Interacción del genotipo del macho con las condiciones de IA sobre la fertilidad y la prolificidad en conejo

Diferencias en sensibilidad al ambiente entre individuos suponen la existencia de lo que se denomina interacción genotipo-medio (GxE). Así, podría existir una interacción entre el genotipo del macho con las condiciones de inseminación y/o el periodo de conservación de las dosis de inseminación artificial (IA). La existencia de esta interacción implicaría que se podrían escoger las condiciones que proporcionan el máximo progreso genético con la finalidad de optimizar el programa de mejora por la fertilidad y la prolificidad del macho en unas determinadas condiciones de utilización del semen. Tras la monta natural o bajo condiciones comerciales de IA (donde se utilizan eyaculados altamente preseleccionados y dosis en fresco de elevada conservación espermática) la contribución del macho a la fertilidad y a la prolificidad es casi nula. Sin embargo, es bien conocido que fallos en la fertilización y la posterior embriogénesis son, en parte, de origen seminal. Cuando se utiliza IA, la tasa de fertilización depende del número y de la calidad de los espermatozoides entorno al momento de la inseminación. La variación genética individual de la fertilidad y la prolificidad del macho podría ser mejor observada bajo condiciones limitantes de IA, como por ejemplo, utilizar una baja concentración de espermatozoides, hacer una nula o muy leve preselección de los eyaculados por calidad seminal o alargar el periodo de conservación de las dosis. El primer objetivo de éste estudio fue determinar si existen diferencias en varianza genética para la fertilidad y la prolificidad tras la IA utilizando semen a distintas concentraciones espermáticas y determinar si hay una interacción del genotipo del macho con este factor. El segundo objetivo del estudio fue determinar si existe una interacción entre el genotipo del macho y otros factores involucrados en el proceso de inseminación en su conjunto (condiciones y duración del periodo de conservación de las dosis, tipo genético de la hembra y condiciones ambientales de granja). El éxito o fracaso a la concepción (F) y el número total de gazapos nacidos por camada (TB) tras la IA bajo diferentes condiciones fueron considerados caracteres distintos y analizados en dos grupos de análisis independientes. Con la finalidad de determinar el efecto de la concentración de espermatozoides en estos caracteres, se realizaron inseminaciones a 10 y 40 x106 espermatozoides/mL resultando en 6,613 y 3,379 datos para F y TB, respectivamente. Para la determinación del efecto de las otras condiciones de IA, se realizaron inseminaciones con dosis que diferían en período de conservación, diluyente y condiciones ambientales resultando en 13,156 y 7,704 datos para F y TB, respectivamente. Se asumieron modelos umbral y lineales bivariantes para F y TB, respectivamente. La concentración de espermatozoides en la dosis de IA tuvo un claro efecto sobre la F y el TB, a favor de la concentración más alta (-0.13% y -1.25 gazapos nacidos, respectivamente), siendo TB más sensible que la F a esta reducción. La heredabilidades obtenidas fueron 0.09 para la F con ambas concentraciones y 0.08 y 0.06 para TB para baja y alta concentración, respectivamente. No se encontró interacción del genotipo del macho con la concentración de espermatozoides en la dosis de inseminación. Por lo tanto, la selección para mejorar la F y el TB podría realizarse a cualquier concentración espermática, dentro del intervalo estudiado. Sin embargo, bajo estas condiciones, la selección para mejorar la F y el TB podría ser mayor que la respuesta esperada tras la monta natural o tras la IA con dosis no conservadas, dado que la varianza aditiva del macho obtenida fue mayor. Además de la concentración espermática, otros factores relacionados con el proceso de IA también tuvieron un efecto importante sobre la F, pero éste no se detectó para TB. No obstante, en ambos casos, había evidencia de interacción entre el genotipo del macho y las condiciones de IA, indicando que, probablemente, haya diferencias entre machos en la capacidad de mantener las características seminales durante el periodo de conservación, que resulten en diferencias en F y TB. Así, sería posible cambiar la resistencia del semen a la conservación por selección genética.Tusell Palomero, L. (2009). Interacción del genotipo del macho con las condiciones de IA sobre la fertilidad y la prolificidad en conejo. http://hdl.handle.net/10251/11585Archivo delegad

Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (trn.1); genotypes of PB sires and average of CB records per PB sire (trn.2); and individual CB genotypes and phenotypes (trn.3). The average of CB offspring records was the trait to be predicted from PB sire’s genotype using cross-validation. Single nucleotide polymorphisms (SNPs) were ranked based on the Spearman Rank correlation with the trait. Subsets with an increasing number (from 50 to 2,000) of the most informative SNPs were used as predictor variables in SVM. Prediction performance was the median of the Spearman correlation (SC, interquartile range in brackets) between observed and predicted phenotypes in the testing set. The best predictive performances were obtained when sire phenotypic information was included in trn.1 (0.22 [0.03] for RFI with SVM and 250 SNPs, and 0.12 [0.05] for ADG with SVM and 500–1,000 SNPs) or when trn.3 was used (0.29 [0.16] with Genomic best linear unbiased prediction (GBLUP) for RFI, and 0.15 [0.09] for ADG with just 50 SNPs). Animals from the last two generations were assigned to the testing set and remaining animals to the training set. Individual’s PB own phenotype and genotype improved the prediction ability of CB offspring of young animals for ADG but not for RFI. The highest SC was 0.34 [0.21] and 0.36 [0.22] for RFI and ADG, respectively, with SVM and 50 SNPs. Predictive performance using CB data for training leads to a SC of 0.34 [0.19] with GBLUP and 0.28 [0.18] with SVM and 250 SNPs for RFI and 0.34 [0.15] with SVM and 500 SNPs for ADG. Results suggest that PB candidates could be evaluated for CB performance with SVM and low-density SNP chip panels after collecting their own RFI or ADG performances or even earlier, after being genotyped using a reference population of CB animals.info:eu-repo/semantics/publishedVersio

Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allows identifying relevant markers and designing low-density SNP chips to evaluate selection candidates. In this research, several univariate and multivariate FS algorithms combined with various parametric and non-parametric learners were applied to the prediction of feed efficiency in growing pigs from high-dimensional genomic data. The objective was to find the best combination of feature selector, SNP subset size, and learner leading to accurate and stable (i.e., less sensitive to changes in the training data) prediction models. Genomic best linear unbiased prediction (GBLUP) without SNP pre-selection was the benchmark. Three types of FS methods were implemented: (i) filter methods: univariate (univ.dtree, spearcor) or multivariate (cforest, mrmr), with random selection as benchmark; (ii) embedded methods: elastic net and least absolute shrinkage and selection operator (LASSO) regression; (iii) combination of filter and embedded methods. Ridge regression, support vector machine (SVM), and gradient boosting (GB) were applied after pre-selection performed with the filter methods. Data represented 5,708 individual records of residual feed intake to be predicted from the animal’s own genotype. Accuracy (stability of results) was measured as the median (interquartile range) of the Spearman correlation between observed and predicted data in a 10-fold cross-validation. The best prediction in terms of accuracy and stability was obtained with SVM and GB using 500 or more SNPs [0.28 (0.02) and 0.27 (0.04) for SVM and GB with 1,000 SNPs, respectively]. With larger subset sizes (1,000–1,500 SNPs), the filter method had no influence on prediction quality, which was similar to that attained with a random selection. With 50–250 SNPs, the FS method had a huge impact on prediction quality: it was very poor for tree-based methods combined with any learner, but good and similar to what was obtained with larger SNP subsets when spearcor or mrmr were implemented with or without embedded methods. Those filters also led to very stable results, suggesting their potential use for designing low-density SNP chips for genome-based evaluation of feed efficiency.info:eu-repo/semantics/publishedVersio

SNP-based mate allocation strategies to maximize total genetic value in pigs

International audienceAbstractBackgroundMate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored.ResultsGenetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE.ConclusionsGenomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain

Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW). Recently, prediction using single-output machine learning algorithms and information from SNPs as predictor variables have been proposed for genomic selection in growing pigs, but like in other species, the prediction quality achieved for RFI has been generally poor. However, it has been suggested that it could be improved through multi-output or stacking methods. For this purpose, four strategies were implemented to predict RFI. Two of them correspond to the computation of RFI in an indirect way using the predicted values of its components obtained from (i) individual (multiple single-output strategy) or (ii) simultaneous predictions (multi-output strategy). The other two correspond to the direct prediction of RFI using (iii) the individual predictions of its components as predictor variables jointly with the genotype (stacking strategy), or (iv) using only the genotypes as predictors of RFI (single-output strategy). The single-output strategy was considered the benchmark. This research aimed to test the former three hypotheses using data recorded from 5828 growing pigs and 45,610 SNPs. For all the strategies two different learning methods were fitted: random forest (RF) and support vector regression (SVR). A nested cross-validation (CV) with an outer 10-folds CV and an inner threefold CV for hyperparameter tuning was implemented to test all strategies. This scheme was repeated using as predictor variables different subsets with an increasing number (from 200 to 3000) of the most informative SNPs identified with RF. Results showed that the highest prediction performance was achieved with 1000 SNPs, although the stability of feature selection was poor (0.13 points out of 1). For all SNP subsets, the benchmark showed the best prediction performance. Using the RF as a learner and the 1000 most informative SNPs as predictors, the mean (SD) of the 10 values obtained in the test sets were: 0.23 (0.04) for the Spearman correlation, 0.83 (0.04) for the zero–one loss, and 0.33 (0.03) for the rank distance loss. We conclude that the information on predicted components of RFI (DFI, ADG, MW, and BFT) does not contribute to improve the quality of the prediction of this trait in relation to the one obtained with the single-output strategy.info:eu-repo/semantics/publishedVersio

Estimation of additive and dominant variance of egg quality traits in pure-line layers

Improved performances are partly due to heterosis effects. One of the basis of heterosis is dominance, which cannot be inherited.However, it can be exploited to boost the total genetic merit of the animals. This has a special interest in avian selection schemeswhere commercial animals are crossbred. In this study, we have estimated additive and dominance genetic variances for severalegg quality traits in pure-line layers.Around 10,500 egg quality performances were used, collected from 1,148 female Rhode Island layers, phenotyped at 70 weeksold and genotyped using a 600K high density SNP chip. Five egg quality traits were analysed: egg weight (EW), egg shell color(ESC), egg shell strength (ESS), albumen height (AH) and egg shell shape (ESShape). Additive and dominance genetic varianceswere estimated via EM-REML with univariate models. That included an inbreeding coefficient and an additive and a dominancerandom effect. Dominance variance explained a small fraction of the phenotypic variance (between 2 to 4 % across all traits).However, it represented a relevant fraction of the total genetic variance for some of the traits (16%, 10%, 35%, 2.4% and 15% ofthe total genetic variance for EW, ESC, ESS, AH, ESShape, respectively).Further research will estimate additive and dominance genetic correlations between the traits to maximize the total genetic gainof these traits simultaneously. In addition, a genomic BLUP with dominance effects is envisaged for the joint analyses of purebredand crossbred performances, to evaluate the potential to generate superior crossbred performances

Predictive ability of genome-assisted statistical models under various forms of gene action

Recent work has suggested that the performance of prediction models for complex traits may depend on the architecture of the target traits. Here we compared several prediction models with respect to their ability of predicting phenotypes under various statistical architectures of gene action: (1) purely additive, (2) additive and dominance, (3) additive, dominance, and two-locus epistasis, and (4) purely epistatic settings. Simulation and a real chicken dataset were used. Fourteen prediction models were compared: BayesA, BayesB, BayesC, Bayesian LASSO, Bayesian ridge regression, elastic net, genomic best linear unbiased prediction, a Gaussian process, LASSO, random forests, reproducing kernel Hilbert spaces regression, ridge regression (best linear unbiased prediction), relevance vector machines, and support vector machines. When the trait was under additive gene action, the parametric prediction models outperformed non-parametric ones. Conversely, when the trait was under epistatic gene action, the non-parametric prediction models provided more accurate predictions. Thus, prediction models must be selected according to the most probably underlying architecture of traits. In the chicken dataset examined, most models had similar prediction performance. Our results corroborate the view that there is no universally best prediction models, and that the development of robust prediction models is an important research objective

