Variance Components Research Articles

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

Comparison of different animal models for estimating genetic parameters for early growth traits and reproductive traits in Tianmu Sainuo sheep

As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Unearthing hybridization opportunities for augmenting biomass yield and harvest index in rajmash (Phaseolus vulgaris L.)

In present study, 21 F1 hybrids derived from crossing between seven female genotypes and three male testers following a line × tester mating design were evaluated for analyzing the component of genetic variation and heterosis of yield contributing traits. The components of variances due to gca and sca revealed the predominance of non-additive gene action for biomass and harvest index. The average better parent heterosis (BPH) and standard heterosis (SH) for Biomass yield (BY) was 43.71 and 114.67, respectively, and the average BPH and SH for the Harvest index (HI) was -13.04 and -20.45, respectively. The higher magnitude of the BPH and SH recorded for BY indicated that the scope for increasing the HI in Rajmash is limited and that further increases in productivity have to come mainly through enhanced BY. The parent RR21-01 and GR 1 were good general combiners for biomass, TRCR 3, TRCR 2, SKAU-R-19 and RKR 1033 for harvest index. Cross combinations IPR-277- 19 × HUR recorded positively significant sca effect for BY and IPR-277-19 × GR, TRCR 3 × GR 1, TRCR 2 × GR 1, IPR-205-19 × HUR, IPR-277-19 × GR 1, SKAU-R-19 ×RKR 1033, RR-21-01 × HUR and RR-21-12 × GR 1 for HI. These results demonstrate that the selection of parents should not only be based on per se performance but also on combining ability and heterosis for achieving genetic improvement in Rajmash,

Decomposing the variance in early maladaptive schemas: the major role of one general factor, the minor role of domains, and their differential relations to facial emotion recognition

IntroductionDespite the growing interest in the early maladaptive schemas, the progress in understanding their impacts is decelerated by a lack of clear understanding of their structure. Different composite scores are calculated without a solid ground or a clarified meaning. Here we explain that the schema variance can be theoretically decomposed into three components: schema-specific, domain-specific due to the unmet core needs, and the common variance we call general susceptibility; each can differentially correlate with other substantive variables. Using this framework, we empirically examine the structure of schemas and their relationships to facial emotion recognition, a crucial ability that can widely affect our social interactions.MethodsA sample of adults completed an emotion recognition task and the Young Schema Questionnaire. Using different factor models, the specific and shared variance across schemas was analyzed. Then, the relation of these variance components to facial emotion recognition was explored.ResultsA general factor explained 27%, 40%, and 64% of the total variance in items, schemas, and domains, respectively. Partialling out the common variance, there was little domain-specific variance remained. Regarding facial emotion recognition, they were not correlated with specific schemas; however, the general susceptibility factor was correlated with anger recognition.DiscussionThe variance decomposition approach to schemas, which uses the bifactor model, may offer a clearer way to explore the impacts of schemas. While domain scores are widely used, their reliability, validity, and meaning are questionable. The generic factor, which is consistently extractable from empirical data, requires further attention.

Oxygen targets following immediate percutaneous coronary intervention in comatose survivors of out-of-hospital cardiac arrest - a BOX sub-study

