scholarly journals The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to Indirect Genetic Effects dominates heritable variation and response to selection

Genetics ◽  
2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries D Hulst ◽  
Mart C M de Jong
Keyword(s):  
Genetic Variation ◽  
Infectious Diseases ◽  
Quantitative Genetics ◽  
Disease Status ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

Abstract Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently we investigate the population level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

scholarly journals The quantitative genetics of the endemic prevalence of infectious diseases: Indirect Genetic Effects dominate heritable variation and response to selection

2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries Hulst ◽  
Mart C. M. de Jong
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Status ◽  
Host Population ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

AbstractPathogens have profound effects on life on earth, both in nature and agriculture. Despite the availability of well-established epidemiological theory, however, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of the transmission dynamics of infectious diseases for heritable variation and response to selection of the host population, our current theoretical framework of quantitative genetics does not include these dynamics. As a consequence, we do not know which genetic effects of the host population determine the prevalence of an infectious disease, and have no concepts of breeding value and heritable variation for endemic prevalence.Here we propose a quantitative genetic theory for the endemic prevalence of infectious diseases. We first identify the genetic factors that determine the prevalence of an infectious disease, using an approach founded in epidemiological theory. Subsequently we investigate the population level effects of individual genetic variation on R0 and on the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for both prevalence and individual binary disease status, and show how these parameters depend on the endemic prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases when prevalence approaches zero, while heritability of individual disease status goes to zero. We show that response of prevalence to selection accelerates considerably when prevalence goes down, in contrast to predictions based on classical genetic models. Finally, we show that most of the heritable variation in the endemic prevalence of the infection is due to indirect genetic effects, suggestion a key role for kin-group selection both in the evolutionary history of current populations and for genetic improvement strategies in animals and plants.

58 Integrating Quantitative Genetics and Epidemiology: Why Selection Against Infectious Diseases Is More Promising Than We Think

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 31-32
Author(s):  
Piter Bijma ◽  
Piter Bijma
Keyword(s):  
Infectious Diseases ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Disease Status ◽  
Genetic Theory ◽  
Breeding Values ◽  
Quantitative Genetic ◽  
The Mean ◽  
Low Prevalence ◽  
Simple Average

Abstract Pathogens have profound effects on livestock. The low heritabilities of individual binary disease status suggest limited prospects for genetic improvement. However, a proper quantitative genetic theory for infectious diseases, including transmission dynamics, is currently lacking. Here we present a quantitative genetic theory for endemic infectious diseases, focussing on the genetic factors that determine the prevalence (P; the mean fraction of the population that is infected). We present simple expressions for breeding values and genetic parameters for the prevalence. Without genetic variation in infectiousness, breeding values for prevalence are a factor 1/P greater than the ordinary breeding values for individual binary disease status (0/1). Hence, even though prevalence is the simple average of individual binary disease status, breeding values for prevalence show much greater variation than our ordinary breeding values. This implies that the genetic variance that determines the potential response of prevalence to selection is largely due to indirect genetic effects (IGE), and thus hidden to ordinary genetic analysis and selection. Hence, the genetic variance that determines the potential of livestock populations to respond to selection must be much greater than currently believed, particularly at low prevalence. We evaluated this implication using simulation of endemics following standard methods in epidemiology. Results show that response of prevalence to selection increases very strongly when prevalence decreases, and is much greater than predicted by our ordinary breeding values. These results supports our theoretical findings, and show that selection against infectious diseases is much more promising than currently believed.

scholarly journals Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Genetics ◽  
2021 ◽  
Author(s):  
Andries D Hulst ◽  
Mart C M de Jong ◽  
Piter Bijma
Keyword(s):  
Infectious Diseases ◽  
Herd Immunity ◽  
Genetic Selection ◽  
Disease Status ◽  
Response To Selection ◽  
Feedback Effects ◽  
Feedback Mechanisms ◽  
Quantitative Genetic ◽  
The Common ◽  
Selection For

Abstract Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).

