China is facing serious experimental monkey shortage during the COVID-19 lockdown.

This report aims to analyze the experimental monkey shortage generated by the COVID-19 lockdown. The supply capability of the monkey breeding farms is insufficient to meet demand, and the sales prices have skyrocketed since 2018. The contradiction will be further aggravated with import prohibition although the countermeasures suggested.

A method for deriving the genetic matrix needed for selection across combinations of breeds.

A method is described for deriving the genetic matrix G needed for breeders to select across breeds for any breeding objective. The matrix comprises partitions among selection criteria (G11 ), between selection criteria and the breeding objective traits (G12 ), and among the breeding objective traits (G22 ). A weighting procedure is used to combine the G matrix of individual breeds with known breed differences. Between-breed variances are added to the diagonals of the matrix. Selection across many breeds can be quite different from that across a subset of the breeds. The particular breeds, and the numbers of each breed, that the breeder is prepared to consider in selection changes the genetic matrix and the selection index that should be used.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.

Performance of pedigree and various forms of marker-derived relationship coefficients in genomic prediction and their correlations.

In recent years, with development and validation of different genotyping panels, several methods have been proposed to build efficient similarity matrices among individuals to be used for genomic selection. Consequently, the estimated genetic parameters from such information may deviate from their counterpart using traditional family information. In this study, we used a pedigree-based numerator relationship matrix (A) and three types of marker-based relationship matrices ( G ) including two identical by descent, that is G K and G M and one identical by state, G V as well as four Gaussian kernel ( GK ) similarity kernels with different smoothing parameters to predict yet to be observed phenotypes. Also, we used different kinship matrices that are a linear combination of marker-derived IBD or IBS matrices with A, constructed as K = λ G + 1 - λ A , where the weight ( λ ) assigned to each source of information varied over a grid of values. A Bayesian multiple-trait Gaussian model was fitted to estimate the genetic parameters and compare the prediction accuracy in terms of predictive correlation, mean square error and unbiasedness. Results show that the estimated genetic parameters (heritability and correlations) are affected by the source of the information used to create kinship or the weight placed on the sources of genomic and pedigree information. The superiority of GK-based model depends on the smoothing parameters (θ) so that with an optimum θ value, the GK-based model statistically yielded better performance (higher predictive correlation, lowest MSE and unbiased estimates) and more stable correlations and heritability than the model with IBD, IBS or A kinship matrices or any of the linear combinations.

Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle.

The objectives of this study were to compare different models for analysing body weight (BW) and average daily feed intake (ADFI) data collected during a 70-day feedlot test period and to explore whether genetic parameters change over time to evaluate the implications of selection response. (Co)variance components were estimated using repeatability and random regression models in 2,071 Angus steers. Models included fixed effects of contemporary group, defined as herd-year-observation_date-age, with additive genetic and permanent environmental components as random effects. Models were assessed based on the log likelihood, Akaike's information criterion and the Bayesian information criterion. For both traits, random regression models (RRMs) presented a better fit, indicating that genetic parameters change over the test period. Using a two-trait RRM, the heritability from day 1 up to day 70 for BW increased from 0.40 to 0.50, while for ADFI, it decreased from 0.44 to 0.33. The genetic correlation increased from 0.53 at day 1 up to 0.79 at day 70. Selection based on an index assuming no change in genetic parameters would yield a 2.78%-3.13% lower selection response compared to an index using parameters estimated with RRMs and assuming these genetic parameters are correct. Results imply that it may be beneficial to implement RRMs to account for the change of parameters across the feedlot period in feed efficiency traits.

Farmers' choice of genotypes and trait preferences in tropically adapted chickens in five agro-ecological zones in Nigeria.

