Estimate Variance Components Research Articles

MINQUE Method Variance Component Estimation for the Mixed Additive and Multiplicative Random Error Model

MINQUE Method Variance Component Estimation for the Mixed Additive and Multiplicative Random Error Model

27 Quality Variation in Beef Chuck Eye Rolls Sourced from a Commercial Processor

Abstract The objective of this study was to evaluate quality variation in beef Chuck Eye rolls (IMPS 116D). Beef Chuck Rolls (IMPS 116A) were purchased from a commercial meat packer and fabricated to obtain beef Chuck Eye Rolls (IMPS 116D). Cases were transported to the Illinois State University Meat Lab and hand-sliced into 2.54 cm boneless beef Chuck Eye steaks (IMPS 1116D) (n = 161). Each roll contained between 7 to 10 steaks for use in further meat quality analyses in accordance with in-plant procedures. Upon slicing, steaks were allowed to bloom for 15 minutes and then used in subsequent quality analysis including color, cook loss, and tenderness evaluation. Instrumental color measurements [L* (lightness), a* (redness), b* (yellowness)] were taken for each steak in duplicate with a HunterLab Miniscan XE Plus Spectrophotometer using a D65 illuminant, 100 observer, and 35-mm aperture. All steaks were cooked to a final internal temperature of 71° C. Steaks then underwent cook loss evaluation as well as tenderness analysis via Warner-Bratzler shear force (WBSF). Statistical analysis was performed using the Proc MIXED procedure of SAS to estimate variance components of steak quality in a nested design (steak within roll within case). For all response variables, the standard error was large for the case term. Ranges of observed response values were: L* 28.03-52.15, a* 14.55-27.82, b* 12.78-25.32, cook loss 17.58-61.11, shearforce 2.10-5.86. Variance components in L* and a* values followed similar patterns in which the greatest amount of variance was contributed by steak, followed by case, followed by roll (L* 13.541 (P < 0.001) vs. 9.035 (P = 0.1790) vs. 1.958 (P = 0.1839) and a* 4.055 (P < 0.0001) vs. 1.605 ( P= 0.2124) vs. 0.632 (P = 0.1746). Case, roll, and steak contributed variance almost equally for b* though two variance components were statistically different from zero σ2case =2.133 (P = 0.2344) σ2roll = 1.627 (P = 0.0335) σ2steak = 2.37 (P < 0.0001). Case contributed approximately 1.5 times the variance in cook loss when compared with steak (σ2case = 43.687 (P = 0.1387) vs. σ2steak=29.471 (P < 0.0001)) and approximately 3.5 times more than roll ( σ2case = 43.687 vs. σ2roll = 12.077 (P = 0.0565)). The largest impact on variance in shearforce values came from steak whereas case and roll contributed almost equally (shearforce σ2case = 0.0256 (P = 0.3894)σ2roll = 0.0333 (P = 0.2040) σ2steak = 0.2454 (P < 0.0001)). Some correlations were observed: L*:a* (-0.46, P < 0.0001), L*:b* (-0.07, P = 0.3979), L*:cookloss (0.166, P = 0.0422), and cookloss:shearforce (0.274, P = 0.0007). As lower-cost alternatives to traditional center of the plate cuts continue to grow in popularity among large volume purchasers, it is important to evaluate potential differences in quality attributes. Further evaluation of the impact of case as well as steak within Chuck Eye roll on individual steak quality is warranted.

Bayes estimates of variance components in mixed linear model

This paper proves that in mixed linear model, the analysis of variance estimation (ANOVAE), the minimum norm quadratic unbiased estimation (MINQUE), the spectral decomposition estimation (SDE) and the restricted maximum likelihood estimation (RMLE) of variance components are the same under some conditions. Based on this result, we construct a linear Bayes estimation (LBE) for the parameter vector consisting of variance components and establish its superiorities. Numerical computations and an illustration show that the LBE is comparable to Lindley's approximation, Tierney and Kadane's approximation and the usual Bayes estimation (UBE) obtained by the MCMC method and easy to use as well.

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits.

Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

Genomic-enhanced breeding values for heat stress tolerance in Girolando cattle in Brazil

Heat stress is a major challenge for dairy herds and can lead to significant production reduction, mainly in tropical climate. Differences in decreased milk production in animals is associated with genetic variation. The overall goal was to investigate the effect of heat stress in milk yield of Girolando cattle in tropical climate and to identify the most appropriate statistical approach for evaluation and selection for heat tolerance in different breed compositions of Girolando. Therefore, to quantify heat stress effects on milk yield loss in Girolando cattle, random regression models were applied on test-day milk yield records (TDMY) and weather data. In addition, different statistical models were evaluated for estimating variance components and predicting breeding values for heat stress (HS). The data comprised 648,072 TDMY of 69,431 first-lactation Girolando cows of different breed compositions, and 21,147 genotyped animals, with daily temperature and humidity measurements from weather stations closest to the tested herds, between 2000 and 2020. We used daily mean values of temperature-humidity index (THI) for the day of test and the 3 previous days as the measure of HS. Heat stress thresholds estimated for the different breed compositions of Girolando in this study were THI 80 for 1/4H, 1/2H and 5/8H; THI 78 for 3/4H animals and THI 77 for 7/8H animals. Average milk production can decrease by up to 34% due to heat stress. Estimated average heritability for TDMY regressed to the THI was 0.21, while the heritability estimated for TDMY regressed to the DIM was 0.29. A correlation of 0.55 was observed between the GEBVs of HS tolerance and GEBV milk yield. The parameters estimated in this study indicate that genomic selection for heat tolerance in dairy cattle is a step towards ensuring valuable tropical production efficiency and improving animal welfare in the face of predicted increases in heat stress events and increase tropical production efficiency.

Variability of high dry matter orange‐fleshed sweetpotato [Ipomoea batatas (L.) Lam.] in later breeding stages and allocation of breeding resources in the humid tropics of Peru

AbstractOrange‐fleshed sweetpotato (OFSP) [Ipomoea batatas (L.) Lam] often lacks high dry matter (DM) in elite breeding material. The objectives were to examine the potential to combine yield with high root DM and high root β‐carotene (BC) content by variance‐covariance analysis and optimization of breeding scenarios for “OFSP dry and starchy,” which was exemplarily shown for the humid tropics of Peru. The trial series across seasons suggested a good chance to combine yield and quality (high DM and high BC). The unique data were used to estimate variance components and correlations among four yield traits and four quality traits. For the optimization of selections in our breeding program, we used the selection response by Cochran's approach and developed a small R script. Responses to selection were predicted for storage root yield and a modified Elston index aggregating eight traits. The differences between two‐ and three‐stage selection scenarios were small. For storage root yield, the recommended optimized two‐stage selection increased the response relative to the standard (one‐stage selection 100%) to 141.6% for 2268 plots and to 189.1% for 540 plots; the corresponding optimized three‐stage selection increased the response to 144.3% for 2268 plots and to 190.5% for 540 plots. A two‐stage selection is recommended with two to three locations at the first stage and 6–12 locations at the second stage, according to low‐ or high‐test capacity, respectively. Optimization of breeding schemes is much more important for smaller breeding programs than those with larger test capacity, and this allows testing even larger numbers of genotypes in a small breeding program.

Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets

ABSTRACT The occurrence of collapses and landslides due to underground mining has its unique failure mechanism, especially in the Karst mountainous regions of China. Spaceborne and airborne remote sensing observations provide rapid and effective tools for assessing surface changes and monitoring surface deformation of such landslides. In this study, we take the Jianshanying landslide, a typical mining-induced and fast-deformed landslide, as an example, and reveal the failure mechanism of such landslide by investigating the historical surface displacement. First, the complete evolution of the landslide surface was investigated from its original state to the overall sliding. The data include the satellite and Unmanned Aerial Vehicle (UAV) optical images, UAV three-dimensional (3-D) real scene models, high-resolution Light Detection and Ranging (LiDAR) DEM, and field survey. The results show that the head region entered the high deformation stage after 2019, the maximum deformation rate was 12.3 m/yr. The landslide morphology was formed after the overall slide occurred in September 2020. Then, the pre-event 3-D surface deformation after the landslide entered the high deformation stage was recovered using Interferometric Synthetic Aperture Radar (InSAR), differential DEM, and SAR/optical offset-tracking techniques. The vertical deformation was recovered around −30 m from 2019 to 2020. In particular, we solved the problem of unequal accuracy of SAR and optical offset-tracking observations in 3-D deformation inversion by employing the Helmert variance component estimation method. The maximum deformation was 6 m and 3 m within 4 months in the NS and EW directions, respectively. Finally, we revealed the failure mechanism of the Jianshanying landslide based on the disparity of horizontal and vertical deformation. That is, underground mining causes a significant subsidence of the rear part of the landslide body, resulting in different stress changes in the rear and front parts of the landslide body, which eventually led to sliding of the front part of the slope along the free surface. This work investigates and monitors the typical underground mining-induced Jianshanying landslide by using multi-sensor remote sensing approaches to trace the pre-event surface motions and to reveal its failure mechanism.

