Optimizing sheep growth curves using a meta-heuristic algorithm.

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research.

Genome-wide diversity, linkage disequilibrium, and admixture in the main Colombian Creole pig breeds.

Colombian Creole pigs have adapted to tropical conditions for over 500 years. They have been modified by natural and artificial selection in different regions. At present, the diversity and current introgression status are unknown. The objective was to estimate the genomic diversity, linkage disequilibrium, population structure, and admixture of four Colombian pig breeds and their relationship with other breeds worldwide. Three Colombian pig breeds (SPE-San Pedreño, 11 samples; ZUN-Zungo, 11 samples; CM-Casco de Mula, ten samples) from the conservation nucleus and one biotype not recognized as a breed (CCH-Criollo Chocoano, seven samples) were genotyped using the Illumina GGP-Porcine80K chip. Open-access data from seven international breeds were also included. Colombian Creole pigs showed moderate genetic differentiation (FST 0.14) globally, but several groups of animals separated, suggesting local clustering due to geographical isolation or different founding effects. Colombian Creole pigs showed breed imprinting and specific grouping in all analyses except for CCH, which, like the Ecuadorian Creole, was a cluster of admixtures. The Colombian Creole pigs revealed a significant relationship with the Iberian pig and some other breeds to varying degrees. However, good maintenance of the conservation nucleus was evidenced. Potential adaptive genes, mainly related to immunological functions, were found, according to FST and pcadapt analyses. This study provides a foundation and scientific data for policy decisions on zoogenetic resources.

Genomic diversity study of highly crossbred cattle population in a Low and Middle Tropical environment.

Milk production in tropical regions plays a crucial role both economically and socially. Typically, animals are utilized for dual purposes and are genetically obtained by an intense crossbreeding between Zebu and/or locally adapted breeds, alongside specialized breeds for dairy production. However, uncontrolled mating and crossbreeding may affect the establishment of an effective animal breeding program. The objective of this study was to evaluate Genomic diversity of highly crossbred cattle population in a Low and Middle Tropical environment. All sampled animals were genotyped using the Genessek GGP Bovine 100 chip (n = 859) and public genomic information from eight breeds were employed as reference. The genetic structure of the population was estimated using a Principal Component, Bayesian clustering and a linkage disequilibrium analysis. PCA results revealed that PC1 explained 44.39% of the variation, associated with the indicus/taurus differentiation, and PC2 explained 14.6% of the variation, attributed to the differentiation of Creole and European components. This analysis underscored a low population structure, attributed to the absence of genealogical tracking and the implementation of non-directed crossbreeding. The clustering shows an average contribution of Zebu, Creole, and European Taurine components in the population was 53.26%, 27.60%, and 19.13%, respectively. While an average LD of 0.096 was obtained for a maximum distance of 400 kb. The LD value was low in this population, probably due to the almost no selection applied and the recombination events that occurred during its development. These findings underscore the value of crossbreeding in tropical dairy production but emphasize the importance of directing the mattings.

SNP Markers: Analysis of Genetic Diversity and Identification of Genomic Regions in Pantaneiro Sheep and Texel Sheep Under Natural Selection.