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation The Lundbeck Foundation. Background Treatment with supplemental oxygen in patients with acute myocardial infarction (AMI) has previously been associated with poorer outcomes. However, in patients with AMI resuscitated from out-of-hospital cardiac arrest (OHCA) and undergoing immediate revascularization, evidence regarding specific oxygen targets is sparse. Purpose To assess the impact of a restrictive versus a liberal oxygen target on infarct size in OHCA patients undergoing immediate percutaneous coronary intervention (PCI). Methods The current study is a sub-study of the BOX trial, a randomized trial with a 2-by-2 factorial design, in which comatose OHCA patients with a presumed cardiac cause of arrest were randomly assigned to either a restrictive oxygen target (partial pressure of arterial oxygen (Pao2) of 9 to 10 kPa) or a liberal oxygen target (Pao2 of 13 to 14 kPa). In this sub-study we included patients who underwent immediate revascularization with PCI. The primary outcome was myocardial infarct size as assessed by the cardiac enzyme troponin-T (TnT). TnT was measured repeatedly during the first 72 hours and analyzed with a repeated measures variance component model with baseline adjustment. Secondary outcome was one-year all-cause mortality assessed by Kaplan-Meier and Cox proportional hazards analyses. Results A total of 329 patients underwent immediate PCI, whereof 172 patients were randomized to the restrictive oxygen target and 157 to the liberal oxygen target. The oxygen intervention was successful with a significantly higher PaO2 in the liberal oxygen target group during the first 72 hours (p<0.0001). Baseline characteristics and findings on coronary angiography (CAG) were similar in the two oxygen target groups. Thus, median age was 63 (interquartile range (IQR) 55–71), 81% were male, and estimated left ventricular ejection fraction on arrival to hospital was 35% (IQR 25–45%). Further, 74% of the patients had an initial postresuscitation electrocardiogram with ST-segment elevations, and the left anterior descending artery was deemed as culprit lesion in 49% of the patients. There was no difference in peak TnT between the restrictive and liberal oxygen target group (median 2540 (IQR 925–6420) versus 3410 (792–8630), ng/l, p=0.23), nor did TnT differ between the groups during the first 72 hours after admission (Figure 1, p=1.00). Within one year from admission, 120 patients (36.5%) had died. There was no difference in mortality between the restrictive (34%) and liberal (39%) oxygen target group (hazard ratio, 0.80; 95% CI, 0.56-1.14; p=0.21) (Figure 2). Conclusion Targeting a restrictive versus a liberal oxygen level in comatose survivors of OHCA who underwent immediate revascularization did not affect infarct size nor did it result in a significant difference in one-year mortality rate.Figure 1:Troponin-T levelsFigure 2:Kaplan-Meier curves

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

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

Estimation of genetic parameters for reproductive traits evaluated by ultrasonography in Senepol heifers

Studies on reproductive tract score (RTS) and antral follicle count (AFC) in Senepol cattle are limited. These traits are essential as they are linked to the reproductive ability and reproductive performance of animals. Understanding the heritability and genetic correlation of these traits is crucial for guiding selection programs and selecting animals with greater reproductive efficiency and higher production potential. Therefore, this study aimed to estimate the genetic parameters of weight, RTS, and AFC in Senepol cattle using transrectal ultrasonography. Reproductive data were collected from 850 female Senepol cattle aged 10-16 months. At the time of evaluation, the following were measured: RTS traits (scores from 1 to 5), AFC (0 to 100), and female weight (kg). Variance and covariance components, genetic heritability, and correlations were estimated by univariate and bivariate analyses using the bull model. Heritability estimates were moderate for AFC (0.52) and weight (0.46), and low for RTS (0.12). The genetic correlation between RTS and AFC was almost zero (-0.05). The estimated genetic correlation between weight and AFC was -0.41, while the correlation between weight and RTS was 0.48. Selective breeding of AFC and RTS can enhance the reproductive efficiency of the herd and result in animals with earlier pubertal age and increased fertility.

Exploring the reliability and validity of multiple mini-interviews in admission to teacher education

ABSTRACT This study addressed the need to explore the reliability and validity of the recently renewed aptitude test based on the multiple mini-interview (MMI) format in Finnish teacher education programmes. Utilising a nationwide dataset (N = 3306), we explored the reliability of the format using intraclass correlations and multilevel modelling. Furthermore, we validated the intended structure of the MMI with confirmatory factor analysis and contrasted the scores of the MMI with other admission scores, i.e. cognitive measures. The results showed mostly small effects of clustering of the applicants according to the different interviewers and circuits. The largest variance component for the MMI total score was the applicant (73.3%), followed by measurement error (15.09%). The intended structure was validated, and the MMI seemed to measure different attributes from other admission measures. Thus, the study demonstrated satisfactory reliability and validity of MMI in admission to teacher education, yet the stations need further development.

