scholarly journals Genetic variance in fitness and its cross-sex covariance predict adaptation during experimental evolution

2020 ◽  
Author(s):  
Eva L. Koch ◽  
Sonja H. Sbilordo ◽  
Frédéric Guillaume
Keyword(s):  
Experimental Evolution ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Additive Genetic Variance ◽  
Relative Fitness ◽  
Male Fitness ◽  
Adaptive Potential ◽  
Genetic Covariance ◽  
Flour Beetles ◽  
Single Stress

AbstractIn presence of rapid environmental changes, it is of particular importance to assess the adaptive potential of populations, which is mostly determined by the additive genetic variation (VA) in fitness. In this study we used Tribolium castaneum (red flour beetles) to investigate its adaptive potential in three new environmental conditions (Dry, Hot, Hot-Dry). We tested for potential constraints that might limit adaptation, including negative genetic covariance between female and male fitness. Based on VA estimates for fitness, we expected the highest relative fitness increase in the most stressful condition Hot-Dry and similar increases in single stress conditions Dry and Hot. High adaptive potential in females in Hot was reduced by a negative covariance with male fitness. We tested adaptation to the three conditions after 20 generations of experimental evolution and found that observed adaptation mainly matched our predictions. Given that body size is commonly used as a proxy for fitness, we also tested how this trait and its genetic variance (including non-additive genetic variance) were impacted by environmental stress. In both traits, variances were sex and condition dependent, but they differed in their variance composition, cross-sex and cross-environment genetic covariances, as well as in the environmental impact on VA.

scholarly journals Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

2015 ◽  
Vol 282 (1819) ◽  
pp. 20151119 ◽  
Author(s):  
Vincent Careau ◽  
Matthew E. Wolak ◽  
Patrick A. Carter ◽  
Theodore Garland
Keyword(s):  
Covariance Matrix ◽  
Adaptive Response ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Wheel Running ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Genetic Covariance ◽  
Variance Covariance Matrix

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix ( G ). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset ( n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.

scholarly journals The ‘algebra of evolution’: the Robertson–Price identity and viability selection for body mass in a wild bird population

2020 ◽  
Vol 375 (1797) ◽  
pp. 20190359 ◽  
Author(s):  
G. K. Hajduk ◽  
C. A. Walling ◽  
A. Cockburn ◽  
L. E. B. Kruuk
Keyword(s):  
Phenotypic Diversity ◽  
Additive Genetic Variance ◽  
Natural Populations ◽  
Phenotypic Selection ◽  
Relative Fitness ◽  
Price Equation ◽  
Genetic Covariance ◽  
Selection Gradient ◽  
Viability Selection ◽  
Temporal Correlations

By the Robertson–Price identity, the change in a quantitative trait owing to selection, is equal to the trait's covariance with relative fitness. In this study, we applied the identity to long-term data on superb fairy-wrens Malurus cyaneus , to estimate phenotypic and genetic change owing to juvenile viability selection. Mortality in the four-week period between fledging and independence was 40%, and heavier nestlings were more likely to survive, but why? There was additive genetic variance for both nestling mass and survival, and a positive phenotypic covariance between the traits, but no evidence of additive genetic covariance. Comparing standardized gradients, the phenotypic selection gradient was positive, β P = 0.108 (0.036, 0.187 95% CI), whereas the genetic gradient was not different from zero, β A = −0.025 (−0.19, 0.107 95% CI). This suggests that factors other than nestling mass were the cause of variation in survival. In particular, there were temporal correlations between mass and survival both within and between years. We suggest that use of the Price equation to describe cross-generational change in the wild may be challenging, but a more modest aim of estimating its first term, the Robertson–Price identity, to assess within-generation change can provide valuable insights into the processes shaping phenotypic diversity in natural populations. This article is part of the theme issue ‘Fifty years of the Price equation’.

scholarly journals Estimation of Genetic Variance in Fitness, and Inference of Adaptation, When Fitness Follows a Log-Normal Distribution

