Permanent Environment Research Articles

A Study on the Relationship between Recreation Benefit Perception and Frequency of Participation in Recreation Activities

The objective of this study is to investigate university students' perceptions of the benefits of recreational activities and their frequency of participation in such activities and to explain a relationship between these two variables. A quantitative research method was employed in this study, with a preference for utilizing a questionnaire as the data collection technique. The participants in the study were undergraduate students from Harran University Osmanbey Campus. A survey was conducted with 683 participants. The study revealed significant differences in the frequency of participants' engagement in activities based on their demographic characteristics, including participants' gender, age, school of higher education or faculty, grade level, weighted grade point averages, monthly income, hometown region, permanent residence environment and place of residence. The study also demonstrated significant differences in the benefit perceived by the participants from recreation activities based on their demographic characteristics, including participants' age, school of higher education or faculty, grade level, monthly income, hometown region and place of residence. Additionally, the study found a significant positive relationship between the frequency of participation in recreational activities and all sub-dimensions of the perceived benefits of such activities.

Quantifying the effects of the mitochondrial genome on milk production traits in dairy cows: empirical results and modelling challenges

Significant advances in livestock traits have been achieved primarily through selection strategies targeting variation in the nuclear genome, with little attention given to mitogenome variation. We analyzed the influence of the mitogenome on milk production traits of Holstein cattle in Croatia based on strategically generated next-generation sequencing data for 109 cows pedigree-linked to 7115 milk production records (milk, fat and protein yield) from 3006 cows (first 5 lactations). Since little is known about the biology of the relationship between mitogenome variation and production traits, our quantitative genetic modeling was complex. Thus, the proportion of total variance explained by mitogenome inheritance was estimated using 5 different models: (1) cytoplasmic model with maternal lineages (CYTO), (2) haplotypic model with mitogenome sequences (HAPLO), (3) amino acid model with unique amino acid sequences (AMINO), (4) evolutionary model based on a phylogenetic analysis using Bayesian Evolutionary Analysis Sampling Trees phylogenetic analysis (EVOL), and (5) mitogenome SNP model (SNPmt). The polygenic autosomal and X chromosome additive genetic effects based on pedigree were modeled, together with the effects of herd-year-season interaction, permanent environment, location, and age at first calving. The estimated proportions of phenotypic variance explained by mitogenome in 4 different models (CYTO, HAPLO, AMINO, and SNPmt) were found to be substantial given the size of mitogenome, ranging from 5% to 7% for all 3 milk traits. At the same time, a negligible proportion of the phenotypic variance was explained by mitogenome with the EVOL model. Similarly, in all models, no proportion of phenotypic variance was explained by the X chromosome. Although our results should be confirmed in other dairy cattle populations, including a large number of sequenced mitogenomes and nuclear genomes, the potential of utilizing mitogenome information in animal breeding is promising, especially as the acquisition of complete genome sequences becomes cost-effective.

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.

87 Estimation of genetic parameters for activity levels in North American Holstein cattle

Abstract High-producing dairy cows face various challenges during their productive life, including metabolic and infectious diseases, heat stress, and intensive management practices. Therefore, dairy cattle breeding programs have begun to focus on novel traits related to the overall resilience of the cows. Resilience has been defined as the ability of an animal to be minimally affected or to recover quickly from environmental disturbances while maintaining a certain level of productivity. In dairy cattle, resilience can be evaluated using measurements of longitudinal indicator traits, such as activity level, which are repeatedly measured throughout the lifetime of the animal. Variability in activity (defined in this study as average steps per hour and daily steps taken) might be a key indicator of resilience, due to activity of contemporary group animals deviating from normal behavior in less resilient cattle. Before resilience indicators are defined based on variability in certain longitudinal traits, there is a need for evaluating the genetic background of the source variable. Therefore, the primary objective of this study was to estimate variance components and genetic parameters of activity traits in lactating Holstein cows. The data was collected from a large dairy farm located in Indiana, using milking systems. Records from 6,332 Holstein cows for the traits of average activity per hour and number of daily steps were used in the analyses. The models used for the estimation of variance components included the significant (P < 0.05) fixed effects of contemporary group defined based on the concatenation of calving year and season, parity number (1, 2, 3+), pregnancy group (0 mo, 1-3 mo, 4-6 mo, and 7-9 mo), and days in milk (2-wkclasses), and the random effects of direct additive genetic, permanent environment, and residual effects. Genetic parameters were estimated for average activity per hour and daily steps for all parities together. Heritability estimates were calculated based on single-trait repeatability models and the (AI) REML method implemented in the BLUPF90+ software. Daily steps taken had a heritability (± SE) of 0.05 (± 0.02) and a repeatability of 0.29 (± 0.02). Activity per hour had a heritability estimate of 0.12 (± 0.04) and a repeatability of 0.57 (± 0.01). These results indicate that activity level is heritable in North American Holstein cattle and can be added to selection indexes if proven to be a key auxiliary trait for improving dairy cattle sustainability. As a next step, we will investigate the genetic relationship of activity traits with other relevant traits and derive resilience indicators based on variability in activity level.

