Variance Components Research Articles

A common component of Fama and French factor variances

This is the first study that explicitly explores the risk of the Fama and French equity factors in terms of their realized variances. Our results show that realized factor variances exhibit strong power-law behavior. A striking commonality is that the power-law exponents are close to α ≈ 2 regardless of which factor variance is analyzed. Notably, our novel joint test designed to test Mandelbrot’s infinite variance hypothesis in the cross-section of realized factor variances shows that the null hypothesis of α = 1.9 cannot be rejected, which further corroborates the evidence that (a) there exist a common component governing factor variance risk, and (b) factor variance risk is statistically undefined. Further evidence derived from co-fractality analysis shows that (c) risk diversification appears to be very limited as factor variances tend to exhibit power-law behavior coincidently. We argue that our study has several theoretical and practical implications—especially due to the fact that factor investing reached $5 trillion in assets under management.

Estimation of Genetic Parameters for Milk Production Rate and Its Stability in Holstein Population.

Milk production rate (MPR) refers to the rate of milk secretion per hour (kg/h), calculated from the harvested milk yield and milking interval, and it is considered an appropriate measure to evaluate the production potential of cows. The objective of this study was to estimate the phenotypic and genetic parameters of milk production rate traits. In this study, the milking records of 4760 Holstein cows were collected, and four milk yield traits and six milk production rate traits were defined. The MIXED procedure was used to detect the impacts of non-genetic effects on milk yield and milk production rate traits, including parity, measured season and lactation stage. Variance and covariance components for milk yield and milk production rate traits were estimated using a univariate linear repeatability model. Parity, measurement season and lactation stage had significant effects (p < 0.01) on milk yield, milk production rate and its stability. Milk yield and milk production traits had high heritability, and ranged from 0.25 to 0.39. The stability of milk production rate had low heritability (0.04~0.05). Milk production rate is beneficial for the devolving novel trait in dairy breeding and provides new insights for herd management and genetic selection of production performance of dairy cattle.

Evaluating genotype by environment interaction for growth traits in Limousine cattle

Environmental conditions affect the growth and health of animals, making it crucial to quantify heat stress and the genetic basis of heat tolerance in animal breeding. The main objective of this study was to evaluate heat stress on beef cattle growth and investigate the genetic background of tolerance to harsh environmental conditions in the Italian Limousine beef cattle. Three growth traits were analysed: average daily gain (ADG), weaning weight (WW), and yearling weight (YW). Data were collected from animals raised between 1991 and 2022 and combined with 14 environmental covariates. Records for ADG, WW, and YW encompassed 108 205, 100 058, and 24 939 individuals, respectively, with 4 617, 4 670, and 2 048 genotyped individuals. Climatic variables were compared for inclusion in a linear mixed model using the Deviance Information Criterion. Multiple-trait models and genomic information incorporated environmental conditions with the largest impact on the studied traits Genotype by environment interaction (G × E) was detected in all the studied traits, showing substantial heterogeneity of the variance components across the different environments (Env). Heritability for WW remains constant among Env; instead, for ADG and YW decreased under uncomfortable environmental conditions. YW showed the lowest genetic correlation (0.28) between divergent conditions (Env 2 and Env 5,) for ADG and WW correlations dropped below 0.50 among Env. The values of genetic correlations indicate that growth traits are moderately to strongly affected by G × E. Eigenvalue decomposition of the additive genetic (co)variance matrix for ADG, WW, and YW indicated that three components accounted for over 0.80 of the proportion of the variance explained, suggesting different animal performances across Env. Spearman rank correlations showed potential re-ranking of genotyped sires, because ADG, WW, and YW showed correlations between Env below 0.80. The accuracy of single-step genomic EBV was higher compared to EBV for al traits. Overall, the result confirms the existence of G × E for growth traits in the Italian Limousine population. Including G × E in the model allows for more environment-aware predictions, helping breeders understand how different genetic bases respond to varying conditions. Genomic predictions incorporating G × E could accelerate genetic gains and improve response to selection for heat tolerance.

