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Related Concept Videos

Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Hardy-Weinberg Principle

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Complex population dynamics and the coalescent under neutrality.

Erik M Volz1

  • 1Department of Epidemiology, University of Michigan, Ann Arbor, Michigan 48109, USA. erik@erikvolz.info

Genetics
|November 2, 2011
PubMed
Summary
This summary is machine-generated.

Estimating effective population size (N(e)) is challenging with complex population dynamics. This study introduces a new coalescent model for accurate demographic history inference, improving population genetics analyses.

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Area of Science:

  • Population Genetics
  • Mathematical Biology
  • Ecology

Background:

  • Estimates of effective population size (N(e)) often poorly correlate with true population sizes.
  • Complex population dynamics, with time-varying birth and death rates, complicate demographic inference.
  • Nonparametric methods like skyline plots may fail to capture accurate demographic histories.

Purpose of the Study:

  • To develop a flexible coalescent model for inferring demographic history in populations with complex dynamics.
  • To provide a theoretical framework for integrating population genetics with mathematical population dynamics.

Main Methods:

  • A novel coalescent model is developed for populations described by deterministic nonlinear dynamical systems of arbitrary dimension.
  • The model accommodates time-varying birth/death rates, population structure, and heterochronous sampling.
  • Methods are derived for calculating coalescence rates, gene genealogy likelihoods, and simulating coalescent trees.

Main Results:

  • The developed model accurately reproduces demographic histories even with complex population dynamics.
  • It provides a unified framework applicable to diverse ecological and epidemiological models.
  • The approach facilitates the derivation of coalescence rates and likelihoods for complex demographic scenarios.

Conclusions:

  • Model-based estimation using this new coalescent framework offers improved efficiency over nonparametric methods for complex populations.
  • This theoretical advancement bridges population genetics with mathematical population dynamics.
  • The framework is valuable for understanding population size changes and evolutionary processes.