Estimation of Genetic Parameters of Early Growth Traits in Dumeng Sheep.

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.

Genetic parameter estimates for growth traits in Texel sheep of Brazil

The present study aimed to estimate the genetic parameters for direct and maternal influences on birth weight (BW), weaning weight (WW) and postweaning weight (PW) of Texel sheep, raised in an extensive system from Brazil. Data file used in this study consisted of 834 body weight records of 834 Texel lambs (376 males and 458 females) born from 2020 to 2021, offspring of 475 ewes and 45 rams. Multivariate animal model was conducted to estimate the variance components, heritability and genetic correlations using the restricted maximum likelihood method in ASReml-R software, while the effects of fixed factors were analyzed by the general linear model procedure of statistical analysis system. Five different animal models, with or without maternal genetic effect, maternal permanent environment effect and covariance between additive genetic and maternal genetic effects, were fitted on the growth traits data of the Texel sheep population. AIC and BIC test results were used to evaluate the best fit models for growth traits. The effect of lamb's sex was significant on BW, WW and PW while birth type and year of lambing effects were on all traits (P < 0.05). Model 4, incorporating direct additive genetic, maternal additive genetic and maternal permanent environmental effects showed the minimum AIC estimate for all growth traits. The direct additive genetic heritability for BW, WW and PW were 0.11 ± 0.03, 0.37 ± 0.05 and 0.31 ± 0.08, respectively and maternal heritability for these traits were 0.00 ± 0.00, 0.04 ± 0.02 and 0.00 ± 0.00, respectively. The total heritability estimates for BW, WW and PW were 0.11 ± 0.03, 0.39 ± 0.05 and 0.31 ± 0.08, respectively. The estimates of the direct additive genetic correlation between BW-WW, BW-PW and WW-PW were 0.68 ± 0.12, 0.66 ± 0.12 and 0.78 ± 0.07, respectively. The maternal permanent environmental correlation between BW-WW, BW-PW and WW-PW were 0.38 ± 0.27, 0.29 ± 0.20 and 0.74 ± 0.16, respectively. Phenotypic correlation ranged from 0.43 ± 0.03 to 0.75 ± 0.02. It is concluded that due to the existence of genetic variation for BB, WW and PW and strong genetic correlation among them, genetic progress for these traits is possible by selection in Texel sheep from Brazil.

Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.

Genetic parameters for milk yield and reproductive traits in Honduran Holstein cattle.

The aim of this study was to estimate the (co)variance components and genetic parameters for milk yield adjusted to 305d (MY305), calving-to-conception interval (CCI), number of services per conception (NSC) and calving interval (CI) of Honduran Holstein cows, by fitting a bivariate animal model using Maximum Restricted Likelihood procedures. Model included the fixed effects of calving number, the contemporary calving group (farm-season-year of calving and the cow age as covariate). The estimated means and standard deviations for MY, CCI, NSC and CI were, 5098.60 ± 1564.32kg, 168.27 ± 104.71 days, 2.46 ± 1.69 services, and 448.73 ± 109.16 days, respectively; and their estimated heritabilities were 0.21 ± 0.05, 0.03 ± 0.028, 0.02 ± 0.024 and 0.06 ± 0.04, respectively. The genetic correlations between MY305 and CCI, NSC and CI were positive and antagonist, with values of 0.64 ± 0.52, 0.99 ± 0.56, and 0.32 ± 0.24 respectively. Even though moderate to low heritability was estimated for MY305, systematic selection for milk yield, with a reduction in reproductive efficiency, if considered as the only selection criterion is important to be considered. By including reproductive traits and considering permanent environment effects into the breeding program, might yield a slow, but constant and permanent improvement over time.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B-spline functions.

