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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...

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

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Published on: February 3, 2023

quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a

Samuel Neuenschwander1, Frédéric Hospital, Frédéric Guillaume

  • 1Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland. samuel.neuenschwander@unil.ch

Bioinformatics (Oxford, England)
|May 3, 2008
PubMed
Summary

quantiNemo is a flexible simulation program for studying quantitative genetics. It models selection, mutation, recombination, and drift in structured populations, aiding research on trait evolution.

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Area of Science:

  • Population genetics
  • Quantitative genetics
  • Evolutionary biology

Background:

  • Quantitative genetics research requires sophisticated simulation tools.
  • Existing software may lack flexibility in modeling complex scenarios.

Purpose of the Study:

  • To introduce quantiNemo, a novel simulation program.
  • To investigate the effects of evolutionary forces on quantitative traits.

Main Methods:

  • Individual-based, genetically explicit stochastic simulation.
  • Object-oriented programming in C++ for flexibility.
  • Modeling of selection, mutation, recombination, and drift.

Main Results:

  • quantiNemo allows detailed investigation of trait architecture.
  • The program accommodates structured populations, migration, and heterogeneous environments.
  • High flexibility in population, selection, and genetic map parameters.

Conclusions:

  • quantiNemo provides a powerful and adaptable platform for quantitative genetics research.
  • Its design facilitates studies on the evolution of complex traits.