeding clones were non-replicated, and the parents and cultivars were placed in all blocks between the former. The genomic prediction abilities (r) for tuber weight per plant were 0.5944 and 0.6776 in T2 at Helgegården and Umeå, respectively, when T1 at Umeå was used as the training population. On average, r was larger in inbred than in hybrid offspring at both breeding sites. The r was also estimated using multi-environment data (involving at least one S1 and one F1 ) for T2 performance at both breeding sites. The r was strongly influenced by the genotype in both S1 and F1 offspring irrespective of the breeding site

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson’s correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data

Resposta reprodutiva de ovelhas Pelibuey à aplicação de somatotropina bovina recombinante e de um reconstituinte metabólico

The objective of this work was to evaluate the effect of recombinant bovine somatotropin (rBST) and of the metabolic restorative preparation Metabolase (MR) on the reestablishment of the post-partum ovarian activity of Pelibuey sheep. Ninety-four ewes, with their respective lambs, were randomly assigned to one of the following treatments: T1, continuous suckling (CS); T2, CS + MR; T3, CS + rBST; and T4, CS + MR + rBST. Ovulating percentages, weight changes in ewes and lambs, incidence of estrus, onset and return to estrus, calving, fecundity, and prolificacy were evaluated. The highest ovulation percentages were recorded for CS in T1 and T2, and the lowest ones for rBST in T3 and T4. The treatments had a significant effect on lamb weight. Ewes in T3 had the lowest incidence of estrus (52.9%), besides a greater onset (26.8±1.9 hours) and return to estrus (66.6%). Calving (86.2%) and fecundity (1.8±0.2) were significantly higher in T2. The application of rBST in ewes increases lamb body weight, due to increased milk production, but affects negatively post-partum reproductive activity due to the loss of ewe body weight.O objetivo deste trabalho foi avaliar o efeito da somatotropina bovina recombinante (rBST) e do reconstituinte metabólico Metabolase (MR) no reestabelecimento da atividade ovariana pós-parto de ovelhas Pelibuey. Noventa e quatro ovelhas, com seus respectivos cordeiros, foram distribuídas ao acaso a um dos seguintes tratamentos: T1, amamentação continua (AC); T2, AC + MR; T3, AC + rBST; e T4, AC + MR + rBST. Foram avaliados percentagem de ovulação, alterações de peso em ovelhas e carneiros, incidência do estro, início e retorno ao estro, parição, fecundidade e prolificidade. As maiores percentagens de ovulação foram registradas para AC em T1 e T2, e as menores para rBST em T3 e T4. Os tratamentos tiveram efeito significativo sobre peso dos cordeiros. As ovelhas em T3 apresentaram menor incidência de estro (52,9%), além de maior início (26,8±1,9 horas) e retorno ao estro (66,6%). A parição (86,2%) e a fecundidade (1,8±0,2) foram significativamente maiores em T2. A aplicação da rBST nas ovelhas incrementa o peso corporal dos cordeiros, em razão do aumento na produção de leite, mas afeta negativamente a atividade reprodutiva pós-parto, em razão da perda de peso corporal das ovelhas