Genetic analysis of a population of Tribolium. IX. Maximization of population size and the concept of a stochastic equilibrium

Genome ◽  
1990 ◽  
Vol 33 (4) ◽  
pp. 571-580 ◽  
Author(s):  
Robert A. Desharnais ◽  
Brian Dennis ◽  
Robert F. Costantino
Keyword(s):  
Natural Selection ◽  
Population Size ◽  
Density Function ◽  
Corn Oil ◽  
Genetic Selection ◽  
Stochastic Theory ◽  
Gamma Density ◽  
Laboratory Populations ◽  
Experimental Treatments ◽  
Stochastic Population Model

Motivated by the genetic hypothesis that natural selection results in the maximization of the equilibrium population size, we quantified this latter equilibrium for laboratory populations of the flour beetle Tribolium castaneum, using the gamma probability density function. Gamma density functions were fitted to adult numbers for each of the experimental treatments that were started with frequencies of the corn oil sensitive (cos) allele in the range 0–1 at intervals of 0.1. The gamma density function adequately described all observed distributions. However, contrary to theory, statistical comparisons of the fitted distribution indicate that the polymorphic populations did not converge to the same identical distribution and that the polymorphic populations are intermediate in population size to the two homozygous groups. The need for a stochastic theory that combines both population size and genetic selection is discussed.Key words: natural selection, Tribolium, gamma distribution, maximum population size, stationary distribution, stochastic differential equation, stochastic population model.

scholarly journals The comparatives of growth and carcass performance of the Thai native chicken between economic selection (Chee KKU12) and natural selection (Chee N)

2020 ◽  
Vol 19 (2) ◽  
pp. 247-257
Author(s):  
Doungnapa Promket ◽  
◽  
Khanitta Ruangwittayanusorn ◽  
Keyword(s):  
Natural Selection ◽  
Growth Performance ◽  
Genetic Selection ◽  
High Growth ◽  
Carcass Composition ◽  
Daily Weight Gain ◽  
Carcass Quality ◽  
Economic Traits ◽  
Native Chicken ◽  
Carcass Performance

Chee is 1 of 4 important native chicken breed in Thailand. Genetic selection can be used to improve growth and carcass performance. The objective of this study was to compare growth and carcass performance of native chickens (Chee) with a population selected for economic traits (Chee KKU12) and natural selection (Chee N). Two hundred Chee KKU12 and Chee N chickens were divided into 5 replicates, 20 chickens per replication. Record growth and carcass quality for data analysis. The results showed that at 12 weeks of age, Chee KKU12 chickens (1,279.484 g) had a higher body weight than did Chee N chickens (1,180.212 g). The averages daily weight gain at 4-6, 6-8, and 0-12 weeks of age of Chee KKU12 chicken (17.861,19.230, and 14.843 grams) was higher than Chee N chickens (16.284, 17.497, and 13.668 g) (P<0.05). The carcass quality with mixed gender showed that Chee KKU12 chickens had higher breast (20.859%) and abdominal fat (0.659%) than Chee N chicken (19.585% and 0.217%, respectively) (P<0.05), but Chee KKU12 chicken thigh (17.007%) was significantly lower than Chee N chickens (18.627%). Regression analysis revealed that the selection of Chee KKU12 chickens for gain in weight will result in better carcass composition including wing (0.074 g), breast (0.089 g), and drumstick (0.134 g), while Chee N chicken had better thigh (0.189 g) when selected for high growth performance (P<0.05). It was concluded that chicken population selected for economic traits has a better growth performance in open housing conditions than naturally selected chickens.

scholarly journals Numerical simulation of the two-locus Wright-Fisher stochastic differential equation with application to approximating transition probability densities

2020 ◽  
Author(s):  
Zhangyi He ◽  
Mark Beaumont ◽  
Feng Yu
Keyword(s):  
Differential Equation ◽  
Natural Selection ◽  
Probability Density Function ◽  
Stochastic Differential Equation ◽  
Probability Density ◽  
Density Function ◽  
Genetic Recombination ◽  
Transition Probability ◽  
Superior Performance ◽  
Transition Probability Density

AbstractOver the past decade there has been an increasing focus on the application of the Wright-Fisher diffusion to the inference of natural selection from genetic time series. A key ingredient for modelling the trajectory of gene frequencies through the Wright-Fisher diffusion is its transition probability density function. Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time, which presents opportunities for investigating natural selection while accounting for genetic recombination and local linkage. However, most existing methods for computing the transition probability density function of the Wright-Fisher diffusion are only applicable to one-locus problems. To address two-locus problems, in this work we propose a novel numerical scheme for the Wright-Fisher stochastic differential equation of population dynamics under natural selection at two linked loci. Our key innovation is that we reformulate the stochastic differential equation in a closed form that is amenable to simulation, which enables us to avoid boundary issues and reduce computational costs. We also propose an adaptive importance sampling approach based on the proposal introduced by Fearnhead (2008) for computing the transition probability density of the Wright-Fisher diffusion between any two observed states. We show through extensive simulation studies that our approach can achieve comparable performance to the method of Fearnhead (2008) but can avoid manually tuning the parameter ρ to deliver superior performance for different observed states.

