scholarly journals Low repeatability of aversive learning in zebrafish (Danio rerio)

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 ◽  
Additive 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.

scholarly journals Low Repeatability of Aversive Learning in Zebrafish (Danio rerio)

2020 ◽  
Author(s):  
Dominic Mason ◽  
Susanne Zajitschek ◽  
Hamza Anwer ◽  
Rose E O’Dea ◽  
Daniel Hesselson ◽  
...  
Keyword(s):  
Individual Differences ◽  
Danio Rerio ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Aversive Conditioning ◽  
Baseline Period ◽  
Learning Ability ◽  
Aversive Learning ◽  
Intra Class Correlation ◽  
The Stability

AbstractAversive learning – avoiding certain situations based on negative experiences – can profoundly increase fitness in animal species. The extent to which this cognitive mechanism could evolve depends upon individual differences in aversive learning being stable through time, and heritable across generations, yet no published study has quantified the stability of individual differences in aversive learning using the repeatability statistic, R (also known as the intra-class correlation). 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 twice conditioned to the same colour, blue conditioning was more repeatable than green conditioning (R = 0.15 and R = 0.02). In contrast to the low repeatability estimates for aversive learning, zebrafish showed moderately consistent individual differences in colour preference during the baseline period (i.e. prior to aversive conditioning; R ~ 0.45). Overall, aversive learning responses of zebrafish were weak and variable (difference in time spent near the aversive cue <6 seconds per minute), but individual differences in learning ability did not explain substantial variability. We speculate that either the effect of aversive learning was too weak to quantify consistent individual differences, or directional selection might have eroded additive genetic variance. Finally, we discuss how confounded repeatability assays and publication bias could have inflated average estimates of repeatability in animal behaviour publications.Summary StatementZebrafish exhibit low repeatability (intra-class correlation) in an aversive learning assay possibly due to past selection pressure exhausting genetic variance in this learning trait.

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

2021 ◽  
Author(s):  
Lisandro Milocco ◽  
Isaac Salazar-Ciudad
Keyword(s):  
Evolutionary Biology ◽  
Genetic Variance ◽  
Learning Algorithm ◽  
Additive Genetic Variance ◽  
Fruit Fly ◽  
Directional Selection ◽  
Prediction Errors ◽  
Evolution Experiment ◽  
Novel Method ◽  
The Mean

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.

scholarly journals Longitudinal modeling of genetic and environmental influences on self-reported availability of psychoactive substances: alcohol, cigarettes, marijuana, cocaine and stimulants

2007 ◽  
Vol 37 (7) ◽  
pp. 947-959 ◽  
Author(s):  
NATHAN A. GILLESPIE ◽  
KENNETH S. KENDLER ◽  
CAROL A. PRESCOTT ◽  
STEVEN H. AGGEN ◽  
CHARLES O. GARDNER ◽  
...  
Keyword(s):  
Individual Differences ◽  
Ordinal Data ◽  
Genetic Variance ◽  
Environmental Control ◽  
Additive Genetic Variance ◽  
Social Constraints ◽  
Adult Males ◽  
Environmental Variance ◽  
Components Of Variance ◽  
Over Time

Background. Although an obvious environmental factor influencing drug use, the sources of individual differences in drug availability (DA) are unknown.Method. This report is based on 1788 adult males from the Mid-Atlantic Twin Registry who participated in a structured telephone interview that included retrospective assessments of DA (cigarette, alcohol, marijuana, cocaine and stimulants) between ages 8 and 25. We fitted a biometric dual change score (DCS) model, adapted for ordinal data, to model latent growth and estimate the genetic and environmental components of variance over time.Results. DA, despite being considered an environmental risk factor, is under both genetic and environmental control. For cigarette, alcohol, marijuana and cocaine availability, there was an overall increase in additive genetic variance and a decline in shared environmental variance over time. Non-shared environmental variance remained steady. Stimulant availability did not follow this pattern. Instead, there was an upswing in shared environmental effects with increasing age.Conclusion. We have modeled the genetic and environmental architecture of changes in DA across adolescence. The rise in additive genetic variance over time coincides with acceleration in the expression of individual differences, probably brought on by an increase in personal freedom and a reduction in social constraints. Understanding the etiology of DA is likely to reveal key components, acting directly or indirectly, in the pathway(s) leading to drug initiation, abuse and dependence.

scholarly journals Recombination load associated with selection for increased recombination

1996 ◽  
Vol 67 (1) ◽  
pp. 27-41 ◽  
Author(s):  
B. Charlesworth ◽  
N. H. Barton
Keyword(s):  
Transposable Elements ◽  
Direct Effect ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Recombination ◽  
Additive Genetic Variance ◽  
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.

scholarly journals The Evolution of Non-Additive Genetic Variance Under Artificial Selection I. Modification of Dominance At a Single Autosomal Locus

1964 ◽  
Vol 17 (2) ◽  
pp. 427 ◽  
Author(s):  
BDH Latter
Keyword(s):  
Artificial Selection ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Directional Selection ◽  
Autosomal Locus ◽  
Laboratory Experimentation ◽  
Allelic Interaction ◽  
The Stability ◽  
Selection Of ◽  
General Method

The papers to be published in this series are concerned with the behaviour under directional selection of genetic systems showing non-allelic interaction. The aim is to describe in quantitative terms the conditions under which overdorninance may evolve under the usual conditions of laboratory experimentation, and to examine the stability of the resultant populations. In this first paper, a general method of approach to problems of the modification of dominance is outlined, and the necessary theory developed.

scholarly journals Genetic and environmental architecture of conscientiousness in adolescence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yusuke Takahashi ◽  
Anqing Zheng ◽  
Shinji Yamagata ◽  
Juko Ando
Keyword(s):  
Individual Differences ◽  
Effortful Control ◽  
Genetic Variance ◽  
Genetic Factors ◽  
Common Factor ◽  
Genetic Correlations ◽  
Self Control ◽  
Genetic Analyses ◽  
Environmental Correlations ◽  
Per Se

AbstractUsing a genetically informative design (about 2000 twin pairs), we investigated the phenotypic and genetic and environmental architecture of a broad construct of conscientiousness (including conscientiousness per se, effortful control, self-control, and grit). These four different measures were substantially correlated; the coefficients ranged from 0.74 (0.72–0.76) to 0.79 (0.76–0.80). Univariate genetic analyses revealed that individual differences in conscientiousness measures were moderately attributable to additive genetic factors, to an extent ranging from 62 (58–65) to 64% (61–67%); we obtained no evidence that shared environmental influences were observed. Multivariate genetic analyses showed that for the four measures used to assess conscientiousness, genetic correlations were stronger than the corresponding non-shared environmental correlations, and that a latent common factor accounted for over 84% of the genetic variance. Our findings suggest that individual differences in the four measures of conscientiousness are not distinguishable at both the phenotypic and behavioural genetic levels, and that the overlap was substantially attributable to genetic factors.

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