Control chart for detecting the scale parameter of the zero‐inflated Poisson model

AbstractWhen monitoring risk in public health, count data commonly exhibit an excessive number of zero, and the zero‐inflated Poisson (ZIP) model is often used to fit this type of data. Most previous methods for monitoring of the ZIP model have focused on the changes in the location parameter and the existence of the scale parameter and usually assumed that the scale parameter is zero in the H0 stage. However, in an objective environment, data often have certain fluctuations, meaning that the scale parameter always exists. Therefore, it is more meaningful to monitor the changes in the scale parameter on top of the predefined baseline than to monitor its existence. In this study, we derive a score test statistic based on the generalized Henderson's joint likelihood function, construct a risk‐adjusted exponentially weighted moving average (EWMA) control chart to monitor the variability of the random effects variance component in the ZIP mixed‐effects model. And the convergence property of the score test statistic is proved through derivation, which shows that the new method has theoretical reliability. The simulation results Indicate that when the scale parameter has different predefined baselines and different variation amplitudes, the proposed method is more effective than the existing RA‐ZIP and PR‐ZIP control charts. In addition, the proposed method is applied to real data from a Hong Kong hospital for online influenza surveillance to demonstrate its practicability.

230 Using phenotypes with uncertain identification leveraging computer vision models

Abstract There has been an increase in high throughput phenotyping using sensors in livestock farming. The major obstacle when analyzing phenotypic data in studies using cameras is identifying the animals using computer vision. To overcome this problem, an analysis method that uses keypoint detection in conjunction with classification algorithms can identify animals in images. However, these algorithms generally output a prediction probability for different classes, reflecting uncertainty. In most practices, the highest probability will be assigned as the putative identification (ID). In some cases, this may introduce incorrect identification and affect downstream decisions. Here, we propose an analytical approach to incorporate all the uncertainty in the putative ID distribution into downstream inferences. We used a publicly available keypoint detection dataset of 17 keypoints detected in 3,226 images from 30 diverse thoroughbred horses. We performed feature engineering with Linear Discriminant Analysis (LDA) using 5-fold cross-validation to build horse classifiers. The best model selected had an accuracy of 88.12% to identify animals in the testing set. Afterward, we performed Monte Carlo simulations to add a phenotype that was assumed independent of the image keypoints. This generated a set of plasmodes to evaluate methods that accounted for ID uncertainty. Each simulation included different means for two groups of 15 horses. In total nine simulation scenarios were considered including varying effect sizes of group mean difference and various repeatability values (horse variance to total variance ratio). We analyzed the simulated data with three different mixed models: 1) Fixed and random effects were based on using ground truth assignment of ID, 2) Fixed and random effects were based on using the putative ID with the highest posterior probability from the best classifier, 3) Finally, prediction probabilities were used to account for putative ID uncertainty. As expected, the model that used ground truth ID had the highest power to detect group differences, and it had the lowest root mean square error (rMSE) to estimate variance components across all scenarios. The linear model that used highest posterior probability putative ID, showed the lowest power and highest rMSE. For instance, as shown in Table 1, the mean rMSE value for group mean differences in the model using ground truth is 0.01 and the model using highest posterior probabilities is 0.13. The model that incorporated ID uncertainty had intermediate properties, showing a rMSE of 0.03 for group differences. Overall, incorporating uncertainty in Animal ID results in improved performance in ultimate test statistics used in animal studies. This is very important in breeding and precision livestock farming contexts where animal identification is challenging. We will further investigate this.

Equivalence of variance components between standard and recursive genetic models using LDL′ transformations

BackgroundRecursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL′ or block-LDL′ transformations.ResultsThe procedure was employed on a dataset comprising five traits (birth weight—BW, weight at 90 days—W90, weight at 210 days—W210, cold carcass weight—CCW and conformation—CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models.ConclusionsThe LDL′ or block-LDL′ transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.

Negative variance components and intercept-slope correlations greater than one in magnitude: How do such "non-regular" random intercept and slope models arise, and what should be done when they do?

Statistical models with random intercepts and slopes (RIAS models) are commonly used to analyze longitudinal data. Fitting such models sometimes results in negative estimates of variance components or estimates on parameter space boundaries. This can be an unlucky chance occurrence, but can also occur because certain marginal distributions are mathematically identical to those from RIAS models with negative intercept and/or slope variance components and/or intercept-slope correlations greater than one in magnitude. We term such parameters "pseudo-variances" and "pseudo-correlations," and the models "non-regular." We use eigenvalue theory to explore how and when such non-regular RIAS models arise, showing: (i) A small number of measurements, short follow-up, and large residual variance increase the parameter space for which data (with a positive semidefinite marginal variance-covariance matrix) are compatible with non-regular RIAS models. (ii) Non-regular RIAS models can arise from model misspecification, when non-linearity in fixed effects is ignored or when random effects are omitted. (iii) A non-regular RIAS model can sometimes be interpreted as a regular linear mixed model with one or more additional random effects, which may not be identifiable from the data. (iv) Particular parameterizations of non-regular RIAS models have no generality for all possible numbers of measurements over time. Because of this lack of generality, we conclude that non-regular RIAS models can only be regarded as plausible data-generating mechanisms in some situations. Nevertheless, fitting a non-regular RIAS model can be acceptable, allowing unbiased inference on fixed effects where commonly recommended alternatives such as dropping the random slope result in bias.

Abstract PO1-02-08: Comprehensive Molecular Profiling of Breast Cancer: A real-time PCR assay for identifying ESR1, PGR, ERBB2, MKI67 and a novel proliferative signature in core needle biopsies and resected invasive breast cancer tissues

Abstract Background: Accurate status determination of breast cancer (BC) biomarkers, including estrogen receptor (ER/ESR1), progesterone receptor (PR/PGR), human epidermal growth factor receptor 2 (HER2/ERBB2), and marker of proliferation Ki67 (MKI67), is crucial for guiding therapeutic decisions. However, current “gold standard” methods such as immunohistochemistry (IHC) for assessing these biomarkers in formalin-fixed paraffin-embedded (FFPE) tissues face challenges in standardization and exhibit substantial inter- and intra-laboratory variability, particularly for Ki67. To address these limitations, APIS Breast Cancer Subtyping Kit has been developed, utilizing reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) to evaluate the status of ER, PR, HER2, KI67, and a novel proliferative signature in breast cancer core needle biopsy (CNB) and resected specimens. The APIS Breast Cancer Subtyping Kit offers enhanced accuracy, improved reproducibility, and the ability to provide results in a timely manner. Herein we report the development and validation of the APIS Breast Cancer Subtyping Kit, assessing clinical performance using retrospective and prospective sample collection. Furthermore, reproducibility and reliability across three testing sites were evaluated. By comparing the results of the kit with those obtained through standard IHC, we demonstrate the reliability and utility of the APIS Breast Cancer Subtyping Kit for precise breast cancer subtyping and guiding personalized patient management decisions. Methods: FFPE tissue sections (n=652) taken by CNB or resection underwent histological analysis in accordance with laboratory’s standard of care methods. Samples with a HER2 score of 2+ were referred to fluorescence in situ hybridization (FISH) to determine ERBB2 amplification, with the FISH result replacing the IHC result in determining the HER2 status (positive/negative) of the sample. Tumors with greater than 1% positive stained nuclei for ER/PR were scored positive. A cut-off of 20% Ki67 staining was used for positive/negative status. The performance was assessed as the agreement with IHC/ISH status and reported as OPA (overall percent agreement) with corresponding 95% confidence intervals (95% CI). To assess the precision of the assay, a panel of RNA pools (samples) was repeatedly measured on different days across three independent laboratories. Reproducibility was evaluated using variance components analysis. Results: The APIS Breast Cancer Subtyping Kit showed high concordance (OPA) with IHC: 93.1% (95% CI: 90.9-94.8) for ER, 86.8% (95% CI: 84.0-89.2) for PR, 94.2% (95% CI: 92.2-95.8) for HER2 (IHC/FISH), 78.3% (95% CI: 75.0-81.3) for Ki67, and 80.1% (95% CI: 76.8-83.1) for proliferative signature (assessed against MKI67 IHC status). Inter-site reproducibility demonstrated excellent agreement in quantitative measurements, with a total SD ranging from 0.00 to 0.22. Binary single-marker status (positive/negative) calling achieved 100% reproducibility for ER, PR, and HER2 for all tested samples (negative/low positive/medium positive), as well as for most Ki67 samples (87% agreement for negative samples). Conclusions: APIS Breast Cancer Subtyping Kit demonstrated a high level of concordance with standard of care IHC/FISH in assessing breast cancer biomarker status. These findings suggest that the APIS Breast Cancer Subtyping Kit provides highly precise and reproducible quantitative assessment of BC biomarkers and molecular subtypes. Inclusion and exclusion criteria Citation Format: Anna Gasior, Joanna Gorniak, Sara Rollinson, Leanne Gough, Anne-Sophie Wegscheider, Axel Niendorf. Comprehensive Molecular Profiling of Breast Cancer: A real-time PCR assay for identifying ESR1, PGR, ERBB2, MKI67 and a novel proliferative signature in core needle biopsies and resected invasive breast cancer tissues [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-02-08.

