Optimal Family Contributions and a Linear Approximation

Wei, R. P.; Lindgren, D.

doi:10.1006/tpbi.1995.1033

Cited by 14 publications

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“…Nevertheless, there is a wide range of intermediate selection methods that differ in the magnitude of the variance in family size. Following Wei and Lindgren (1995), consider a breeding population comprising k families of finite size. The breeding value of an individual can be partitioned into a family component b i and a within-family component w ij .…”

Section: Managing the Contributions Of Individualsmentioning

confidence: 99%

Management of genetic diversity in small farm animal populations

Fernández

Meuwissen

Toro

et al. 2011

Animal

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Many local breeds of farm animals have small populations and, consequently, are highly endangered. The correct genetic management of such populations is crucial for their survival. Managing an animal population involves two steps: first, the individuals who will be permitted to leave descendants are to be chosen and the number offspring they will be permitted to produce has to be determined; second, the mating scheme has to be identified. Strategies dealing with the first step are directed towards the maximisation of effective population size and, therefore, act jointly on the reduction in the loss of genetic variation and in the increase of inbreeding. In this paper, the most relevant methods are summarised, including the so-called 'Optimum Contribution' methodology (contributions are proportional to the coancestry of each individual with the rest), which has been shown to be the best. Typically, this method is applied to pedigree information, but molecular marker data can be used to complete or replace the genealogy. When the population is subjected to explicit selection on any trait, the above methodology can be used by balancing the response to selection and the increase in coancestry/inbreeding. Different mating strategies also exist. Some of the mating schemes try to reduce the level of inbreeding in the short term by preventing mating between relatives. Others involve regular (circular) schemes that imply higher levels of inbreeding within populations in the short term, but demonstrate better performance in the long term. In addition, other tools such as cryopreservation and reproductive techniques aid in the management of small populations. In the future, genomic marker panels may replace the pedigree information in measuring the coancestry. The paper also includes the results of several experiments and field studies on the effectiveness and on the consequences of the use of the different strategies.

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Section: Managing the Contributions Of Individualsmentioning

confidence: 99%

Management of genetic diversity in small farm animal populations

Fernández

Meuwissen

Toro

et al. 2011

Animal

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“…The relative gain becomes large when the measure of the corresponding diversity goes far away from l / w l , the diversity index associated with uniform deployment (Magurran, 1988). After allowance for effective population size, linear deployment, which is defined as a special case in the present study, has been shown to be superior over uniform deployment in early studies (Lindgren and Matheson, 1986;Lindgren et al, 1989;Bondesson and Lindgren, 1993;Wei and Lindgren, 1995).…”

Section: Discussionmentioning

confidence: 81%

Optimal Diversity‐Dependent Contributions of Genotypes to Mixtures

Wei

Yeh

1999

Biometrics

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Deployment of genotypes to a production population decisively depends on the measure of diversity, a consideration that parallels genetic gain in the management of genotype mixtures. Optimal diversity-dependent deployment has been developed in this study for a family of diversity indices in the genetical and ecological context. The optimal solution at given diversity was expressed as the relationship between genotype contributions and their genetic performances, which maximized genetic gain. Numerical calculations were performed by using genotypes generated from normal order statistics. An optimal deployment in one situation could be nonoptimum in another. Classical uniform deployment, where superior genotypes equally contribute to the mixtures, was the limit of optimal deployment. Comparisons were made between optimal and uniform deployment and between optimal and nonoptimal deployment where genotypes contributed proportionally to the mixtures in accordance to their genetic superiority. The superiority in gain of optimal deployment over that of uniform deployment increased as the difference between the diversity measure under optimal deployment and the contributing number (N) of genotypes under uniform deployment became large and as the diversity measure and N under optimal deployment increased. The superiority over nonoptimal deployment increased rapidly at low diversity, reaching a maximum somewhere at diversity between 1 and N. Scale of superiority depended on the similitude between optimum and nonoptimum deployment; the larger the distinctiveness, the greater the superiority.

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“…Parameter-based models have been excellent tools to help guide breeders in their examination of optimum strategies for mating, testing and selection (Mullin and Park 1995;Wei and Lindgren 1995;Kerr et al 1998;Rosvall and Anderson 1999;Lstiburek et al 2005) over early generations, as significant changes to genetic variances and covariances are generally not expected. However, for more than one to three generations, the use of locus-based 'gene' models may help improve our understanding of the changes to genetic diversity under different selection regimes over several generations, as well as highlight some of the genetic mechanisms behind biological constraints (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multivariate selection under adverse genetic correlations: impacts of population sizes and selection strategies on gains and coancestry in forest tree breeding

Yanchuk

Sanchez

2011

Tree Genetics & Genomes

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We examined gene models for two traits with and without antagonistic pleiotropy using a locus-based simulation model to investigate the effects of different population sizes, heritabilities and economic weights, using index selection, and index selection with optimum selection (OS), over 10 generations. Gene models included additive and dominance gene action, with equal and varying initial allele frequencies with and without pleiotropy for a fixed level of resources (i.e. founder sizes each generation of 40, 80 and 160 with progeny arrays that totaled 800 per generation). Pleiotropy (with an initial r g of −0.5), reduced gain by~8-10% when heritabilities for both traits were the same (0.2), relative to non-pleiotropic cases. When traits had different heritabilities (i.e. 0.2 and 0.4), gains in the lower heritability trait were substantially lower, especially with pleiotropy present. In general, OS with slightly larger population sizes could offset losses in gain, but rarely overrode the large effects of different heritabilities or economic weights. Pleiotropy increased response variance among traits, which was magnified when heritabilities were different. Identifying an appropriate weight on relatedness in the OS process is important to manage coancestry expectations around the loss of alleles (or fixation of recessive alleles) and to minimise response variance. The dynamics of selection intensity, drift, rate of coancestry build-up, response variance, etc. are complex for multi-trait selection; however, a few economically viable strategies could reduce the adverse effects of selecting against genetic correlations without drastically impairing gain.

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Optimal Family Contributions and a Linear Approximation

Cited by 14 publications

References 0 publications

Management of genetic diversity in small farm animal populations

Management of genetic diversity in small farm animal populations

Optimal Diversity‐Dependent Contributions of Genotypes to Mixtures

Multivariate selection under adverse genetic correlations: impacts of population sizes and selection strategies on gains and coancestry in forest tree breeding

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