Related Experiment Video
Updated: May 19, 2026
06:18
Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
Published on: July 11, 2025
CLOSED-FORM ASYMPTOTIC SAMPLING DISTRIBUTIONS UNDER THE COALESCENT WITH RECOMBINATION FOR AN ARBITRARY NUMBER OF LOCI
1University of California, Berkeley.
Advances in Applied Probability
|August 4, 2012
Summary
This study develops a new combinatorial method to find exact mathematical formulas for multi-locus genetic sampling distributions. This advances understanding of population genetics, especially with recombination.
Area of Science:
- Population Genetics
- Theoretical Biology
- Computational Biology
Background:
- Deriving closed-form sampling distributions for the coalescent model with recombination is mathematically complex.
- Previous work focused on two-locus models using asymptotic series for moderate to high recombination rates.
- A generalizable method for an arbitrary number of loci was lacking.
Purpose of the Study:
- To develop a method for obtaining closed-form expressions for multi-locus sampling distributions.
- To extend existing frameworks to handle an arbitrary number of genetic loci.
- To provide universal expressions applicable across different mutation models.
Main Methods:
- Employed combinatorial approaches to analyze the multi-locus sampling distribution.
- Derived closed-form expressions for the initial terms of an asymptotic expansion.
- Focused on the coalescent model with recombination for an arbitrary number of loci.
Main Results:
- Successfully obtained closed-form expressions for the first few terms of the asymptotic expansion.
- Demonstrated the universality of these expressions, linking them to marginal one-locus distributions.
- The derived formulas are applicable to both finite- and infinite-alleles mutation models.
Conclusions:
- The combinatorial approach provides a powerful tool for tackling complex multi-locus population genetics problems.
- The derived expressions offer a significant advancement in calculating genetic sampling distributions.
- This work lays the foundation for more accurate modeling of genetic diversity and evolution.
Related Concept Videos
01:49Hardy-Weinberg Principle
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
01:26Distributions 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...
01:09Mutation, 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).
01:20Cluster 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...
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...
01:12Sampling 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...
02:53Conservative Site-specific Recombination and Phase Variation
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
The recognition sites for Cre recombinase called LoxP...