This study aimed at determining chicken genotypes of choice and traits preference in chicken by smallholder farmers in Nigeria. Data were obtained from a total of 2063 farmers using structured questionnaires in five agro-ecological zones in Nigeria. Chi square (χ2) statistics was used to explore relationships between categorical variables. The mean ranks of the six genotypes and twelve traits of preference were compared using the non-parametric Kruskal-Wallis H (with Mann-Whitney U test for post hoc separation of mean ranks), Friedman, and Wilcoxon signed-rank (with Bonferroni's adjustments) tests. Categorical principal component analysis (CATPCA) was used to assign farmers into groups. Gender distribution of farmers was found to be statistically significant (χ2 = 16.599; P ≤ 0.002) across the zones. With the exception of Shika Brown, preferences for chicken genotypes were significantly (P ≤ 0.01) influenced by agro-ecological zone. However, gender differentiated response was only significant (P ≤ 0.01) in Sasso chicken with more preference by male farmers. Overall, FUNAAB Alpha, Sasso, and Noiler chicken were ranked 1st, followed by Kuroiler (4th), Shika Brown (5th), and Fulani birds (6th), respectively. Within genotypes, within and across zones and gender, preferences for traits varied significantly (P ≤ 0.005 and P ≤ 0.01). Traits of preference for selection of chicken breeding stock tended towards body size, egg number, egg size, and meat taste. Spearman's rank order correlation coefficients of traits of preference were significant (P ≤ 0.01) and ranged from 0.22 to 0.90. The two PCs extracted, which explained 65.3% of the variability in the dataset, were able to assign the farmers into two groups based on preference for body size of cock and hen and the other ten traits combined. The present findings may guide the choice of appropriate chicken genotypes while the traits of economic importance may be incorporated into future genetic improvement and conservation programs in Nigeria.

Genomic selection strategies for breeding adaptation and production in dairy cattle under climate change.

Livestock production both contributes to and is affected by global climate change, and substantial modifications will be required to increase its climate resilience. In this context, reliance on dominant commercial livestock breeds, featuring small effective population sizes, makes current production strategies vulnerable if their production is restricted to environments, which may be too costly to support under future climate scenarios. The adaptability of animal populations to future environments will therefore become important. To help evaluate the role of genetics in climate adaptation, we compared selection strategies in dairy cattle using breeding simulations, where genomic selection was used on two negatively correlated traits for production (assumed to be moderately heritable) and adaptation (assumed to have low heritability). Compared with within-population breeding, genomic introgression produced a more positive genetic change for both production and adaptation traits. Genomic introgression from highly adapted but low production value populations into highly productive but low adaptation populations was most successful when the adaptation trait was given a lower selection weight than the production trait. Genomic introgression from highly productive population to highly adapted population was most successful when the adaptation trait was given a higher selection weight than the production trait. Both these genomic introgression schemes had the lowest risk of inbreeding. Our results suggest that both adaptation and production can potentially be improved simultaneously by genomic introgression.

Fluctuating survival selection explains variation in avian group size.

Most animal groups vary extensively in size. Because individuals in certain sizes of groups often have higher apparent fitness than those in other groups, why wide group size variation persists in most populations remains unexplained. We used a 30-y mark-recapture study of colonially breeding cliff swallows (Petrochelidon pyrrhonota) to show that the survival advantages of different colony sizes fluctuated among years. Colony size was under both stabilizing and directional selection in different years, and reversals in the sign of directional selection regularly occurred. Directional selection was predicted in part by drought conditions: birds in larger colonies tended to be favored in cooler and wetter years, and birds in smaller colonies in hotter and drier years. Oscillating selection on colony size likely reflected annual differences in food availability and the consequent importance of information transfer, and/or the level of ectoparasitism, with the net benefit of sociality varying under these different conditions. Averaged across years, there was no net directional change in selection on colony size. The wide range in cliff swallow group size is probably maintained by fluctuating survival selection and represents the first case, to our knowledge, in which fitness advantages of different group sizes regularly oscillate over time in a natural vertebrate population.

A robust DF-REML framework for variance components estimation in genetic studies.

MOTIVATION: In genetic association studies, linear mixed models (LMMs) are used to test for associations between phenotypes and candidate single nucleotide polymorphisms (SNPs). These same models are also used to estimate heritability, which is central not only to evolutionary biology but also to the prediction of the response to selection in plant and animal breeding, as well as the prediction of disease risk in humans. However, when one or more of the underlying assumptions are violated, the estimation of variance components may be compromised and therefore so may the estimates of heritability and any other functions of these. Considering that datasets obtained from real life experiments are prone to several sources of contamination, which usually induce the violation of the assumption of the normality of the errors, a robust derivative-free restricted-maximum likelihood framework (DF-REML) together with a robust coefficient of determination are proposed for the LMM in the context of genetic studies of continuous traits. RESULTS: The proposed approach, in addition to the robust estimation of variance components and robust computation of the coefficient of determination, allows in particular for the robust estimation of SNP-based heritability by reducing the bias and increasing the precision of its estimates. The performance of both classical and robust DF-REML approaches is compared via a Monte Carlo simulation study. Additionally, three examples of application of the methodologies to real datasets are given in order to validate the usefulness of the proposed robust approach. Although the main focus of this article is on plant breeding applications, the proposed methodology is applicable to both human and animal genetic studies. AVAILABILITY AND IMPLEMENTATION: Source code implemented in R is available in the Supplementary Material. CONTACT: vmml@fct.unl.pt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Economic evaluation of sexed semen use in Iranian dairy farms according to field data.