2019 ◽  
Vol 110 (4) ◽  
pp. 383-395 ◽  
Author(s):  
Timothée Bonnet ◽  
Michael B Morrissey ◽  
Loeske E B Kruuk
Keyword(s):  
Natural Selection ◽  
Animal Models ◽  
Normal Distribution ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Relative Fitness ◽  
Parameter Estimates ◽  
Log Normal ◽  
Change In Mean ◽  
Log Normal Distribution

AbstractAdditive genetic variance in relative fitness (σA2(w)) is arguably the most important evolutionary parameter in a population because, by Fisher’s fundamental theorem of natural selection (FTNS; Fisher RA. 1930. The genetical theory of natural selection. 1st ed. Oxford: Clarendon Press), it represents the rate of adaptive evolution. However, to date, there are few estimates of σA2(w) in natural populations. Moreover, most of the available estimates rely on Gaussian assumptions inappropriate for fitness data, with unclear consequences. “Generalized linear animal models” (GLAMs) tend to be more appropriate for fitness data, but they estimate parameters on a transformed (“latent”) scale that is not directly interpretable for inferences on the data scale. Here we exploit the latest theoretical developments to clarify how best to estimate quantitative genetic parameters for fitness. Specifically, we use computer simulations to confirm a recently developed analog of the FTNS in the case when expected fitness follows a log-normal distribution. In this situation, the additive genetic variance in absolute fitness on the latent log-scale (σA2(l)) equals (σA2(w)) on the data scale, which is the rate of adaptation within a generation. However, due to inheritance distortion, the change in mean relative fitness between generations exceeds σA2(l) and equals (exp⁡(σA2(l))−1). We illustrate why the heritability of fitness is generally low and is not a good measure of the rate of adaptation. Finally, we explore how well the relevant parameters can be estimated by animal models, comparing Gaussian models with Poisson GLAMs. Our results illustrate 1) the correspondence between quantitative genetics and population dynamics encapsulated in the FTNS and its log-normal-analog and 2) the appropriate interpretation of GLAM parameter estimates.

scholarly journals Effects on phenotypic variability of directional selection arising through genetic differences in residual variability

2004 ◽  
Vol 83 (2) ◽  
pp. 121-132 ◽  
Author(s):  
WILLIAM G. HILL ◽  
XU-SHENG ZHANG
Keyword(s):  
Genetic Variance ◽  
Environmental Variability ◽  
Phenotypic Variability ◽  
Additive Genetic Variance ◽  
Directional Selection ◽  
Frequency Change ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Genetic Covariance ◽  
Mean And Variance

In standard models of quantitative traits, genotypes are assumed to differ in mean but not variance of the trait. Here we consider directional selection for a quantitative trait for which genotypes also confer differences in variability, viewed either as differences in residual phenotypic variance when individual loci are concerned or as differences in environmental variability when the whole genome is considered. At an individual locus with additive effects, the selective value of the increasing allele is given by ia/σ+½ixb/σ2, where i is the selection intensity, x is the standardized truncation point, σ2 is the phenotypic variance, and a/σ and b/σ2 are the standardized differences in mean and variance respectively between genotypes at the locus. Assuming additive effects on mean and variance across loci, the response to selection on phenotype in mean is iσAm2/σ+½ixcovAmv/σ2 and in variance is icovAmv/σ+½ixσ2Av/σ2, where σAm2 is the (usual) additive genetic variance of effects of genes on the mean, σ2Av is the corresponding additive genetic variance of their effects on the variance, and covAmv is the additive genetic covariance of their effects. Changes in variance also have to be corrected for any changes due to gene frequency change and for the Bulmer effect, and relevant formulae are given. It is shown that effects on variance are likely to be greatest when selection is intense and when selection is on individual phenotype or within family deviation rather than on family mean performance. The evidence for and implications of such variability in variance are discussed.

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.