BACKGROUND: Sheep farming is growing substantially in Brazil, driven by the increasing demand for sheep meat. This rising demand has heightened the focus on sheep, making them the subject of numerous studies, including those centered on genetic analysis. A notable research area involves Pantaneiro sheep, which are indigenous to the Pantanal region of Mato Grosso do Sul and other locations. These sheep are of particular interest due to their adaptation to the unique environmental conditions of the Pantanal, a floodplain characterized by its distinctive climatic and ecological features. This study primarily aimed to conduct a comprehensive genomic analysis of Pantanal sheep subjected to natural selection within the Pantanal region and compare different sample herds using methodological approaches. METHODS: Genomic analysis was performed to examine genetic diversity and structure via GGP50K single nucleotide polymorphism (SNP) analysis. A sample of 192 adult sheep over 4 years old was categorized into seven populations based on location: Six populations comprised Pantaneiro sheep with one Texel sheep population. Outlier SNPs were assessed to pinpoint regions under natural selection, with comparisons between the Pantaneiro and the commercial Texel breeds. All data analyses were conducted using the R programming language, employing specialized genetic analysis packages. These outlier SNPs were detected using three methodologies, PCAdapt, OutFLANK, and FDIST2/fsthet, with false discovery rate (FDR) corrections applied to ensure result accuracy. Each method was evaluated, and the genes associated with the identified SNPs were cross-referenced with the most recent sheep genome database, focusing specifically on genes with known phenotypic traits. RESULTS: Analysis of a sample comprising 192 adult individuals revealed greater genetic variability within the Pantaneiro breed than the Texel breed, highlighting the adaptation of the Pantaneiro breed to the unique Pantanal environment. Conversely, the Texel breed exhibited significantly higher levels of inbreeding, attributed to its controlled breeding practices. Outlier SNPs were detected with notable variation across different methodologies, underscoring the importance of FDR correction in ensuring the reliability and concentration of identified outliers. These outlier SNPs facilitated the identification of genes associated with key phenotypic traits, including hair growth, tissue regeneration, pigmentation regulation, and muscle capacity. CONCLUSION: The integrated analysis of methodologies demonstrated significant efficiency in elucidating the genomic landscape of Pantanal sheep, highlighting the genetic richness inherent in sheep from the Pantanal region of Mato Grosso do Sul. The techniques employed effectively identified outlier SNPs associated with phenotypically relevant genes. These findings, which reveal greater genetic variability and adaptability, underscore the potential of these animals for future research and their significance within Brazilian sheep farming. The Texel breed served as a valuable comparative group, illustrating the limited genetic variability in highly controlled breeding environments.

Nutritional composition, fatty acids profile and immunoglobulin G concentrations of mare milk of the Chilean Corralero horse breed.

The objective of the present study was to characterize the nutritional composition, fatty acid profile, and IgG concentration of the milk produced by Chilean Corralero horse (CCH) mares from breeding farms located in southern Chile. Forty-five milk samples were collected from three of the biggest breeding farms (coded as A, B and C) specialized in breeding and selection of CCH in Chile (15 mares sampled per farm). Farms differed in days in milk (DIM). A negative association between DIM and ash, milk protein, milk solids, saturated fatty acids (SFA), and gross energy (GE) was found, whereas DIM had a positive association with monounsaturated fatty acids (MUFA). Milk components like fat, lactose, and energy content varied independently of DIM, indicating other influencing factors such as farm-specific management practices. Offspring sex moderately affected GE content, with milk from mares bearing female offspring having higher GE. Macronutrient profiles of the CCH mares' milk were within the reported range for other horse breeds but tended to have lower fat and total solids. Compared to cow and human milk, horse milk is richer in lactose and lower in fat and protein. Immunoglobulin G concentration was only affected by the farm (B > A) which could be linked to dietary factors and pasture composition rather than maternal parity or other known factors. Overall, CCH mare milk has notable nutritional characteristics, with implications for both foal health and potential human consumption, posing less cardiac risk compared to cow's milk as indicated by lower atherogenic and thrombogenic indices.

Influence of different environmental challenges on the expression of reproductive traits in Holstein cattle in Southern Brazil.

The aim of this study was to assess the impact of genotype-environment interaction (GEI) on the manifestation of traits such as age at first calving (AFC), age at first service (AFS), and calving interval (CI) through the application of the reaction norm model in Holstein cattle raised in Paraná state, Brazil. Utilizing data from the milk testing service of the Paraná Association of Holstein Cattle Breeders (APCBRH), this study analyzed records from 179,492 animals undergoing their first, second, and third lactations from the years 2012 to 2022. These animals were part of 513 herds spread across 72 municipalities in Paraná. The environmental gradient was established by normalizing contemporary group solutions, derived from the animal model, with the 305-day-corrected milk yield serving as the dependent variable. Subsequently, reaction norms were determined utilizing a Random Regression Model. Spearman's correlation was then applied to compare the estimates of breeding values across different environmental gradients for the studied traits. The highest EG (+ 4) indicates the least challenging environments, where animals experience better environmental conditions. Conversely, lower EG (-4) values represent the most challenging environments, where animals endure worse conditions. The only trait that exhibited a moderate heritability magnitude was AFC (0.23) in the least challenging environmental condition. The other traits were classified as having low heritability magnitudes regardless of the evaluated environmental gradient. While minimal evidence was found for the influence of GEI on CI, a clear GEI effect was observed for AFC and AFS across all environmental gradients examined. A reversal in genotype ranking occurred under extreme environmental conditions. The findings suggest that the best-performing genotype under one environmental gradient may not necessarily excel under another.

High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery.

Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle.