Maintenance of Quantitative Genetic Variation

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Genetic Variation ◽  
Quantitative Genetics ◽  
Stabilizing Selection ◽  
Quantitative Genetic ◽  
Wide Range

One of the major unresolved issues in quantitative genetics is what accounts for the amount of standing genetic variation in traits. A wide range of models, all reviewed in this chapter, have been proposed, but none fit the data, either giving too much variation or too little apparent stabilizing selection.

scholarly journals Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Thinh Tuan Chu ◽  
Mark Henryon ◽  
Just Jensen ◽  
Birgitte Ask ◽  
Ole Fredslund Christensen
Keyword(s):  
Statistical Model ◽  
Statistical Models ◽  
Genetic Effects ◽  
Response To Selection ◽  
Heritable Variation ◽  
Fixed Rate ◽  
Random Group ◽  
Breeding Programs ◽  
The Difference ◽  
Family Groups

Abstract Background Social genetic effects (SGE) are the effects of the genotype of one animal on the phenotypes of other animals within a social group. Because SGE contribute to variation in economically important traits for pigs, the inclusion of SGE in statistical models could increase responses to selection (RS) in breeding programs. In such models, increasing the relatedness of members within groups further increases RS when using pedigree-based relationships; however, this has not been demonstrated with genomic-based relationships or with a constraint on inbreeding. In this study, we compared the use of statistical models with and without SGE and compared groups composed at random versus groups composed of families in genomic selection breeding programs with a constraint on the rate of inbreeding. Results When SGE were of a moderate magnitude, inclusion of SGE in the statistical model substantially increased RS when SGE were considered for selection. However, when SGE were included in the model but not considered for selection, the increase in RS and in accuracy of predicted direct genetic effects (DGE) depended on the correlation between SGE and DGE. When SGE were of a low magnitude, inclusion of SGE in the model did not increase RS, probably because of the poor separation of effects and convergence issues of the algorithms. Compared to a random group composition design, groups composed of families led to higher RS. The difference in RS between the two group compositions was slightly reduced when using genomic-based compared to pedigree-based relationships. Conclusions The use of a statistical model that includes SGE can substantially improve response to selection at a fixed rate of inbreeding, because it allows the heritable variation from SGE to be accounted for and capitalized on. Compared to having random groups, family groups result in greater response to selection in the presence of SGE but the advantage of using family groups decreases when genomic-based relationships are used.

scholarly journals Evolution of quantitative traits in the wild: mind the ecology

2010 ◽  
Vol 365 (1552) ◽  
pp. 2431-2438 ◽  
Author(s):  
Josephine M. Pemberton
Keyword(s):  
Quantitative Genetics ◽  
Quantitative Traits ◽  
Natural Populations ◽  
Response To Selection ◽  
Genetic Response ◽  
Quantitative Genetic ◽  
Animal Populations ◽  
In The Wild ◽  
Gradient Variation

Recent advances in the quantitative genetics of traits in wild animal populations have created new interest in whether natural selection, and genetic response to it, can be detected within long-term ecological studies. However, such studies have re-emphasized the fact that ecological heterogeneity can confound our ability to infer selection on genetic variation and detect a population's response to selection by conventional quantitative genetics approaches. Here, I highlight three manifestations of this issue: counter gradient variation, environmentally induced covariance between traits and the correlated effects of a fluctuating environment. These effects are symptomatic of the oversimplifications and strong assumptions of the breeder's equation when it is applied to natural populations. In addition, methods to assay genetic change in quantitative traits have overestimated the precision with which change can be measured. In the future, a more conservative approach to inferring quantitative genetic response to selection, or genomic approaches allowing the estimation of selection intensity and responses to selection at known quantitative trait loci, will provide a more precise view of evolution in ecological time.

scholarly journals A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution

2021 ◽  
Author(s):  
Joel W McGlothlin ◽  
Erol Akcay ◽  
Edmund D Brodie ◽  
Allen J Moore ◽  
Jeremy Van Cleve
Keyword(s):  
Game Theory ◽  
Quantitative Genetics ◽  
Evolutionary Game Theory ◽  
Social Evolution ◽  
Evolutionary Game ◽  
Genetic Effects ◽  
Social Selection ◽  
Selection Gradients ◽  
Quantitative Genetic ◽  
Indirect Genetic Effects

Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory, and in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

scholarly journals Linking genetic merit to sparse behavioral data: behavior and genetic effects on lamb growth in Soay sheep

2019 ◽  
Author(s):  
Charlotte E Regan ◽  
Josephine M Pemberton ◽  
Jill G Pilkington ◽  
Per T Smiseth ◽  
Alastair J Wilson
Keyword(s):  
Genetic Variation ◽  
Parental Care ◽  
Natural Populations ◽  
Genetic Effects ◽  
Breeding Value ◽  
Offspring Performance ◽  
Genetic Studies ◽  
Soay Sheep ◽  
Lamb Growth

Abstract Wild quantitative genetic studies have focused on a subset of traits (largely morphological and life history), with others, such as behaviors, receiving much less attention. This is because it is challenging to obtain sufficient data, particularly for behaviors involving interactions between individuals. Here, we explore an indirect approach for pilot investigations of the role of genetic differences in generating variation in parental care. Variation in parental genetic effects for offspring performance is expected to arise from among-parent genetic variation in parental care. Therefore, we used the animal model to predict maternal breeding values for lamb growth and used these predictions to select females for field observation, where maternal and lamb behaviors were recorded. Higher predicted maternal breeding value for lamb growth was associated with greater suckling success, but not with any other measures of suckling behavior. Though our work cannot explicitly estimate the genetic basis of the specific traits involved, it does provide a strategy for hypothesis generation and refinement that we hope could be used to justify data collection costs needed for confirmatory studies. Here, results suggest that behavioral genetic variation is involved in generating maternal genetic effects on lamb growth in Soay sheep. Though important caveats and cautions apply, our approach may extend the ability to initiate more genetic investigations of difficult-to-study behaviors and social interactions in natural populations.

scholarly journals Heritable variation for sex ratio under environmental sex determination in the common snapping turtle (Chelydra serpentina).

Genetics ◽  
1992 ◽  
Vol 131 (1) ◽  
pp. 155-161 ◽  
Author(s):  
F J Janzen
Keyword(s):  
Genetic Variation ◽  
Sex Ratio ◽  
Sex Determination ◽  
Sex Ratios ◽  
Chelydra Serpentina ◽  
Heritable Variation ◽  
Snapping Turtle ◽  
Primary Sex Ratio ◽  
Quantitative Genetic ◽  
The Common

Abstract The magnitude of quantitative genetic variation for primary sex ratio was measured in families extracted from a natural population of the common snapping turtle (Chelydra serpentina), which possesses temperature-dependent sex determination (TSD). Eggs were incubated at three temperatures that produced mixed sex ratios. This experimental design provided estimates of the heritability of sex ratio in multiple environments and a test of the hypothesis that genotype x environment (G x E) interactions may be maintaining genetic variation for sex ratio in this population of C. serpentina. Substantial quantitative genetic variation for primary sex ratio was detected in all experimental treatments. These results in conjunction with the occurrence of TSD in this species provide support for three critical assumptions of Fisher's theory for the microevolution of sex ratio. There were statistically significant effects of family and incubation temperature on sex ratio, but no significant interaction was observed. Estimates of the genetic correlations of sex ratio across environments were highly positive and essentially indistinguishable from + 1. These latter two findings suggest that G x E interaction is not the mechanism maintaining genetic variation for sex ratio in this system. Finally, although substantial heritable variation exists for primary sex ratio of C. serpentina under constant temperatures, estimates of the effective heritability of primary sex ratio in nature are approximately an order of magnitude smaller. Small effective heritability and a long generation time in C. serpentina imply that evolution of sex ratios would be slow even in response to strong selection by, among other potential agents, any rapid and/or substantial shifts in local temperatures, including those produced by changes in the global climate.

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