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Related Concept Videos

Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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...
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).
Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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...

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Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
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Published on: May 13, 2012

A sequentially Markov conditional sampling distribution for structured populations with migration and recombination.

Matthias Steinrücken1, Joshua S Paul, Yun S Song

  • 1Department of Statistics, University of California, Berkeley, CA 94720, USA. steinrue@stat.berkeley.edu

Theoretical Population Biology
|September 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational framework for population genetics, extending conditional sampling distributions (CSDs) to model migration between populations. This allows for more accurate inference of population structure and migration rates in genomic analyses.

Keywords:
Conditional sampling distributionHidden Markov modelMigrationRecombinationSequentially Markov coalescentStructured coalescent

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Genomic Analysis

Background:

  • Conditional sampling distributions (CSDs) are fundamental to population genomic analyses, particularly for inferring population structure.
  • Existing CSD frameworks have not adequately incorporated the explicit exchange of migrants between populations in a principled manner.
  • Recent advancements introduced sequentially Markov CSDs for single panmictic populations, approximating diffusion processes related to coalescent theory.

Purpose of the Study:

  • To extend the sequentially Markov CSD framework to accommodate subdivided population structures.
  • To develop an efficiently computable CSD that provides a genealogical interpretation linked to structured coalescent models with migration and recombination.
  • To demonstrate the utility of the developed CSD for accurate estimation of migration rates.

Main Methods:

  • Extension of the sequentially Markov CSD framework to model population subdivision.
  • Integration of migration and recombination processes within the CSD.
  • Empirical validation of the CSD's performance in estimating migration rates.

Main Results:

  • An efficiently computable CSD was developed for subdivided populations.
  • The new CSD framework admits a genealogical interpretation aligned with structured coalescent theory.
  • Empirical results confirmed accurate estimation of diverse migration rates using the developed CSD.

Conclusions:

  • The extended sequentially Markov CSD framework effectively models migration in subdivided populations.
  • This approach offers a principled and computationally efficient method for population genomic analyses.
  • The CSD provides a valuable tool for inferring migration rates and understanding population structure.