scholarly journals A quantitative genetics approach to the evolution of phenotypic (co)variance under limited dispersal, with an application to socially synergistic traits

2018 ◽  
Author(s):  
Charles Mullon ◽  
Laurent Lehmann
Keyword(s):  
Quantitative Genetics ◽  
Darwinian Evolution ◽  
Classical Dynamics ◽  
Multiple Traits ◽  
Fitness Effects ◽  
Correlational Selection ◽  
Social Traits ◽  
Trait Distribution ◽  
Limited Dispersal ◽  
Specific Variation

AbstractDarwinian evolution consists of the gradual transformation of heritable quantitative traits due to natural selection and the input of random variation by mutation. Here, we use a quantitative genetics approach to investigate the coevolution of multiple traits under selection, mutation, and limited dispersal. We track the dynamics of trait means and variance-covariances between traits that experience frequency-dependent selection. Assuming a multivariate-normal trait distribution, we recover classical dynamics of quantitative genetics, as well as stability and evolutionary branching conditions of invasion analyses, except that due to limited dispersal, selection depends on indirect fitness effects and relatedness. In particular, correlational selection that associates different traits within-individuals depends on the fitness effects of such associations between-individuals. These kin selection effects can be as relevant as pleiotropy for correlation between traits. We illustrate this with an example of the coevolution of two social traits whose association within-individual is costly but synergistically beneficial between-individuals. As dispersal becomes limited and relatedness increases, associations between-traits between-individuals become increasingly targeted by correlational selection. Consequently, the trait distribution goes from being bimodal with a negative correlation under panmixia to unimodal with a positive correlation under limited dispersal. More broadly, our approach can help understand the evolution of intra-specific variation.

scholarly journals Evolutionary stability of jointly evolving traits in subdivided populations

2016 ◽  
Author(s):  
Charles Mullon ◽  
Laurent Keller ◽  
Laurent Lehmann
Keyword(s):  
Quantitative Traits ◽  
Evolutionary Stability ◽  
Population Subdivision ◽  
Multiple Traits ◽  
Diversifying Selection ◽  
Fitness Effects ◽  
Subdivided Populations ◽  
Limited Dispersal ◽  
Indirect Fitness ◽  
Dispersal Cost

The evolutionary stability of quantitative traits depends on whether a population can resist invasion by any mutant. While uninvadability is well understood in well-mixed populations, it is much less so in subdivided populations when multiple traits evolve jointly. Here, we investigate whether a spatially subdivided population at a monomorphic equilibrium for multiple traits can withstand invasion by any mutant, or is subject to diversifying selection. Our model also explores the among traits correlations arising from diversifying selection and how they depend on relatedness due to limited dispersal. We find that selection favours a positive (negative) correlation between two traits, when the selective effects of one trait on relatedness is positively (negatively) correlated to the indirect fitness effects of the other trait. We study the evolution of traits for which this matters: dispersal that decreases relatedness, and helping that has positive indirect fitness effects. We find that when dispersal cost is low and the benefits of helping accelerate faster than its costs, selection leads to the coexistence of mobile defectors and sessile helpers. Otherwise, the population evolves to a monomorphic state with intermediate helping and dispersal. Overall, our results highlight the importance of population subdivision for evolutionary stability and correlations among traits.

scholarly journals An evolutionary quantitative genetics model for phenotypic (co)variances under limited dispersal, with an application to socially synergistic traits

Evolution ◽  
2019 ◽  
Vol 73 (9) ◽  
pp. 1695-1728 ◽  
Author(s):  
Charles Mullon ◽  
Laurent Lehmann
Keyword(s):  
Quantitative Genetics ◽  
Limited Dispersal

scholarly journals simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Samuel B. Fernandes ◽  
Alexander E. Lipka
Keyword(s):  
Quantitative Genetics ◽  
Multivariate Analyses ◽  
Statistical Analyses ◽  
Multivariate Statistical Analyses ◽  
Multiple Traits ◽  
Multivariate Statistical ◽  
Marker Data ◽  
Data Formats ◽  
Wide Range ◽  
Genomic Regions

Abstract Background Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. Results We illustrate simplePHENOTYPES’ ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. Conclusions simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES.

scholarly journals Oxytocin and vasotocin receptor variation sheds light into the evolution of human prosociality

2018 ◽  
Author(s):  
Constantina Theofanopoulou ◽  
Alejandro Andirkó ◽  
Cedric Boeckx ◽  
Erich D. Jarvis
Keyword(s):  
Balancing Selection ◽  
Modern Human ◽  
Primate Species ◽  
Human Populations ◽  
Modern Humans ◽  
Social Traits ◽  
Pathogenicity Prediction ◽  
Vasopressin Receptors ◽  
Specific Variation ◽  
Major Allele