PREDICTIONS OF THE EFFECTIVENESS OF INTRAPOPULATION SELECTION VERSUS SELECTION FOR SPECIFIC COMBINING ABILITY IN SWINE

1984 ◽  
Vol 64 (4) ◽  
pp. 799-806
Author(s):  
R. M. McKAY ◽  
G. W. RAHNEFELD
Keyword(s):  
Combining Ability ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Effective Means ◽  
Average Daily Gain ◽  
Mass Selection ◽  
Genetic Covariance ◽  
Selection For ◽  
Sib Selection ◽  
Daily Gain

Additive genetic variance estimates for purebred (Lacombe) and crossbred (Lacombe × Yorkshire) populations and the additive genetic covariance between purebred and crossbred progeny were calculated for postweaning average daily gain, total probe fat, total carcass fat, and litter size in swine. These estimates were used to predict the effectiveness of four methods of intrapopulation selection (IP) relative to selection for specific combining ability (SCA) to determine the most effective means of improving crossbred performance. The intrapopulation methods were mass selection based on information from both sexes (BS), mass selection based on information from one sex (OS), full-sib selection (FS), and half-sib selection (HS). The Lacombe population was selected over 12 generations for increased postweaning average daily gain and the Lacombe × Yorkshire population was generated by breeding Lacombe boars with randomly selected gilts from a Yorkshire control population. Selection for combining ability was the most effective means of improving average daily gain except when information was available on both sexes and the relative selection intensity (SCA/IP) was less than 0.60. Mass selection was superior to SCA for improving total probe fat except when information was restricted to one sex and the relative selection intensity was less than 0.47. For total probe fat and total carcass fat, SCA was superior to FS and HS for relative selection intensities less than 0.65 and 0.74, respectively. Selection for combining ability was superior to OS for litter size regardless of the generation interval length. Key words: Intrapopulation selection, selection for combining ability, additive genetic variance, additive genetic covariance, swine

scholarly journals Analogues of the fundamental and secondary theorems of selection, assuming a log-normal distribution of expected fitness

2019 ◽  
Vol 110 (4) ◽  
pp. 396-402 ◽  
Author(s):  
Michael B Morrissey ◽  
Timothée Bonnet
Keyword(s):  
Additive Genetic Variance ◽  
Natural Populations ◽  
Relative Fitness ◽  
Empirical Estimation ◽  
Genetic Covariance ◽  
Quantitative Genetic ◽  
Log Normal ◽  
Methodological Considerations ◽  
Fundamental Theorems ◽  
Log Normal Distribution

Abstract It is increasingly common for studies of evolution in natural populations to infer the quantitative genetic basis of fitness (e.g., the additive genetic variance for relative fitness), and of relationships between traits and fitness (e.g., the additive genetic covariance of traits with relative fitness). There is a certain amount of tension between the theory that justifies estimating these quantities, and methodological considerations relevant to their empirical estimation. In particular, the additive genetic variances and covariances involving relative fitness are justified by the fundamental and secondary theorems of selection, which pertain to relative fitness on the scale that it is expressed. However, naturally-occurring fitness distributions lend themselves to analysis with generalized linear mixed models (GLMMs), which conduct analysis on a different scale, typically on the scale of the logarithm of expected values, from which fitness is expressed. This note presents relations between evolutionary change in traits, and the rate of adaptation in fitness, and log quantitative genetic parameters of fitness, potentially reducing the discord between theoretical and methodological considerations to the operationalization of the secondary and fundamental theorems of selection.

scholarly journals Introduced populations of ragweed show as much evolutionary potential as native populations

2018 ◽  
Author(s):  
Brechann V. McGoey ◽  
John R. Stinchcombe
Keyword(s):  
Genetic Architecture ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Ambrosia Artemisiifolia ◽  
Empirical Literature ◽  
Ecological Problem ◽  
Adaptive Potential ◽  
Evolutionary Potential ◽  
Introduced Populations ◽  
Native Populations

AbstractInvasive species are a global economic and ecological problem. They also offer an opportunity to understand evolutionary processes in a colonizing context. The impacts of evolutionary factors, such as genetic variation, on the invasion process are increasingly appreciated but there remain gaps in the empirical literature. The adaptive potential of populations can be quantified using genetic variance-covariance matrices (G), which encapsulate the heritable genetic variance in a population. Here, we use a multivariate, Bayesian approach to assess the adaptive potential of introduced populations of ragweed, Ambrosia artemisiifolia, a serious allergen and agricultural weed. We compared several aspects of genetic architecture and the structure of G matrices between three native and three introduced populations, based on data collected in the field in a common garden experiment. We find moderate differences in the quantitative genetic architecture among populations, but we do not find that introduced populations suffer from a limited adaptive potential compared to native populations. Ragweed has an annual life history, is an obligate outcrosser, and produces billions of seeds and pollen grains per. These characteristics, combined with the significant additive genetic variance documented here, suggest ragweed will be able to respond quickly to selection pressures in both its native and introduced ranges.

scholarly journals Linkage and the limits to natural selection.

