Learning from your mistakes: a novel method to predict the response to directional selection

Fruit Fly ◽

Directional Selection ◽

Prediction Errors ◽

Evolution Experiment ◽

Novel Method ◽

Predicting how populations respond to selection is a key goal of evolutionary biology. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation. However, differences between the observed responses to selection and those predicted by the breeder’s equation occur. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. A key insight from previous research is that the expected value of these prediction errors is often not zero, in which case the predictions are systematically biased. Here, we propose that this prediction bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a novel method to predict the response to selection, which is built on three key innovations. First, the method predicts change as the breeder’s equation plus a bias term. Second, the method combines information from the breeder’s equation and from the record of past changes in the mean, to estimate the bias and predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a machine-learning algorithm on the record of past changes. We apply the method to data of an artificial selection experiment of the wing of the fruit fly, as well as to an in silico evolution experiment for teeth. We find that the method outperforms the breeder’s equation, and notably provides good predictions even when traits under selection are omitted from the analysis and when additive genetic variance is estimated inaccurately. The proposed method is easy to apply since it only requires recording the mean of the traits over past generations.

How does parental environment influence the potential for adaptation to global change?

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2018.1374 ◽

2018 ◽

Vol 285 (1886) ◽

pp. 20181374 ◽

Cited By ~ 15

Author(s):

Evatt Chirgwin ◽

Dustin J. Marshall ◽

Carla M. Sgrò ◽

Keyne Monro

Keyword(s):

Genetic Variation ◽

Global Change ◽

Adaptive Capacity ◽

Genetic Variance ◽

Life Stage ◽

Parental Exposure ◽

Future Warming ◽

The Mean ◽

Negative Impacts

Parental environments are regularly shown to alter the mean fitness of offspring, but their impacts on the genetic variation for fitness, which predicts adaptive capacity and is also measured on offspring, are unclear. Consequently, how parental environments mediate adaptation to environmental stressors, like those accompanying global change, is largely unknown. Here, using an ecologically important marine tubeworm in a quantitative-genetic breeding design, we tested how parental exposure to projected ocean warming alters the mean survival, and genetic variation for survival, of offspring during their most vulnerable life stage under current and projected temperatures. Offspring survival was higher when parent and offspring temperatures matched. Across offspring temperatures, parental exposure to warming altered the distribution of additive genetic variance for survival, making it covary across current and projected temperatures in a way that may aid adaptation to future warming. Parental exposure to warming also amplified nonadditive genetic variance for survival, suggesting that compatibilities between parental genomes may grow increasingly important under future warming. Our study shows that parental environments potentially have broader-ranging effects on adaptive capacity than currently appreciated, not only mitigating the negative impacts of global change but also reshaping the raw fuel for evolutionary responses to it.

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

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2015.1119 ◽

2015 ◽

Vol 282 (1819) ◽

pp. 20151119 ◽

Cited By ~ 25

Author(s):

Vincent Careau ◽

Matthew E. Wolak ◽

Patrick A. Carter ◽

Theodore Garland

Keyword(s):

Covariance Matrix ◽

Adaptive Response ◽

Genetic Variance ◽

Environmental Changes ◽

Wheel Running ◽

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.

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

Genetics Research ◽

10.1017/s0016672304006640 ◽

2004 ◽

Vol 83 (2) ◽

pp. 121-132 ◽

Cited By ~ 66

Author(s):

WILLIAM G. HILL ◽

XU-SHENG ZHANG

Keyword(s):

Genetic Variance ◽

Environmental Variability ◽

Phenotypic Variability ◽

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.

Measurement of fleece wettability in sheep and its relationship to susceptibility to fleece rot and blowfly strike

Australian Journal of Agricultural Research ◽

10.1071/ar9820141 ◽

1982 ◽

Vol 33 (1) ◽

pp. 141 ◽

Cited By ~ 1

Author(s):

L Pascoe

Keyword(s):