The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ±â€…0.09 for CH4 and 0.21 ±â€…0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.

Genetic selection designed to reduce the amount of enteric methane emission from livestock is a mitigation strategy to ensure more sustainable production over generations since genetic gains are cumulative. Brazil is a large producer of beef, and the Nellore breed (Bos taurus indicus) plays a very important role in this production. There are a few studies evaluating genetic and genomic aspects of enteric methane emission in Nellore cattle. The objectives of the present study were to estimate the heritability of daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to identify genomic regions and candidate genes associated with genetic variation of these traits. The heritability estimates for CH4 and CH4res were of moderate to high magnitude (0.42 ±â€…0.09 and 0.21 ±â€…0.09, respectively). Genome-wide association analyses revealed new loci associated with methane emission in Nellore cattle on chromosomes 5, 11, 17, and 20, where 10 candidate genes were identified, 3 for CH4 and 7 for CH4res. The 2 traits possess sufficient genetic variability to be included as selection criteria in breeding programs.

Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.

BACKGROUND: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Economic utility of Colombian Romosinuano cattle.

The objective of this study was to quantify the economic utility in Romosinuano production systems by developing a bioeconomic model assumed cow-calf, cow-calf plus stocker (CCPS), and complete cycle operations. Each system produced males for sale and females for replacement. Input parameters were established from breed data collected by AGROSAVIA. Revenues were estimated using the official cattle price, and production costs were quantified per activity. In the results, for cow-calf operations, the maximum economic utility was 244.12 USD. CCPS, yielded 231.86 USD, and Complete cycle, 268.94 USD. The genetic progress per generation for W240, W480, W24 and CI was + 3.8 kg, + 5 kg, + 5.9 kg, and -1 d, respectively. The price of livestock was the sensitized variable with the greatest impact on maximum economic utility (± 118.64 USD to ± 155.44 USD), followed by mineral supplementation (16.31 USD to ± 37.34 USD). The sensitized variables with the lowest impact were food (± 1.62 USD to ± 1.8 USD) and health plan supplies (± 6.03 USD to ± 9.13 USD). It is concluded that economic utility defined as a composite trait influenced by the characteristics that shape it favors genetic progress and the identification of animals with optimal performance in different bovine production systems.

Performance and metabolic evaluation in primiparous beef cows under grazing at different breeding ages.

We aimed to evaluate the metabolic and performance differences in primiparous Nellore cows, which became pregnant at 14 or 24-mo old. Thirty-eight cows with 202 ± 5 days of gestation were divided into two treatments according to breeding age: 14 or 24-mo. Cows were evaluated for body weight (BW), body condition score (BCS), carcass characteristics, milk yield, calves's performance, and blood characteristics. The animals were managed in eight paddocks under continuous grazing and evaluated from 90 d before parturition until 240 d after calving. We observed an interaction between breeding age and time (P < 0.01) for cow BW. Both breeding age categories experienced BW loss during parturition, with a concurrent decrease in BCS. However, following their first calving, the BW of 24-mo cows remained stable (P > 0.05), whereas 14-mo cows exhibited a gradual recovery in BW after parturition (P < 0.05). Milk yield was greater in 24-mo animals (P < 0.01), but decreased with increasing milking days (p < 0.05) for both groups. The weight gain calves from the heifers bred at 24-mo was greater (P < 0.01), which reflected in greater BW at weaning. The beta-hydroxybutyrate (ß-OHB) concentration was greater before calving and a marked decrease after parturition (P < 0.05). The 24-mo cows had greater blood ß-OHB (P < 0.01) at prepartum and 30 days after calving. Blood progesterone was greater in 24-mo cows (P > 0.05). Primiparous beef cows that conceive at either 14 or 24-months of age exhibit distinct nutritional requirements and metabolic profiles. Notably, cows that conceive at 24-months of age have the advantage of weaning heavier calves and displaying a more consistent reproductive cycle following their first calving than cows that conceive at 14-months.

Genotype-by-gestational thermal environment interaction and its impact on the future performance of tropical composite beef cattle offspring.

