PSXI-9 Heterotic and breed effects in crossbred lambs

Given its benefits on animal performance, crossbreeding is common commercially. Genetic evaluation of sheep in the U.S. is performed within breed type (terminal, maternal wool, range, hair). While incorporating crossbred records may improve assessment of purebreds, it requires accounting for heterotic and breed effects in the evaluation. The objectives were to i) determine the generalized effects of direct and maternal heterosis on growth traits of crossbred lambs, and ii) estimate covariance components for direct and maternal additive, and uncorrelated maternal environmental, effects among those traits. Data included body weights (BW) at birth (BN; n = 14395), pre-weaning (WN; n = 9298), weaning (WW; n = 9230), and post-weaning (PW; n = 1593). Mean (SD) BW were 5.3 (1.1), 22.2 (8.7), 39.1 (7.2), and 54.2 (8.7) kg for BN, WN, WW, and PW, respectively. Estimates of heterotic effects and covariance components were obtained using a multiple trait animal model. Genetic effects based on founders’ breeds were included, being significant. Estimates of direct heterosis were 3.04 ± 0.61, 2.62 ± 0.64, 3.99 ± 0.54, and 5.97 ± 0.86%, and estimates of maternal heterosis were 1.86 ± 0.87, 4.42 ± 0.79, 3.69 ± 0.66, and 3.77 ± 0.90%, for BN, WN, WW, and PW, respectively. Direct heritability estimates were 0.17 ± 0.02, 0.13 ± 0.02, 0.18 ± 0.02, and 0.47 ± 0.04 for BN, WN, WW, and PW, respectively. Additive maternal effects defined trivial variation in PW. For BN, WN, and WW, respectively, maternal heritability estimates were 0.17 ± 0.02, 0.10 ± 0.02, and 0.07 ± 0.02. Uncorrelated maternal effects defined little variation in any trait. Direct and maternal heterosis had considerable impact on growth traits, emphasizing the value of crossbreeding and the need to account for heterosis, in addition to breed effects, if crossbred lamb information is included in genetic evaluation.

GIBBSTHUR: Software for Estimating Variance Components and Predicting Breeding Values for Ranking Traits Based on a Thurstonian Model

(1) Background: Ranking traits are used commonly for breeding purposes in several equine populations; however, implementation is complex, because the position of a horse in a competition event is discontinuous and is influenced by the performance of its competitors. One approach to overcoming these limitations is to assume an underlying Gaussian liability that represents a horse’s performance and dictates the observed classification in a competition event. That approach can be implemented using Montecarlo Markov Chain (McMC) techniques with a procedure known as the Thurstonian model. (2) Methods: We have developed software (GIBBSTHUR) that analyses ranking traits along with other continuous or threshold traits. The software implements a Gibbs Sampler scheme with a data-augmentation step for the liability of the ranking traits and provides estimates of the variance and covariance components and predictions of the breeding values and the average performance of the competitors in competition events. (3) Results: The results of a simple example are presented, in which it is shown that the procedure can recover the simulated variance and covariance components. In addition, the correlation between the simulated and predicted breeding values and between the estimates of the event effects and the average additive genetic effect of the competitors demonstrates the ability of the software to produce useful predictions for breeding purposes. (4) Conclusions: the GIBBSTHUR software provides a useful tool for the breeding evaluation of ranking traits in horses and is freely available in a public repository (https://github.com/lvaronaunizar/Gibbsthur).

Introduction of covariance components in slip inversion of geodetic data following a non-uniform spatial distribution and application to slip deficit rate estimation in the Nankai Trough subduction zone

SUMMARY Inappropriate mathematical treatment of prediction errors associated with inaccurate forward modelling in an inversion scheme may result in significant unnatural short-wavelength components in the estimated slip distribution, which is a typical consequence of overfitting data. When geodetic data in observation stations following a non-uniform spatial distribution are used in a geodetic slip inversion, the spatial non-uniformity of the observation can possibly influence the distribution pattern of the short-wavelength components significantly, which may be confused with slip patterns that are geophysically meaningful. Such situations often occur when land and seafloor geodetic data are used in combination in slip inversions. To avoid overfitting, this study proposes a method that incorporates covariance components in the covariance matrix of the misfit vector, which originate from prediction errors. Because the proposed method retains the linearity of the inversion problem, widely known approaches that introduce prior constraints to a linear inversion problem are easily combined with the proposed method. This study demonstrates a combination of the newly introduced covariance components with a prior constraint on the smoothness of slip distribution, constructing a Bayesian model with unknown hyperparameters, which are objectively determined by minimizing Akaike’s Bayesian information criterion. In the synthetic tests, the proposed method estimated slip deficit rate (SDR) distributions that are closer to the true one, avoiding overfitting the geodetic data with spatial non-uniformity. By contrast, a conventional approach, which does not introduce covariance components, estimates unnaturally rough SDR distributions using the same synthetic data. The proposed method was applied to the estimation of SDR in the Nankai Trough subduction zone, using geodetic data of displacement rates provided by land GNSS stations and seafloor GNSS-Acoustic stations. This method estimates a reasonably smooth distribution of SDR, avoiding overfitting. The spatial distribution of residuals of the displacement rates suggests that the proposed method avoids overfitting some portions of the observed displacement rates that the forward model set for the analyses could not fundamentally explain.

Maternal effect on sports performance traits in horses

The aim of the study was to estimate the direct additive genetic effect and the additive maternal effect on the level of traits estimated during the Polish Jumping Championships for Young Horses. The investigations involved 541 stallions and 353 mares, which in total started in the Championships 1232 times. Variance and covariance components were estimated using the Gibbs sampling method. Heritability (h²) and repeatability (r²) coefficients as well as maternal effects (m²) were calculated for 7 sports performance traits. There was an additive maternal effect, ranging from 0.11 to 0.39, on the level of traits assessed based on achieved scores. The effect was particularly high in the case of traits the level of which was determined by the animal organism performance and stress resistance. It was also noted that the value of the maternal effect in some traits was similar or higher than the coefficient of heritability, which may indicate a high effect of the mare’s specific environment in determining sport predispositions in the offspring. There is a need to analyse the cause of trait variability in other equestrian disciplines.