A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits

Gompertz growth curves were fitted to the data of 137 rabbits from control (C) and selected (S) lines. The animals came from a synthetic rabbit line selected for an increased growth rate. The embryos from generations 3 and 4 were frozen and thawed to be contemporary of rabbits born in generation 10. Group C was the offspring of generations 3 and 4, and group S was the contemporary offspring of generation 10. The animals were weighed individually twice a week during the first four weeks of life, and once a week thereafter, until 20 weeks of age. Subsequently, the males were weighed weekly until 40 weeks of age. The random samples of the posterior distributions of the growth curve parameters were drawn by using Markov Chain Monte Carlo (MCMC) methods. As a consequence of selection, the selected animals were heavier than the C animals throughout the entire growth curve. Adult body weight, estimated as a parameter of the Gompertz curve, was 7% higher in the selected line. The other parameters of the Gompertz curve were scarcely affected by selection. When selected and control growth curves are represented in a metabolic scale, all differences disappear

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

The eect of selection for growth rate on carcass composition and meat characteristics of rabbits

Abstract The eect of selection for growth rate on carcass composition and meat quality was assessed by comparing two groups of rabbits belonging to dierent generations of a selection experiment. A Bayesian approach was used. Embryos belonging to generations 3 and 4 of selection were frozen and thawed to be contemporary of animals from generation 10. A control group (C), formed from ospring of these embryos, was contemporary to ospring of generations 10 and 11 of selection, chosen at random, which constituted the selected group (S). One hundred and thirty-one contemporary rabbits were slaughtered at approximately the Spanish commercial live weight of 2 kg. Carcasses were dissected and measured according to the norms of the World Rabbit Scienti®c Association. An animal model including eects of genetic group (C, S) and sex, and slaughter weight as a covariate was used. S animals had a higher development of liver, kidneys and of a set of organs consisting of the thymus, trachea, oesophagus, lung and heart, relative to C. For dissectible fat, S animals had less than C: À0.31 g for scapular fat, À1.62 g for perirenal fat and À2.03 g for inguinal fat. S had a lower content (À0.39%) of dissectible fat percentage in the``Reference'' carcass, indicating a lower degree of maturity at slaughter. The meat to bone ratio was not aected by selection, but the meat and bone contents of the hind leg were 3.25 and 0.71 g higher, respectively, in the C group. Selected animals had a lower water holding capacity in the raw meat (À2.10%), a higher water holding capacity in the cooked meat (2.17%), a higher cooking loss (3.31%) and a lower fat percentage in the meat of a hind leg (À0.37%). Females had more fat than males: 0.26 g for scapular fat, 1.02 g for perirenal fat, 1.10 g for inguinal fat, and 0.24% for total dissectible fat percentage of the``Reference'' carcass.

The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efciency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efciency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cageaverage traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efciency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efciency traits, although specifc OTUs taxonomically assigned to 5 diferent phyla have a higher weight. Rabbit growth and feed efciency are infuenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.info:eu-repo/semantics/publishedVersio

Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %.Peer ReviewedPostprint (published version

Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Background: Improving feed efficiency (FE) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs (RFI) should be of value for further research on biological aspects of FE. Here, we present a random regression model that extends the classical definition of RFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several FE components: use of feed for growth (WG), use of feed for backfat deposition (FG), use of feed for maintenance (MW), and unspecific efficiency in the use of feed (RFI). Expected response to alternative selection indexes involving different components is also studied. Results: Based on goodness-of-fit to the available feed intake (FI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, WG and FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of FI. The estimated heritabilities of RFI using the model that accounts for animal-specific needs and the traditional RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for MW, WG and FG, respectively. Estimates of genetic correlations of RFI were positive with amount of feed used for WG and FG but negative for MW. Expected response in overall efficiency, reducing FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of RFI was considered. Conclusions: Expected response in overall efficiency, by reducing FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct FI. The relatively small difference between the traditional RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in FI. Overall, a model that accounts for animal-specific needs for MW, WG and FG is statistically superior and allows for the possibility to act differentially on FE components

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model

Background: Indirect genetic efects (IGE) are important components of various traits in several species. Although the intensity of social interactions between partners likely vary over time, very few genetic studies have investigated how IGE vary over time for traits under selection in livestock species. To overcome this issue, our aim was: (1) to ana‑ lyze longitudinal records of average daily gain (ADG) in rabbits subjected to a 5-week period of feed restriction using a structured antedependence (SAD) model that includes IGE and (2) to evaluate, by simulation, the response to selec‑ tion when IGE are present and genetic evaluation is based on a SAD model that includes IGE or not. Results: The direct genetic variance for ADG (g/d) increased from week 1 to 3 [from 8.03 to 13.47 (g/d)2 ] and then decreased [6.20 (g/d)2 at week 5], while the indirect genetic variance decreased from week 1 to 4 [from 0.43 to 0.22 (g/d)2 ]. The correlation between the direct genetic efects of diferent weeks was moderate to high (ranging from 0.46 to 0.86) and tended to decrease with time interval between measurements. The same trend was observed for IGE for weeks 2 to 5 (correlations ranging from 0.62 to 0.91). Estimates of the correlation between IGE of week 1 and IGE of the other weeks did not follow the same pattern and correlations were lower. Estimates of correlations between direct and indirect efects were negative at all times. After seven generations of simulated selection, the increase in ADG from selection on EBV from a SAD model that included IGE was higher (~30%) than when those efects were omitted. Conclusions: Indirect genetic efects are larger just after mixing animals at weaning than later in the fattening period, probably because of the establishment of social hierarchy that is generally observed at that time. Accounting for IGE in the selection criterion maximizes genetic progress.info:eu-repo/semantics/publishedVersio

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota

The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g., humans, pigs, or cattle), it has not been investigated in rabbits. Using a Bayes factor approach, the aim of this study was to dissect the effects of host genetics, litter and cage on 984 microbial traits that are representative of the rabbit microbiota. Analysis of 16S rDNA sequences of cecal microbiota from 425 rabbits resulted in the relative abundances of 29 genera, 951 operational taxonomic units (OTU), and four microbial alpha-diversity indices. Each of these microbial traits was adjusted with mixed linear and zero-inflated Poisson (ZIP) models, which all included additive genetic, litter and cage effects, and body weight at weaning and batch as systematic factors. The marginal posterior distributions of the model parameters were estimated using MCMC Bayesian procedures. The deviance information criterion (DIC) was used for model comparison regarding the statistical distribution of the data (normal or ZIP), and the Bayes factor was computed as a measure of the strength of evidence in favor of the host genetics, litter, and cage effects on microbial traits. According to DIC, all microbial traits were better adjusted with the linear model except for the OTU present in less than 10% of the animals, and for 25 of the 43 OTU with a frequency between 10 and 25%. On a global scale, the Bayes factor revealed substantial evidence in favor of the genetic control of the number of observed OTU and Shannon indices. At the taxon-specific level, significant proportions of the OTU and relative abundances of genera were influenced by additive genetic, litter, and cage effects. Several members of the genera Bacteroides and Parabacteroides were strongly influenced by the host genetics and nursing environment, whereas the family S24-7 and the genus Ruminococcus were strongly influenced by cage effects. This study demonstrates that host genetics shapes the overall rabbit cecal microbial diversity and that a significant proportion of the taxa is influenced either by host genetics or environmental factors, such as litter and/or cage