With global warming, there are growing challenges for raising taurine and composite beef cattle populations in tropical regions, including elevated temperatures, limited forage availability, parasite infestation, and infectious diseases. These environmental factors can trigger specific physiological responses in the developing fetus, which may have long-term implications on its performance. Therefore, the main objective of this study was to assess the influence of naturally induced thermal stress during the gestation period on the subsequent performance of tropical composite beef cattle progeny. Furthermore, we aimed to investigate the impact of genotype-by-gestational thermal environment interaction (G×Eg) on traits under selection pressure in the breeding population. A total of 157,414 animals from 58 farms located in various Brazilian states were recorded for birth weight (BW), preweaning weight gain (PWG), yearling weight (YW), hip height (HH), scrotal circumference (SC), and days to first calving (DFC). We first applied a linear regression model to the BW data, which revealed that the last 40 d of gestation were suitable for calculating the mean temperature humidity index (THIg). Subsequent regression analyses revealed that for every 10-unit increase in THIg, detrimental effects of approximately 1.13% to 16.34% are expected for all traits evaluated. Genetic parameters were estimated through a reaction norm model using THIg as the environmental descriptor. The posterior means of heritability estimates (SD) were 0.35 (0.07), 0.25 (0.03), 0.31 (0.03), 0.37 (0.01), 0.29 (0.07), and 0.20 (0.09) for the direct effect of BW, PWG, YW, HH, SC, and DFC, respectively. These estimates varied along the range of THIg values, suggesting a variable response to selection depending on the thermal environment during gestation. Genetic correlation estimates between more divergent THIg values were low or negative for YW, PWG, and DFC, indicating that the best-performing individuals at low THIg values may not perform as well at high THIg values and vice versa. Overall, thermal stress during gestation impacts the future performance of beef cattle offspring. Our results indicate the need for developing effective breeding strategies that take into account G×Eg effects and the re-ranking of breeding animals along the THIg scale, particularly for traits such as DFC that are highly sensitive to thermal stress.

With global warming posing increasing challenges in tropical regions, this study aimed to assess the impact of thermal stress during gestation on the performance of composite beef cattle offspring. Environmental factors such as high temperatures, humidity, limited forage availability, and parasite infestation can elicit physiological responses in the developing fetus, affecting its long-term performance and welfare. Using the temperature humidity index (THIg) of the late gestation as a measure of thermal environment, a reaction norm model was applied to analyze the birth weight, preweaning weight gain, yearling weight, hip height, scrotal circumference, and days to first calving (DFC). Results revealed that increasing THIg values were associated with a detrimental effect in these traits. Genotype-by-environment interaction was found to significantly influence trait variability, with DFC showing the strongest effect. Negative genetic correlations were observed between divergent THIg values, suggesting that individuals performing well in mild thermal environments may not excel in high thermal stress conditions. The heritability estimates varied along the THIg scale, indicating that selection response may vary depending on the thermal environment during gestation. These findings emphasize the need for breeding strategies that account for genotype-by-environment effects and consider the impact of thermal stress on cattle performance.

Comparative assessment of growth curves in Brazilian white Morada Nova hair sheep breed.

The Morada Nova sheep breed is essential for the economy of the semi-arid region of Northeast Brazil, standing out for its adaptability, resistance to parasites and reproductive ability. However, the white variant is endangered, highlighting the importance of studies on its productivity to support conservation efforts. This study focuses on the growth curve of the Morada Nova sheep breed, using nonlinear models and analyzing flock profiles. Total of 764 observations of 165 animals from four farms in Ceará and Rio Grande do Norte, Brazil, were analyzed. Canonical discriminant analysis (CDA) was used for the exploratory analysis and four nonlinear models were used to study the growth curve. Weight from birth to 270 days of age, absolute growth rate (AGR), and the impact of sex on growth curves were assessed. Sex and farm are significant discriminating variables (P < 0.05) for the studied effects (weight and age). Weight was the primary phenotypic biomarker that discriminated between the two indicators, while age was a discriminating indicator only for the core effect. The Gompertz model was the most efficient, presenting the lowest residuals and greatest convergence. The study reveals new information about the growth of Morada Nova sheep, the white variety, including weight differences between the sexes at all analyzed ages and an inflection point before 90 days of age. These discoveries contribute to the understanding of the breed's growth and help in the formulation of conservation strategies.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

The genomic estimated breeding value (GEBV) represents an animal's genetic merit calculated using a combination of phenotypes, pedigree, and genomic information through a procedure known as single-step genomic best linear unbiased prediction (ssGBLUP). The accuracy of a GEBV reflects how closely it correlates with the true breeding value. However, calculating accuracies is not computationally feasible for large datasets with genomic information. In this context, methods for approximating accuracies have been proposed and implemented into genetic evaluations. This study aimed to compare 2 algorithms to approximate accuracies for ssGBLUP. In algorithm 1, genomic contributions are based on the diagonal of the genomic relationship matrix (G), combined with contributions from animal records and pedigrees. In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data for this study were provided by the American Angus Association and included datasets of growth, carcass, and marbling traits. Genotypes were available for 1,235,930 animals, and the pedigree had 12,492,581 animals. We showed that algorithm 2 is better suited for approximating accuracies, as its approximations closely matched the exact accuracy values obtained from the inverse of the mixed model equations.

