Comparing the latent state-trait structure of the PANSS in cariprazine-medicated and placebo-controlled patients with acute schizophrenia.

After over a hundred years of research, the question whether the symptoms of schizophrenia are rather trait-like (being a relatively stable quality of individuals) or state-like (being substance to change) is still unanswered. To assess the trait and the state component in patients with acute schizophrenia, one group receiving antipsychotic treatment, the other not. Data from four phase II/III, 6-week, randomized, double-blind, placebo-controlled trials of similar design that included patients with acute exacerbation of schizophrenia were pooled. In every trial, one treatment group received a third-generation antipsychotic, cariprazine, and the other group placebo. To assess symptoms of schizophrenia, the Positive and Negative Symptom Scale (PANSS) was applied. Further analyses were conducted using the five subscales as proposed by Wallwork and colleagues. A latent state-trait (LST) model was developed to estimate the trait and state components of the total variance of the observed scores. All symptom dimensions behaved more in a trait-like manner. The proportions of all sources of variability changed over the course of the observational period, with a bent around weeks 3 and 4. Visually inspected, no major differences were found between the two treatment groups regarding the LST structure of symptom dimensions. This high proportion of inter-individual stability may represent an inherent part of symptomatology that behaves independently from treatment status.

Which emotion regulation strategy is efficient for whom? Reappraisal and suppression efficiency for adaptive and maladaptive personality profiles.

OBJECTIVE: This study aimed to explore the efficiency of different emotion regulation strategies, specifically reappraisal and suppression, in relation to adaptive and maladaptive personality profiles. BACKGROUND: Personality conditions emotions and influences emotion regulation. Of the available regulation strategies, reappraisal (reinterpreting the situation) is described as an efficient strategy, whereas suppression (not displaying the experienced emotion) carries higher physiological and cognitive costs. Little is known, however, about the influence of personality on these efficiencies. METHOD: We tested the personality structure of 102 participants (Meanage = 20.75, SDage = 2.15), based on the Five-Factor Model and the Maladaptive Personality Trait Model. Experience, expressivity, and physiological arousal were recorded during the viewing of emotionally charged positive and negative images while participants reappraised, suppressed, or viewed the images without regulating their emotions. RESULTS: We identified two clusters for adaptive personality ("Adaptive Resilient" and "Anti-resilient") and two for maladaptive personality ("Maladaptive Resilient" and "Under-controlled"). The major finding was for emotional experience in maladaptive personalities, where reappraisal was efficient in the Maladaptive Resilient profile, while none of the strategies brought relief in the Under-controlled profile. CONCLUSION: This study, which systematically contrasts personality and efficiency of emotion regulation strategies, is one of the first attempts to refine the understanding of how personality influences the emotional regulation process.

Estimation of genetic parameters for important traits using a multi-trait model in late-feathering Qingyuan partridge hens in China.

Qingyuan partridge chicken is one of the most well-known Chinese indigenous yellow broilers. In breeding programmes, five traits are usually selected when the chickens are 105 days old, namely body weight (BW), comb height (CH), shank length (SL), shank girth (SG) and feather maturity (FM). The objective of this study was to estimate the genetic parameters of these five traits, especially direct additive genetic correlations, to lay the foundation for balanced selection of Qingyuan partridge chickens. Approximately 9600 records were used for estimation. Variance components for these five traits were estimated using three multi-trait models incorporating different effects via Gibbs sampling. Based on model 1 in which the random effects included direct additive genetic effects and residuals, the estimated direct heritabilities for BW, CH, SL, SG and FM were 0.29 ± 0.04, 0.53 ± 0.04, 0.47 ± 0.04, 0.43 ± 0.05 and 0.18 ± 0.03, respectively. The direct genetic correlations ranged from -0.08 to 0.46. When additionally considering maternal additive genetic effects (model 2), the estimates of direct heritabilities and absolute values of direct additive genetic correlations were smaller. The heritabilities were 0.14 ± 0.04, 0.40 ± 0.02, 0.34 ± 0.05, 0.27 ± 0.05 and 0.12 ± 0.03 for BW, CH, SL, SG and FM, respectively. The direct additive genetic correlations ranged from -0.33 to 0.36. More specifically, the direct additive genetic correlations between BW and CH, SL, SG and FM were 0.19 ± 0.13, 0.15 ± 0.15, 0.36 ± 0.15 and - 0.33 ± 0.21, respectively. The genetic correlations of FM with SL, SG and CH were - 0.15 ± 0.15, -0.08 ± 0.17 and 0.18 ± 0.15, respectively. The direct genetic correlations between CH and SG and SL were - 0.02 ± 0.11 and - 0.20 ± 0.11, respectively, and that between SL and SG was 0.19 ± 0.11. The total heritabilities and maternal additive genetic correlations ranged from 0.16 to 0.44 and from -0.13 to 0.61, respectively. The third model also included the maternal permanent environmental effect for BW. The estimates of direct heritability, direct additive genetic correlation, total heritability and maternal additive genetic correlation were only slightly different from those based on the second model. Therefore, the maternal additive genetic effect has a large effect on the estimation of genetic parameters, and it is better to consider this effect in the genetic evaluation of these five traits. Relatively high direct and maternal additive genetic correlations for most trait pairs suggested that it is better to jointly evaluate these five traits in breeding programmes.