AbstractModern human lifestyle strongly depends on complex social traits like empathy, tolerance and cooperation. These diverse facets of social cognition have been associated with variation in the oxytocin receptor (OTR) and its sister genes, the vasotocin/vasopressin receptors (VTR1A/AVPR1A and AVPR1B/VTR1B). Here, we compared the full genomic sequences of these receptors between modern humans, archaic humans, and 12 non-human primate species, and identified sites that show heterozygous variation in modern humans and archaic humans distinct from variation in other primates, and that have associated literature. We performed variant clustering, pathogenicity prediction, regulation, linkage disequilibrium frequency and selection analyses on data in different modern-human populations. We found five sites with modern human specific variation, where the modern human allele is the major allele in the global population (OTR: rs1042778, rs237885, rs6770632; VTR1A: rs10877969; VTR1B: rs33985287). Among them, the OTR-rs6770632 was predicted to be the most functional. We found two sites where alleles (OTR: rs59190448 and rs237888)1 present only in modern humans and archaic humans are under positive selection in modern humans, with rs237888 predicted to be a highly functional site. We identified three sites of convergent evolution between modern humans and bonobos (OTR: rs2228485 and rs237897; VTR1A: rs1042615), with OTR-rs2228485 ranking very highly in terms of functionality and being under balancing selection in modern humans. Our findings shed light on evolutionary questions of modern human and hominid prosociality, as well as on similarities in the social behavior between modern humans and bonobos.

scholarly journals Dispersal alters the nature and scope of sexually antagonistic variation

2020 ◽  
Author(s):  
Ewan O Flintham ◽  
Vincent Savolainen ◽  
Charles Mullon
Keyword(s):  
Genetic Variation ◽  
Balancing Selection ◽  
Sexual Antagonism ◽  
Previous Theory ◽  
Fitness Effects ◽  
Kin Competition ◽  
Spatial Subdivision ◽  
Subdivided Populations ◽  
Limited Dispersal ◽  
Intralocus Conflict

AbstractIntra-locus sexual conflict, or sexual antagonism, occurs when alleles have opposing fitness effects in the two sexes. Previous theory suggests that sexual antagonism is a driver of genetic variation by generating balancing selection. However, these studies assume that populations are well-mixed, neglecting the effects of spatial subdivision. Here we use mathematical modelling to show that limited dispersal can fundamentally change evolution at sexually antagonistic autosomal and X-linked loci due to inbreeding and sex-specific kin competition. We find that if the sexes disperse at different rates, kin competition within the philopatric sex biases intralocus conflict in favour of the more dispersive sex. Furthermore, kin competition diminishes the strength of balancing selection relative to genetic drift, reducing genetic variation in small subdivided populations. Meanwhile, by decreasing heterozygosity, inbreeding reduces the scope for sexually antagonistic polymorphism due to non-additive allelic effects, and this occurs to a greater extent on the X-chromosome than autosomes. Overall, our results demonstrate that spatial structure is an important factor in predicting where to expect sexually antagonistic alleles. We suggest that observed interspecific and intragenomic variation in sexual antagonism may be explained by sex-specific dispersal ecology and demography.

Quantitative genetics and fitness effects of basal metabolism

2012 ◽  
Vol 27 (2) ◽  
pp. 301-314 ◽  
Author(s):  
Zbyszek Boratyński ◽  
Esa Koskela ◽  
Tapio Mappes ◽  
Eero Schroderus
Keyword(s):  
Quantitative Genetics ◽  
Basal Metabolism ◽  
Fitness Effects

scholarly journals Correlational selection and genetic architecture promote the leaf economics spectrum in a perennial grass

2021 ◽  
Author(s):  
Robert W Heckman ◽  
Jason E Bonnette ◽  
Brandon E Campitelli ◽  
Philip A Fay ◽  
Thomas E Juenger
Keyword(s):  
Genetic Architecture ◽  
Panicum Virgatum ◽  
Perennial Grass ◽  
Multiple Traits ◽  
Evolutionary Mechanisms ◽  
Leaf Economics Spectrum ◽  
Correlational Selection ◽  
Common Gardens ◽  
Close Physical Proximity ◽  
Leaf Economics

The leaf economics spectrum (LES) is hypothesized to result from a trade-off between resource acquisition and conservation. Yet few studies have examined the evolutionary mechanisms behind the LES, perhaps because most species exhibit relatively specialized leaf economics strategies. In a genetic mapping population of the phenotypically diverse grass Panicum virgatum, we evaluate two interacting mechanisms that may drive LES evolution: 1) genetic architecture, where multiple traits are coded by the same gene (pleiotropy) or by genes in close physical proximity (linkage), and 2) correlational selection, where selection acts non-additively on combinations of multiple traits. We found evidence suggesting that shared genetic architecture (pleiotropy) controls covariation between two pairs of leaf economics traits. Additionally, at five common gardens spanning 17 degrees of latitude, correlational selection favored particular combinations of leaf economics traits. Together, these results demonstrate how the LES can evolve within species.

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.

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