In this study, at first, the reproductive consequences of sexed semen use were quantified and then the cost-benefit of sexed semen use on Iranian commercial dairy farms was evaluated. Retrospective data collected during 2006 to 2013 from four large dairy farms in the Isfahan province of Iran were used for this study. These data included of 13,003 heifers records, from which 11.2% used sexed semen from 33 different bulls. All data were analysed using a multivariable logistical regression model, GENMOD procedure from SAS software. The analyses included economic values (EVs) when sexed semen was used in 1, 2 and 3 consecutive services compared with conventional semen use for all insemination. Results showed that rates of female born from sexed semen (86.3%) were 1.8 times higher than those from conventional semen (48.5%). Conception rates were 43.8% for sexed and 59.2% for conventional semen (p < 0.0001). Abortion (4.4% vs. 5.4%) and stillbirth (8.4% vs. 7.2%) rates were not significantly different between sexed and conventional semen (p = 0.09). Dystocia rates were 15.5% for sexed and 19.6% for conventional semen (p = 0.002). Sexed semen use showed negative EVs through all investigated scenarios. The EVs from the implementation of 1, 2 and 3 sexed semen breedings were estimated to be $-6.69, $-14.01 and $-19.08, respectively. Total insemination cost and increased cost of age at first calving were the most important components associated with negative EV for sexed semen. Sensitivity analysis showed that proportion of conception rates of sexed semen to conventional semen and female calf value were the most important biological and economic factors influencing on the EV of sexed semen, respectively. Breakeven would be obtained with 77.4%-79.3% conception rates or female calf value of $719.5-$754.7 through investigated breeding scenarios when all other factors remained the same.

The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows.

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows.

Genetic heteroscedastic models for ordinal traits: application to sheep litter size.

BACKGROUND: Classical genetic canalization models, which accommodate the mean and variance of a trait separately, provide a flexible approach to take heteroscedasticity for continuous traits into account. However, this model is not appropriate for discrete traits. The aim of this work was to propose heteroscedastic threshold models suitable for the genetic analysis of ordinal data. METHODS: In order to first fit the mean and variance of ordinal traits separately, we extended the classical threshold model (TM) for discrete data by introducing non-genetic and genetic factors of heterogeneity on the variance of its underlying variable, which leads to a homothetic threshold model HTM and its alternative parameterization HTM' in which the thresholds of different individuals are linked by a homothetic-translation. Relaxing the constraint between the thresholds led us to propose an independent threshold model ITM that was more flexible than HTM' but required the estimation of more parameters. TM, HTM and ITM models were applied to study 19,671 records on litter size in Romane sheep. RESULTS: Both HTM and ITM were able to disentangle the link between the mean and variance that holds in the classical homoscedastic threshold model. The results obtained for the litter size of Romane ewes showed that the data was best fitted with HTM compared to ITM and TM. The correlations between the observed and predicted variances were equal to 0.6 and 0.2 for HTM and TM, respectively. These analyses showed the existence of a genetic component for the heterogeneity of litter size in sheep that was taken into account in HTM. CONCLUSIONS: HTM is the most suitable model to study the variability of litter size in sheep. It accommodates both the mean and variance separately while requiring the estimation of only a few parameters.

Quantitative genetic analysis of the bTB diagnostic single intradermal comparative cervical test (SICCT).

BACKGROUND: Bovine tuberculosis (bTB) is a disease of significant economic importance and is a persistent animal health problem with implications for public health worldwide. Control of bTB in the UK has relied on diagnosis through the single intradermal comparative cervical test (SICCT). However, limitations in the sensitivity of this test hinder successful eradication and the control of bTB remains a major challenge. Genetic selection for cattle that are more resistant to bTB infection can assist in bTB control. The aim of this study was to conduct a quantitative genetic analysis of SICCT measurements collected during bTB herd testing. Genetic selection for bTB resistance will be partially informed by SICCT-based diagnosis; therefore it is important to know whether, in addition to increasing bTB resistance, this might also alter genetically the epidemiological characteristics of SICCT. RESULTS: Our main findings are that: (1) the SICCT test is robust at the genetic level, since its hierarchy and comparative nature provide substantial protection against random genetic changes that arise from genetic drift and from correlated responses among its components due to either natural or artificial selection; (2) the comparative nature of SICCT provides effective control for initial skin thickness and age-dependent differences; and (3) continuous variation in SICCT is only lowly heritable and has a weak correlation with SICCT positivity among healthy animals which was not significantly different from zero (P > 0.05). These emerging results demonstrate that genetic selection for bTB resistance is unlikely to change the probability of correctly identifying non-infected animals, i.e. the test's specificity, while reducing the overall number of cases. CONCLUSIONS: This study cannot exclude all theoretical risks from selection on resistance to bTB infection but the role of SICCT in disease control is unlikely to be rapidly undermined, with any adverse correlated responses expected to be weak and slow, which allow them to be monitored and managed.

