scholarly journals Multilevel Selection 1: Quantitative Genetics of Inheritance and Response to Selection: TABLE 1

Genetics ◽  
2006 ◽  
Vol 175 (1) ◽  
pp. 277-288 ◽  
Author(s):  
Piter Bijma ◽  
William M. Muir ◽  
Johan A. M. Van Arendonk
Keyword(s):  
Quantitative Genetics ◽  
Multilevel Selection ◽  
Response To Selection

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 The microbiome extends host evolutionary potential

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucas P. Henry ◽  
Marjolein Bruijning ◽  
Simon K. G. Forsberg ◽  
Julien F. Ayroles
Keyword(s):  
Quantitative Genetics ◽  
Host Response ◽  
Evolutionary Models ◽  
Response To Selection ◽  
Evolutionary Potential ◽  
Complex Interplay ◽  
The Mean

AbstractThe microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

scholarly journals Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps

2019 ◽  
Author(s):  
Lisandro Milocco ◽  
Isaac Salazar-Ciudad
Keyword(s):  
Natural Selection ◽  
Quantitative Genetics ◽  
Evolutionary Biology ◽  
Linear Models ◽  
Response To Selection ◽  
Multiple Traits ◽  
Highly Nonlinear ◽  
Nonlinear Maps ◽  
Evo Devo ◽  
Nonlinear Nature

AbstractA fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation (1). Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population (2, 3). The other major field of research concerned with the GPM, developmental evolutionary biology or evo-devo (1, 4–6), has found the GPM to be highly nonlinear and complex (4, 7). Here we quantify how the predictions of quantitative genetics are affected by the complex, nonlinear maps found in developmental biology. We combine a realistic development-based GPM model and a population genetics model of recombination, mutation and natural selection. Each individual in the population consists of a genotype and a multi-trait phenotype that arises through the development model. We simulate evolution by applying natural selection on multiple traits per individual. In addition, we estimate the quantitative genetics parameters required to predict the response to selection. We found that the disagreements between predicted and observed responses to selection are common, roughly in a third of generations, and are highly dependent on the traits being selected. These disagreements are systematic and related to the nonlinear nature of the genotype-phenotype map. Our results are a step towards integrating the fields studying the GPM.

scholarly journals Multilevel Selection 2: Estimating the Genetic Parameters Determining Inheritance and Response to Selection

Genetics ◽  
2006 ◽  
Vol 175 (1) ◽  
pp. 289-299 ◽  
Author(s):  
Piter Bijma ◽  
William M. Muir ◽  
Esther D. Ellen ◽  
Jason B. Wolf ◽  
Johan A. M. Van Arendonk
Keyword(s):  
Genetic Parameters ◽  
Multilevel Selection ◽  
Response To Selection

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.

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