Reliability of a workplace-based assessment for the United States general surgical trainees’ intraoperative performance using multivariate generalizability theory: a psychometric study

The System for Improving and Measuring Procedure Learning (SIMPL), a smartphone-based operative assessment application, was developed to assess the intraoperative performance of surgical residents. This study aims to examine the reliability of the SIMPL assessment and determine the optimal number of procedures for a reliable assessment. In this retrospective observational study, we analyzed data collected between 2015 and 2023 from 4,616 residents across 94 General Surgery Residency programs in the United States that utilized the SIMPL smartphone application. We employed multivariate generalizability theory and initially conducted generalizability studies to estimate the variance components associated with procedures. We then performed decision studies to estimate the reliability coefficient and the minimum number of procedures required for a reproducible assessment. We estimated that the reliability of the assessment of surgical trainees' intraoperative autonomy and performance using SIMPL exceeded 0.70. Additionally, the optimal number of procedures required for a reproducible assessment was 10, 17, 15, and 17 for postgraduate year (PGY) 2, PGY 3, PGY 4, and PGY 5, respectively. Notably, the study highlighted that the assessment of residents in their senior years necessitated a larger number of procedures compared to those in their junior years. The study demonstrated that the SIMPL assessment is reliably effective for evaluating the intraoperative performance of surgical trainees. Adjusting the number of procedures based on the trainees' training stage enhances the assessment process's accuracy and effectiveness.

Migratory birds modulate niche tradeoffs in rhythm with seasons and life history

Movement is a key means by which animals cope with variable environments. As they move, animals construct individual niches composed of the environmental conditions they experience. Niche axes may vary over time and covary with one another as animals make tradeoffs between competing needs. Seasonal migration is expected to produce substantial niche variation as animals move to keep pace with major life history phases and fluctuations in environmental conditions. Here, we apply a time-ordered principal component analysis to examine dynamic niche variance and covariance across the annual cycle for four species of migratory crane: common crane (Grus grus, n = 20), demoiselle crane (Anthropoides virgo, n = 66), black-necked crane (Grus nigricollis, n = 9), and white-naped crane (Grus vipio, n = 9). We consider four key niche components known to be important to aspects of crane natural history: enhanced vegetation index (resources availability), temperature (thermoregulation), crop proportion (preferred foraging habitat), and proximity to water (predator avoidance). All species showed a primary seasonal niche "rhythm" that dominated variance in niche components across the annual cycle. Secondary rhythms were linked to major species-specific life history phases (migration, breeding, and nonbreeding) as well as seasonal environmental patterns. Furthermore, we found that cranes' experiences of the environment emerge from time-dynamic tradeoffs among niche components. We suggest that our approach to estimating the environmental niche as a multidimensional and time-dynamical system of tradeoffs improves mechanistic understanding of organism-environment interactions.

Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.

Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes

BackgroundReproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses.ResultsVariance components analyses showed a low to moderate heritability (h2) for the traits analysed, ranging from 0.014 for endometritis up to 0.271 for stillbirth, indicating variable degrees of variation within the reproduction traits. For genome-wide association studies, we were able to detect genome-wide significant association signals for nine out of 13 analysed traits after Bonferroni correction on chromosome 6, 18 and the X chromosome. In total, we detected over 2700 associated SNPs encircling more than 90 different genes using the imputed whole-genome sequence data. Functional associations were reviewed so far known and potential candidate regions in the proximity of reproduction events were hypothesised.ConclusionOur results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.

Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Rice is an important food crop with a crucial role in global food security. Study on correlation between morphoagronomic traits and yield is an important step in the development of superior rice varieties. Research data were collected from 20 rice varieties grown in the experimental field. A total of 10 morphoagronomic trait variables were measured, including plant height, number of productive tillers, flowering age, dry weight of biomass, hollow grain, 1,000-grain weight, grain weight, panicle number, panicle length, and harvest index. Data were analyzed using variance, correlation, and principal component analyses. The results of PCA showed that 3 principal components (PC) explained 80.88% of the data variation. PC1 was associated with plant height, flowering age, biomass dry weight, empty grain, panicle length, grain weight per clump. PC2 was associated with 1,000-grain weight and harvest index, while PC3 was associated with number of productive tillers and number of panicles. Correlation analysis showed that 1,000-grain weight was positively correlated with harvest index, while biomass dry weight was negatively correlated. These results suggest that all observed morphoagronomic traits except biomass dry weight have an important role in increasing rice yield. Furthermore, the results of this study provide basic informations for the development of high-yielding rice varieties.

Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle.

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

BackgroundIn this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).MethodsStochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.ResultsBased on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.ConclusionsGenotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Financial Accountability, Resources and Competence of Proprietors, and Performance of Private Universities in Uganda

Abstract: The purpose of this study was to examine the effect of financial accountability on performance of private universities in Uganda. The study is guided by a hypothesis, that financial accountability have a statistically significant effect on the performance of private universities. The study taps into the voices of university stakeholders seeking their opinion on how to address the university performance challenges and 162 sample units were selected and participated in the study. The study follows the post-positivism philosophical stance which calls for the use of mixed techniques, while a descriptive, analytical and cross-sectional research design was utilized. Data was analyzed using two statistical software programs: Analysis of Moments of Structures (AMOS) for structural equation modeling (SEM), and SPSS was used for preliminary data analysis. In contrast, content analysis was employed in the qualitative approach. The results were presented using, Scatter plots, regression analysis, correlation coefficients and percentage tables while SEM was used to perform confirmatory factor analysis, principle component analysis, variance analysis, correlation coefficients and regression analysis. There was positive significant correlation between the financial accountability and the University performance (r = 0.641, P&lt;0.01). The results further indicate that financial accountability individually explains 41.1% (R Square Change = .411) of the variations in University performance. The study found out that all dimensions of financial accountability exhibit a positive and significant relationship with performance. Hence value for money, financial reporting and audited accounts can ensure transparent financial transactions avoid diversion of funds, create robust strategies for mobilizing resources and save funds that can be utilized to improve financial sustainability and education quality in private universities. The study recommends that private universities should establish transparent financial reporting, promote active engagement of proprietors in financial decision making and resource mobilization, adherence to fiscal responsibilities, and effective financial governance as well as training proprietors in university financial management.

Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Study of body measurements and components of variance for various body measurements of goats found in Southern Punjab

Variance components are estimated to further compute genetic parameters, and these components reflect variation of traits as well as their transferability to the next generation. This study was conducted to estimate variance components for different body measurements in Teddy goats in the southern Punjab. Data were subjected to mixed model analysis using ASReml software where restricted maximum likely hood procedure was adopted. These body measurements included diagonal body length, withers height, head length, neck length, ear length, ear width, tail length, distance between ears, and canon circumference. Body measurements were recorded using tailor tape and live weight will be measured by spring balance. The following form of animal model was applied; Yijklm= YOBi+ SOBj + Sexk + TOBl + A +Ԑijklm.. The estimates for variance components were used to compute genetic parameters.

PSI-1 Genetic and non-genetic factors influencing beef cattle attractiveness to horn fly

