Temporal and genomic analysis of additive genetic variance in breeding programmes

AbstractGenetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.

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Temporal and genomic analysis of additive genetic variance in breeding programmes

10.1101/2020.08.29.273250 ◽

2020 ◽

Author(s):

Letícia A. de C. Lara ◽

Ivan Pocrnic ◽

R. Chris Gaynor ◽

Gregor Gorjanc

Keyword(s):

Linkage Disequilibrium ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Model Fitting ◽

Genomic Analysis ◽

Principal Component ◽

Complex Trait ◽

Breeding Programme ◽

Empirical Bayesian ◽

Genomic Analyses

AbstractThis study demonstrates a framework for temporal and genomic analysis of additive genetic variance in a breeding programme. Traditionally we used specific experimental designs to estimate genetic variance for a specific group of individuals and a general pedigree-based model to estimate genetic variance for pedigree founders. However, with the pedigree-based model we can also analyse temporal changes in genetic variance by summarising sampled realisations of genetic values from a fitted model. Here we extend this analysis to a marker-based model and build a framework for temporal and genomic analyses of genetic variance. The framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values, and (iii) calculating variance of the sampled genetic values by time and genome partitions. Genome partitions enable estimation of contributions from chromosomes and chromosome pairs and genic and linkage-disequilibrium variances. We demonstrate the framework by analysing data from a simulated breeding programme involving a complex trait with additive gene action. We use the full Bayesian and empirical Bayesian approaches to account for the uncertainty due to model fitting. We also evaluate the use of principal component approximation. Results show good concordance between the simulated and estimated variances for temporal and genomic analyses and give insight into genetic processes. For example, we observe reduction of genic variance due to selection and drift and buildup of negative linkage-disequilibrium (the Bulmer effect) due to directional selection. In this study the popular empirical Bayesian approach estimated the variances well but it underestimated uncertainty of the estimates. The principal components approximation biases estimates, in particular for the genic variance. This study gives breeders a framework to analyse genetic variance and its components in different stages of a programme and over time.

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Asymptotic rates of response from index selection

Animal Science ◽

10.1017/s0003356100032347 ◽

1989 ◽

Vol 49 (2) ◽

pp. 217-227 ◽

Cited By ~ 67

Author(s):

Naomi R. Wray ◽

W. G. Hill

Keyword(s):

Genetic Variance ◽

Additive Genetic Variance ◽

Index Selection ◽

Selection Intensity ◽

Response To Selection ◽

Multiple Trait ◽

Breeding Values ◽

Breeding Programmes ◽

Breeding Schemes ◽

Rate Of Response

ABSTRACTThe reduction in additive genetic variance due to selection is investigated when index selection using family records is practised. A population of infinite size with no accumulation of inbreeding, an infinitesimal model and discrete generations are assumed. After several generations of selection, the additive genetic variance and the rate of response to selection reach an asymptote. A prediction of the asymptotic rate of response is considered to be more appropriate for comparing response from alternative breeding programmes and for comparing predicted and realized response than the response following the first generation of selection that is classically used. Algorithms to calculate asymptotic response rate are presented for selection based on indices which include some or all of the records of an individual, its full- and half-sibs and its parental estimated breeding values. An index using all this information is used to predict response when selection is based on breeding values estimated by using a Best Linear Unbiased Prediction (BLUP) animal model, and predictions agree well with simulation results. The predictions are extended to multiple trait selection.Asymptotic responses are compared with one-generation responses for a variety of alternative breeding schemes differing in population structure, selection intensity and heritability of the trait. Asymptotic responses can be up to one-quarter less than one-generation responses, the difference increasing with selection intensity and accuracy of the index. Between family variance is reduced considerably by selection, perhaps to less than half its original value, so selection indices which do not account for this tend to place too much emphasis on family information. Asymptotic rates of response to selection, using indices including family information for traits not measurable on the individuals available for selection, such as sex limited or post-slaughter traits, are found to be as much as two-fifths less than their expected one-generation responses. Despite this, the ranking of the breeding schemes is not greatly altered when compared by one-generation rather than asymptotic responses, so the one-generation prediction is usually likely to be adequate for determining optimum breeding structure.

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Short-term Changes in the Variance: 1. Changes in the Additive Variance

10.1093/oso/9780198830870.003.0016 ◽

2018 ◽

Cited By ~ 1

Author(s):

Bruce Walsh ◽

Michael Lynch

Keyword(s):

Linkage Disequilibrium ◽

Assortative Mating ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Allele Frequencies ◽

Disruptive Selection ◽

Stabilizing Selection ◽

Short Term ◽

Additive Variance ◽

The Mean

Selection changes the additive-genetic variance (and hence the response in the mean) by both changing allele frequencies and by generating correlations among alleles at different loci (linkage disequilibrium). Such selection-induced correlations can be generated even between unlinked loci, and (generally) are negative, such that alleles increasing trait values tend to become increasingly negative correlated under direction or stabilizing selection, and positively correlated under disruptive selection. Such changes in the additive-genetic variance from disequilibrium is called the Bulmer effects. For a large number of loci, the amount of change can be predicted from the Bulmer equation, the analog of the breeder's equation, but now for the change in the variance. Upon cessation of selection, any disequilibrium decays away, and the variances revert back to their additive-genic variances (the additive variance in the absence of disequilibrium). Assortative mating also generates such disequilibrium.