The Price Equation

2015 ◽  
Author(s):  
James A.R. Marshall
Keyword(s):  
Natural Selection ◽  
Replicator Dynamics ◽  
Genetic Selection ◽  
Single Locus ◽  
Price Equation ◽  
General Description ◽  
Conceptual Tool ◽  
Evolutionary Selection ◽  
Selective Regime ◽  
Fisher’S Fundamental Theorem

This chapter considers a general description of natural selection: the Price equation. Developed by George Price in the late 1960s, the Price equation can be applied to the change of any quantity under any selective regime. It is thus not limited to considering simple haploid single-locus traits, unlike the replicator dynamics, and indeed it is not even limited to considering evolutionary selection. The Price equation provides an instantaneous description of selection in action. The simplicity of the equation makes it a useful conceptual tool for understanding selective processes such as natural selection. The chapter first describes the general Price equation before discussing its use to understand genetic selection. It then shows how the Price equation can be used to derive two classical results from population and quantitative genetics: Fisher's “fundamental theorem of natural selection” and the breeder's equation.

The evolution of animal senescence

1984 ◽  
Vol 62 (9) ◽  
pp. 1661-1667 ◽  
Author(s):  
Michael R. Rose
Keyword(s):  
Natural Selection ◽  
Group Selection ◽  
Antagonistic Pleiotropy ◽  
Physiological Mechanisms ◽  
Beneficial Effects ◽  
Laboratory Populations

Senescence, the endogenous deterioration of health at later ages, can be explained in terms of evolution. Senescence is not due to group selection but to the decline with age in the force of natural selection acting on individuals. This decline allows the spread of alleles with deleterious effects on late health. Such alleles do not appear to have effects confined to later ages. Instead, they are favoured by natural selection because of beneficial effects at early ages, in spite of later deleterious effects due to antagonistic pleiotropy. Manipulation of laboratory populations of Drosophila has shown that senescence can be postponed using selection. There are no absolute, universal, physiological causes of senescence. Laboratory populations with genetically postponed senescence can be used to study proximate physiological mechanisms of senescence in animals.

scholarly journals Selection in a growing bacterial/yeast colony biases results of mutation accumulation experiments

2021 ◽  
Author(s):  
Anjali Mahilkar ◽  
Sharvari Kemkar ◽  
Supreet Saini
Keyword(s):  
Natural Selection ◽  
Population Size ◽  
Effective Population Size ◽  
Colony Growth ◽  
Mutation Accumulation ◽  
Rate Of Change ◽  
Raw Material ◽  
Effective Population ◽  
The Face ◽  
Mean Fitness

AbstractMutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments performed with organisms such asDrosophilahave an effective population size of one. However, in MA experiments with bacteria and yeast, a single founder is allowed to grow to a size of a colony (~108). The effective population size in these experiments is of the order of 10. In this scenario, while it is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth and therefore, the MA experiment; this effect has not been tested explicitly. In this work, we simulate colony growth and perform an MA experiment, and demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over represented by a factor greater than two. The DFE of beneficial and deleterious mutations are accurately captured in an MA experiment. We show that the effect of selection in a growing colony varies non-monotonically and that, in the face of natural selection dictating an MA experiment, estimates of mutation rate of an organism is not trivial. We perform experiments with 160 MA lines ofE. coli, and demonstrate that rate of change of mean fitness is a non-monotonic function of the colony size, and that selection acts differently in different sectors of a growing colony. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

scholarly journals Nucleotide variation and balancing selection at the Ckma gene in Atlantic cod: Analysis with multiple merger coalescent models

2014 ◽  
Author(s):  
Einar Árnason ◽  
Katrín Halldórsdóttir
Keyword(s):  
Natural Selection ◽  
Population Size ◽  
Time Scales ◽  
High Frequency ◽  
Atlantic Cod ◽  
Balancing Selection ◽  
Reproductive Capacity ◽  
Parameter Estimates ◽  
Nucleotide Variation ◽  
High Fecundity