Genetic Variance ◽

Genetic Correlations ◽

Economic Importance ◽

Correlated Response ◽

Response To Selection ◽

Significant Incidence ◽

The Mean ◽

Selection For ◽

The One

Fleece wettability in sheep is a character believed to be related to susceptibility to fleece rot and blowfly strike. The present study was undertaken to investigate that hypothesis and to assess wettability as a possible character for a selection program. Wool samples were taken from two flocks which had been subject to selection for wool quality and resistance to fleece rot and a third flock which was unselected. The wettabilities of about 800 samples were determined. The results were found to be repeatable and the technique was capable of distinguishing between sheep. Some problems of measurement are discussed. In the one flock with a significant incidence of fleece rot, susceptibility to fleece rot was found to be associated with higher wettabilities. The mean wettability and the variance were found to be significantly higher in the unselected flock than in the two selected flocks. The heritability of wettability was estimated in the two selected flocks and was found to be low. It is argued that there is likely to be more additive genetic variance in the unselected flock and that the observed difference in wettability was due to a correlated response to selection for resistance to fleece rot. It is considered that further work on the heritability of wettability and its genetic correlations with other characters of economic importance could be fruitful.

Polygenic adaptation after a sudden change in environment

10.1101/792952 ◽

2019 ◽

Cited By ~ 10

Author(s):

Laura K. Hayward ◽

Guy Sella

Keyword(s):

Genetic Variance ◽

Sudden Change ◽

Directional Selection ◽

Stabilizing Selection ◽

Effective Population ◽

Short Term ◽

Exponential Process ◽

Optimal Value ◽

Polygenic Adaptation ◽

AbstractPolygenic adaptation in response to selection on quantitative traits is thought to be ubiquitous in humans and other species, yet this mode of adaptation remains poorly understood. We investigate the dynamics of this process, assuming that a sudden change in environment shifts the optimal value of a highly polygenic quantitative trait. We find that when the shift is not too large relative to the genetic variance in the trait and this variance arises from segregating loci with small to moderate effect sizes (defined in terms of the selection acting on them before the shift), the mean phenotype’s approach to the new optimum is well approximated by a rapid exponential process first described by Lande (1976). In contrast, when the shift is larger or large effect loci contribute substantially to genetic variance, the initially rapid approach is succeeded by a much slower one. In either case, the underlying changes to allele frequencies exhibit different behaviors short and long-term. Over the short term, strong directional selection on the trait introduces small differences between the frequencies of minor alleles whose effects are aligned with the shift in optimum versus those with effects in the opposite direction. The phenotypic effects of these differences are dominated by contributions from alleles with moderate and large effects, and cumulatively, these effects push the mean phenotype close to the new optimum. Over the longer term, weak directional selection on the trait can amplify the expected frequency differences between opposite alleles; however, since the mean phenotype is close to the new optimum, alleles are mainly affected by stabilizing selection on the trait. Consequently, the frequency differences between opposite alleles translate into small differences in their probabilities of fixation, and the short-term phenotypic contributions of large effect alleles are largely supplanted by contributions of fixed, moderate ones. This process takes on the order of ~4Ne generations (where Ne is the effective population size), after which the steady state architecture of genetic variation around the new optimum is restored.

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 ◽

Allele Frequencies ◽

Disruptive Selection ◽

Stabilizing Selection ◽

Short Term ◽

Additive Variance ◽

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.

The effects of stabilizing selection on the time of development in Drosophila melanogaster

Genetics Research ◽

10.1017/s0016672300003219 ◽

1962 ◽

Vol 3 (3) ◽

pp. 364-382 ◽

Cited By ~ 37

Author(s):

Timothy Prout

Keyword(s):

Drosophila Melanogaster ◽

Development Time ◽

Genetic Variance ◽

Parallel Line ◽

Total Variance ◽

Disruptive Selection ◽

Stabilizing Selection ◽

Environmental Component ◽

The length of time of development, from oviposition to emergence in Drosophila melanogaster was subjected to stabilizing selection. In each generation only the individuals emerging close to the mean development time were used as parents of the next generation. This line was designated the ‘S’ line. In a parallel line disruptive selection was practised; where in each generation the earliest flies to emerge were mated to the flies last to emerge; those emerging at intermediate times were discarded. This line was designated the ‘D’ line. Two control lines were also carried, where the flies were mated at random with respect to time of emergence. The experiment extended for 40 generations and produced the following results:(1) The variance of development time decreased in the S line and increased in the D line, relative to the control lines.(2) The mean development time decreased in the S line and increased in the D line.(3) The coefficients of variation decreased in the S line and increased in the D line.(4) The viability, measured as per cent flies emerging, decreased in the D line.Toward the end of the experiment the amount of additive genetic variance in the selected lines and in the control lines was estimated from the response to directional selection. The estimates showed that (i) the loss of total variance in the S line can be accounted for completely by a loss in additive genetic variance, and (ii) the increase in the total variance of the D line cannot be ascribed to an increase in the additive genetic variance. It was probably due to an increase in the environmental component of variance, i.e. to a loss of ‘buffering capacity’.