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A Population Genetics Model of Marker-Assisted Selection

Genetics ◽

10.1093/genetics/146.3.1173 ◽

1997 ◽

Vol 146 (3) ◽

pp. 1173-1183

Author(s):

Z W Luo ◽

R Thompson ◽

J A Woolliams

Keyword(s):

Linkage Disequilibrium ◽

Marker Assisted Selection ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Phenotypic Selection ◽

Genetic Response ◽

Factors Affecting ◽

Short And Long Term ◽

Trait Locus ◽

Rate Of Dissipation

A deterministic two-loci model was developed to predict genetic response to marker-assisted selection (MAS) in one generation and in multiple generations. Formulas were derived to relate linkage disequilibrium in a population to the proportion of additive genetic variance used by MAS, and in turn to an extra improvement in genetic response over phenotypic selection. Predictions of the response were compared to those predicted by using an infinite-loci model and the factors affecting efficiency of MAS were examined. Theoretical analyses of the present study revealed the nonlinearity between the selection intensity and genetic response in MAS. In addition to the heritability of the trait and the proportion of the marker-associated genetic variance, the frequencies of the selectively favorable alleles at the two loci, one marker and one quantitative trait locus, were found to play an important role in determining both the short- and long-term efficiencies of MAS. The evolution of linkage disequilibrium and thus the genetic response over several generations were predicted theoretically and examined by simulation. MAS dissipated the disequilibrium more quickly than drift alone. In some cases studied, the rate of dissipation was as large as that to be expected in the circumstance where the true recombination fraction was increased by three times and selection was absent.

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Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs

Genes ◽

10.3390/genes13010012 ◽

2021 ◽

Vol 13 (1) ◽

pp. 12

Author(s):

Houssemeddine Srihi ◽

José Luis Noguera ◽

Victoria Topayan ◽

Melani Martín de Hijas ◽

Noelia Ibañez-Escriche ◽

...

Keyword(s):

Litter Size ◽

Genetic Variance ◽

Mixed Model ◽

Additive Genetic Variance ◽

Genetic Correlations ◽

Genomic Analysis ◽

Gxe Interaction ◽

Genomic Regions ◽

Additive Genetic Effects ◽

Selection Of

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

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Genetic structure of growth and phenological traits in Salix viminalis

Canadian Journal of Forest Research ◽

10.1139/x00-175 ◽

2001 ◽

Vol 31 (2) ◽

pp. 276-282 ◽

Cited By ~ 2

Author(s):

Ann Christin Rönnberg-Wästljung

Keyword(s):

Linkage Disequilibrium ◽

Genetic Structure ◽

Genetic Variance ◽

Growth Traits ◽

Additive Genetic Variance ◽

Geographic Origin ◽

Salix Viminalis ◽

Parental Origin ◽

Dominance Genetic Variance ◽

Sexual Propagation

The genetic structure of Salix viminalis L. for different growth traits and bud flush has been studied. The aim was also to study differences in the genetic structure due to geographic origin and type of trait. Two incomplete factorial crossings with parental clones originating from Poland and Sweden were used. Growth traits showed a high amount of dominance genetic variance independent of parental origin. It is suggested that inbreeding depression found in S. viminalis could be a biological cause of the high levels of dominance as an alternative to overestimation due to epistasis. There were significant differences in amount of dominance genetic variance between the two origins for three of the nine growth traits. The higher number of characters influenced by dominance in the pedigree with Swedish origin can possibly be explained by linkage disequilibrium, since S. viminalis in Sweden has been introduced from Germany and Poland, and no sexual propagation of the species in Sweden has been reported. Bud flush showed high additive genetic variance in contrast to the growth traits. In the breeding for biomass production of S. viminalis, the high amount of dominance has to be taken into consideration.

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Selection Under Inbreeding

10.1093/oso/9780198830870.003.0023 ◽

2018 ◽

Author(s):

Bruce Walsh ◽

Michael Lynch

Keyword(s):

Linkage Disequilibrium ◽

Recombination Rate ◽

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Selection Response ◽

Selfing Rate ◽

Evolutionary Forces ◽

Additional Variance

When dominance is presence, the selection response equations under inbreeding can become rather complex, they require additional variance components beyond the additive-genetic variance. Further, both transient and permanent components of response can arise. This chapter examines the theory of both the covariance of relatives under general inbreeding, as well as the expected selection response under inbreeding. Due to the decrease in the effective recombination rate under selfing, the Bulmer effect can be rather dramatic, as any linkage disequilibrium generated by selection is only weakly removed by recombination. Finally, this chapter also examines the evolutionary forces that interact to determine the selfing rate for a given population.

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