The ephemeral microbiota: Ecological context and environmental variability drive the body surface microbiota composition of Magellanic penguins across subantarctic breeding colonies.

Environmental microbes routinely colonize wildlife body surface microbiota. However, animals experience dynamic environmental shifts throughout their daily routine. Yet, the effect of ecological shifts in wildlife body surface microbiota has been poorly explored. Here, we sequenced the hypervariable region V3-V4 of the 16S rRNA gene to characterize the body surface microbiota of wild Magellanic penguins (Spheniscus magellanicus) under two ecological contexts: (1) Penguins walking along the coast and (2) Penguins sheltered underground in their nest, across three subantarctic breeding colonies in the Magellan Strait, Chile. Despite ecological contexts, our results revealed that Moraxellaceae bacteria were the most predominant and abundant taxa associated with penguin body surfaces. Nevertheless, we detected colony-specific core bacteria associated with penguin bodies. The most abundant were: Deinococcus in the Contramaestre colony, Fusobacterium in the Tuckers 1 colony, and Clostridium sensu stricto 1 in the Tuckers 2 colony. Our results give a new perspective on the niche environmental hypothesis for wild seabirds. First, the ecological characteristics of each colony were associated with the microbial communities from the nest soil and the body surface of penguins inside the nests. For example, in the colonies with heterogenous vegetation cover (i.e. the Tuckers Islets), there was a similar microbial composition between the nest soil and the body surface of penguins. In contrast, on the more arid colony (Contramaestre), we detected differences in the microbial communities between the nest soil and the body surface of penguins.

Profile of cattle breed sensitivity to the tick Rhipicephalus microplus.

Among cattle, Bos taurus breeds and their crosses are more sensitive to tick infestations than Bos indicus breeds that are more resistant to infestation and more adaptable to tropical climates. The presence of susceptible individuals in herds and inadequate tick control lead to direct and indirect losses in the meat production chain, in addition to increased mortality due to cattle tick fever. The objective of this study was to describe, compare and rank the sensitivity of different breeds of stabled cattle to the tick Rhipicephalus microplus and to present, as an innovative result, a scale called the Tick Ruler. Secondary data on the number of retrieved engorged females, engorged female ticks' weight, egg mass weight and number of larvae were extracted from research reports of experiments conducted over 18 years with eight breeds to describe and report the sensitivity of the breeds to artificial infestation by R. microplus larvae. For analyses, the recovery rate of engorged female ticks and the percentile of dispersion of individuals in their respective races were calculated, and comparison of these percentiles between races was performed. The ranking of the percentiles resulted in the organization of the breeds by their susceptibility to R. microplus; we call this scale the "Tick Ruler." The ruler is a simple, easy-to-understand tool that can be used by technicians and producers to evaluate the tick sensitivity of a breed of interest and can assist producers in decision-making to find a balance between increased production gains and the risk of economic losses depending on the breed composition in a cattle herd.

Identifying selection strategies based on the practices and preferences of small ruminant farmers to improve the sustainability of their breeding systems.