Genetic parameters for udder conformation traits derived from Cartesian coordinates generated by robotic milking systems in North American Holstein cattle

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems have increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in a xyz plan, which can then be used to derive udder conformation traits. AMS generate a large amount of per milking visit data for individual cows, which contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multi-trait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 single nucleotide polymorphisms. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (±standard error, SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (±SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between the FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection sub-indexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

Sexual dimorphism in the environmental sensitivity of weaning weight in beef cattle

This study investigates the environmental sensitivity and sexual dimorphism in the genetic parameters of weaning weight (WW) in Nelore beef cattle. Five reaction norms models were employed to analyze the 148,131 WW records, considering different definitions of the environmental descriptor (ED). (Co)variance components and genetic parameters were separately estimated for males and females. Results reveal substantial sexual dimorphism in various parameters. Significant divergence across sexes was observed for environmental sensitivity, with males showing an increased response to favorable environments while females genetically responded better to more challenging environments. Including sex in defining the ED can inflate the estimated breeding values of sires with a higher number of male progeny records, highlighting the importance of choosing a reliable ED in analyses of traits influenced by sexual dimorphism and genotype-by-environment interaction (G × E). The observed sexual dimorphism and environmental sensitivity underscore the complexity of genetic evaluation of WW in beef cattle populations, with implications for selection strategies. The findings suggest that a sex-separated trait analysis considering G × E effects is most appropriate for genetic evaluations of WW. However, a simpler single-trait model excluding sex from the ED definition yielded a higher coincidence of selected sires with the sex-separated analysis. In conclusion, understanding sexual dimorphism and environmental sensitivity is crucial for genetic evaluations and selection decisions in beef cattle breeding programs, emphasizing the need for careful model selection and ED definition.

Effect of genetic and sex effect on genomic prediction for horn development in Nellore cattle

This study aimed to evaluate the influence of phenotypic classification of horn development, animal sex effect, and non-autosomal SNP (single nucleotide polymorphism) markers on the genetic parameters and genomic prediction ability for horn development in Nellore cattle using the single-step genomic best linear unbiased prediction method. The polled phenotype was evaluated in two (presence and absence of horns), three (scurs and polled offspring from a horned parent, and the polled and horned animals), and four (absence of horn, polled born to a parent with horn, scurs, and presence of horn) phenotypic categories. A total of 12 statistical models were evaluated. The variance components were estimated using the THRGIBBS1F90 software, and a threshold animal model was used for genomic prediction analyses with the single-step genomic BLUP (ssGBLUP) procedure. Accuracy, bias, and dispersion parameters were evaluated based on the linear regression (LR) method. The highest heritability (0.84) was obtained when the polled character was evaluated as a binary trait. The lowest heritability estimates (0.44 to 0.45) for horn development were obtained when the phenotype was classified into three categories. For the same horn development classification method, the heritability estimates were similar regardless of the genomic evaluated models and fixed effects included in the model. For models considering four and three phenotypic categories for horn development, the inclusion of the sex effect as a fixed effect within the CG did not improve the accuracy, bias, and dispersion of genomic predictions for horn development. Analyzing the trait with binary expression, the highest prediction accuracy was observed when the effect of sex was not included in the CG and without the SNPs in the sex chromosomes. These models displayed the highest dispersion, pointing out the low robustness of genomic prediction. In addition, models that use less than four categories to classify the horn development phenotype, with no discrimination between polled and homozygous polled displayed lower prediction ability. The inclusion of non-autosomal SNPs in the analyses for the models considering four phenotypic categories leads to an improvement in prediction accuracy in 5,26%, bias, and dispersion reduction, 37% and 4,55%, respectively, compared with models that only considered autosomal SNPs. The selection using genomic information for the polled trait is feasible, and it is an alternative to obtaining polled Nellore animals. The binary coding of horn development is an unsuitable oversimplification of polled phenotype, and probably, the genetic background of horn development is more complex than previously proposed. The most adequate prediction model to evaluate the horn development in Nellore cattle was considering four phenotypic categories and including non-autosomal SNP in the analyses for genomic prediction purposes of naturally genetically polled animals. Genetic dehorning can be adopted on a large scale as a low-cost and non-invasive approach to increase the frequency of hornless animals using genomic information and mating strategies.

