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

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.

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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 ◽

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.

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Morphological integration during postnatal ontogeny: implications for evolutionary biology

10.1101/2021.07.13.452198 ◽

2021 ◽

Author(s):

Alex Hubbe ◽

Guilherme Garcia ◽

Harley Sebastiao ◽

Arthur Porto ◽

Fabio Andrade Machado ◽

...

Keyword(s):

Evolutionary Biology ◽

Additive Genetic Variance ◽

Structural Similarity ◽

Morphological Integration ◽

Ontogenetic Stage ◽

Postnatal Ontogeny ◽

Phenotypic Variance ◽

Skull Morphology ◽

Genetic Covariance ◽

Ontogenetic Stages

Understanding how development changes the genetic covariance of complex phenotypes is fundamental for the study of evolution. If the genetic covariance changes dramatically during postnatal ontogeny, one cannot infer confidently evolutionary responses based on the genetic covariance estimated from a single postnatal ontogenetic stage. Mammalian skull morphology is a common model system for studying the evolution of complex structures. These studies often involve estimating covariance between traits based on adult individuals. There is robust evidence that covariances changes during ontogeny. However, it is unknown whether differences in age-specific covariances can, in fact, bias evolutionary analyses made at subadult ages. To explore this issue, we sampled two marsupials from the order Didelphimorphia, and one precocial and one altricial placental at different stages of postnatal ontogeny. We calculated the phenotypic variance-covariance matrix (P-matrix) for each genus at these postnatal ontogenetic stages. Then, we compared within genus P-matrices and also P-matrices with available congeneric additive genetic variance-covariance matrices (G-matrices) using Random Skewers and the Krzanowsky projection methods. Our results show that the structural similarity between matrices is in general high (> 0.7). Our study supports that the G-matrix in therian mammals is conserved during most of the postnatal ontogeny. Thus it is feasible to study life-history changes and evolutionary responses based on the covariance estimated from a single ontogenetic stage. Our results also suggest that at least for some marsupials the G-matrix varies considerably prior to weaning, which does not invalidate our previous conclusion because specimens at this stage would experience striking differences in selective regimes than during later ontogenetic stages.

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

10.1101/2020.02.26.966119 ◽

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.

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Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Genetics ◽

10.1093/genetics/iyab122 ◽

2021 ◽

Author(s):

Marnin D Wolfe ◽

Ariel W Chan ◽

Peter Kulakow ◽

Ismail Rabbi ◽

Jean-Luc Jannink

Keyword(s):

Mate Selection ◽

Selection Criteria ◽

Genetic Variance ◽

Selection Index ◽

Dominance Model ◽

Genetic Covariance ◽

Good Correspondence ◽

Mean And Variance ◽

Genetic Distribution ◽

Selection Of

Abstract Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses were accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).

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ESTİMATİON OF GENETİC AND ENVİRONMENTAL PARAMETERS AFFECTİNG PRODUCTİVİTY İN MORKARAMAN SHEEP AND ECONOMİC EVALUATİON OF PARAMETERS

Journal of Global Innovations in Agriculture Sciences ◽

10.22194/jgias/9.958 ◽

2021 ◽

pp. 155-161

Author(s):

Ufuk Karadavut ◽

Burhan Bahadır ◽

Volkan Karadavut ◽

Galip Şimşek ◽

Hakan İnci

Keyword(s):

Genetic Parameters ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Environmental Parameters ◽

Error Variance ◽

Economic Importance ◽

Phenotypic Variance ◽

Economic Aspects ◽

Evaluation Of Parameters ◽

The Relationship

This study was carried out to protect the continuity of productivity in morkaraman sheep raised in Turkey and determine their economic importance. Morkaraman sheep are concentrated in the Eastern Regions of the country. The province of Bingöl, where the study was conducted, is located in this region and has an important morkaraman population. The study was carried out between 2008-2018. Sixty-eight morkaraman sheep were used during the study period out of 317 lambing lambs. In the study, the total number of lambs born per sheep (TNLBS), the number of weaned lambs (NWL), the weights of the lambs weaned per sheep (WLWS) and the total weight of the lambs weaned in the first period (TWLWFP) were determined. In addition, Additive genetic variance, Error variance, Phenotypic variance, Heritability and Ratio of error variation were determined for these variables. As a result, the correlation between the examined variables was significant and positive, except for the relationship between TNLBS and TWLWFP. The relationship between these two variables was significant but negative. Significant changes were also observed in terms of genetic parameters. It was concluded that the economic aspects of the examined variables should not be ignored in terms of sustainability. Keywords: Sheep, morkaraman, sustainability, genotypic and phenotypic variance.

