scholarly journals On the relevance of three genetic models for the description of genetic variance in small populations undergoing selection

1998 ◽  
Vol 30 (1) ◽  
pp. 59 ◽  
Author(s):  
Florence Fournet-Hanocq ◽  
Jean-Michel Elsen
Keyword(s):  
Genetic Variance ◽  
Genetic Models ◽  
Small Populations

scholarly journals Directional selection rather than functional constraints can shape the G matrix in rapidly adapting asexuals

2018 ◽  
Author(s):  
Kevin Gomez ◽  
Jason Bertram ◽  
Joanna Masel
Keyword(s):  
Linkage Disequilibrium ◽  
Population Genetic ◽  
Genetic Variance ◽  
Genetic Models ◽  
Relative Fitness ◽  
Functional Constraints ◽  
Selective Sweeps ◽  
Genetic Covariance ◽  
Wave Models ◽  
Genetic Covariances

ABSTRACTGenetic covariances represent a combination of pleiotropy and linkage disequilibrium, shaped by the population’s history. Observed genetic covariance is most often interpreted in pleiotropic terms. In particular, functional constraints restricting which phenotypes are physically possible can lead to a stable G matrix with high genetic variance in fitness-associated traits and high pleiotropic negative covariance along the phenotypic curve of constraint. In contrast, population genetic models of relative fitness assume endless adaptation without constraint, through a series of selective sweeps that are well described by recent traveling wave models. We describe the implications of such population genetic models for the G matrix when pleiotropy is excluded by design, such that all covariance comes from linkage disequilibrium. The G matrix is far less stable than has previously been found, fluctuating over the timescale of selective sweeps. However, its orientation is relatively stable, corresponding to high genetic variance in fitness-associated traits and strong negative covariance - the same pattern often interpreted in terms of pleiotropic constraints but caused instead by linkage disequilibrium. We find that different mechanisms drive the instabilities along versus perpendicular to the fitness gradient. The origin of linkage disequilibrium is not drift, but small amounts of linkage disequilibrium are instead introduced by mutation and then amplified during competing selective sweeps. This illustrates the need to integrate a broader range of population genetic phenomena into quantitative genetics.

scholarly journals INTERACTION BETWEEN NATURAL SELECTION FOR HETEROZYGOTES AND DIRECTIONAL SELECTION

Genetics ◽  
1974 ◽  
Vol 76 (1) ◽  
pp. 163-168
Author(s):  
Margrith Wehrli Verghese
Keyword(s):  
Natural Selection ◽  
First Generation ◽  
Genetic Variance ◽  
Selection Response ◽  
Directional Selection ◽  
Simple Function ◽  
Small Populations ◽  
Gene Frequencies ◽  
Selection For

ABSTRACT When directional selection for an additively inherited trait is opposed by natural selection favoring heterozygous genotypes a selection plateau may be reached where genetic variance is present. The amount of response when this plateau is reached is a simple function of the selection response in the first generation and the intensity of natural selection. When selection is practiced in small populations, the sizes of the initial equilibrium gene frequencies are at least as important as the intensity of natural selection in determining the probability of fixing desirable alleles.

scholarly journals Evolution of genetic variability in a spatially heterogeneous environment: effects of genotype–environment interaction

1987 ◽  
Vol 49 (2) ◽  
pp. 147-156 ◽  
Author(s):  
Sara Via ◽  
Russell Lande
Keyword(s):  
Genetic Variability ◽  
Genetic Correlation ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Genetic Models ◽  
Quantitative Character ◽  
Disruptive Selection ◽  
Environment Interaction ◽  
The Mean

SummaryClassical population genetic models show that disruptive selection in a spatially variable environment can maintain genetic variation. We present quantitative genetic models for the effects of disruptive selection between environments on the genetic covariance structure of a polygenic trait. Our models suggest that disruptive selection usually does not alter the equilibrium genetic variance, although transient changes are predicted. We view a quantitative character as a set of character states, each expressed in one environment. The genetic correlation between character states expressed in different environments strongly affects the evolution of the genetic variability. (1) If the genetic correlation between character states is not ± 1, then the mean phenotype expressed in each environment will eventually attain the optimum value for that environment; this is the evolution of phenotypic plasticity (Via & Lande, 1985). At the joint phenotypic optimum, there is no disruptive selection between environments and thus no increase in the equilibrium genetic variability over that maintained by a balance between mutation and stabilizing selection within each environment. (2) If, however, the genetic correlation between character states is ± 1, the mean phenotype will not evolve to the joint phenotypic optimum and a persistent force of disruptive selection between environments will increase the equilibrium genetic variance. (3) Numerical analyses of the dynamic equations indicate that the mean phenotype can usually be perturbed several phenotypic standard deviations from the optimum without producing transient changes of more than a few per cent in the genetic variances or correlations. It may thus be reasonable to assume a roughly constant covariance structure during phenotypic evolution unless genetic correlations among character states are extremely high or populations are frequently perturbed. (4) Transient changes in the genetic correlations between character states resulting from disruptive selection act to constrain the evolution of the mean phenotype rather than to facilitate it.