Analytical and statistical consideration on the use of the ISAG-ICAR-SNP bovine panel for parentage control, using the Illumina BeadChip technology: example on the German Holstein population.

BACKGROUND: Parentage control is moving from short tandem repeats- to single nucleotide polymorphism (SNP) systems. For SNP-based parentage control in cattle, the ISAG-ICAR Committee proposes a set of 100/200 SNPs but quality criteria are lacking. Regarding German Holstein-Friesian cattle with only a limited number of evaluated individuals, the exclusion probability is not well-defined. We propose a statistical procedure for excluding single SNPs from parentage control, based on case-by-case evaluation of the GenCall score, to minimize parentage exclusion, based on miscalled genotypes. Exclusion power of the ISAG-ICAR SNPs used for the German Holstein-Friesian population was adjusted based on the results of more than 25,000 individuals. RESULTS: Experimental data were derived from routine genomic selection analyses of the German Holstein-Friesian population using the Illumina BovineSNP50 v2 BeadChip (20,000 individuals) or the EuroG10K variant (7000 individuals). Averages and standard deviations of GenCall scores for the 200 SNPs of the ISAG-ICAR recommended panel were calculated and used to calculate the downward Z-value. Based on minor allelic frequencies in the Holstein-Friesian population, one minus exclusion probability was equal to 1.4×10⁻¹° and 7.2×10⁻²6, with one and two parents, respectively. Two monomorphic SNPs from the 100-SNP ISAG-ICAR core-panel did not contribute. Simulation of 10,000 parentage control combinations, using the GenCall score data from both BeadChips, showed that with a Z-value greater than 3.66 only about 2.5% parentages were excluded, based on the ISAG-ICAR recommendations (core-panel: ≥ 90 SNPs for one, ≥ 85 SNPs for two parents). When applied to real data from 1750 single parentage assessments, the optimal threshold was determined to be Z = 5.0, with only 34 censored cases and reduction to four (0.2%) doubtful parentages. About 70 parentage exclusions due to weak genotype calls were avoided, whereas true exclusions (n = 34) were unaffected. CONCLUSIONS: Using SNPs for parentage evaluation provides a high exclusion power also for parent identification. SNPs with a low GenCall score show a high tendency towards intra-molecular secondary structures and substantially contribute to false exclusion of parentages. We propose a method that controls this error without excluding too many parent combinations from the evaluation.

Reproductive and productive performances of Santa Inês ewes submitted to breeding in different periods of the Amazonian humid tropical climate.

The objective of this study was to evaluate the reproductive and productive performance of Santa Inês ewes bred at different times of the year in humid tropical climate. One hundred and forty-eight Santa Inês ewes were grouped according to the time of the year of their breeding season (i.e., mating period) (dry/wet, wet, wet/dry, and dry season). The service type was natural mating and the ewes and rams were kept together every night for 45 days. Reproductive efficiency was assessed by service, pregnancy, lambing, prolificacy, twinning, pregnancy loss, weaning, and lamb mortality rates. Ewes were weighed at the beginning and at the end of the breeding season and before and after parturition, and sequential weighing of the lambs was performed (at birth, 15, 30, 60, and 90 days). Reproductive efficiency index (number of lambs weaned/total of served ewes) and productive efficiency (kg of weaned lamb/kg of served or lambed ewes) were calculated. All ewes expressed estrus early in the breeding season; however, a higher percentage (53.5 and 7.1 % at 30 and 45 days, respectively) of ewes returned to estrus during the wet/dry period. The lower rates (13.9 %) of return to estrus at 30 days were during the wet season (P < 0.05). There were no (P > 0.05) effects of breeding seasons on the remaining reproductive rates. Ewes that lambed during the wet/dry transition period weighted less, before (40.5 ± 2.5 kg) and after (38.6 ± 1.6 kg) parturition, than those of other groups (P < 0.05). Lamb weight at birth did not vary between groups, however, weight at weaning was higher (15.6 ± 2.1 kg) in lambs born during the wet season (P < 0.05). The reproductive efficiency index was lower (0.66) when the breeding season took place during the dry/wet period (P < 0.05). Productive rates were significantly higher (0.29 and 0.33 for kg of weaned lamb/kg of served and lambed ewes, respectively; P < 0.05) in ewes served in the dry season. The reproductive performance of Santa Inês ewes was not significantly influenced by the period of the year in which the breeding seasons took place, allowing for four breeding seasons a year in the Amazon region. Variations between periods in return to estrus rates, weight of ewes close to parturition and lamb weight at weaning indicate that climate changes can also affect reproductive rates.