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize.

Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.

Disentangling trait, occasion-specific, and accumulated situational effects of psychological distress in adulthood: evidence from the 1958 and 1970 British birth cohorts.

BACKGROUND: The trajectories of psychological distress differ between individuals, but these differences can be difficult to understand because the measures contain both consistent and situational features; however, in longitudinal studies these sources of information can be disentangled. In addition to occasion-specific features, interindividual differences can be decomposed into two sources of information: trait and carry-over effects between neighboring occasions that are not related to the trait (i.e. accumulated situational effects). METHODS: To disentangle these three sources of variance throughout adulthood, the consistency (trait and accumulated situational effects) and occasion specificity of nine indicators of psychological distress from the Malaise Inventory were examined in two birth cohorts, the 1958 National Child Development Study (NCDS58), and the 1970 British Cohort Study (BCS70). RESULTS: The scale was administered at ages 23, 33, 42, and 50 in NCDS58 (n = 7147), and at ages 26, 30, 34, and 42 in BCS70 (n = 6859). For each psychological symptom, more variance was consistent than occasion-specific. The majority of the consistency was due to trait variance as opposed to accumulated situational effects, indicating that an individual predisposed to be distressed at the beginning of the study remained more likely to be distressed over the whole period. Symptoms of rage were notably more consistent among males than females in both cohorts (78.1% and 81.3% variance explained by trait in NCDS58 and BCS70, respectively), and among females in the NCDS58 (69%). CONCLUSIONS: Symptoms of psychological distress exhibited high stability throughout adulthood, especially among men, due mostly to interindividual trait differences.

Establishment process of a magic trait allele subject to both divergent selection and assortative mating.

Sexual selection and divergent selection are among the major driving forces of reproductive isolation, which could eventually result in speciation. A magic trait is defined such that a single trait is subject to both divergent selection and mate choice through phenotype-based assortative mating. We are here interested in the evolutionary behavior of alleles at a genetic locus responsible for a magic trait in a finite population. We assume that, in a pair of homogeneous subpopulations, a mutant allele arises at the magic trait locus, and theoretically obtain the probability that the new allele establishes in the population, or the establishment probability. We also show an analytical expression for the trajectory of allele frequency along the establishment, from which the time required for the establishment is obtained, or the establishment time. Under this model, divergent selection simply favors the new allele to fix where it is beneficial, whereas assortative mating works against rare alleles. It is theoretically demonstrated that the fate of the new allele is determined by the relative contributions of the two selective forces, divergent selection and assortative mating, when the allele is rare so that the two selective forces counteract. Our theoretical results for the establishment probability and time allow us to understand the relative role of random genetic drift in the establishment process of a magic trait allele in a finite population.

Assessing the reliability of predicted plant trait distributions at the global scale.

AIM: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. RESULTS: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait-environment relationships and trait-trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. MAIN CONCLUSIONS: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.

Genotype-by-environment (temperature-humidity) interaction of milk production traits in Australian Holstein cattle.