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2 ) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2 ) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

Possibilities of Using “BUGA” Named Software for Breeding Value Estimation of Anatolian Water Buffalo Population of Istanbul

In this study, variance components and breeding value estimations of Anatolian water buffaloes were estimated from the data obtained within the scope of community based water buffalo improvement program of Anatolian water buffalo population of İstanbul. The "BUGA" named software, which was started to be developed with the support of Harran University Scientific Research Project Unit (HUBAP) for use in the estimation of (co)variance components and breeding value were used in this study. The BUGA software used in the research can predict genetic and environmental effects together with variance components as Best Linear Unbiased Prediction (BLUP) with the Restricted Maximum Log likelihood (REML) method. Unlike its counterparts of another software of foreign origin, it has a visual interface. The language of use is Turkish. The dataset includes records such as city, district, village, genealogy information, date of birth, gender, calving date, lactation parity, number of test day milk yield, lactation milk yield and previous calving date of 442 Anatolian buffaloes raised in this population. The records were analysed using the repeatability model with BUGA software and the additive genetic and permanent environmental effects of all animals in the dataset were estimated. Random environment variance, genotypic variance and permanent environment variance values were estimated as 107112.60 kg2, 20045.22 kg2 and 1259.50 kg2, respectively, while heritability (h2) and degree of repeatability (r) were determined as 0.1561 and 0.1659, respectively. The results obtained as an example of case study applied for the possibility of usage the BUGA software in the estimation of genetic parameters and breeding values showed that BUGA software can be easily used in farm recording conditions with the advantage of friendly user features.

Impact of Metaverse in Educational Environment

The purpose of this review is to define of metaverses and to explain their potential and limitations for pedagogical applications in educational environment. The Metaverse has infinite possibilities as a new social communication space. . In other words, the Metaverse is a post-reality universe, an eternal and permanent multi-user environment that combines physical reality and digital virtuality. Regarding the educational use of the metaverse, the following future tasks are proposed: First, teachers need to carefully analyze how students understand the metaverse. Next, teachers should design classes that encourage students to collaboratively and creatively solve problems and complete projects. Third, a educational metaverse platform should be developed to prevent the misuse of student data.

What does lime tell us about cadaveric remains?

ABSTRACT Lime is commonly used in human burials, but its effects on bodies are not clear at all. Different short-time experimental studies concluded that lime does not inhibit the decomposition of bodies. To provide the long-term effects of lime on bodies, this work presents the results of six burials of pig carcasses completely or partially covered with lime during 5 to 5 years. These experiments were carried out in the experimental facilities of Taphos-m project. After that period of time, all pig carcasses were completely or partially skeletonised, and the Bradford score values were about 90% (active decomposition). Lime casts were observed, surrounding and covering some pig cadaveric remains. Energy Dispersive Spectroscopy (EDS) confirmed preservation of lime remains on bones in the cases when no lime casts were observed in the burial. Also, persistence of unexpected anatomical position (bone spatial distribution) by lime could be observed. The cortical surface of the bones was flaked and cracked, related to dryness in a permanent alkaline environment produced by lime inside burials.

Impact of heat stress on genetic evaluation of oocyte and embryo production in Gir dairy cattle.

