Evolutionary consequences of indirect genetic effects

Wolf, Jason B.; Brodie, Edmund D.; Cheverud, James M.; Moore, Allen J.; Wade, Michael J.

doi:10.1016/s0169-5347(97)01233-0

Cited by 751 publications

(838 citation statements)

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“…These models are trait-based, in the sense that they specify the indirect effect on the phenotype of the focal individual as a direct function of the phenotypic trait values of its social partners, and have been referred to as 'interacting phenotypes' (Moore et al, 1997;Wolf et al, 1998;McGlothlin et al, 2010). In the simple case where the trait of interest is also the single trait causing the IGE and the interaction is between two individuals, the trait-based model equals (Moore et al, 1997) …”

Section: Modelling Igesmentioning

confidence: 99%

“…An indirect genetic effect (IGE) is a genetic effect of an individual on trait values of other individuals (Griffing, 1967;Moore et al, 1997;Wolf et al, 1998). The term IGE is commonly used to refer to such effects on individuals belonging to the same species.…”

Section: Introductionmentioning

confidence: 99%

“…No attempt is made to fully review the existing literature on IGEs. Readers entirely unfamiliar with IGEs are referred to the introduction of Moore et al (1997) and to Wolf et al (1998) for a broader introduction. The following contains (i) a basic summary on quantitative genetic IGE models, (ii) a discussion of trait-based vs variance-component models of IGEs, (iii) a discussion on statistical estimation issues, (iv) a section on the interpretation and estimation of W, (v) a section on the interpretation of the magnitude of IGEs in relation to response to selection and (vi) a discussion of the genetic architecture of IGEs.…”

Section: Introductionmentioning

confidence: 99%

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The quantitative genetics of indirect genetic effects: a selective review of modelling issues

2013

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Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient W (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient W may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

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Section: Modelling Igesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The quantitative genetics of indirect genetic effects: a selective review of modelling issues

2013

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“…We use results from the field of IGEs to define breeding value for R 0 . An IGE is heritable effect of an individual on the trait value of another individual (Griffing, 1967(Griffing, , 1976(Griffing, , 1981Moore et al, 1997;Wolf et al, 1998;Muir, 2005). Hence, infectivity is an IGE, as an individual's infectivity affects the disease status of its contacts.…”

Section: Dynamic Model Of Infection With Genetic Heterogeneitymentioning

confidence: 99%

On the definition and utilization of heritable variation among hosts in reproduction ratio R0 for infectious diseases

2014

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Infectious diseases have a major role in evolution by natural selection and pose a worldwide concern in livestock. Understanding quantitative genetics of infectious diseases, therefore, is essential both for understanding the consequences of natural selection and for designing artificial selection schemes in agriculture. The basic reproduction ratio, R 0 , is the key parameter determining risk and severity of infectious diseases. Genetic improvement for control of infectious diseases in host populations should therefore aim at reducing R 0 . This requires definitions of breeding value and heritable variation for R 0 , and understanding of mechanisms determining response to selection. This is challenging, as R 0 is an emergent trait arising from interactions among individuals in the population. Here we show how to define breeding value and heritable variation for R 0 for genetically heterogeneous host populations. Furthermore, we identify mechanisms determining utilization of heritable variation for R 0 . Using indirect genetic effects, next-generation matrices and a SIR (Susceptible, Infected and Recovered) model, we show that an individual's breeding value for R 0 is a function of its own allele frequencies for susceptibility and infectivity and of population average susceptibility and infectivity. When interacting individuals are unrelated, selection for individual disease status captures heritable variation in susceptibility only, yielding limited response in R 0 . With related individuals, however, there is a secondary selection process, which also captures heritable variation in infectivity and additional variation in susceptibility, yielding substantially greater response. This shows that genetic variation in susceptibility represents an indirect genetic effect. As a consequence, response in R 0 increased substantially when interacting individuals were genetically related. Heredity (2014) 113, 364-374; doi:10.1038/hdy.2014.38; published online 14 May 2014 INTRODUCTIONInfectious diseases are widespread in humans, animals and plants. In natural populations, infectious diseases have a major role in the process of evolution by natural selection (Haldane, 1949;O'Brien and Evermann, 1988). In domestic populations, particularly in livestock, infectious diseases are imposing a worldwide concern owing to their impact on the welfare and productivity of livestock, and in the case of zoonosis, also because of the threat for human health. To contain the threat imposed by infectious diseases, different control strategies such as vaccination, antibiotic treatments and management practices have been implemented widely. However, the evolution of resistance to antibiotics by bacteria, evolution of resistance to vaccines by viruses and undesirable environmental impacts of antibiotic treatment put these strategies under question (Gibson and Bishop, 2005). Thus, there is a need to investigate additional control strategies, so as to extend the repertoire of possible interventions. A greater repertoire is favourable (1) because ...

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“…If there is a maternal genetic effect, this will contribute to the heritable variation and may influence adaption and evolution of the trait (Kirkpatrick and Lande 1989; Mousseau and Fox 1998; Wolf et al. 1998; Wolf 2003). However, as identifying maternal effects requires a large amount of data and certain pedigree structures, it has not been much studied in an evolutionary context (Reale et al.…”

Section: Introductionmentioning

confidence: 99%

Is my study system good enough? A case study for identifying maternal effects

Holand

Steinsland

2016

Ecology and Evolution

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In this paper, we demonstrate how simulation studies can be used to answer questions about identifiability and consequences of omitting effects from a model. The methodology is presented through a case study where identifiability of genetic and/or individual (environmental) maternal effects is explored. Our study system is a wild house sparrow (Passer domesticus) population with known pedigree. We fit pedigree‐based (generalized) linear mixed models (animal models), with and without additive genetic and individual maternal effects, and use deviance information criterion (DIC) for choosing between these models. Pedigree and R‐code for simulations are available. For this study system, the simulation studies show that only large maternal effects can be identified. The genetic maternal effect (and similar for individual maternal effect) has to be at least half of the total genetic variance to be identified. The consequences of omitting a maternal effect when it is present are explored. Our results indicate that the total (genetic and individual) variance are accounted for. When an individual (environmental) maternal effect is omitted from the model, this only influences the estimated (direct) individual (environmental) variance. When a genetic maternal effect is omitted from the model, both (direct) genetic and (direct) individual variance estimates are overestimated.

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Evolutionary consequences of indirect genetic effects

Cited by 751 publications

References 23 publications

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

On the definition and utilization of heritable variation among hosts in reproduction ratio R0 for infectious diseases

Is my study system good enough? A case study for identifying maternal effects

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