Dairying in Australia is practiced in highly diverse climatic conditions and production systems, which means that re-ranking of genotypes could occur across environments that vary in temperature and humidity-that is, genotype-by-environment interactions (G × E) may exist. The objective of this study was to investigate G × E for heat tolerance with respect to milk production traits in Australian Holsteins. A total of 6.7 million test-day milk yield records for first, second, and third lactations from 491,562 cows and 6,410 sires that had progeny in different climatic environments were included in the analysis. The environmental gradient used was the temperature-humidity index (THI) calculated from climate data from 163 Australian public weather stations between 2003 and 2017. Data were analyzed using univariate reaction norm (RM) sire model, and the results were compared with multi-trait model (MT). The MT analysis treated test-day yields at 5th percentile (THI = 61; i.e., thermoneutral conditions), 50th percentile (THI = 67; i.e., moderate heat stress conditions), and 95th percentile (THI = 73; i.e., high heat stress conditions) of the trajectory of THI as correlated traits. A THI series of 61, 67, and 73, for example, is equivalent to average temperature and relative humidity of approximately 20°C and 45%, 25°C and 45%, and 31°C and 50%, respectively. We observed some degree of heterogeneity of additive (AG) and permanent environmental (PE) variance over the trajectory THI from RM analysis, with estimates decreasing at higher THI values more steeply for PE than for AG variance. The genetic correlations of the tests between the 5th and 95th percentiles of THI for milk, protein, and fat yield from RM were 0.88 ± 0.01 (standard error), 0.79 ± 0.01, and 0.86 ± 0.01, respectively, whereas the corresponding estimates from MT were 0.86 ± 0.02, 0.84 ± 0.03, and 0.87 ± 0.03. We observed lower genetic correlations between the 5th and 95th percentiles of THI for milk tests from recent years (i.e., 2009 and 2017) compared with earlier years (i.e., 2003 and 2008), which suggests that the level of G × E is increasing in the studied population and should be monitored especially in anticipation of future expected increase in daily average temperature and frequency of heat events. Overall, our results indicate presence of G × E at the upper extreme of the trajectory of THI, but the current extent of sire re-ranking may not justify providing separate genetic evaluations for different levels of heat stress. However, variations observed in the sire sensitivity to heat stress suggest that dairy herds in high heat load conditions could benefit more from using heat-tolerant or resilient sires.

Arabic Version of the Personality Inventory for the DSM-5 (PID-5) in a Community Sample of United Arab Emirates Nationals.

BACKGROUND: Section III of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) proposes a model for conceptualizing personality disorders in which they are characterized by impairments in personality functioning and maladaptive personality traits. The Personality Inventory for DSM-5 (PID-5) is a self-report measure that assesses the presence and severity of these maladaptive personality traits. OBJECTIVE: The current study examined the reliability and validity of the Arabic version of the Personality Inventory for DSM-5 (PID-5) to measure maladaptive personality traits in the Emirati population of the United Arab Emirates. METHODS: The Arabic version of the PID-5 was administered to a community sample of 1,090 United Arab Emirates nationals (89.5% female and 10.5% male, mean age = 22.44 years old). The descriptive measures, internal consistency, test-retest reliability, convergent validity with NEO - Five Factor Inventory, as well as PID-5's factor structure, were all addressed. RESULTS: The PID-5facets and domains mean scores were higher in the Emirati sample compared to the original US sample. Internal consistency of the PID-5 scales was acceptable to high and test-retest coefficients ranged from 0.84 (facets) to 0.87 (domains). As expected, the five domains of the Arabic version of the PID-5 correlated significantly with all Five-Factor Model domains of personality. Additionally, the Arabic version of the PID-5 confirmed a five-factor structure that resembles the PID-5 domains. CONCLUSION: The findings of this study provided initial support for the use of the Arabic version of the PID-5 to assess maladaptive personality traits in the Emirati population of the United Arab Emirates.

A Bayesian approach for individual-level drug benefit-risk assessment.

In existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually identified and defined separately based on aggregated clinical data and therefore ignore the individual-level differences as well as the association among the criteria. We proposed a Bayesian multicriteria decision-making method for BRA of drugs using individual-level data. We used a multidimensional latent trait model to account for the heterogeneity of treatment effects with latent variables introducing the dependencies among outcomes. We then applied the stochastic multicriteria acceptability analysis approach for BRA incorporating imprecise and heterogeneous patient preference information. We adopted an efficient Markov chain Monte Carlo algorithm when implementing the proposed method. We applied our method to a case study to illustrate how individual-level benefit-risk profiles could inform decision-making.

Combining adverse pregnancy and perinatal outcomes for women exposed to antiepileptic drugs during pregnancy, using a latent trait model.

