| 1. |
- Hallander, Jon, et al.
(författare)
-
Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions
- 2010
-
Ingår i: Genetics. - : Oxford University Press (OUP). - 0016-6731 .- 1943-2631. ; 185, s. 645-654
-
Tidskriftsartikel (refereegranskat)abstract
- It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.
|
|
| 2. |
- Hallander, Jon
(författare)
-
Novel methods for improved tree breeding
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The development and implementation of statistical tools to improve inference in sustainable forest tree breeding are presented here. By combining classical quantitative genetic theory and novel statistical methods, a number of parameters are optimized. The results obtained are compared to those achieved by traditional methods for visualizing improvements to genetic parameters. The methods are tested on both simulated data and on a real Scots pine pedigree. Modeling non-additive gene action using a finite loci model indicates that the development of the additive variance component does not decay initially as the underlying theory predicts. This phenomenon is shown for different sets of genetic components and models. In addition, variable numbers of loci were used so that different numbers of interactions could be captured. To draw inferences about the genetic parameters, a powerful Bayesian Markov chain Monte Carlo method was developed. The method utilizes transformation of the genetic covariance structure to improve computational speed. By combining two different Bayesian Gibbs samplers, a useful hybrid sampler was developed; this was found to enhance convergence statistics and computational speed. A method that finds the number of trees and their respective mating proportions that will maximize genetic gain was implemented and modified to handle a large number of selection candidates. When testing the selection method on a real pedigree an increase in genetic gain of up to 30 % was found compared to traditional methods in which the same restrictions were placed on relatedness. In order to provide a long-term breeding perspective, the selection method was combined with various mating schemes to examine the development of genetic parameters. A modified minimum coancestry mating scheme resulted in a level of genetic gain closest to the theoretically achievable limit while reducing the level of inbreeding in the population.
|
|
| 3. |
- Hallander, Jon, et al.
(författare)
-
Optimization of selection contribution and mate allocations in monoecious tree breeding populations
- 2009
-
Ingår i: BMC Genetics. - : Springer Science and Business Media LLC. - 1471-2156. ; 10
-
Tidskriftsartikel (refereegranskat)abstract
- Conclusion: The main reason why MCM schemes yielded higher genetic gains was the improvement in managing the long term genetic contribution of founders in the population; this was achieved by connecting unrelated families. In addition, the accumulation of inbreeding was reduced by MCM schemes since the variance in long term genetic contributions of founders was smaller than in the other schemes. Consequently, by combining an MCM scheme with an algorithm that optimizes contributions of the selected individuals, a higher long term response is obtained while reducing the risk within the breeding program.
|
|
| 4. |
- Hallander, Jon, et al.
(författare)
-
Optimum contribution selection in large general tree breeding populations with an application to Scots pine
- 2009
-
Ingår i: TAG Theoretical and Applied Genetics. - : Springer Science and Business Media LLC. - 0040-5752 .- 1432-2242. ; 118, s. 1133-1142
-
Tidskriftsartikel (refereegranskat)abstract
- Development of selection methods that optimises selection differential subject to a constraint on the increase of inbreeding (or coancestry) in a population is an important part of breeding programmes. One such method that has received much attention in animal breeding is the optimum contribution (OC) dynamic selection method. We implemented the OC algorithm and applied it to a diallel progeny trial of Pinus sylvestris L. (Scots pine) focussing on two traits (total tree height and stem diameter). The OC method resulted in a higher increase in genetic gain (8-30%) compared to the genetic gain achieved using standard restricted selection method at the same level of coancestry constraint. Genetic merit obtained at two different levels of restriction on coancestry showed that the benefit of OC was highest when restriction was strict. At the same level of genetic merit, OC decreased coancestry with 56 and 39% for diameter and height, respectively, compared to the level of coancestry obtained using unrestricted truncation selection. Inclusion of a dominance term in the statistical model resulted in changes in contribution rank of trees with 7 and 13% for diameter and height, respectively, compared to results achieved by using a pure additive model. However, the genetic gain was higher for the pure additive model than for the model including dominance for both traits.
|
|
| 5. |
|
|