Abstract Horn flies are the most common summer pests of cattle, leading to substantial economic losses due to impaired production and decreased animal welfare. Genetic and non-genetic factors have been reported to influence cattle susceptibility to horn flies. However, the wide range of heritability estimates, and few identified genomic regions associated with cattle resistance to horn fly may be due to subjective assessment in collecting the fly load phenotype and the small size of the population assessed. Here, beef calves from the UF purebred Brahman (BRA) and Multibreed Angus-Brahman (MAB) populations were video recorded at weaning using a GoPro camera mounted on the top of the chute. Fly infestation was determined as the average fly count obtained from five longitudinal images of each animal using ImageJ software. A total of 748 crossbred and 415 purebred calves born from 2021 to 2023 were evaluated during summer (August or September). Analysis of variance (ANOVA) was performed within herd by fitting linear models that included collection date, age, skin color, coat color, and pre-weaning average daily gain (ADG) as fixed effects, and residual as a random effect. For MAB calves, breed groups (BG) determined based on the percentage of Brahman breed, with BG1 ranging from 0 to 19%, BG2 from 20 to 39%, BG3 from 40 to 59%, BG4 from 60 to 79%, BG5 from 80 to 100%, were also fit in the model. Fly count was transformed to a natural log value (log[count +1]) ensuring normally distribute residuals. As expected, darker colors were significantly more attractive to horn flies, while animals with greaterr percentage of Brahman were less susceptible than animals with greater percentage of Angus breed. Considerable variability in fly load was observed within purebred Brahmans, indicating that additional genetic factors, other than breed, influence cattle resistance to flies. Positive estimates were observed between horn fly load and average daily gain in both herds, which can be due to the fly load below the previously reported economic injury level of 200 flies (BRA: 28.78 ± 38.20, MAB: 19.31 ± 26.72). Variance components were estimated within herds by fitting the same aforementioned effects and a genomic-based relationship matrix (G) constructed with genotypes from the GeneSeek Genomic Profiler F250 SNP chip. The moderate heritability estimate demonstrates the feasibility of selecting cattle resistant to horn flies. Overall, our findings have the potential to contribute to reducing losses and costs in beef cattle operations, while reducing the impact on the environment of overuse of insecticides.

PSLBII-20 Estimation of genetic parameters for methane emissions and residual feed intake in Nellore cattle

Abstract The objective of this study was to obtain (co)variance components, heritability, and genetic correlation estimates for enteric methane emissions, residual feed intake, growth, carcass and reproductive indicator traits. Data from 43 feed efficiency trials conducted over a 13-yr period on a single purebred Nellore herd in Central Brazil, comprising 3,400 intact males and 2,167 females, were analyzed. Trial data along with diet NDF content were used to estimate the methane emission (g CH4/kg carcass gain) for each individual, using an equation developed by Medeiros et al., 2014: CH4 (g/d) = -0.1011 + 0.02062*DMI (kg/d) + 0.001648*NDF (% of DMI). Other data included pedigree information, body weight (BW), carcass traits, and reproductive indicator traits. Continuous traits were analyzed using a linear animal model, and threshold traits were analyzed using a threshold animal model. Two-trait analyses were performed using the restricted maximum likelihood to estimate the variance components, heritabilities and genetic and phenotypic correlations among traits. The estimated heritability of methane emissions was 0.136 ± 0.0314. The genetic correlations of methane emissions with growth and carcass traits were low and not different from zero: BW at 450 d (0.064 ± 0.140), scrotal circumference at 365 d (0.188 ± 0.155), longissimus muscle area (-0.090 ± 0.132), 12th-13th rib fat (-0.122 ± 0.142), rump fat (0.080 ± 0.119), intramuscular fat % (-0.108 ± 0.152), BW at 210 d (-0.077 ± 0.168). By contrast, there were the low genetic correlation of methane emissions with early calving probability (-0.260; -0.504 – 0.026); moderate genetic correlations of methane emissions with age at first calving (0.519 ± 0.202) and DM intake (0.512 ± 0.116); and the high correlation of methane with residual feed intake (RFI; 0.832 ± 0.073). Thus, selection to improve weaning and yearling weights, and carcass traits would not affect methane emissions. Genetic selection to reduce methane emissions is feasible and would also reduce DMI and RFI, as well as female sexual precocity traits. Conversely, selection to improve RFI can be used to identify animals with decreased methane emissions. Medeiros, S. R, L. G. Baioni, A. Berndt, M. C. Freua, T. Z. Albertini, C. Costa Jr., e G. B. Feltrin (2014) Modeling enteric methane emission from beef cattle in Brazil: A proposed equation performed by principal component analyses and mixed modeling multiple regression. Proceedings, Livestock, Climate Change and Food Security Conference, Madrid.

PSVII-11 Genetic parameter estimates for lamb survival in prolific and nonprolific ewes

Abstract Given the increasing global demand for animal products, increasing lamb survival is a key breeding objective for sheep. However, high prolificacy can be associated with increases in fetal and lamb mortality. Therefore, the goal of this study was to estimate the variance components and genetic parameters for lamb survival (LS), using a: 1) single-trait model, where all ewes are analyzed together; and 2) two-trait model, where ewes are grouped in two groups based on birth type [i.e., Group 1 (G1) = single lamb/ewe, and Group 2 (G2) = multiple lambs/ewe]. In both models, LS was defined as the survival of lamb from birth to weaning and analyzed as a threshold trait. A total of 1,040,629 records (215,075 from single and 825,554 from multiple birth) from 319,467 ewes from several breeds/breed groups were available for the study. There were no females with two different types of births. Variance components were estimated through Bayesian inference via Gibbs sampling implemented in the GIBBSF90+ program, fitting a two-trait animal model with maternal effects, in which breed/breed groups were included as fixed effects. Average number of lambs/ewe in G2 was 2.57 (SD = 0.72). Heritability estimated (± SE) for LS on the underlying scale using the single-trait (0.04 ± 0.10) and two-trait model (0.010 ± 0.001 for LSG1 and 0.031 ± 0.004 for LSG2) were low. Maternal heritability estimated by the single-trait model was 0.007 ± 0.043, while by the two-trait model the estimates were 0.010 ± 0.006 and 0.040 ± 0.005 for LSG1 and LSG2, respectively. The genetic correlation estimated between LSG1 and LSG2 was weak and positive (0.25 ± 0.21). The low non-significant genetic correlation between single and multiple birth groups suggests that lamb survival is a different genetic trait in different birth types. Therefore, selecting for improving LS in nonprolific ewes may not necessarily translate to improving LS in prolific ewes. This underscores the need for careful consideration and targeted breeding strategies to achieve desired outcomes in nonprolific and prolific sheep simultaneously. Further studies to define the optimal statistical approach to genetically evaluate LS in prolific and nonprolific ewes are warranted.

PSX-4 Exploring the impact of age and body condition score on the genetic analysis of mature cow weight in American Angus cattle

Abstract Selecting sires with optimal genetic merit for mature weight (MWT) is crucial for profitability of beef producers, ensuring cost-effective production and increased economic returns per animal. The main objective of this study was to determine how different statistical approaches [e.g., including the fixed effects (FE) of age and body condition score (BCS) in the statistical model and/or pre-adjusting the weights for these effects] affect variance components estimation and genetic parameters for MWT, and their impact in the posterior selection decisions. The dataset provided by the American Angus Association (AAA) comprised 80,296 MWT records from 37,934 cows aged 2 to 15 yr. Contemporary groups (CG) were defined by concatenating herd, year, and season of measurement. Model 1 analyzed unadjusted MWT with CG and BCS as categorical FE and age as a covariable (quadratic). Model 2 was model 1 without BCS, model 3 was model 1 without age, and model 4 considered CG as the only FE. In model 5, MWT was pre-adjusted for 6 yr of age and BCS of 6, and CG was the only FE included in the statistical model. Additive genetic and permanent environment (pe) random effects were included in all statistical models except model 5, which did not include pe (only one adjusted record per animal). Variance components were calculated using REML implemented in the BLUPf90+ family of programs. Sires with at least 10 offspring (n = 1,483) were ranked according to their breeding values (EBVs) predicted for MWT, and all and the top 10% sires were used to estimate the rank correlation across models. The heritability (± SE) estimated for MWT was 0.43 (± 0.011), 0.42 (± 0.011), 0.70 (± 0.008), 0.72 (± 0.082), and 0.37 (± 0.013) for models 1, 2, 3, 4, and 5, respectively. Repeatability values were 0.64, 0.67, 0.72, and 0.76 for models 1, 2, 3, and 4, respectively. The EBV ranks were highly correlated between models 1 and 2 (Top 10%: 0.96; All: 0.99), and models 3 and 4 (Top 10%: 0.99; All: 0.99). Inflated heritabilities in models 3 and 4 suggest that the effect of age is confounded with the additive genetic effect, which will not translate to greater genetic progress. Therefore, disregarding age in the genetic evaluation of MWT is not recommended. Similarities between models 1 and 2 suggest that BCS has a relatively small impact on MWT, probably related to the relatively low genetic correlation between these traits. Models 1 and 5 cannot be directly compared, as they use slightly different response variables (body weight vs. pre-adjusted weight) and may lead to slightly different selection decisions (correlation between Top 10%: 0.85; All: 0.95). Further investigations in this field are warranted.

Economic Evaluation of Conservation through Use of an Araucaria angustifolia Provenance and Progeny Test.

Araucaria angustifolia is a species known for its valuable wood and nuts, but it is threatened with extinction. The plantation of forests for genetic resource conservation is a complementary strategy designed to reduce the species' genetic variability loss. This study aimed to evaluate the technical and economic viability of A. angustifolia for genetic conservation through use. The analyzed provenance and progeny trial was established in 1982 in Itapeva, Brazil. It was structured using a compact family blocks design with 110 open-pollinated progenies from five natural populations, three replicates, ten plants per subplot, and 3.0 m × 2.0 m spacing. After 33 years, the trial was evaluated for total height, diameter at breast height, wood volume, and survival. The variance components and genetic parameter estimates were performed using Restricted Maximum Likelihood/Best Linear Unbiased Prediction methods (REML/BLUP) methods with the Selegen software (version 2014). The production and management scenarios were obtained using the SisAraucaria software (version 2003). Sensitivity analysis and economic parameter estimates were obtained through various economic evaluation methods using the Planin software (version 1995). In general, the genetic parameters indicated that the population has enough variability for both conservation and breeding purposes, suggesting technical viability for the establishment of a seed orchard. The economic parameters indicated that the commercialization of wood and araucaria nuts proved to be more profitable than wood production by itself. In conclusion, araucaria genetic conservation through use is a technically and economically viable ex situ conservation strategy.

PSX-7 Heritability estimates for longevity-related traits in U.S. Katahdin ewes

Abstract Genetic selection for increased ewe longevity can directly contribute to improved flock profitability and welfare. Therefore, this study aimed to estimate heritabilities for eight longevity-related traits in 12,149 Katahdin ewes. The ewes were daughters of 1,157 sires and 6,147 dams and were born between 1989 and 2020 in 58 flocks located across the U.S. The National Sheep Improvement Program provided the data. Age at last lambing (ALL); productive life (PL; number of days between the first and last lambing), total number of litters (TLI), lambs born (TNB), and lambs weaned (TNW) over the lifetime of the ewe; total litter weight at lambing (TLB) and weaning (TLW) over the lifetime of the ewe, and TLW divided by the weight of the ewe adjusted for 120 d (TLWadj) were evaluated (Table 1). Body weights (BW) at lambing and weaning (WW) were adjusted for sex effect, while WW was also adjusted for 60 d old. Ewes with lambing interval and age at the first lambing greater than 720 d and 1,095 d, respectively, were excluded. Moreover, only flocks that reported lambing records across at least 7 consecutive years and an average of 20 or more lambing records over the yr were evaluated. Fixed effects of contemporary group (flock, year, and season of birth of the ewe), the combined effect of the birth type and rearing type of the ewe, and age class at first lambing, as well as the animal random effect, were included in the animal model fitted. Contemporary groups with less than three ewes were excluded. Moreover, we excluded records deviating ± ± 3.0 SD from the mean. Variance components were estimated by restricted maximum likelihood based on pedigree (by fitting the A matrix) and genomic information [by fitting an H matrix including pedigree and genomic relationships from 10,032 animals genotyped with a 50K single nucleotide polymorphisms (SNP) chip]. The criteria used to exclude SNP were MAF &amp;lt; 0.05, call rate &amp;lt; 0.95 (for both SNP and animals), and a maximum difference of 0.15 between the observed and expected heterozygosity (Hardy-Weinberg equilibrium). After quality control, 31,182 SNP were used for the analyses. The analyses were performed using the BLUPF90+ programs. The pedigree-based heritability estimates were: ALL (0.10 ± 0.017), PL (0.11 ± 0.023), TLI (0.10 ± 0.017), TNA (0.11 ± 0.017), TNW (0.11 ± 0.018), TLB (0.12 ± 0.020), TLW (0.10 ± 0.024), and TLWadj (0.07 ± 0.031). The genomic-based heritability estimates were: ALL (0.13 ± 0.018), PL (0.14 ± 0.024), TLI (0.13 ± 0.018), TNB (0.13 ± 0.018), TNW (0.14 ± 0.018), TLB (0.15 ± 0.021), TLW (0.11 ± 0.024), and TLWadj (0.07 ± 0.032). The relative standard error (RSE) of the estimates obtained with the A matrix ranged from 15.5% (TNB) to 44.3% (TLWadj) and from 12.9% (TNW) to 45.7% (TLWadj) when fitting the H matrix. In conclusion, longevity-related traits in U.S. Katahdin ewes are heritable, and estimates are slightly greater (with reduced RSE) when incorporating genomic information to estimate variance components.

PSX-5 Repeatability of overreach, term and acuteness in Peruvian Paso horse

Abstract The Peruvian Paso Horse is considered one of the best saddle horses worldwide due to the gait that makes this horse perform one of the smoothest walks. The overreach, term, and acuteness are the most characteristic movements in the gait, making them the most desirable attributes in its breeding and reproduction. Repeatability of overreach, term, and acuteness in Peruvian Paso horses (PPH) have not been determined to date. It is important to estimate this parameter for application in PPH breeding, therefore, the aim of this study was to estimate the repeatability of overreach, term, and acuteness of PPHs. The study included records of 137 stallions and mares. All measurements were recorded in MP4 video format with a resolution of 1920 x 1080 megapixels and at 60 frames per second. All traits were measured three times (once per stride), and each trait was analyzed. KINOVEA software version 0.9.5 was used to analyze the measurements. A multivariate repeated measures animal model with sex effect was used to estimate the variance components for each trait using WOMBAT software. Repeatability was high in all traits (&amp;gt; 0.78; Table 1). The low standard error values close to 0.02 indicate high precision in all traits (0.904, 0.855, and 0.903 for overreach, term, and acuteness, respectively), being defined as the variance of the mean of “n” measurements as a percentage of the variance of one measurement, indicating that this parameter has little effect on the temporal environment of the three traits. These results demonstrate that previous training can influence the regularity of the gait, in agreement with the present study, in which only adult animals that have already had previous training were evaluated. Furthermore, our results corroborate that traits with high repeatability require few measurements (3 in this study) to obtain greater precision, and an increase in the number of measurements may be irrelevant for parameter estimation. In a breeding plan, this high precision may be due to an increase in the additive genetic variance, indicating that two or more measurements have greater heritability than a single measurement, allowing a better breeding value estimate to be obtained. Considering the high repeatability values found, this parameter can be used as an indicator of how effective the selection process can be, considering its relationship with heritability, as repeatability values are higher than heritability, due to the inclusion of permanent environmental variance (within-individual variance) in its estimation. High values also indicate that individuals tend to have consistent performance, and therefore, obtaining several measures for evaluation may be impractical. It can be concluded that overreach, term, and acuteness have high repeatability values, which allows these traits to respond better in a selection process and performance evaluation for subsequent competitions.