BACKGROUND: Application of latent variable models in medical research are becoming increasingly popular. A latent trait model is developed to combine rare birth defect outcomes in an index of infant morbidity. METHODS: This study employed four statewide, retrospective 10-year data sources (1999 to 2009). The study cohort consisted of all female Florida Medicaid enrollees who delivered a live singleton infant during study period. Drug exposure was defined as any exposure to Antiepileptic drugs (AEDs) during pregnancy. Mothers with no AED exposure served as the AED unexposed group for comparison. Four adverse outcomes, birth defect (BD), abnormal condition of new born (ACNB), low birth weight (LBW), and pregnancy and obstetrical complication (PCOC), were examined and combined using a latent trait model to generate an overall severity index. Unidimentionality, local independence, internal homogeneity, and construct validity were evaluated for the combined outcome. RESULTS: The study cohort consisted of 3183 mother-infant pairs in total AED group, 226 in the valproate only subgroup, and 43,956 in the AED unexposed group. Compared to AED unexposed group, the rate of BD was higher in both the total AED group (12.8% vs. 10.5%, P < .0001), and the valproate only subgroup (19.6% vs. 10.5%, P < .0001). The combined outcome was significantly correlated with the length of hospital stay during delivery in both the total AED group (Rho = 0.24, P < .0001) and the valproate only subgroup (Rho = 0.16, P = .01). The mean score for the combined outcome in the total AED group was significantly higher (2.04 ± 0.02 vs. 1.88 ± 0.01, P < .0001) than AED unexposed group, whereas the valproate only subgroup was not. CONCLUSIONS: Latent trait modeling can be an effective tool for combining adverse pregnancy and perinatal outcomes to assess prenatal exposure to AED, but evaluation of the selected components is essential to ensure the validity of the combined outcome.

Estimation of Trait-Model Parameters in a MOD Score Linkage Analysis.

BACKGROUND/AIMS: Theoretically, the trait-model parameters (disease allele frequency and penetrance function) can be estimated without bias in a MOD score linkage analysis. We aimed to practically evaluate the MOD score approach regarding its ability to provide unbiased trait-model parameters for various pedigree-type and trait-model scenarios. We further investigated the ability of the MOD score approach to detect imprinting using affected sib pairs (ASPs) and affected half-sib pairs (AHSPs) when all parental genotypes are missing. METHODS: Simulated pedigree data were analyzed using the GENEHUNTER-MODSCORE software package. Parameter estimation performance in terms of bias and variability was evaluated with regard to trait-model type and pedigree complexity. RESULTS: Generally, parameters were estimated with lower bias and variability with increasing pedigree complexity, especially for recessive and overdominant models. However, dominant and additive models could hardly be distinguished even when using 3-generation pedigrees. Imprinting could clearly be detected for mixtures of mainly ASPs and only few AHSPs with the common parent of the imprinted sex, even though no parental genotypes were available. CONCLUSION: Our results provide guidance to researchers regarding the possibility to estimate trait-model parameters by a MOD score analysis, including the degree of imprinting, with certain types of pedigrees.

Genetic trends for fertility, udder health and protein yield in Swedish red cattle estimated with different models.

The aim of this study was to estimate and compare genetic trends in Swedish Red cattle using a full multiple-trait (MT) model and trait-group-wise models for female fertility, udder health and protein yield. Field data for maiden heifers from 1989 and cows with a first and second lactation between 1990 and 2007 were included. (Co)variance components were estimated prior to prediction of breeding values. The estimated genetic trends were clearly favourable for protein yield and udder conformation, and in most cases neutral to favourable for clinical mastitis and calving to first insemination. In maiden heifers, the trends were neutral for number of inseminations per service period. Unfavourable genetic trends were estimated for number of inseminations in the first two lactations, but the trends seemed less unfavourable from evaluations within trait groups compared with when using the full MT model. Excluding maiden heifer data affected genetic trends less than using trait-group-wise analyses instead of a full MT model. Unfavourable genetic trends in functional traits may be missed unless the traits are evaluated in a MT model including traits under strong selection.

Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models.

OBJECTIVE: This study estimated the genetic parameters for productive and reproductive traits. METHODS: The data included production and reproduction records of animals that have calved between 1979 and 2013. The genetic parameters were estimated using multivariate mixed models (DMU) package, fitting univariate and multivariate mixed models with average information restricted maximum likelihood algorithm. RESULTS: The estimates of heritability for milk production traits from the first three lactation records were 0.03±0.03 for lactation length (LL), 0.17±0.04 for lactation milk yield (LMY), and 0.15±0.04 for 305 days milk yield (305-d MY). For reproductive traits the heritability estimates were, 0.09±0.03 for days open (DO), 0.11±0.04 for calving interval (CI), and 0.47±0.06 for age at first calving (AFC). The repeatability estimates for production traits were 0.12±0.02, for LL, 0.39±0.02 for LMY, and 0.25±0.02 for 305-d MY. For reproductive traits the estimates of repeatability were 0.19±0.02 for DO, and to 0.23±0.02 for CI. The phenotypic correlations between production and reproduction traits ranged from 0.08±0.04 for LL and AFC to 0.42±0.02 for LL and DO. The genetic correlation among production traits were generally high (>0.7) and between reproductive traits the estimates ranged from 0.06±0.13 for AFC and DO to 0.99±0.01 between CI and DO. Genetic correlations of productive traits with reproductive traits were ranged from -0.02 to 0.99. CONCLUSION: The high heritability estimates observed for AFC indicated that reasonable genetic improvement for this trait might be possible through selection. The h2 and r estimates for reproductive traits were slightly different from single versus multi-trait analyses of reproductive traits with production traits. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended.

Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein.

Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, ß-CN, κ-CN, α-lactalbumin, and ß-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for ß-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage.

An alternative approach to modeling genetic merit of feed efficiency in dairy cattle.

Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A widely accepted yet occasionally contested measure of FE in dairy cattle is residual feed intake (RFI). The use of RFI is limiting for several reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI, and potential differences in genetic versus nongenetic relationships between dry matter intake (DMI) and FE component traits. Hence, analyses focusing on DMI as the response are often preferred. We propose an alternative multiple-trait (MT) modeling strategy that exploits the Cholesky decomposition to provide a potentially more robust measure of FE. We demonstrate that our proposed FE measure is identical to RFI provided that genetic and nongenetic relationships between DMI and component traits of FE are identical. We assessed both approaches (MT and RFI) by simulation as well as by application to 26,383 weekly records from 50 to 200 d in milk on 2,470 cows from a dairy FE consortium study involving 7 institutions. Although the proposed MT model fared better than the RFI model when simulated genetic and nongenetic associations between DMI and FE component traits were substantially different from each other, no meaningful differences were found in predictive performance between the 2 models when applied to the consortium data.

Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep.

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525 d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

Currently, multiple-trait (MT) models are used in large-scale genetic evaluation of growth traits, where body weight traits are defined as separate traits at a finite number of fixed ages. Random regression (RR) models are expected to be superior since they can handle repeated measurements of weight and model these as a function of the actual age of measurement. These two models were compared in predicting breeding values for the body weight of Australian meat sheep. Phenotypic variation and estimated breeding values (EBVs) estimated at specific ages between 60 and 525 d with RR and MT models were compared and EBVs were validated in progeny data. The accuracy of EBVs in forecasting the performance of progeny was not statistically different between the two models. Other benefits of the RR model include the use of multiple records per animal, estimation of EBVs for early and late growth, with no need for age correction. Hence, RR models can be useful for the genetic evaluation of growth traits of sheep in Australia, but they do not necessarily predict breeding values at different ages more accurately than MT models.

Efficiency of computerized adaptive testing with a cognitively designed item bank.

An item bank is key to applying computerized adaptive testing (CAT). The traditional approach to developing an item bank requires content experts to design each item individually, which is a time-consuming and costly process. The cognitive design system (CDS) approach offers a solution by automating item generation. However, the CDS approach has a specific way of calibrating or predicting item difficulty that affects the measurement efficiency of CAT. A simulation study was conducted to compare the efficiency of CAT using both calibration and prediction models. The results show that, although the predictive model (linear logistic trait model; LLTM) shows a higher root mean square error (RMSE) than the baseline model (Rasch), it requires only a few additional items to achieve comparable RMSE. Importantly, the number of additional items needed decreases as the explanatory rate of the model increases. These results indicate that the slight reduction in measurement efficiency due to prediction item difficulty is acceptable. Moreover, the use of prediction item difficulty can significantly reduce or even eliminate the need for item pretesting, thereby reducing the costs associated with item calibration.

A penalized likelihood approach to pooling estimates of covariance components from analyses by parts.

Estimates of covariance matrices for numerous traits are commonly obtained by pooling results from a series of analyses of subsets of traits. A penalized maximum-likelihood approach is proposed to combine estimates from part analyses while constraining the resulting overall matrices to be positive definite. In addition, this provides the scope for 'improving' estimates of individual matrices by applying a penalty to the likelihood aimed at borrowing strength from their phenotypic counterpart. A simulation study is presented showing that the new method performs well, yielding unpenalized estimates closer to results from multivariate analyses considering all traits, than various other techniques used. In particular, combining results for all sources of variation simultaneously minimizes deviations in phenotypic estimates if sampling covariances can be approximated. A mild penalty shrinking estimates of individual covariance matrices towards their sum or estimates of canonical eigenvalues towards their mean proved advantageous in most cases. The method proposed is flexible, computationally undemanding and provides combined estimates with good sampling properties and is thus recommended as alternative to current methods for pooling.

Estimation of heritabilities and additive genetic correlations for reproduction traits in swine: insights for tropical commercial production systems using multiple trait animal models.

OBJECTIVE: This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand. METHODS: Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual. RESULTS: Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07±0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4. CONCLUSION: The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand.