Summary A computationally tractable approach to complex segregation analysis for large data sets is described. This is then used to extend the value of DNA tests for major locus genotype, by calculating probabilities of belonging to each genotype class for all animals not DNA tested. Under the conditions simulated, this approach led to an approximate doubling of the number of animals genotyped with 100% confidence, the ability to exclude many more animals from one genotype class, plus a high probability (> 90%) of belonging to a given genotype class for many other animals. Zusammenfassung Verwendung von Spaltungsanalyse für rationelle Anwendung von DNA Tests für Hauptgene Ein rechnermäßig traktabler Ansatz zu komplexer Spaltungsanalyse großer Datenmengen wird beschrieben. Dieser wird verwendet, um den Wert der DNA Tests für Hauptgene zu erweitern und zwar dadurch, daß die Wahrscheinlichkeiten der Zugehörigkeit zu jeder Genotypenklasse für Tiere ohne DNA Diagnose berechnet wird. Die Simulation ergab eine Verdopplung der Zahl der Tiere, die mit 100% Sicherheit genotypisiert werden konnten sowie Ausschluß von mehr Tieren aus einer gegebenen Genotypenklasse und schließlich eine hohe Wahrscheinlichkeit (> 90%) der Zuweisung vieler Tiere zu gegebenen Genotypenklassen.

Genetic improvement schemes in livestock are based on the assumption that the expression of relevant genes is independent of parent of origin. Until now no evidence has been found to reject this assumption. The present study on three purebred pig populations, however, shows that a significant proportion of the phenotypic variance in backfat thickness (5-7%) can be explained by genes subject to paternal imprinting. The implication is that there are genes affecting backfat that are expressed only when derived from the paternal gamete. Paternal imprinted effects explained 1-4% of the phenotypic variation for growth rate. Maternal imprinted effects were heavily confounded with heritable maternal environmental effects. When modelled separately, these effects explained 2-5% and 3-4% of the phenotypic variance in backfat thickness and growth rate, respectively. Gametic imprinting may have consequences for the optimization of breeding programmes, especially in crossbreeding systems with specialized sire and dam lines.

Mixed models incorporating the inverse of a numerator relationship matrix (NRM) are widely used to estimate genetic parameters and to predict breeding values in animal breeding. A simple and quick method to directly calculate the inverse of the NRM has been historically developed for diploid animal species. Mixed models are less used in plant breeding partly because the existing method for diploids is not applicable to autopolyploid species. This is because of the phenomenon of double reduction and the possibility that gametes carry alleles which are identical by descent. This paper generalises the NRM and its inverse for autopolyploid species, so it can be easily incorporated into their genetic analysis. The technique proposed is to first calculate the kinship coefficient matrix and its inverse as a precursor to calculating the NRM and its inverse. This allows the NRM to be calculated for populations containing individuals of mixed ploidy levels. This generalization can also accommodate uncertain parentage by generating the "average" relationship matrix. The possibility that non-inbred parents can produce inbred progeny (double reduction) is also discussed. Rules are outlined that are applicable for any level of ploidy. Examples of use of the matrix are provided using simulated pedigrees.

Indirect genetic effects (IGEs) are heritable effects of individuals on trait values of their conspecifics. IGEs may substantially affect response to selection, but empirical studies on IGEs are sparse and their magnitude and correlation with direct genetic effects are largely unknown in plants. Here we used linear mixed models to estimate genetic (co)variances attributable to direct and indirect effects for growth and foliar disease damage in a large pedigreed population of Eucalyptus globulus. We found significant IGEs for growth and disease damage, which increased with age for growth. The correlation between direct and indirect genetic effects was highly negative for growth, but highly positive for disease damage, consistent with neighbour competition and infection, respectively. IGEs increased heritable variation by 71% for disease damage, but reduced heritable variation by 85% for growth, leaving nonsignificant heritable variation for later age growth. Thus, IGEs are likely to prevent response to selection in growth, despite a considerable ordinary heritability. IGEs change our perspective on the genetic architecture and potential response to selection. Depending on the correlation between direct and indirect genetic effects, IGEs may enhance or diminish the response to natural or artificial selection compared with that predicted from ordinary heritability.

An individual's genes may influence the phenotype of neighboring conspecifics. Such indirect genetic effects (IGEs) are important as they can affect the apparent total heritable variance in a population, and thus the response to selection. We studied these effects in a large, pedigreed population of Eucalyptus globulus using variance component analyses of Mycosphearella leaf disease, diameter growth at age 2 years, and post-infection diameter growth at ages 4 and 8 years. In a novel approach, we initially modeled IGEs using a factor analytic (FA) structure to identify the most influential neighbor positions, with the FA loadings being position-specific regressions on the IGEs. This involved sequentially comparing FA models for the variance-covariance matrices of the direct and indirect effects of each neighbor. We then modeled IGEs as a distance-based, combined effect of the most influential neighbors. This often increased the magnitude and significance of indirect genetic variance estimates relative to using all neighbors. The extension of a univariate IGEs model to bivariate analyses also provided insights into the genetic architecture of this population, revealing that: (1) IGEs arising from increased probability of neighbor infection were not associated with reduced growth of neighbors, despite adverse fitness effects being evident at the direct genetic level; and (2) the strong, genetic-based competitive interactions for growth, established early in stand development, were highly positively correlated over time. Our results highlight the complexities of genetic-based interactions at the multi-trait level due to (co)variances associated with IGEs, and the marked discrepancy occurring between direct and total heritable variances.