Optimal mass selection policies for schemes with overlapping generations and restricted inbreeding

<p>Abstract</p> <p>Optimum breeding schemes for maximising the rate of genetic progress with a restriction on the rate of inbreeding (per year or per generation) are investigated for populations with overlapping generations undergoing mass selection. The optimisation is for the numbers of males and females to be selected and for their distribution over age classes. Expected rates of genetic progress (Δ<it>G</it>) are combined with expected rates of inbreeding (Δ<it>F</it>) in a linear objective function (Φ = Δ<it>G </it>- λΔ<it>F</it>) which is maximised. A simulated annealing algorithm is used to obtain the solutions. The restriction on inbreeding is achieved by increasing the number of parents and, in small schemes with severe restrictions, by increasing the generation interval. In the latter case the optimum strategy for obtaining the maximum genetic gain is far from truncation selection across age classes. In most situations, the optimum mating ratio is one but the differences in genetic gain obtained with different mating ratios are small. Optimisation of schemes when restricting the rate of inbreeding per generation leads to shorter generation intervals than optimisation when restricting the rate of inbreeding per year.</p

Genetic gain of pure line selection and combined crossbred purebred selection with constrained inbreeding

Using deterministic methods, rates of genetic gain (ýG) and inbreeding (ýF) were compared between pure line selection (PLS) and combined crossbred purebred selection (CCPS), for the sire line of a three-way crossbreeding scheme. Purebred performance and crossbred performance were treated as genetically correlated traits assuming the infinitesimal model. Breeding schemes were compared at a fixed total number of purebred selection candidates, i.e. including crossbred information did not affect the size of the purebred nucleus. Selection was by truncation on estimated breeding values for crossbred performance. Rates of genetic gain were predicted using a pseudo-BLUP selection index. Rates of inbreeding were predicted using recently developed methods based on long-term genetic contributions. Results showed that changing from PLS to CCPS may increase ýF by a factor of 2·14. In particular with high heritabilities and low purebred-crossbred genetic correlations, CCPS requires a larger number of parents than PLS, to avoid excessive ýF. The superiority of CCPS over PLS was judged by comparing ýG from both selection strategies at the same ýF. At the same ýF, CCPS was superior to PLS and the superiority of CCPS was only moderately reduced compared with the situation without a restriction on ýF. This paper shows that the long-term genetic contribution theory can be used to balance ýF and ýG in animal breeding schemes within very limited computing time

On the relation between gene flow theory and genetic gain

In conventional gene flow theory the rate of genetic gain is calculated as the summed products of genetic selection differential and asymptotic proportion of genes deriving from sex-age groups. Recent studies have shown that asymptotic proportions of genes predicted from conventional gene flow theory may deviate considerably from true proportions. However, the rate of genetic gain predicted from conventional gene flow theory was accurate. The current note shows that the connection between asymptotic proportions of genes and rate of genetic gain that is embodied in conventional gene flow theory is invalid, even though genetic gain may be predicted correctly from it.Note sur la relation entre le calcul de flux des gènes et le progrès génétique. Dans la méthode classique de calcul de la transmission des gènes, le taux de progrès génétique est calculé comme la somme des produits de la différentielle de sélection génétique et de la proportion asymptotique des gènes provenant des groupes âge-sexe. Des études récentes ont montré que les proportions asymptotiques de gènes prédites à partir de la méthode classique de calcul des flux de gènes peuvent dévier considérablement des proportions réelles. Par contre, le progrès génétique est prédit à partir de cette même méthode avec une bonne précision. La présente note montre que le lien entre le flux des gènes et le progrès génétique, tel qu'il apparaît dans la méthode classique de calcul des flux des gènes n'est donc pas correct, même si le progrès génétique peut être correctement prédit à partir de ladite méthode

Radiance uncertainty characterisation to facilitate climate data record creation

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented

Bias, accuracy, and impact of indirect genetic effects in infectious diseases

Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals’ underlying susceptibility. In a previous study, we showed that with an indirect genetic effect (IGE) model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1) make expression of infectivity dependent on the individuals’ disease status (Case Model) and (2) to include timing of infection (Case-ordered Model). The models are evaluated by comparing impact of selection, bias, and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact, and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case-Ordered model performed considerably worse than the Standard and the Case Models, pointing toward limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding