Genetic parameters of sow feed efficiency during lactation and its underlying traits in a Duroc population

As a result of the genetic selection for prolificacy and the improvements in the environment and farms management, litter size has increased in the last few years so that energy requirements of the lactating sow are greater. In addition, selection for feed efficiency of growing pigs is also conducted in maternal lines, and this has led to a decrease in appetite and feed intake that is extended to the lactation period, so the females are not able to obtain the necessary energy and nutrients for milk production and they mobilize their energetic reserves. When this mobilization is excessive, reproductive and health problems occur which ends up in an early sow culling. In this context, it has been suggested to improve feed efficiency at lactation through genetic selection. The aim of this study is to know, in a Duroc population, the genetic determinism of sow feed efficiency during lactation and traits involved in its definition, as well as genetic and environmental associations between them. The studied traits are daily lactation feed intake (dLFI), daily sow weight balance (dSWB), backfat thickness balance (BFTB), daily litter weight gain (dLWG), sow residual feed intake (RFI) and sow restricted residual feed intake (RRFI) during lactation. Data corresponded to 851 parities from 581 Duroc sows. A Bayesian analysis was performed using Gibbs sampling. A four-trait repeatability animal model was implemented including the systematic factors of batch and parity order, the standardized covariates of sow weight (SWf) and litter weight (LWs) at farrowing for all traits and lactation length for BFTB. The posterior mean (posterior SD) of heritabilities were: 0.09 (0.03) for dLFI, 0.37 (0.07) for dSWB, 0.09 (0.03) for BFTB, 0.22 (0.05) for dLWG, 0.04 (0.02) for RFI and null for RRFI. The genetic correlation between dLFI and dSWB was high and positive (0.74 (0.11)) and null between dLFI and BFTB. Genetic correlation was favourable between RFI and dLFI and BFTB (0.71 (0.16) and −0.69 (0.18)), respectively. The other genetic correlations were not statistically different from zero. The phenotypic correlations were low and positive between dLFI and dSWB (0.27 (0.03), dSWB and BFTB (0.25 (0.04)), and between dLFI and dLWG (0.16 (0.03)). Therefore, in the population under study, the improvement of the lactation feed efficiency would be possible either using RFI, which would not have unfavourable correlated effects, or through an index including its component traits.info:eu-repo/semantics/acceptedVersio

Use of group records of feed intake to select for feed efficiency in rabbit

Models for genetic evaluation of feed efficiency (FE) for animals housed in groups when they are either fed ad libitum (F) or on restricted (R) feeding were implemented. Definitions of FE on F included group records of feed intake (¯FI_F) and individual records of growth rate (GF) and metabolic weight (MF). Growth rate (GR) as FE measurement on R was used. Data corresponded to 5,336 kits from a rabbit sire line, from 1,255 litters in 14 batches and 667 cages. A five-trait mixed model (also with metabolic weight on R, MR) was implemented including, for each trait, the systematic effects of batch, body weight at weaning, parity order and litter size; and the random effects of litter, additive genetic and individual. A Bayesian analysis was performed. Conditional traits such as ¯FI_F |M_F,G_F and G_F |M_F,¯FI_F were obtained from elements of additive genetics ( ( ¯FI_F |M_F,G_F )_g and ( G_F |M_F,¯FI_F )_g ) or phenotypic (( ¯FI_F |M_F,G_F )_p and ( G_F |M_F,¯FI_F )_p ) (co)variance matrices. In the first case, heritabilities were low (0.07 and 0.06 for ( ¯FI_F |M_F,G_F )_g and ( G_F |M_F,¯FI_F )_g, respectively) but null genetic correlation between the conditional and conditioning traits is guaranteed. In the second case, heritabilities were higher (0.22 and 0.16 for ( ¯FI_F |M_F,G_F )_p and ( G_F |M_F,¯FI_F )_p, respectively) but the genetic correlation between ( ¯FI_F |M_F,G_F )_p and G_F was moderate (0.58). Heritability of GR was low (0.08). This trait was negatively correlated with ( G_F |M_F,¯FI_F )_p and ( G_F |M_F,¯FI_F )_gof animals on F, which indicate a different genetic background. The correlation between GR and GF was also low to moderate (0.48) and the additive variance of GF was almost 4 times that of GR, suggesting the presence of a substantial genotype by feeding regimen interaction.info:eu-repo/semantics/acceptedVersio

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

Background: 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. Results: 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. Conclusions: 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. © 2022, The Author(s)

A Thioredoxin Domain-Containing Protein Interacts with Pepino mosaic virus Triple Gene Block Protein 1