Interacción genotipo x tipo de dosis de inseminación artificial para la fertilidad del macho de conejo

El objetivo de este trabajo fue estimar los parámetros genéticos de la fertilidad tras la IA con 3 tipos de dosis obtenidas de eyaculados de machos de la línea Caldes: 1) tipo 10: con 10 x 106 espermatozoides/ml y 24h de conservación en un diluyente comercial tipo A. 2) tipo 40: con 40 x 106 espermatozoides/ml y las mismas condiciones de conservación que las del tipo 10. 3) tipo X: dosis preparadas tras diluir los eyaculados con un diluyente comercial tipo B (1:5) siendo desconocida la concentración y sin periodo de conservación. Se realizaron 3,628 IA con dosis del tipo 10 sobre hembras cruzadas, 3,027 con dosis del tipo 40 y la misma población de hembras, y 5,779 con dosis del tipo X sobre hembras puras de la línea Caldes. La fertilidad tras la IA con dosis del tipo 10 (F10), 40 (F40) y X (FX) fue considerada un carácter distinto en cada caso, de tipo binario. Los datos se analizaron utilizando un modelo umbral tri-carácter. La estima de la media de la distribución marginal posterior (DMP) de F10 menos F40 fue de -0.13. Este resultado indica un claro efecto de la concentración sobre la fertilidad, que podría no ser lineal. Las medias de la DMP de F10 menos FX y F40 menos FX fueron -0.37 y -0.23, respectivamente, lo que indica que el efecto de las condiciones de conservación sobre la fertilidad podría ser más importante que el de la concentración ya que FX fue muy próxima a la fertilidad tras la MN y la concentración del tipo de dosis X sería en promedio de unos 50 x 106 espermatozoides/ml. Las heredabilidades parecen ser similares para F10 y F40 y ambas mayores que las correspondientes a la fertilidad tras la MN y a FX. La interacción del genotipo x concentración de la dosis de IA es prácticamente despreciable debido a que las varianzas genéticas fueron similares para F10 y F40 y a que su correlación genética fue próxima a 1. Sin embargo, la interacción podría ser de mayor importancia entre el genotipo y las condiciones de conservación

Exploring the genetics of the efficiency of fertile AI dose production in rabbits

Exploring the genetics of the efficiency of fertile AI dose production in rabbits The general aim of this thesis has been to analyse sources of variation for some of the most important components of fertile artificial insemination (AI) dose production in order to explore the interest and limitations of different strategies for their genetic improvement in a paternal line of rabbits selected for growth rate. These components refer to seminal production and quality traits, being considered the male reproductive performance (fertility and prolificacy) as the final expression of the effect of the seminal characteristics and the effect of the interaction among them and with the female. Genetic analyses of the seminal traits involved in AI dose production and growth rate were modelled using threshold and linear multiple-trait mixed models. Relationship between fertility and pH of the semen was analysed either using mixed or recursive mixed models. Male and female genetic contributions to fertility were estimated using additive or product threshold models and both models were compared by its ability of predicting fertility data. Existence of genotype x artificial insemination conditions for male effect on fertility and prolificacy was estimated under a Character state model. Finally, the product threshold model was used for estimating separately the effect of the environmental temperature on male and on female contributions to fertility. All inferences of this thesis have been done under a Bayesian approach. Male libido and variables related to the quality of the ejaculate such as presence of urine and calcium carbonates in the ejaculate, individual sperm motility, semen pH and suitability for AI of the ejaculate (which involves the subjective combination of several semen quality traits) were found to be lowly heritable, but repeatable.Tusell Palomero, L. (2011). Exploring the genetics of the efficiency of fertile AI dose production in rabbits [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11842Palanci