Small ruminant farming is of socio-economic and environmental importance to many rural communities around the world. The SMARTER H2020 project aims to redefine genetic selection criteria to increase the sustainability of the sector. The objective of this study was to analyse the selection and breeding management practices of small ruminant producers and breeders, linked with socio-technical elements that shape them. The study is based on farm surveys using semi-structured interviews conducted in five countries (France, Spain, Italy, Greece, and Uruguay) across 272 producers and breeders of 13 sheep and goat breeds, and 15 breed × system combinations. The information was collected in four sections. The first and second sections dealt with general elements of structure and management of the system and the flock/herd. The third section focused on selection and breeding management practices: criteria for culling and replacement of females, selection criteria for males, use of estimated breeding values and global indexes, and preferences for indexing new traits to increase the sustainability of their system. The fourth section aimed to collect socio-technical information. We used a data abstraction method to standardise the representation of these data. A mixed data factor analysis followed by a hierarchical ascending classification allowed the characterisation of three profiles of selection and breeding management: (1) a profile of producers (n = 93) of small flocks/herds, with little knowledge or use of genetic selection and improvement tools (selection index, artificial insemination, performance recording); these farmers do not feel that new traits are needed to improve the sustainability of their system. (2) a profile of producers (n = 34) of multibreed flocks/herds that rely significantly on grazing; they are familiar with genetic tools, they currently use AI; they would like the indexes to include more health and robustness characteristics, to make their animals more resistant and to increase the sustainability of their system. And (3) a profile of producers or breeders (n = 145) of large flocks/herds, with specific culling criteria; these farmers are satisfied with the current indexes to maintain the sustainability of their system. These results are elements that can be used by private breeding companies and associations to support the evolution of selection objectives to increase the resilience of animals and to improve the sustainability of the small ruminant breeding systems.

Estimation of genetic parameters for maintenance energy requirements and residual feed intake in Nellore cattle.

We estimated heritabilities and genetic and phenotypic correlation estimates for maintenance energy requirements (NEmR), residual feed intake (RFI), growth, carcass and reproductive indicator traits, using data from 41 feed efficiency trials in Brazil, comprising 4381 males and females. Continuous traits were analysed using a linear animal model and threshold traits were analysed using a threshold animal model. The heritability estimates were low for RFI (0.190) and NEmR (0.193); other heritabilities were mainly moderate (growth and carcass traits) or high (sexual precocity traits). The genetic correlation of RFI with NEmR was high (0.701). The genetic correlations of NEmR were low with carcass and reproductive traits, and moderate with growth traits. Thus, selection to improve weaning weight and female sexual precocity indicator traits would not affect maintenance energy requirement. Genetic selection to reduce maintenance energy requirements is feasible and would also reduce DMI and RFI. Selection to improve RFI can be used to identify animals with lower maintenance energy requirements. Long-term selection to reduce RFI and NEmR would have favourable effects on yearling weight, carcass muscle indicator traits and female sexual precocity. Genetic (co)variance component estimates for NEmR, in conjunction with economic values of selection criteria, may be used to develop novel approaches for genetic selection to improve efficiency of beef production.

Genetic parameters for reproductive traits and somatic cell score in U.S. dairy goats.

There are no studies regarding the estimation of genetic parameters and genetic trends for reproductive traits and somatic cells in goats. Their knowledge allows optimization of selection schemes. The objective of this study was to estimate genetic parameters and genetic and phenotypic trends for age at first kidding (AFK), kidding interval (KIN) and somatic cell score (SCS). Analyses were conducted within and across seven US goat breeds, namely, Nubian (NU), Alpine (AL), LaMancha (LM), Toggenburg (TO), Saanen (SA), Nigerian Dwarf (ND) and Oberhasli (OB), and a set of all of these breeds (AB). The restricted maximum likelihood methodology and trivariate animal models were used. Genetic and phenotypic trends were estimated using regression models. The average and standard deviation of AFK, KIN and SCS for AB were 573.6 ± 178.5 days, 418.8 ± 125.5 days and 4.67 ± 2.23 Log2, respectively. The heritabilities (h2) and standard errors of AFK, KIN and SCS for AB were 0.28 ± 0.02, 0.04 ± 0.02 and 0.22 ± 0.01, respectively. The h2 ranged from 0.15 (SA) to 0.37 (NU) for AFK, from 0.04 (AB) to 0.10 (AL) for KIN, and from 0.11 (TO) to 0.26 (LM and ND) for SCS. Genetic correlations between AFK and KIN and between AFK and SCS for AB were positive and weak (0.07 and 0.12, respectively) but significant (P < 0.01). Genetic correlations between SCS and KIN were significant (P < 0.01) for all the breeds and ranged from -0.15 (NU) to 0.44 (AL). Genetic correlations between AFK and SCS in the NU and AL breeds were similar (approximately 0.21). A positive genetic trend was found for KIN in the SA breed, which caused an increase in the number of days between consecutive kiddings. The genetic trend of SCS for the NU, AL and ND breeds was negative and decreased annually, which is beneficial for producers. These first results show the intensity and direction of some favorable/unfavorable relationships between AFK or KIN and SCS Log2 in some U.S. goat genetic groups.