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A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

10.1101/318451 ◽

2018 ◽

Cited By ~ 3

Author(s):

Caroline E. Thomson ◽

Isabel S. Winney ◽

Oceane C. Salles ◽

Benoit Pujol

Keyword(s):

Animal Model ◽

Genetic Variance ◽

Genetic Relatedness ◽

Additive Genetic Variance ◽

Phenotypic Traits ◽

Phenotypic Variance ◽

Data Types ◽

Evolutionary Potential ◽

Recent Developments

AbstractNon-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.

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

10.32942/osf.io/tkq9w ◽

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.

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Estimation of variance components for bodyweight of grasscutters using expectation maximization algorithm of restricted maximum likelihood

Nigerian Journal of Animal Production ◽

10.51791/njap.v44i5.1545 ◽

2020 ◽

Vol 44 (5) ◽

pp. 5-8

Author(s):

I. Udeh

Keyword(s):

Body Weight ◽

Maximum Likelihood ◽

Expectation Maximization ◽

Variance Components ◽

Genetic Variance ◽

Phenotypic Variability ◽

Additive Genetic Variance ◽

Expectation Maximization Algorithm ◽

Estimation Of Variance ◽

Selection Of

The objective of this study was to estimate the variance components and heritability of bodyweight of grasscutters at 4, 6 and 8 months of age using EM algorithm of REML procedures. The data used for the study were obtained from the bodyweight records of 20 grasscutters from four families at 4, 6 and 8 months of age. The heritability of bodyweight of grasscutters at 4, 6 and 8 months of age were 0.14, 0.10 and 0.12 respectively. This implies that about 10 – 14 % of the phenotypic variability of body weight in this grasscutter population was accounted by additive genetic variance while environmental and gene combination variance made a larger contribution. The implication is that selection of grasscutters in this population should not be based on the information on the animals alone but also information fromits relatives.

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Genetic and phenotypic variability of cherry plum (Prunus cerasifera Ehrh) pomological characteristics

Genetika ◽

10.2298/gensr0303155c ◽

2003 ◽

Vol 35 (3) ◽

pp. 155-160 ◽

Cited By ~ 3

Author(s):

Slavica Colic ◽

Gordan Zec ◽

Dejan Marinkovic ◽

Zoran Jankovic

Keyword(s):

Genetic Variance ◽

Phenotypic Variability ◽

Sugar Content ◽

Plant Diseases ◽

Raw Material ◽

Phenotypic Variance ◽

Dry Matter Content ◽

Native Population ◽

Total Phenotypic Variance ◽

Prunus Cerasifera

Cherry plum (Prunus cerasifera Ehrh) is one of the most widely spread fruit species in our country. The fruits are mostly used for brandy production and the seed is used for rootstock production in fruit culture. As cherry plum is resistant to plant diseases and pests, chemical protection is not required. Concerning that, cherry plum is reach and cheap source for the production of healthy food. The objective of this research was the analysis of genetic and phenotypic variability, as well as study on correlation of pomological traits of 49 cherry plum genotypes selected from the native population in Serbia. It was measured mat the highest genetic variance in total phenotypic variance was for the fruit height and total sugar content. The lowest genetic variance in total phenotypic variance was for the length of the stalk and dry matter content. The highest genetic variance coefficient (CVg = 22.93%) was calculated for the total acid content although the lowest value of genetic and phenotypic variance was for the fruit width (CVg = 0.69%; CVf = 0.80%). The highest coefficient of phenotypic and genetic correlation was calculated between the weight and height of the fruit. Native population of cherry plum in Serbia and Montenegro is specific because of the extensive variability of the forms which is highly important for the selection of raw material in breeding process.

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Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

The Journal of Agricultural Science ◽

10.1017/s0021859618000497 ◽

2018 ◽

Vol 156 (4) ◽

pp. 565-569

Author(s):

H. Ghiasi ◽

R. Abdollahi-Arpanahi ◽

M. Razmkabir ◽

M. Khaldari ◽

R. Taherkhani

Keyword(s):

Dairy Cows ◽

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Genetic Effects ◽

Information Criteria ◽

Phenotypic Variance ◽

Dominance Variance ◽

Dominance Genetic Variance ◽

Estimated Heritability

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

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