Genotype-environment interactions and the maintenance of polygenic variation.

Genetics ◽

10.1093/genetics/121.1.129 ◽

1989 ◽

Vol 121 (1) ◽

pp. 129-138 ◽

Cited By ~ 25

Author(s):

J H Gillespie ◽

M Turelli

Keyword(s):

Genetic Variance ◽

Balancing Selection ◽

Natural Populations ◽

Extensive Literature ◽

Multilocus Genotype ◽

Polygenic Inheritance ◽

Mean Fitness ◽

The Mean ◽

Polygenic Variation

Abstract Genotype-environment interactions may be a potent force maintaining genetic variation in quantitative traits in natural populations. This is shown by a simple model of additive polygenic inheritance in which the additive contributions of alleles vary with the environment. Under simplifying symmetry assumptions, the model implies that the variance of the phenotypes produced across environments by a multilocus genotype decreases as the number of heterozygous loci increases. In the region of an optimal phenotype, the mapping from the quantitative trait into fitness is concave, and the mean fitness of a genotype will increase with the number of heterozygous loci. This leads to balancing selection, polymorphism, and potentially high levels of additive genetic variance, even though all allelic effects remain additive within each specific environment. An important implication of the model is that the variation maintained by genotype-environment interactions is difficult to study with the restricted range of environments represented in typical experiments. In particular, if fluctuations in allelic effects are pervasive, as suggested by the extensive literature on genotype-environment interactions, efforts to estimate genetic parameters in a single environment may be of limited value.

Low repeatability of aversive learning in zebrafish (Danio rerio)

Journal of Experimental Biology ◽

10.1242/jeb.240846 ◽

2021 ◽

Author(s):

Dominic Mason ◽

Susanne Zajitschek ◽

Hamza Anwer ◽

Rose E. O'Dea ◽

Daniel Hesselson ◽

...

Keyword(s):

Individual Differences ◽

Publication Bias ◽

Danio Rerio ◽

Electric Shock ◽

Animal Species ◽

Genetic Variance ◽

Directional Selection ◽

Aversive Learning ◽

Negative Experiences

Aversive learning – avoiding certain situations based on negative experiences – can profoundly increase fitness in animal species, yet no studies have systematically quantified its repeatability. Therefore, we assessed the repeatability of aversive learning by conditioning approximately 100 zebrafish (Danio rerio) to avoid a colour cue associated with a mild electric shock. Across eight different colour conditions zebrafish did not show consistent individual differences in aversive learning (R=0.04). Within conditions, when zebrafish were conditioned to the same colour, blue conditioning was more repeatable than green conditioning (R=0.15 and R=0.02). Overall, aversive learning responses of zebrafish were weak and variable. We speculate that the effect of aversive learning might have been too weak to quantify consistent individual differences, or directional selection might have eroded additive genetic variance. We also discuss how confounded repeatability assays and publication bias could have inflated estimates of repeatability in the literature.

Recombination load associated with selection for increased recombination

Genetics Research ◽

10.1017/s0016672300033450 ◽

1996 ◽

Vol 67 (1) ◽

pp. 27-41 ◽

Cited By ~ 70

Author(s):

B. Charlesworth ◽

N. H. Barton

Keyword(s):

Transposable Elements ◽

Direct Effect ◽

Quantitative Traits ◽

Genetic Variance ◽

Genetic Recombination ◽

Directional Selection ◽

Additive Variance ◽

Linkage Disequilibria ◽

Selection For

SummaryExperiments on Drosophila suggest that genetic recombination may result in lowered fitness of progeny (a ‘recombination load’). This has been interpreted as evidence either for a direct effect of recombination on fitness, or for the maintenance of linkage disequilibria by epistatic selection. Here we show that such a recombination load is to be expected even if selection favours increased genetic recombination. This is because of the fact that, although a modifier may suffer an immediate loss of fitness if it increases recombination, it eventually becomes associated with a higher additive genetic variance in fitness, which allows a faster response to directionselection. This argument applies to mutation-selection balance with synergistic epistasis, directional selection on quantitative traits, and ectopic exchange among transposable elements. Further experiments are needed to determine whether the selection against recombination due to trie immediate load is outweighed by the increased additive variance in fitness produced by recombination.