A high-fecundity organisms, such as Atlantic cod, can withstand substantial natural selection and can at any time simultaneously replace alleles at a number of loci due to their excess reproductive capacity. High-fecundity organisms may reproduce by sweepstakes leading to highly skewed heavy-tailed offspring distribution. Under such reproduction the Kingman coalescent of binary mergers breaks down and models of multiple merger coalescent are more appropriate. Here we study nucleotide variation at the Ckma (Creatine Kinase Muscle type A) gene in Atlantic cod. The gene shows extreme differentiation between the North (Canada, Greenland, Iceland, Norway, Barents Sea) and the South (Faroe Islands, North-, Baltic-, Celtic-, and Irish Seas) with a between regions FST > 0.8 whereas neutral loci show no differentiation. This is evidence for natural selection. The protein sequence is conserved by purifying selection whereas silent and non-coding sites show extreme differentiation. Relative to outgroup the site-frequency spectrum has three modes, a mode at singleton sites and two high frequency modes at opposite frequencies representing divergent branches of the gene genealogy that is evidence for balancing selection. Analysis with multiple-merger coalescent models can account for the high frequency of singleton sites and indicate reproductive sweepstakes. Coalescent time scales with population size and with the inverse of variance in offspring number. Parameter estimates using multiple-merger coalescent models show fast time-scales. Time-scales of mitochondrial DNA are about square root of the effective population size and time-scales of nuclear genes are much faster.

scholarly journals Demography and natural selection have shaped genome-wide variation in the widely distributed conifer Norway Spruce (Picea abies)

2019 ◽  
Author(s):  
Xi Wang ◽  
Carolina Bernhardsson ◽  
Pär K. Ingvarsson
Keyword(s):  
Genetic Diversity ◽  
Natural Selection ◽  
Picea Abies ◽  
Population Size ◽  
Effective Population Size ◽  
Norway Spruce ◽  
Demographic History ◽  
Sequencing Data ◽  
Effective Population ◽  
Genome Wide

AbstractUnder the neutral theory, species with larger effective population sizes are expected to harbour higher genetic diversity. However, across a wide variety of organisms, the range of genetic diversity is orders of magnitude more narrow than the range of effective population size. This observation has become known as Lewontin’s paradox and although aspects of this phenomenon have been extensively studied, the underlying causes for the paradox remain unclear. Norway spruce (Picea abies) is a widely distributed conifer species across the northern hemisphere and it consequently plays a major role in European forestry. Here, we use whole-genome re-sequencing data from 35 individuals to perform population genomic analyses in P. abies in an effort to understand what drives genome-wide patterns of variation in this species. Despite having a very wide geographic distribution and an enormous current population size, our analyses find that genetic diversity of P.abies is low across a number of populations (p=0.005-0.006). To assess the reasons for the low levels of genetic diversity, we infer the demographic history of the species and find that it is characterised by several re-occurring bottlenecks with concomitant decreases in effective population size can, at least partly, provide an explanation for low polymorphism we observe in P. abies. Further analyses suggest that recurrent natural selection, both purifying and positive selection, can also contribute to the loss of genetic diversity in Norway spruce by reducing genetic diversity at linked sites. Finally, the overall low mutation rates seen in conifers can also help explain the low genetic diversity maintained in Norway spruce.

Why Ecology and Evolution Occupy Distinct Epistemic Niches

2019 ◽  
Vol 47 (1) ◽  
pp. 143-165
Author(s):  
Stefan Linquist ◽  
Keyword(s):  
Natural Selection ◽  
Population Size ◽  
Effective Population Size ◽  
Rapid Evolution ◽  
Directional Selection ◽  
Effective Population ◽  
Trait Variation ◽  
Ecological Relationships ◽  
Generally True ◽  
Evolutionary Explanations

Recent examples of rapid evolution under natural selection seem to require that the disciplines of ecology and evolution become better integrated. This inference makes sense only if one’s understanding of these disciplines is based on Hutchinson’s two-speed model of the ecological theater and the evolutionary play. Instead, these disciplines are more accurately viewed as occupying distinct “epistemic niches.” When so understood, we see that rapid evolution under selection, even if it is generally true, does not imply that evolutionary explanations are improved by the inclusion of ecological details. Nor are ecological explanations necessarily improved by the inclusion of information about trait variation, heritability, effective population size, or other standard evolutionary factors. To illustrate, I develop a version of Kitcher’s (1984) “gory details” argument to show that, even for some trait that is under strong directional selection, a dynamically sufficient explanation of its ecological relationships should ignore most of the information explaining why that trait is evolving. The wholesale integration of ecology and evolution looks even less appealing when empirical sufficiency, a purely practical requirement, is taken into account. As a way forward, I propose an eco-evo partitioning framework. This strategy enables researchers to estimate the empirical sufficiency of a purely ecological, a purely evolutionary, or a combined eco-evo approach.

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