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Related Concept Videos

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.
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).
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A spatial dirichlet process mixture model for clustering population genetics data.

Brian J Reich1, Howard D Bondell

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. reich@stat.ncsu.edu

Biometrics
|September 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian clustering method that combines genetic and spatial data. The approach effectively identifies homogeneous groups, as demonstrated by clustering wolverine populations.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Identifying homogeneous groups is crucial in population genetics.
  • Spatial information can enhance clustering algorithms.
  • Existing methods may not fully leverage combined genetic and spatial data.

Purpose of the Study:

  • To develop a novel Bayesian clustering algorithm incorporating both genetic and spatial information.
  • To classify individuals into homogeneous clusters for advanced population genetic studies.
  • To evaluate the performance of the proposed clustering method.

Main Methods:

  • Development of a Bayesian clustering algorithm utilizing a Dirichlet process prior.
  • Integration of genetic markers (microsatellite data) and spatial coordinates.
  • Performance evaluation through simulation studies.
  • Application to a real-world dataset of wolverines in Western Montana.

Main Results:

  • The proposed Bayesian method effectively utilizes both genetic and spatial data for clustering.
  • Simulation studies confirm the method's performance in identifying homogeneous groups.
  • Successful clustering of wolverine individuals in Western Montana was achieved.

Conclusions:

  • The developed Dirichlet process-based Bayesian algorithm offers an effective approach for population genetic clustering.
  • Combining genetic and spatial information improves the accuracy of individual classification.
  • The method has practical applications in wildlife population studies, such as wolverine research.