Pepino mosaic virus (PepMV) is a mechanically-transmitted tomato pathogen of importance worldwide. Interactions between the PepMV coat protein and triple gene block protein (TGBp1) with the host heat shock cognate protein 70 and catalase 1 (CAT1), respectively, have been previously reported by our lab. In this study, a novel tomato interactor (SlTXND9) was shown to bind the PepMV TGBp1 in yeast-two-hybrid screening, in vitro pull-down and bimolecular fluorescent complementation (BiFC) assays. SlTXND9 possesses part of the conserved thioredoxin (TRX) active site sequence (W__PC vs. WCXPC), and TXND9 orthologues cluster within the TRX phylogenetic superfamilyclosesttophosducin-likeprotein-3. InPepMV-infectedandhealthyNicotianabenthamiana plants,NbTXND9mRNAlevelswerecomparable,andexpressionlevelsremainedstableinbothlocal and systemic leaves for 10 days post inoculation (dpi), as was also the case for catalase 1 (CAT1). To localize the TXND9 in plant cells, a polyclonal antiserum was produced. Purified α-SlTXND9 immunoglobulin (IgG) consistently detected a set of three protein bands in the range of 27–35 kDa, in the 1000 and 30,000 g pellets, and the soluble fraction of extracts of healthy and PepMV-infected N. benthamiana leaves, but not in the cell wall. These bands likely consist of the homologous protein NbTXND9 and its post-translationally modified derivatives. On electron microscopy, immuno-gold labellingofultrathinsectionsofPepMV-infectedN.benthamianaleavesusingα-SlTXND9IgGrevealed particle accumulation close to plasmodesmata, suggesting a role in virus movement. Taken together, this study highlights a novel tomato-PepMV protein interaction and provides data on its localization in planta. Currently, studies focusing on the biological function of this interaction during PepMV infection are in progress

Genotype x dose of artificial insemination interaction for buck fertility

Ponencia publicada en ITEA, vol.104El 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.The aim of this research was to estimate genetic parameters of male fertility after AI with three different types of AI doses obtained from ejaculates of bucks belonging to the Caldes line: 1) type 10: doses with 10 x 106 spermatozoa/ml and a period of 24 h of storage at 18ºC in a saline extender A. 2) type 40: doses with 40 x 106 spermatozoa/ml and the same storage conditions as type 10. 3) type X, doses prepared with semen diluted (1:5) with a saline extender B, but with unknown sperm dosage and no storage period. 3,628 AI were performed with the type 10 doses using crossbred females, 3,027 with the type 40 doses and the same population of females, and 5,779 with the type X doses, using purebred M. Piles et al. ITEA (2008), Vol. 104 (2), 160-168 16 females from the Caldes line in a different farm. Fertility after AI with type 10 doses (F10), type 40 doses (F40) and type X doses (FX) was considered as three different binary traits. Data were analyzed under a three-trait threshold model. The mean of the marginal posterior distribution (MPD) for F10 minus F40 was estimated to be -0.13. This result indicates a clear effect of the sperm dosage on fertility, which could be non-linear. The mean of the MPD of F10 minus FX and F40 minus FX were respectively, -0.37 and -0.23 which indicates that the effect of the storage conditions on fertility could be even more important on fertility than sperm dosage, since FX was very close to fertility after NM and sperm dosage of this type of doses was in average lower than 50 x 106 spermatozoa/ml. Heritabilities seem to be similar for F10 and F40 and both of them could be higher than heritability of male fertility after NM and FX. Variance of the genotype x sperm dosage interaction was almost negligible since additive variances were similar for F10 and F40 and their genetic correlation was close to 1. However, this interaction could be more important between the genotype and the storage conditions

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.