Characterization of smallholder pig breeding practices within a rural commune of North Central Vietnam.

This case study focused on a pig production system in a rural area of North Central Vietnam, with a focus on describing household pig breeding practices and estimating herd demographic parameters, particularly on reproduction. One hundred five households undertaking small-scale piglet production were surveyed, with information gathered on 3268 individual pigs. Pig keeping contributed variably to the overall household livelihood portfolio, with female household members as the main decision makers, contributors to labor, and beneficiaries of income from the pig enterprise. All households kept between one and four young or adult sows, with 69% of these sows of a local breed type (predominantly Mong Cai), 28% a cross between a local sow and an exotic sow (predominantly Large White), and the remainder (3%) as exotic sows. Eighty-eight percent of the piglets produced were cross-bred, while 12% were local breed. No adult males were kept by the surveyed households, reflecting the common use of artificial insemination for mating purposes. The most common breeding system practiced-the keeping of Mong Cai females and production of cross-bred piglets-capitalizes on the small body size and high fecundity of the sows and the fast growth rate and leanness of the cross-bred piglets. The survey tool used, which was based on farmer recall of events over the preceding 12-month period, appeared to give reasonable results although some recall bias could be detected. This case study will serve as an entry point to planned broader scale characterization and development of pig breeding systems in North Central Vietnam.

Georeferenced evaluation of genetic breeding value patterns in Brazilian Holstein cattle.

The aim of this study was to analyze the relationship between environmental and genetic values for milk production and type traits in Holstein cattle in Brazil. The genetic value of 65,383 animals for milk production and 53,626 for type classification were available. Socioeconomic and environmental data were obtained from the Brazilian Institute of Geography and Statistics, the Food and Agriculture Organization of the United Nations, the National Aeronautics and Space Administration, and the National Institute of Meteorology. Five to six clusters were generated for each of the groups of type traits and production levels. The relationships between these traits were assessed using the STEPDISC, DISCRIM and CANDISC procedures in SAS(®). Traits within the clusters behaved differently, but, in general, animals with lower genetic values were found in environments that were more stressful for animal production. These differences were mainly associated with temperature, humidity, precipitation and the Normalized Difference Vegetative Index. Genetic values for milk production showed best discrimination between different environments, while type traits showed poor discrimination, possibly because farmers mainly select for milk production. Environmental variations for genetic values in dairy cattle in Brazil should be further examined.

Bayesian methods for estimating GEBVs of threshold traits.

Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories=2, incidence=30%, number of quantitative trait loci=50, h² = 0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for GS of threshold traits.

Breeding return times and abundance in capture-recapture models.

For many long-lived animal species, individuals do not breed every year, and are often not accessible during non-breeding periods. Individuals exhibit site fidelity if they return to the same breeding colony or spawning ground when they breed. If capture and recapture is only possible at the breeding site, temporary emigration models are used to allow for only a subset of the animals being present in any given year. Most temporary emigration models require the use of the robust sampling design, and their focus is usually on probabilities of annual survival and of transition between breeding and non-breeding states. We use lake sturgeon (Acipenser fulvescens) data from a closed population where only a simple (one sample per year) sampling scheme is possible, and we also wish to estimate abundance as well as sex-specific survival and breeding return time probabilities. By adding return time parameters to the Schwarz-Arnason version of the Jolly-Seber model, we have developed a new likelihood-based model which yields plausible estimates of abundance, survival, transition and return time parameters. An important new finding from investigation of the model is the overestimation of abundance if a Jolly-Seber model is used when Markovian temporary emigration is present.

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats.

One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.