Genetics ◽  
1995 ◽  
Vol 140 (2) ◽  
pp. 821-841 ◽  
Author(s):  
N H Barton
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Large Population ◽  
Substantial Effect ◽  
Relative Fitness ◽  
Fixation Probability ◽  
Deleterious Mutations ◽  
Effective Population ◽  
Probability Of Fixation ◽  
General Method

Abstract The probability of fixation of a favorable mutation is reduced if selection at other loci causes inherited variation in fitness. A general method for calculating the fixation probability of an allele that can find itself in a variety of genetic backgrounds is applied to find the effect of substitutions, fluctuating polymorphisms, and deleterious mutations in a large population. With loose linkage, r, the effects depend on the additive genetic variance in relative fitness, var(W), and act by reducing effective population size by (N/Ne) = 1 + var(W)/2r2. However, tightly linked loci can have a substantial effect not predictable from Ne. Linked deleterious mutations reduce the fixation probability of weakly favored alleles by exp (-2U/R), where U is the total mutation rate and R is the map length in Morgans. Substitutions can cause a greater reduction: an allele with advantage s < scrit = (pi 2/6) loge (S/s) [var(W)/R] is very unlikely to be fixed. (S is the advantage of the substitution impeding fixation.) Fluctuating polymorphisms at many (n) linked loci can also have a substantial effect, reducing fixation probability by exp [square root of 2Kn var(W)/R] [K = -1/E((u-u)2/uv) depending on the frequencies (u,v) at the selected polymorphisms]. Hitchhiking due to all three kinds of selection may substantially impede adaptation that depends on weakly favored alleles.

Evaluation of repeatability and pre-structured repeatability models for genetic analyses of repeated records of fat and protein contents of milk in Iranian Holstein cows

2018 ◽  
Vol 58 (11) ◽  
pp. 1983
Author(s):  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Holstein Cows ◽  
Phenotypic Correlation ◽  
Genetic Covariance ◽  
Future Studies ◽  
Phenotypic Correlations ◽  
Variance Structure

Fat and protein content of milk measurements from first to fifth lactations of Iranian Holstein cows were analysed using repeatability and several pre-structured repeatability models that varied in additive genetic variance structure and fitted heterogeneous residual co (variance). For this research, a total of 257 197 fat and 218 688 protein records were used. The records were measured on 116 531 cows born between 2010 and 2014. The animals originated from 2355 sires and 91 212 dams. Pre-structured repeatability models with heterogeneous residual co (variance) and the respective genetic variance structure were the best models for genetic analysis of the fat and protein data. The results derived from these models showed that heritability of both fat and protein are decreased from first to fifth lactations. Heritability of fat measured at first, second, third, fourth and fifth locations were between 0.10 and 0.19 and those for protein were between 0.07 and 0.24. Moderate to high phenotypic correlations were estimated between the repeated records of the fat and protein. Values of 0.13 and 0.16 were estimated for heritability of fat and protein using repeatability model. Phenotypic correlations among the repeated records of fat and protein were estimated to be 0.30 and 0.33, respectively when this model was applied. The results showed the genetic variance, heritability and phenotypic correlation of the fat and protein are changed over the lactations but the genetic parameters derived from the repeatability model are homogenous whereas in both models unity genetic correlations are assumed among the repeated records. The results of this study show that the repeatability model is not an appropriate model for genetic analysis of the repeated records of fat and protein in the population investigated and can be improved when pre-structured repeatability model is used. In the present study homogenous genetic covariance was assumed among the fat and protein taken at the different lactations which can be modelled in future studies for more improving the models.

Sign in / Sign up

Export Citation Format

Share Document