Prediction of effective population size in open nucleus breeding systems

Nomura, Tetsuro

doi:10.1111/j.1439-0388.1997.tb00519.x

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Other extensions for the prediction of the effective population size under selection refer to sex-linked loci (Nomura, 1997b;Wang, 1998), gynodioecious species (that is, species which have both hermaphrodite and female individuals, Laporte et al, 2000), open nucleus schemes (Nomura, 1997c;Bijma and Woolliams, 1999) and selection on traits affected by maternal effects (Rönnegård and Woolliams, 2003).…”

Section: Populations Under Selectionmentioning

confidence: 99%

Prediction and estimation of effective population size

2016

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Effective population size (N e ) is a key parameter in population genetics. It has important applications in evolutionary biology, conservation genetics and plant and animal breeding, because it measures the rates of genetic drift and inbreeding and affects the efficacy of systematic evolutionary forces, such as mutation, selection and migration. We review the developments in predictive equations and estimation methodologies of effective size. In the prediction part, we focus on the equations for populations with different modes of reproduction, for populations under selection for unlinked or linked loci and for the specific applications to conservation genetics. In the estimation part, we focus on methods developed for estimating the current or recent effective size from molecular marker or sequence data. We discuss some underdeveloped areas in predicting and estimating N e for future research.Heredity ( INTRODUCTIONThe concept of effective population size, introduced by Sewall Wright (1931, 1933), is central to plant and animal breeding (Falconer and Mackay, 1996), conservation genetics (Frankham et al., 2010;Allendorf et al., 2013) and molecular variation and evolution (Charlesworth and Charlesworth, 2010), as it quantifies the magnitude of genetic drift and inbreeding in real-world populations. A substantial number of extensions to the basic theory and predictions were made since the seminal work of Wright, with main early developments by James Crow and Motoo Kimura (Kimura and Crow, 1963a;Crow and Kimura, 1970) and later by a list of contributors. Several review papers (Crow and Denniston, 1988;Caballero, 1994;Wang and Caballero, 1999;Nomura, 2005a) and population genetic books (Fisher, 1965;Wright, 1969;Ewens, 1979;Nagylaki, 1992) have summarised the existing theory in predicting the effective size of a population at different spatial and timescales under various inheritance modes and demographies. Comparatively, methodological developments (reviewed by Schwartz et al., 1999;Beaumont, 2003a;Wang, 2005;Palstra and Ruzzante, 2008;Luikart et al., 2010;Gilbert and Whitlock, 2015) in estimating the effective size of natural populations from genetic data lag behind but are accelerating in the past decade, thanks to the rapid developments of molecular biology.The classical developments of effective population size theory are based on the rate of change in gene frequency variance (genetic drift) or the rate of inbreeding. The effective population size is defined in reference to the Wright-Fisher idealised population, that is, a hypothetical population with very simplifying characteristics where genetic drift is the only factor in operation, and the dynamics of allelic and genotypic frequencies across generations merely depend on the population census (N) size. The effective size of a real population is then defined as the size of an idealised population, which would give rise to the rate of inbreeding and the rate of change in variance of gene frequencies actually observed in the population under consideration,

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Section: Populations Under Selectionmentioning

confidence: 99%

Prediction and estimation of effective population size

2016

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“…Animal breeding programs frequently show a hierarchical structure, in which genetic improvement is made in a nucleus herd, and the improvement is transferred by migration to base or commercial. Extending the transition matrix approach, Nomura (1997c) derived a prediction equation for the effective size of populations with hierarchical structure. As pointed out by Bijma and Woolliams (1999), Nomura's (1997c) derivation is based on an assumption that genetic contributions of parental groups (nucleus and commercial animals) to progeny remain unchanged after selection.…”

Section: Extensions To Several Practical Casesmentioning

confidence: 99%

“…Extending the transition matrix approach, Nomura (1997c) derived a prediction equation for the effective size of populations with hierarchical structure. As pointed out by Bijma and Woolliams (1999), Nomura's (1997c) derivation is based on an assumption that genetic contributions of parental groups (nucleus and commercial animals) to progeny remain unchanged after selection. This could be a critical assumption, and especially in populations with overlapping generations it is likely to be strongly violated (Bijma & Woolliams 1999).…”

Section: Extensions To Several Practical Casesmentioning

confidence: 99%

Developments in prediction theories of the effective size of populations under selection

Nomura

2005

Animal Science Journal

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The effective population size is a key parameter in the definition of selection programs, because the magnitude of this parameter determines both the rate of inbreeding and the amount of genetic drift in the population. Prediction of the effective size of selected populations is complicated by the fact that selection has a cumulative effect on the effective size. In the present article, two basic approaches to predict the effective size of populations under selection were summarized, and the interrelation among them was clarified. Several extensions to practical situations relevant to animal breeding, such as non-random mating, index selection and marker-assisted selection, were also reviewed.

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Prediction of Genetic Contributions and Generation Intervals in Populations With Overlapping Generations Under Selection

Bijma

Woolliams

1999

Genetics

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A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear regression on breeding value. Total genetic contributions of age classes are modeled using a modified gene flow approach and long-term predictions are obtained assuming equilibrium genetic parameters. Generation interval was defined as the time in which genetic contributions sum to unity, which is equal to the turnover time of genes. Accurate predictions of long-term genetic contributions of individual animals, as well as total contributions of age classes were obtained. Due to selection, offspring of young parents had an above-average breeding value. Long-term genetic contributions of youngest age classes were therefore higher than expected from the age class distribution of parents, and generation interval was shorter than the average age of parents at birth of their offspring. Due to an increased selective advantage of offspring of young parents, generation interval decreased with increasing heritability and selection intensity. The method was compared to conventional gene flow and showed more accurate predictions of long-term genetic contributions.

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Prediction of effective population size in open nucleus breeding systems

Cited by 3 publications

References 13 publications

Prediction and estimation of effective population size

Prediction and estimation of effective population size

Developments in prediction theories of the effective size of populations under selection

Prediction of Genetic Contributions and Generation Intervals in Populations With Overlapping Generations Under Selection

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