Estimated genetic parameters for mature cow live weights during lactation in a multi-breed dairy cattle population

ABSTRACT Live weight (LWT) is an important trait for dairy production systems as it influences both maintenance feed requirements and beef revenue from culled cows and from offspring surplus to replacements. The breeding goal for the New Zealand dairy industry includes LWT. This study estimated genetic parameters for LWT during lactation from some animals in the New Zealand multi-breed dairy cattle population. A total of 31,922 LWT records from 11,222 lactating dairy cattle born from 1988 to 2015 were used for the analysis. Estimation of (co)variance components used Gibbs sampling from pedigree-based models. The posterior mean heritability estimates using scale weights were consistently high across six lactations and ranged from 0.57 to 0.64. There were high genetic correlations between the scale weights measured during different lactations (>0.95). We further estimated the posterior mean heritability for subjective liveweight scores recorded from first lactation using trained inspectors and the estimate was 0.36, lower than those heritability estimates from scaled weight. Further, the genetic correlations between subjective liveweight score and scale weights at various lactations ranged from 0.91-0.92. It is recommended that only scale weights be used for national genetic evaluation.

Genetic associations between stayability to consecutive calvings and traits of economic interest in taurine and zebu breeds.

Stayability (STAY) is a way to evaluate the productive longevity of females. Measuring the STAY at each cow calving allows earlier indicators of longevity to be obtained. Our objective with this study was to verify the association between STAY and consecutive calvings and traits potentially used as selection criteria in beef cattle, such as age at first calving (AFC), days to calving (DC), weaning weight (WW), and yearling weight (YW). Data from the Nelore, Angus/Brangus, and Hereford/Braford breeds were used. The estimation of variance components and subsequent prediction of breeding values were performed for all traits. The estimated breeding values (EBV) were used to analyse the association between STAY and the other traits. The Pearson's correlation estimated between the EBV for the intercept coefficient for STAY to consecutive calvings and those of AFC, DC, WW (direct and maternal effects), and YW was favourable and of low magnitude (<0.25) depending on the breed studied. The influence of the genetic merit of AFC on the chance of selection for STAY was favourable and relevant regardless of the intensity of selection and breed. DC and WW (maternal effect) traits were favourably influenced by the chance of selection for STAY, irrespective of breed. The WW (direct effect) did not affect the chance of selection for STAY for the Nelore and Hereford/Braford breeds and negatively influenced, but to a small extent, the Angus/Brangus breed. For YW, an increase in genetic merit affected the chances of selection for STAY, depending on the breed and selection intensity evaluated. The influence of the genetic merit for AFC, DC, and WW (maternal effect) on the chance of selection for STAY to consecutive calvings was favourable and relevant regardless of the selection intensity scenario evaluated. The WW (direct effect) did not influence the chance of selection for STAY. For the scenario with high selection intensity, the selection for YW favourably influenced the chance of selection for STAY in Angus/Brangus and Hereford/Braford breeds but not in Nelore.

Analyzing Complete Generalizability Theory Designs Using Structural Equation Models

ABSTRACT We illustrate how to analyze complete generalizability theory (GT) designs using structural equation modeling software (lavaan in R), compare results to those obtained from numerous ANOVA-based packages, and apply those results in practical ways using data obtained from a large sample of respondents, who completed the Self-Perception Profile for College Students (Neemann & Harter, 2012) on two occasions. Results revealed that estimates of variance components, generalizability coefficients, dependability coefficients, and proportions of measurement error derived from lavaan were essentially equivalent to those produced by the GT packages GENOVA and gtheory in R and variance component programs in SPSS, SAS, and R. Within the article and extended online Supplemental Material, we illustrate how indices obtained from these resources can be used for either norm- and criterion-referencing purposes and for estimating effects of changes made to measurement procedures. We further describe ways to use structural equation models for applications of GT beyond what conventional ANOVA-based packages would typically permit.

Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S.

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m-3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m-3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m-3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM.

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project GenTORE and Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake.

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Estimation of Expectations and Variance Components in Two-Level Nested Simulation Experiments

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm n observations are generated for the parameters, and in the inner level m observations of the simulation model are generated with the values of parameters fixed at the values generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent and show how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand, and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments with this forecast model. We found that, given a fixed number of total observations nm, the choice of only one replication in the inner level (m=1) is recommended to obtain a more accurate estimator for the expectation of a performance variable.

