Article

# Increased accuracy of artificial selection by using the realized relationship matrix.

Biosciences Research Division, Department of Primary Industries Victoria, 1 Park Drive, Bundoora 3083, Australia.

Genetics Research (Impact Factor: 2). 03/2009; 91(1):47-60. DOI: 10.1017/S0016672308009981 Source: PubMed

- [Show abstract] [Hide abstract]

**ABSTRACT:**This study compared genomic predictions based on imputed high-density markers (∼777,000) in the Nordic Holstein population using a genomic BLUP (GBLUP) model, 4 Bayesian exponential power models with different shape parameters (0.3, 0.5, 0.8, and 1.0) for the exponential power distribution, and a Bayesian mixture model (a mixture of 4 normal distributions). Direct genomic values (DGV) were estimated for milk yield, fat yield, protein yield, fertility, and mastitis, using deregressed proofs (DRP) as response variable. The validation animals were split into 4 groups according to their genetic relationship with the training population. Groupsmgs had both the sire and the maternal grandsire (MGS), Groupsire only had the sire, Groupmgs only had the MGS, and Groupnon had neither the sire nor the MGS in the training population. Reliability of DGV was measured as the squared correlation between DGV and DRP divided by the reliability of DRP for the bulls in validation data set. Unbiasedness of DGV was measured as the regression of DRP on DGV. The results showed that DGV were more accurate and less biased for animals that were more related to the training population. In general, the Bayesian mixture model and the exponential power model with shape parameter of 0.30 led to higher reliability of DGV than did the other models. The differences between reliabilities of DGV from the Bayesian models and the GBLUP model were statistically significant for some traits. We observed a tendency that the superiority of the Bayesian models over the GBLUP model was more profound for the groups having weaker relationships with training population. Averaged over the 5 traits, the Bayesian mixture model improved the reliability of DGV by 2.0 percentage points for Groupsmgs, 2.7 percentage points for Groupsire, 3.3 percentage points for Groupmgs, and 4.3 percentage points for Groupnon compared with GBLUP. The results showed that a Bayesian model with intense shrinkage of the explanatory variable, such as the Bayesian mixture model and the Bayesian exponential power model with shape parameter of 0.30, can improve genomic predictions using high-density markers.Journal of Dairy Science 05/2013; · 2.57 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Abstract – Quantitative genetic techniques are contributing greatly to our understanding of evolutionary processes in the wild. However, the vast majority of applications of quantitative genetic methodologies to natural populations have been conducted in very narrow ranges of the ‘life history space’ expressed by organisms in nature. Recent and ongoing technological and analytical advances are making expansion of the taxonomic scope of such studies both possible and highly desirable. I review a number of ways in which fishes can be exploited to better understand the phenotypic expression of genetic variation in the wild. In particular, I argue that the culturability of fishes can be exploited in the execution of study designs where pedigrees can be manipulated, while maintaining the ecological relevance of phenotypic and genetic variation as expressed in the wild. In general, the model organisms which are currently most intensively studied in a quantitative genetic framework in the wild are not easily culturable. Thus, at least among vertebrates, the proposed approaches are uniquely suited to fishes. I highlight how fishes may provide particularly good models with which to study the genetic basis of variation in phenotypic plasticity, the genetic basis of the environmental dependence of genetic parameters and to detect adaptive phenotypic microevolution.Ecology of Fresh Water Fish 10/2010; 20(3):328 - 345. · 1.94 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Genomic selection (GS) is a powerful method for exploitation of DNA sequence polymorphisms in breeding improvement, through the prediction of breeding values based on all markers distributed genome‐wide. Forage grasses and legumes provide important targets for GS implementation, as many key traits are difficult or expensive to assess, and are measured late in the breeding cycle. Generic attributes of forage breeding programmes are described, along with status of genomic resources for a representative species group (ryegrasses). Two schemes for implementing GS in ryegrass breeding are described. The first requires relatively little modification of current schemes, but could lead to significant reductions in operating cost. The second scheme would allow two rounds of selection for key agronomic traits within a time period previously required for a single round, potentially leading to doubling of genetic gain rate, but requires a purpose‐designed reference population. In both schemes, the limited extent of linkage disequilibrium (LD), which is the major challenge for GS implementation in ryegrass breeding, is addressed. The strategies also incorporate recent advances in DNA sequencing technology to minimize costs.Plant Breeding 01/2013; 132(2). · 1.18 Impact Factor

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.