Identifying and selecting genotypes tolerant to heat stress might improve reproductive traits in dairy cattle, including oocyte and embryo production. The temperature-humidity index (THI) was used, via random regression models, to investigate the impact of heat stress on genetic parameters and breeding values of oocyte and embryo production in Gir dairy cattle. We evaluated records of total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryos (VE) from dairy Gir donors. Twenty-four models were tested, considering age at ovum pick-up (AOPU) and THI means as a regressor in the genetic evaluation. We computed THI in eight periods, from 0 to 112days before ovum pick-up, which were adjusted by different orders of Legendre polynomials (second, third, and fourth). The best-fit model according to Akaike's information criterion (AIC) and Model Posterior Probabilities (MPP) considered Legendre polynomials of third order and THI means of 112days for TO, fourth order and 56days for VO, second order and 28days for CE, and second order and 42days for VE, respectively. The heritability (h2) estimates across AOPU and THI scales ranged from 0.34 to 0.62 for TO, 0.31 to 0.58 for VO, 0.26 to 0.39 for CE, and 0.15 to 0.26 for VE, respectively. The fraction of the phenotypic variance explained by the permanent environment in different AOPU and THI scales ranged from 0.03 to 0.25 for TO, 0.05 to 0.26 for VO, 0.09 to 0.36 for CE, and 0.15 to 0.27 for VE, respectively. Spearman's rank correlation between the estimated breeding values in different AOPU and THI scale from the top 5% sires and females ranged from 0.18 to 0.90 for TO, 0.31 to 0.95 for VO, 0.14 to 0.85 for CE, and 0.47 to 0.94 for VE, respectively. The h2estimates for all evaluated traits varied from moderate to high magnitude across AOPU and THI scales, indicating that genetic selection can result in rapid genetic progress for the evaluated traits. There was a reranking among the best animals in different AOPU and THI. It is possible to select dairy Gir cattle tolerant to heat stress to improve oocyte and embryo production.

142 Genotype-By-Environment Interactions in Dairy Cattle Raised in California and New England

Abstract Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that the traits should not be split in the current genetic evaluation. In conclusion, no critical effect of genotype-by-environment interaction was observed, even though top bulls may differ across regions. The next step of this project is to evaluate GxE for somatic cell score and to include genomic information in the evaluations. Finally, a genome-wide association study across the two regions will be performed to investigate changes in genomic regions associated with the phenotypes.

PSXIII-26 Investigating the Genetic Background of Juniper Consumption in Goats

Abstract Junipers (Juniperus spp.) are woody invasive plants that have caused encroachment problems in the U.S. western rangeland, decreasing forage productivity and biodiversity. A potential solution to this issue is the use of goats in target grazing programs. Selecting goats for juniper consumption is of great interest to rangeland farmers. Therefore, our main objectives were to 1) estimate genetic parameters, including heritability and repeatability, for juniper consumption in Angora (ANG) and Boer x Spanish (BxS) composite population of goats grazing on West Texas rangelands (USA); and, 2) perform a genome-wide association study (GWAS) to identify candidate genes associated with juniper consumption. Fecal samples were collected multiple times between 2008 and 2017 and analyzed using near-infrared reflectance spectroscopy (NIRS) to estimate the percentage of juniper in the diet of goats, using calibration equations previously developed in controlled studies. We analyzed a dataset comprising 2,327 records from 459 ANG and 8,714 records from 1,996 BxS. The pedigree file included 1,601 and 2,426 ANG and BxS goats, respectively. Genomic information for 50,957 single nucleotide polymorphisms (SNP) from 93 ANG and 238 BxS goats was included in the analyses. Sampling date and sex were fitted as categorical fixed effects and age as a linear covariate in the statistical models. Animal direct additive genetic and permanent environment were considered as random effects. Genetic parameters were calculated based on the Restricted Maximum Likelihood method under the single-step genomic BLUP procedure implemented in the BLUPf90+ family of programs. We used de-regressed estimated breeding values and whole-genome sequence data for the GWAS analyses with 8 million SNP for the ANG and 5.3 million SNP for the BxS. The software GCTA was employed for these analyses. Juniper percentage in their diets varied across the years and ranged from 2.14% (± 7.51%) to 14.6% (± 11.0%) for ANG and from 10.5% (± 14.7) to 33.8% (± 18.1) for the BxS population. The heritability estimate for juniper consumption was 0.19 (± 0.038) for ANG and 0.28 (± 0.022) for BxS, indicating that juniper consumption can be increased through selection. The repeatability values were 0.21 for ANG and 0.37 for BxS. The GWAS revealed 1,257 significant SNP located within or close to 220 genes in ANG, and 878 SNP related to 159 genes in BxS, suggesting their association with juniper consumption. These findings suggest that juniper consumption is under genetic control and has a polygenic inheritance pattern influenced by multiple genes of small effect. The heritability and repeatability estimates indicate that genetic improvement of juniper consumption is feasible in both goat populations.