Variability in Morphological, Biochemical, and Proximate Yield Composition among Predominant Amaranthus hybridus Cultivars in South-West Nigeria

Variability in Morphological, Biochemical, and Proximate Yield Composition among Predominant Amaranthus hybridus Cultivars in South-West Nigeria

Development and Validation of the Coronary Heart Disease Scale Among the System of Quality of Life Instruments for Chronic Diseases QLICD-CHD (V2.0) Based on Classical Test Theory and Generalizability Theory.

Coronary heart disease (CHD) is a common and frequent disease with a long and incurable course, and the quality of life of patients is severely reduced. This study was to develop and validate a quality of life scale for patients with CHD based on the Chinese context. The scale QLICD-CHD (V2.0) was developed based on the QLICD-CHD (V1.0), using a programmed decision procedures. Based on the data measuring QoL 3 times before and after treatments from 189 patients with CHD, the psychometric properties of the scale were evaluated with respect to validity, reliability and responsiveness employing correlation analysis, multi-trait scaling analysis, structural equation modeling, t-test and also G-study and D-study of generalizability theory analysis. The SF-36 scale was used as the criterion to evaluate the criterion-related validity. Paired t tests were conducted to evaluate the responsiveness on each domain/facet as well as the total of the scale, with Standardized Response Mean (SRM) being calculated. The QLICD-CHD (V2.0) has been developed with 42 items in 4 domains. The Cronbach's α of the general module, the specific module and the total scale were 0.91, 0.92 and 0.91 respectively. The overall score and the test-retest reliability coefficients in all domains are higher than 0.60, except for the specific module. Correlation and factor analysis confirmed good construct validity and criterion-related validity. After treatments, the overall score and score of all domains have statistically significant changes (P<0.01). The SRM of domain-level score ranges from 0.27 to 0.50. Generalizability Theory further confirm the reliability of the scale through more accurate variance component studies. The QLICD-CHD (V2.0) could be used as a useful instrument in assessing QoL for patients with CHD, with good psychometric properties.

Genetic analysis of health traits and their associations with longevity, fertility, production, and conformation traits in Holstein cattle

Health traits have high economic values in dairy cattle breeding, which can cause considerable financial loss through involuntary culling. In this study, fourteen health traits were analyzed, including five composite health traits: reproductive disorders (RD), udder health (UH), digestive disorders (DD), metabolic disorders (MD), locomotory diseases (LD), and nine independent health traits: gestation disorders and peripartum disorders (GDPD), irregular estrus cycle and sterility (IECS), metritis (ME), mastitis (MA), abomasal displacement (AD), enteritis (EN), and ketosis (KE), claw diseases (CD), laminitis complex (LC). This study analysed variance components for health traits through both single and bivariate repeatability animal models. All health traits showed low heritability, ranging from 0.001 to 0.025. Most of health traits in five categories showed negative genetic correlations, ranging from -0.012 (CD and EN) to -0.634 (ME and EN). Strong positive genetic correlations appeared within the same category, ranging from 0.469 (EN and AD) to 0.994 (UH and MA, LD and CD). Furthermore, approximate genetic correlations were evaluated between health traits and routinely collected traits (longevity, fertility, production, and conformation). In general, the low to moderate approximate genetic correlations were estimated between health traits and routinely collected traits. The estimated correlations between health traits and longevity, fertility, production, conformation traits could provide an indirect reference for disease resistance breeding in Holstein cattle.