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

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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Faculty Opinions recommendation of Very low additive genetic variance and evolutionary potential in multiple populations of two rainforest Drosophila species.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1033401.374438 ◽

2006 ◽

Author(s):

Richard Frankham

Keyword(s):

Genetic Variance ◽

Additive Genetic Variance ◽

Drosophila Species ◽

Evolutionary Potential ◽

Multiple Populations

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Variance component analysis of body mass in a wild population of deer (Odocoileus virginianus): results from two decades of research

Wildlife Research ◽

10.1071/wr12224 ◽

2013 ◽

Vol 40 (7) ◽

pp. 588 ◽

Cited By ~ 1

Author(s):

Stephen L. Webb ◽

Kenneth L. Gee ◽

Randy W. DeYoung ◽

Seth M. Harju

Keyword(s):

Odocoileus Virginianus ◽

Body Mass ◽

Environmental Effects ◽

Phenotypic Traits ◽

Phenotypic Variance ◽

Components Of Variance ◽

Habitat Improvement ◽

Capture Recapture ◽

Sources Of Variation

Context Long-term studies of large, vertebrate mammals using capture–recapture data are scarce, even though long-term ecological studies are requisite to understanding quantitative genetics and evolutionary processes that can be applied as part of management programs. Aims Objectives were to (1) partition components of variation in body mass to understand the differential effects of environmental variation on the sexes during ontogeny, to better prescribe habitat-improvement projects, and (2) estimate repeatability to assess potential for selection on body mass. Methods We used a 23-year dataset (1983–2005) of capture–recapture records of wild white-tailed deer (Odocoileus virginianus) to estimate components of variance and repeatability of body mass. We used an animal-model approach that employed the use of general linear mixed models and restricted maximum likelihood to adjust for the effects of age (i.e. fixed effect), and to partition the total phenotypic variance into among-individual (i.e. the deer), permanent environmental (i.e. year of birth) and temporary environmental (i.e. year of measurement and residual) effects (all modelled as random effects). Key results We found that body mass increased with age in both sexes, repeatability of body mass was 0.595 for females and 0.716 for males, and among-individual variation was more influential on body mass than were permanent and temporary environmental effects combined. Year of birth was more important in males than females, but changed during the course of ontogeny for both sexes. Year of measurement did not influence post-rut body mass in males, but did contribute to variation in body mass of females. Conclusions These long-term data offer insights into the sources of variation that influence body mass of deer, which can be used to understand how environmental sources of variation influence phenotypic traits, and for developing management plans and making selection decisions. Implications Knowledge of repeatability (as an upper limit to heritability) can be used to make management decisions related to selection, culling and breeding, whereas understanding environmental effects can lead to better management recommendations (e.g. habitat-improvement projects).

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What contributes to differences in phenotypic variation between generations?

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200030398 ◽

2009 ◽

Vol 2009 ◽

pp. 200-200

Author(s):

A Wolc ◽

I White ◽

M Lisowski ◽

W G Hill

Keyword(s):

Animal Model ◽

Environmental Effects ◽

Environmental Conditions ◽

Phenotypic Variation ◽

Variance Components ◽

Genetic Variance ◽

Phenotypic Variance ◽

Base Population ◽

Substantial Heterogeneity ◽

Over Time

Under the animal model genetic variance is estimated in the base population taking into account inbreeding and is otherwise assumed to remain unchanged over generations. In practice, phenotypic variation differs randomly or systematically over time. Intuitively, such changes would be attributed mostly to environmental effects, and so lower heritability would be expected when variation is inflated. Studies in dairy cattle show contradictory results (e.g. Boldman and Freeman, 1990). Laying hens are kept under environmental conditions intended to be constant, but show substantial heterogeneity in phenotypic variance (VP) over generations. The aim was to investigate how variance components change.

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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 ◽

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.

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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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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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Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽

10.3390/ani9121055 ◽

2019 ◽

Vol 9 (12) ◽

pp. 1055 ◽

Cited By ~ 2

Author(s):

Ying Liu ◽

Lei Xu ◽

Zezhao Wang ◽

Ling Xu ◽

Yan Chen ◽

...

Keyword(s):

Beef Cattle ◽

Genomic Prediction ◽

Complex Traits ◽

Genetic Variance ◽

Association Studies ◽

Additive Genetic Variance ◽

Additive Models ◽

Carcass Weight ◽

Phenotypic Variance ◽

Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

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VERY LOW ADDITIVE GENETIC VARIANCE AND EVOLUTIONARY POTENTIAL IN MULTIPLE POPULATIONS OF TWO RAINFOREST DROSOPHILA SPECIES

Evolution ◽

10.1554/05-710.1 ◽

2006 ◽

Vol 60 (5) ◽

pp. 1104 ◽

Cited By ~ 73

Author(s):

Vanessa M. Kellermann ◽

Belinda van Heerwaarden ◽

Ary A. Hoffmann ◽

Carla M. Sgrò

Keyword(s):

Genetic Variance ◽

Additive Genetic Variance ◽

Drosophila Species ◽

Evolutionary Potential ◽

Multiple Populations

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The Changes in Genetic and Environmental Variance With Inbreeding in Drosophila melanogaster

Genetics ◽

10.1093/genetics/152.1.345 ◽

1999 ◽

Vol 152 (1) ◽

pp. 345-353 ◽

Cited By ~ 3

Author(s):

Michael C Whitlock ◽

Kevin Fowler

Keyword(s):

Drosophila Melanogaster ◽

Large Scale ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Inbred Lines ◽

Residual Variance ◽

Phenotypic Variance ◽

Population Bottlenecks ◽

Environmental Variance ◽

Components Of Variance

Abstract We performed a large-scale experiment on the effects of inbreeding and population bottlenecks on the additive genetic and environmental variance for morphological traits in Drosophila melanogaster. Fifty-two inbred lines were created from the progeny of single pairs, and 90 parent-offspring families on average were measured in each of these lines for six wing size and shape traits, as well as 1945 families from the outbred population from which the lines were derived. The amount of additive genetic variance has been observed to increase after such population bottlenecks in other studies; in contrast here the mean change in additive genetic variance was in very good agreement with classical additive theory, decreasing proportionally to the inbreeding coefficient of the lines. The residual, probably environmental, variance increased on average after inbreeding. Both components of variance were highly variable among inbred lines, with increases and decreases recorded for both. The variance among lines in the residual variance provides some evidence for a genetic basis of developmental stability. Changes in the phenotypic variance of these traits are largely due to changes in the genetic variance.

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Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs

Animals ◽

10.3390/ani11020481 ◽

2021 ◽

Vol 11 (2) ◽

pp. 481

Author(s):

Valentina Bonfatti ◽

Roberta Rostellato ◽

Paolo Carnier

Keyword(s):

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Model Fitting ◽

Genetic Effects ◽

Phenotypic Variance ◽

Genetic Response ◽

Dominance Variance ◽

Dry Cured Ham ◽

Genetic Evaluations

Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litter effects, but no effects on animal ranking and accuracy of breeding values were detected. Accounting for litter effects in the models for genetic evaluations would be sufficient to prevent the overestimation of the genetic variance while ensuring computational efficiency.

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