99 Heritability and Repeatability of Structural Foot and Leg Traits in Sheep

Abstract Similar to other livestock species, sheep are subject to a range of structural faults that contribute to lameness and involuntary culling. Record-keeping in the sheep industry is generally sparse, particularly for structural traits; thus, the extent of the problem is ill-defined. Furthermore, the few studies centered on foot and leg structure have typically involved small numbers of animals measured only once. Consequently, estimates of heritability for pasterns, and for claw and hoof conformation scores in sheep are sparse in the literature, with little consideration of potential score changes across an animal’s lifetime. Using a large dataset of repeatedly-measured scores, our objective was to reliably estimate the heritability and repeatability of several foot and leg traits indicative of structural soundness. Pedigree and performance data on 28,300 commercial crossbreed ewes were supplied by Focus Genetics, Ltd., New Zealand. The subjective foot and leg scores were recorded multiple times from first lambing to culling, with as many as 5 sets of records collected on individual ewes. The conformation traits evaluated were overall foot and leg conformation, rear pastern angles, front pastern angles, rear claw and hoof shapes, and front claw and hoof shapes. We fitted a repeatability model with fixed effects of recording age, age of dam, birth rank, rearing rank, heterosis (as a covariate), and contemporary group defined as the combination of birth year, birth flock, and recording mob. Random effects were direct additive, permanent environment, and residual. Heritability estimates ranged between 0.05 ± 0.01 for front claw and hoof shape score to 0.18 ± 0.01 for front pastern angle score. The corresponding repeatability estimates ranged from 0.13 ± 0.01 to 0.30 ± 0.01. The heritabilities estimated in our study were very similar to those obtained by Sheep Improvement Ltd., New Zealand; their estimates varied from 0.06 to 0.20 for the same traits. Based on the low heritability and moderate repeatability for these traits, culling animals based on their first recorded structural foot and leg scores may be risky. However, through their incorporation into a designed breeding program, genetic selection offers a viable approach to improve structural soundness in sheep.

Hind feet position score: A novel trait to genetically reduce lameness incidence

Lameness is an important health and welfare issue that causes considerable economic losses in dairy herds. The objective of this study was to investigate whether the hind feet position score (HFPS) can be used as an auxiliary trait for genetic evaluation of lameness. The HFPS is evaluated by visual scoring of the position of both the hind-digits to the mid-line of the cow's body. The higher the heel height of the lateral claw, the higher is the HFPS, and the higher is the risk for development of lameness. In total, 3,478 records from 1,064 Fleckvieh cows from 35 farms were obtained between September 1, 2021, and March 5, 2022. Data collection was carried out by the regional milk recording organizations. Hind feet position was scored visually by trained personnel during routine milk performance testing in the milking parlor using a 3-class scoring system: score 1 = 0° to <17° indicating a balanced heel height of both the medial and the lateral claw; score 2 = angle of 17° to 24°; score 3 = angle of >24°. After all cows had been milked, locomotion scoring was performed for each animal using a 5-class scoring system with locomotion scores ranging between 1 (normal) and 5 (severely lame). Using HFPS, sensitivity and specificity were 69.5% and 66.8%, respectively, for detecting lameness defined by locomotion score ≥2. For genetic analyses, a bivariate linear animal model was fitted with fixed effects of herd, parity, lactation stage, and classifier, and random effects of animal and permanent environment. Heritabilities for HFPS and locomotion score were 0.07 and 0.10, respectively, and the genetic correlation between the 2 traits studied was 0.80. These results suggest that the HFPS could be used for genetic evaluations to reduce lameness incidence in dairy cattle.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to: 1) compare single-trait random regression models under a Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) setting based on alternative covariance functions (i.e., Wood - WD, Wilmink - WL, and Ali-and-Schaeffer - AS) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); 2) combine the best functions for each trait under a multiple-trait framework; 3) estimate time-dependent SNP effects for all the studied longitudinal traits; and, 5) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The WD model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across DIM for all traits ranged from -0.06 (186 to 215 DIM for MY-SCS) to 0.78 (66 to 95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.