scholarly journals The Correlation Between Relatives on the Supposition of Genomic Imprinting

Genetics ◽  
2002 ◽  
Vol 161 (1) ◽  
pp. 411-417 ◽  
Author(s):  
Hamish G Spencer
Keyword(s):  
Genomic Imprinting ◽  
Standard Error ◽  
Statistical Power ◽  
Genetic Variance ◽  
Genetic Model ◽  
Genetic Models ◽  
Single Locus ◽  
Genetic Analyses ◽  
Quantitative Genetic ◽  
Quantitative Genetic Model

Abstract Standard genetic analyses assume that reciprocal heterozygotes are, on average, phenotypically identical. If a locus is subject to genomic imprinting, however, this assumption does not hold. We incorporate imprinting into the standard quantitative-genetic model for two alleles at a single locus, deriving expressions for the additive and dominance components of genetic variance, as well as measures of resemblance among relatives. We show that, in contrast to the case with Mendelian expression, the additive and dominance deviations are correlated. In principle, this correlation allows imprinting to be detected solely on the basis of different measures of familial resemblances, but in practice, the standard error of the estimate is likely to be too large for a test to have much statistical power. The effects of genomic imprinting will need to be incorporated into quantitative-genetic models of many traits, for example, those concerned with mammalian birthweight.

scholarly journals GENETIC MODELS FOR THE ANALYSIS OF DATA FROM THE FAMILIES OF IDENTICAL TWINS

Genetics ◽  
1976 ◽  
Vol 83 (4) ◽  
pp. 811-826
Author(s):  
Walter E Nance ◽  
Linda A Corey
Keyword(s):  
Least Squares ◽  
Maternal Effects ◽  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Weighted Least Squares ◽  
Identical Twins ◽  
Genetic Models ◽  
Family Unit ◽  
Least Squares Estimates

ABSTRACT Genetic models are described which exploit the unique relationships that exist within the families of identical twins to obtain weighted least squares estimates of additive, dominance and epistatic components of genetic variance as well as estimates of the contributions of X-linked genes, maternal effects and three sources of environmental variation. Since all of the relationships required to achieve a resolution of these variance components are contained within each family unit, the model would appear to be superior to previous approaches to the analysis of quantitative traits in man.

scholarly journals The Genetic Architecture of Quantitative Traits Cannot Be Inferred From Variance Component Analysis

2016 ◽  
Author(s):  
Wen Huang ◽  
Trudy F.C. Mackay
Keyword(s):  
Genetic Variation ◽  
Variance Components ◽  
Variance Component ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Component Analysis ◽  
Genetic Models ◽  
Variance Component Analysis ◽  
Components Of Variance

AbstractClassical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals. The genetic variance components are defined in terms of genotypic values reflecting underlying genetic architecture (additive, dominance and epistatic genotypic effects) and allele frequencies. However, the dependency of the definition of genetic variance components on the underlying genetic models is not often appreciated. Here, we show how the partitioning of additive and non-additive genetic variation is affected by the genetic models and parameterization of allelic effects. We show that arbitrarily defined variance components often capture a substantial fraction of total genetic variation regardless of the underlying genetic architecture in simulated and real data. Therefore, variance component analysis cannot be used to infer genetic architecture of quantitative traits. The genetic basis of quantitative trait variation in a natural population can only be defined empirically using high resolution mapping methods followed by detailed characterization of QTL effects.

COMPARISON OF PREDICTED AND SIMULATED RESPONSES TO S1 SELECTION IN A DIPLOID, CROSS-FERTILIZED SPECIES

1981 ◽  
Vol 61 (1) ◽  
pp. 9-15 ◽  
Author(s):  
T. M. CHOO ◽  
L. W. KANNENBERG
Keyword(s):  
Coefficient Of Variation ◽  
Variance Component ◽  
Genetic Variance ◽  
Recurrent Selection ◽  
Genetic Models ◽  
Variance Component Analysis ◽  
Narrow Sense Heritability ◽  
Complete Dominance ◽  
Per Se ◽  
Good Agreement

Computer simulation was used to test the accuracy of mathematical formulae for predicting mean response and variance of response to S1 per se recurrent selection under both additive and complete dominance genetic models. S1 selection was simulated at two levels of selection intensity (5 and 25%) and two levels of narrow sense heritability (0.2 and 0.6) for 15 cycles. In each cycle, 400 S1 families were evaluated in simulated trials consisting of one replication of 10-plant plots at each of four locations. The character under selection was controlled by 40 independently assorted loci. For both genetic models, variance component analysis provided good estimates of genetic variance and the predicted gains were in good agreement with the simulated gains. The simulated coefficient of variation of response was small and was very close to the predicted coefficient of variation in each of the four selection regimes under both models.

scholarly journals Response to selection while maximizing genetic variance in small populations

2016 ◽  
Vol 48 (1) ◽  
Author(s):  
Isabel Cervantes ◽  
Juan Pablo Gutiérrez ◽  
Theo H.E. Meuwissen
Keyword(s):  
Genetic Variance ◽  
Small Populations ◽  
Response To Selection
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