Individual efficiency for the use of feed resources in rabbits

[EN] A Bayesian procedure, which allows consideration of the individual variation in the feed resource allocation pattern, is described and implemented in 2 sire lines of rabbit (Caldes and R). The procedure is based on a hierarchical Bayesian scheme, where the first stage of the model consists of a multiple regression model of feed intake on metabolic BW and BW gain. In a second stage, an animal model was assumed including batch, parity order, litter size, and common environmental litter effects. Animals were reared during the fattening period (from weaning at 32 d of age to 60 d of age) in individual cages on an experimental farm, and were fed ad libitum with a commercial diet. Body weight (g) and cumulative feed intake (g) were recorded weekly. Individual BW gain (g) and average BW (ABW, g) were calculated from these data for each 7-d period. Metabolic BW (g(0.75)) was estimated as ABW(0.75). The number of animals actually measured was 444 and 445 in the Caldes and R lines, respectively. Marginal posterior distributions of the genetic parameters were obtained by Gibbs sampling. Posterior means (posterior SD) for heritabilities for partial coefficients of regression of feed intake on metabolic BW and feed intake on BW gain were estimated to be 0.35 (0.17) and 0.40 (0.17), respectively, in the Caldes line and 0.26 (0.19) and 0.27 (0.14), respectively, in line R. The estimated posterior means (posterior SD) for the proportion of the phenotypic variance due to common litter environmental effects of the same coefficients of regression were respectively, 0.39 (0.14) and 0.28 (0.13) in the Caldes line and 0.44 (0.22) and 0.49 (0.14) in line R. These results suggest that efficiency of use of feed resources could be improved by including these coefficients in an index of selection.Research was supported by INIA SC00-011. The authors acknowledge comments and suggestions made by M. Baselga and A. Blasco from the Universidad Politécnica de Valencia (Spain) and R. Rekaya for his assistance in solving numerical problems.Piles, M.; García-Tomas, M.; Rafel, O.; Ibañez Escriche, N.; Ramon, J.; Varona, L. (2007). Individual efficiency for the use of feed resources in rabbits. Journal of Animal Science. 85(11):2846-2853. https://doi.org/10.2527/jas.2006-218S284628538511Blasco, A. (2001). The Bayesian controversy in animal breeding. Journal of Animal Science, 79(8), 2023. doi:10.2527/2001.7982023xBlasco, A., Piles, M., & Varona, L. (2003). A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits. Genetics Selection Evolution, 35(1). doi:10.1186/1297-9686-35-1-21Cameron, N. D., & Thompson, R. (1986). Design of multivariate selection experiments to estimate genetic parameters. Theoretical and Applied Genetics, 72(4), 466-476. doi:10.1007/bf00289528Estany, J., Camacho, J., Baselga, M., & Blasco, A. (1992). Selection response of growth rate in rabbits for meat production. Genetics Selection Evolution, 24(6), 527. doi:10.1186/1297-9686-24-6-527Gelman, A., & Rubin, D. B. (1992). Inference from Iterative Simulation Using Multiple Sequences. Statistical Science, 7(4), 457-472. doi:10.1214/ss/1177011136Geyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science, 7(4), 473-483. doi:10.1214/ss/1177011137Gianola, D., & Sorensen, D. (2004). Quantitative Genetic Models for Describing Simultaneous and Recursive Relationships Between Phenotypes. Genetics, 167(3), 1407-1424. doi:10.1534/genetics.103.025734MIGNON-GRASTEAU, S. (1999). Genetic parameters of growth curve parameters in male and female chickens. British Poultry Science, 40(1), 44-51. doi:10.1080/00071669987827Paracchini, V., Pedotti, P., & Taioli, E. (2005). Genetics of Leptin and Obesity: A HuGE Review. American Journal of Epidemiology, 162(2), 101-114. doi:10.1093/aje/kwi174Piles, M., Gianola, D., Varona, L., & Blasco, A. (2003). Bayesian inference about parameters of a longitudinal trajectory when selection operates on a correlated trait1. Journal of Animal Science, 81(11), 2714-2724. doi:10.2527/2003.81112714xPiles, M., Gomez, E. A., Rafel, O., Ramon, J., & Blasco, A. (2004). Elliptical selection experiment for the estimation of genetic parameters of the growth rate and feed conversion ratio in rabbits1. Journal of Animal Science, 82(3), 654-660. doi:10.2527/2004.823654xRauw, W. M., Luiting, P., Verstegen, M. W. A., Vangen, O., & Knap, P. W. (2000). Differences in food resource allocation in a long-term selection experiment for litter size in mice 2. Developmental trends in body weight against food intake. Animal Science, 71(1), 39-47. doi:10.1017/s1357729800054874Rauw, W. M., Knap, P. W., Verstegen, M. W. A., & Luiting, P. (2002). Food resource allocation patterns in lactating females in a long-term selection experiment for litter size in mice. Genetics Selection Evolution, 34(1). doi:10.1186/1297-9686-34-1-83Rekaya, R., Carabaño, M. J., & Toro, M. A. (2000). Bayesian Analysis of Lactation Curves of Holstein-Friesian Cattle Using a Nonlinear Model. Journal of Dairy Science, 83(11), 2691-2701. doi:10.3168/jds.s0022-0302(00)75163-0Rekaya, R., Weigel, K. A., & Gianola, D. (2001). Hierarchical nonlinear model for persistency of milk yield in the first three lactations of Holsteins. Livestock Production Science, 68(2-3), 181-187. doi:10.1016/s0301-6226(00)00239-6Varona, L., Moreno, C., Garcia Cortes, L. A., & Altarriba, J. (1998). Bayesian Analysis of Wood’s Lactation Curve for Spanish Dairy Cows. Journal of Dairy Science, 81(5), 1469-1478. doi:10.3168/jds.s0022-0302(98)75711-xWakefield, J. C., Smith, A. F. M., Racine-Poon, A., & Gelfand, A. E. (1994). Bayesian Analysis of Linear and Non-Linear Population Models by Using the Gibbs Sampler. Applied Statistics, 43(1), 201. doi:10.2307/298612

Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations

The final publication is available at Springer via http://dx.doi.org/10.1016/j.rse.2016.02.048Earth Observation (EO) technology is today at a maturity level that allows deriving operational estimates of Surface Soil Moisture (SSM) from a variety of sensors; yet, such products are provided at present at a coarse spatial and/or temporal resolution, which restricts their use in local or regional scale studies and practical applications. Herein, a methodology to derive SSM estimates from space at previously unattained spatio-temporal resolutions is proposed. The method is based on a variant of thePeer ReviewedPostprint (author's final draft