Variance–covariance analysis of two high-resolution regional least-squares quasi-geoid models

This paper investigates the full variance–covariance (VC) matrix of two high-resolution regional quasi-geoid models, utilizing a spherical radial basis function parameterization. Model parameters were estimated using weighted least-squares techniques and variance component estimation (VCE) for data weighting. The first model, known as the “RCR model,” is computed through the remove–compute–restore method, incorporating various local gravity and radar altimeter datasets. The second model, the “combined model,” includes the GOCO05s satellite-only global geopotential model as an additional dataset with a full-noise VC matrix. Validation of the noise VC matrix scaling for each quasi-geoid model is achieved by comparing observed and formal noise standard deviations of differences between geometric and gravimetric height anomalies at GPS height markers in the Netherlands. Analysis of the noise VC matrix of height anomalies at grid nodes reveals significantly smaller formal noise standard deviations for the RCR model compared to the combined model. This difference is attributed to VCE assigning larger weights to the GOCO05s dataset, which exhibits greater noise standard deviations for the specific spatial scales used. Additionally, the formal noise standard deviations of height anomaly differences, relevant for GNSS-heighting, favor the RCR model. However, the disparity between the two models is smaller than implied by the height anomaly noise standard deviations. This is due to the combined model’s noise autocorrelation function displaying a longer correlation length (67 km) in contrast to the RCR model’s (17 km). Consequently, the combined model exhibits a greater reduction in noise variance for height anomaly differences relative to white noise compared to the RCR model.

An Application of Theril Indexes for the Interrater Reliability: A Comparison with Intraclass Correlations

This study proposes to apply the Theil-index ratios for the interrater reliability. We discuss the theoretical foundations and examine its function empirically using real data. Our analyses show that Theil-index rations and intraclass correlation (ICC) estimates are highly correlated. However, ICC may underestimate the interrater reliability by some extreme disagreement among raters and be more likely to be influenced by the extreme disagreement. As Theil-index ratios overcome the limitations of ICC to some degree, it seems that Theil-index ratios provide an alternative to evaluating interrater reliability, at least under certain conditions, e.g., when outliers exist in the data, it is difficult to obtain the variance component estimates, or ICC underestimates interrater reliability.

English

English

Estimation of genetic parameters for milking temperament in Holstein-Gyr cows

IntroductionDairy cattle with poor temperament can cause several inconveniences during milking, leading to labor difficulties, increasing the risk of accidents with animals and workers, and compromising milk yield and quality. This study aimed to estimate variance components and genetic parameters for milking temperament and its genetic correlations with milk yield in crossbred Holstein-Gyr cattle.MethodsData were collected at three commercial farms, resulting in 5,904 records from 1,212 primiparous and multiparous lactating cows. Milking temperament (MT), measured as the milking temperament of each cow, was assessed during pre-milking udder preparation (RP) and when fitting the milking cluster (RF) by ascribing scores from 1 (cow stands quietly) to 8 (the cow is very agitated, with vigorous movements and frequent kicking). The number of steps and kicks were also recorded during pre-milking udder preparation (SRPand KRP, respectively) and when fitting the milking cluster (SRFand KRF, respectively). Milk yield (MY) was obtained from each farm database. In two of them, MY was recorded during the monthly milk control (that could or could not coincide with the date when the milking temperament assessments were carried out) and in the remaining farm, MY was recorded on the same day that the milking temperament assessments were made. Genetic parameters were estimated using the THRGibbs1f90 program applying a threshold model, which included 89 contemporary groups as fixed effects, animal age at the assessment day and the number of days in milking as covariates, and direct additive genetic and residual effects as random effects.Results and discussionsThe heritability estimates were MT= 0.14 ± 0.03 (for both, MRPand MRF), MY= 0.11 ± 0.08, SRP= 0.05 ± 0.03, KRP= 0.14 ± 0.05, SRF= 0.10 ± 0.05, and KRF= 0.32 ± 0.16. The repeatability estimates were 0.38 ± 0.05, 0.42 ± 0.02, and 0.84 ± 0.006 for MTRP, MTRF, and MY, respectively; and 0.38 ± 0.02, 0.30 ± 0.07, 0.52 ± 0.02, and 0.46 ± 0.15 for SRP, KRP, SRF, and KRF, respectively. The estimates of most genetic correlation coefficients between MTRP-MTRFwere all strong and positive (MTRR-MTRF= 0.63 ± 0.10, MTRP-SRP= 0.65 ± 0.12, MTRP-KRP= 0.56 ± 0.16, MTRF-SRF= 0.77 ± 0.06, and MTRF-KRF= 0.56 ± 0.34) except for MY (MTRP-MY= 0.26 ± 0.26 and MTRF-MY= 0.21 ± 0.23). Despite the low magnitude of MT heritability, it can be included as a selection trait in the breeding program of Holsteins-Gyr cattle, although its genetic progress will be seen only in the long term. Due to the low accuracy of the genetic correlation estimates between MT and MY and the high range of the 95% posterior density interval, it cannot be affirmed by this study that the selection of a milking temperament trait will infer on milk yield. More data is therefore needed per cow and more cows need to be observed and measured to increase the reliability of the estimation of these correlations to be able to accurately interpret the results.

