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Search Results: 1 - 10 of 32120 matches for " González-Recio Oscar "
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Genome-wide prediction of discrete traits using bayesian regressions and machine learning
Oscar González-Recio, Selma Forni
Genetics Selection Evolution , 2011, DOI: 10.1186/1297-9686-43-7
Abstract: This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO) and two machine learning algorithms (boosting and random forest) to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability.The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data.The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions. Boosting with a pseudo Huber loss function showed high accuracy, whereas Random Forest produced more consistent results and an interesting predictive ability. Nonetheless, the best method may be case-dependent and a initial evaluation of different methods is recommended to deal with a particular problem.The availability of thousands of markers from high throughput genotyping platforms offers an exciting prospect to predict the outcome of complex traits in animal breeding using genomic informat
Genetic relationship of discrete-time survival with fertility and production in dairy cattle using bivariate models
González-Recio Oscar,Alenda Rafael
Genetics Selection Evolution , 2007, DOI: 10.1186/1297-9686-39-4-391
Abstract: Bivariate analyses of functional longevity in dairy cattle measured as survival to next lactation (SURV) with milk yield and fertility traits were carried out. A sequential threshold-linear censored model was implemented for the analyses of SURV. Records on 96 642 lactations from 41 170 cows were used to estimate genetic parameters, using animal models, for longevity, 305 d-standardized milk production (MY305), days open (DO) and number of inseminations to conception (INS) in the Spanish Holstein population; 31% and 30% of lactations were censored for DO and INS, respectively. Heritability estimates for SURV and MY305 were 0.11 and 0.27 respectively; while heritability estimates for fertility traits were lower (0.07 for DO and 0.03 for INS). Antagonist genetic correlations were estimated between SURV and fertility (-0.78 and -0.54 for DO and INS, respectively) or production (-0.53 for MY305), suggesting reduced functional longevity with impaired fertility and increased milk production. Longer days open seems to affect survival more than increased INS. Also, high productive cows were more problematic, less functional and more liable to being culled. The results suggest that the sequential threshold model is a method that might be considered at evaluating genetic relationship between discrete-time survival and other traits, due to its flexibility.
Trans-Generational Effect of Maternal Lactation during Pregnancy: A Holstein Cow Model
Oscar González-Recio, Eva Ugarte, Alex Bach
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0051816
Abstract: Epigenetic regulation in mammals begins in the first stages of embryogenesis. This prenatal programming determines, in part, phenotype expression in adult life. Some species, particularly dairy cattle, are conceived during the maternal lactation, which is a period of large energy and nutrient needs. Under these circumstances, embryo and fetal development compete for nutrients with the mammary gland, which may affect prenatal programming and predetermine phenotype at adulthood. Data from a specialized dairy breed were used to determine the transgenerational effect when embryo development coincides with maternal lactation. Longitudinal phenotypic data for milk yield (kg), ratio of fat-protein content in milk during first lactation, and lifespan (d) from 40,065 cows were adjusted for environmental and genetic effects using a Bayesian framework. Then, the effect of different maternal circumstances was determined on the residuals. The maternal-related circumstances were 1) presence of lactation, 2) maternal milk yield level, and 3) occurrence of mastitis during embryogenesis. Females born to mothers that were lactating while pregnant produced 52 kg (MonteCarlo standard error; MCs.e. = 0.009) less milk, lived 16 d (MCs.e. = 0.002) shorter and were metabolically less efficient (+0.42% milk fat/protein ratio; MCs.e.<0.001) than females whose fetal life developed in the absence of maternal lactation. The greater the maternal milk yield during embryogenesis, the larger the negative effects of prenatal programming, precluding the offspring born to the most productive cows to fully express their potential additive genetic merit during their adult life. Our data provide substantial evidence of transgenerational effect when pregnancy and lactation coincide. Although this effect is relatively low, it should not be ignored when formulating rations for lactating and pregnant cows. Furthermore, breeding, replacement, and management strategies should also take into account whether the individuals were conceived during maternal lactation because, otherwise, their performance may deviate from what it could be expected.
Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens
Oscar González-Recio, Daniel Gianola, Guilherme JM Rosa, Kent A Weigel, Andreas Kranis
Genetics Selection Evolution , 2009, DOI: 10.1186/1297-9686-41-3
Abstract: Genome-wide association studies of diseases and complex traits [1] have permeated into animal breeding, and genome-assisted selection has become a major focus of research [2,3]. However, genome-based artificial selection poses several challenges. For instance, methods for prediction of genetic merit or phenotype using a large number of markers must be contrasted and improved. Also, biological and economical advantages of genome-assisted selection in a breeding program must be quantified (this second problem is not addressed herein).A very important issue is how to deal with a much larger number of markers (p) than of individuals that are genotyped (n). Some proposals include treating marker effects as random, with shrinkage of estimates of non-informative markers to zero. This is done naturally in a Bayesian context, where all unknowns are treated as random variables (e.g., Gianola and Fernando, [4]). On the one hand, Bayesian regression methods, such as Bayes A and Bayes B [2], or the special case of Bayes A described by Xu [5] have recently gained attention. However, all of these procedures involve strong assumptions a priori. On the other hand, non-parametric methods have been suggested as an alternative for predicting genomic breeding values, because these methods may require weaker assumptions when modeling complex quantitative traits [6].These non-parametric approaches have been applied to simulated [7] and field [8] data, and results seem promising. The simulations from Gianola et al. [7] involved 100 biallelic markers and additive × additive interactions between five pairs of loci. Gonzalez-Recio et al. [8] used 24 pre-selected SNPs from a filter and wrapper feature subset selection algorithm [9] in a reproducing kernel Hilbert spaces (RKHS) regression model. However, these non-parametric methods have not been tested yet using a large number of SNPs and field data. Inclusion of a large number of SNPs in these non-parametric models must be studied. Further, e
Combining Genomic and Genealogical Information in a Reproducing Kernel Hilbert Spaces Regression Model for Genome-Enabled Predictions in Dairy Cattle
Silvia Teresa Rodríguez-Ramilo, Luis Alberto García-Cortés, óscar González-Recio
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0093424
Abstract: Genome-enhanced genotypic evaluations are becoming popular in several livestock species. For this purpose, the combination of the pedigree-based relationship matrix with a genomic similarities matrix between individuals is a common approach. However, the weight placed on each matrix has been so far established with ad hoc procedures, without formal estimation thereof. In addition, when using marker- and pedigree-based relationship matrices together, the resulting combined relationship matrix needs to be adjusted to the same scale in reference to the base population. This study proposes a semi-parametric Bayesian method for combining marker- and pedigree-based information on genome-enabled predictions. A kernel matrix from a reproducing kernel Hilbert spaces regression model was used to combine genomic and genealogical information in a semi-parametric scenario, avoiding inversion and adjustment complications. In addition, the weights on marker- versus pedigree-based information were inferred from a Bayesian model with Markov chain Monte Carlo. The proposed method was assessed involving a large number of SNPs and a large reference population. Five phenotypes, including production and type traits of dairy cattle were evaluated. The reliability of the genome-based predictions was assessed using the correlation, regression coefficient and mean squared error between the predicted and observed values. The results indicated that when a larger weight was given to the pedigree-based relationship matrix the correlation coefficient was lower than in situations where more weight was given to genomic information. Importantly, the posterior means of the inferred weight were near the maximum of 1. The behavior of the regression coefficient and the mean squared error was similar to the performance of the correlation, that is, more weight to the genomic information provided a regression coefficient closer to one and a smaller mean squared error. Our results also indicated a greater accuracy of genomic predictions when using a large reference population.
Air Quality Trends in Metropolitan Zones in Veracruz, México  [PDF]
Sergio Natan González Rocha, Juan Cervantes Pérez, José M. Baldasano Recio
Open Journal of Air Pollution (OJAP) , 2016, DOI: 10.4236/ojap.2016.52007
Abstract: Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO2, O3, SO2, PM10 and PM2.5. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM10 and PM2.5 surpassed the WHO guidelines and Mexican standards. O3 and SO2 in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO2 did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM10 and PM2.5 concentrations were the main problem. Others pollutants that influenced the AQ were O3, NO2 and SO2 in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO2 and SO2 are attributed mainly to road transport.
Distribución y dispersión del mielero (Coereba Flaveola, Aves: Coerebidae) en la ciudad de Lima, Perú
Oscar González M.
Ecología Aplicada , 2002,
Abstract: El mielero (Coereba flaveola), es un ave introducida en la ciudad de Lima desde 1992, la cual tiene un patrón de dispersión selectivo. Hasta la fecha (Marzo 1999), no ha colonizado todos los distritos localizados en el Norte u Oeste de la ciudad. Esta selectividad podría deberse a que estos lugares no ofrecen un hábitat óptimo y/o a la falta de corredores naturales para su dispersión. En la actualidad, el mielero se ubica únicamente en parques y jardines de considerable cobertura arbórea.
Oscar González
The Biologist (Lima) , 2012,
Abstract: I comment on the importance of producing, publishing and reviewing scientific literature. Science and Biology in particular, owes its new findings to the published results of the researchers. However in this process there could be some faults that are necessary to correct. Lack of integrity and conflict of interest may obstruct the adequate criteria to produce or review a scientific publication. It is necessary to avoid that. The Biologist's reputation should be known by his professional ethics and his production, which should be portrayed in scientific publications of high quality.
El cálculo infinitesimal leibniciano: Una sí-ntesis de las perspectivas de Brunschvicg E Ishiguro
González Gilmas Oscar
Signos filosóficos , 2004,
Abstract: Resumen: Este artí-culo estudia el tratamiento que dio Leibniz a los infinitésimos, utilizándolos para los cálculos, por una parte, pero considerándolos como inexistentes por otra. A partir de los comentarios previos de Brunschvicg y de Ishiguro acerca de este paradójico estatuto de los infinitésimos en Leibniz, se propone una sí-ntesis de ambas argumentaciones, basada en el carácter algorí-tmico de los infinitesimales y en la presuposición del principio de continuidad, el cual permite la aplicación del cálculo infinitesimal a la fí-sica. El cálculo infinitesimal leibniciano se muestra así- como uno de los mejores ejemplos de su Caracterí-stica Universal, en especial por su utilidad para el Arte de Inventar. Abstract: This article studies Leibniz"s treatment of infinitesimals: their application to the calculus and his opinion that they did not exist. In partial agreement with Brunschvicg"s and Ishiguro "s commentaries on the paradoxical status of Leibniz′s infinitesimals, this study proposes a synthesis of both interpretations, which is based on the algorithmic nature of infinitesimals and on the assumption of continuity, and which renders possible the application of the Infinitesimal Calculus to physics. Leibnizian Infinitesimal Calculus is one of the best examples of his Universal Characteristic, particularly because of its usefulness in the Art of Invention. Keywords diferencial --- incomparabilidad --- indivisible --- infinitesimal --- infinito
La reforma de la Ley 100: más expectativa que realidad
Oscar Orlando González Vega
Revista Colombiana de Gastroenterologia , 2007,
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