Population structure and genomic analyses of carcass back Length, shoulder width and leg width in purebred and crossbred German Merino sheep

In order to derive suitable breeding strategies for complex traits, it is indispensable that their genetic architecture as well as population specific parameters are thoroughly investigated. Large samples of genotyped individuals are required in order to produce reliable results and to draw adequate inferences. Whereas in cattle, pig and chicken such data are available for many populations, such datasets are rare for sheep. Except for some sheep breeding nations where genomic selection is applied to sheep, the majority of datasets of genotyped sheep come from experiments with often small sample sizes. Pooling data from different breeds and crosses is one way to increase the sample size for genomic analyses but is somehow challenging due to the multi-breed composition of the datasets. Population structure and genetic diversity has an impact on the results of genomic analyses but is often not studied in the populations in which genomic analyses are conducted. Genetically diverse populations as found in sheep often compromise the analyses and the interpretation of results. Here, we investigated the genetic diversity and population structure of purebred Merino and several Merino crossbred lambs as well as in their pooled data and conducted separate and joint genetic analyses. Linkage disequilibrium was studied in the pooled data as well as in the single datasets. Consistency of phase was calculated among crosses. Population structure assessed by principal component analysis. Variance components were estimated and genome-wide association studies were performed for three carcass traits. All investigations based on 50k genotype data. Large genetic diversity was found between and also within crosses. The populations showed low levels of linkage disequilibrium (average r² between 0.155 and 0.171 within crosses and 0.138 in the pooled dataset for inter-marker distances up to 0.059 Mbp) which decayed fast with increasing inter-marker distance which was particularly pronounced when the data were pooled. The population genetic structure of the purebred Merino sample was similar as in Merino crosses and the genetic differentiation between the different populations were small. Variance components could solely be estimated at acceptable standard errors for some of the single crosses and hence genome-wide association studies could not be performed for all crosses. Pooling data drastically reduced standard errors in variance component estimation but did only marginally improve gene mapping results in terms of power but not precision. The heritabilities estimated in the pooled dataset were 0.30, 0.18 and 0.18 for back length, shoulder width and leg width. Only one genome-wide significant SNP for shoulder width was detected. Further, across all traits and datasets, 25 SNPs were found to be putatively trait associated. Larger datasets and denser marker panels are required to utilize such datasets for gene mapping analyses at high power and precision and would improve population genetic investigations. If possible, and to avoid the application of too complex models, data pooling should rather be done by combining several populations of the same breed instead of multiple crosses.

Using social interaction models for genetic analysis of skin damage in gilts.

Skin damage is a trait of economic and welfare importance that results from social interactions between animals. These interactions may produce wound signs on the gilt's skin as a result of damage behavior (i.e., fighting), biting syndromes (i.e., tail, vulva, or ear biting), and swine inflammation and necrosis syndrome. Although current selection for traits that are affected by social interactions primarily focuses on improving direct genetic effects, combined selection on direct and social genetic effects could increase genetic gain and avoid a negative response to selection in cases of competitive behavior. The objectives of this study were to (1) estimate variance components for combined skin damage (CSD), with or without accounting for social genetic effects, (2) investigate the impact of including genomic information on the prediction accuracy, bias, and dispersion of CSD estimated breeding values, and (3) perform a single-step genome-wide association study (ssGWAS) of CSD under a classical and a social interaction model. Our results show that CSD is heritable and affected by social genetic effects. Modeling CSD with social interaction models increased the total heritable variance relative to the phenotypic variance by three-fold compared to the classical model. Including genomic information increased the prediction accuracy of direct, social, and total estimated breeding values for purebred sires by at least 21.2%. Bias and dispersion of estimated breeding values were reduced by including genomic information in classical and social interaction models but remained present. The ssGWAS did not identify any single nucleotide polymorphism that was significantly associated with social or direct genetic effects for CSD. Combined skin damage is heritable, and genetic selection against this trait will increase the welfare of animals in the long term. Combined skin damage is affected by social genetic effects, and modeling this trait with a social interaction model increases the potential for genetic improvement. Including genomic information increases the prediction accuracy of estimated breeding values and reduces their bias and dispersion, although some biases persist. The results of the genome-wide association study indicate that CSD has a polygenic architecture and no major quantitative trait locus was detected.