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Related Concept Videos

Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Hardy-Weinberg Principle01:49

Hardy-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.In the early 20th century,...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
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Estimation of selection intensity under overdominance by Bayesian methods.

Erkan Ozge Buzbas1, Paul Joyce, Zaid Abdo

  • 1University of Idaho, USA. buzbas@gmail.com

Statistical Applications in Genetics and Molecular Biology
|July 4, 2009
PubMed
Summary

Researchers developed new Bayesian methods to estimate selection intensity under overdominance, a type of genetic selection. These methods are applied to the Human Leukocyte Antigen (HLA) loci, advancing population genetics inference.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Statistical Genetics

Background:

  • Balanced allele frequencies can indicate selection, particularly overdominance.
  • Existing methods lack likelihood-based approaches for inferring selection intensity.
  • Overdominance is of interest in population genetics but difficult to quantify.

Purpose of the Study:

  • To introduce novel Bayesian methods for estimating selection intensity under overdominance.
  • To develop a flexible framework accommodating multiple loci and alleles.
  • To provide tools for robust inference in population genetics.

Main Methods:

  • Developed Bayesian inference framework for k-allele models with overdominance.
  • Incorporated hierarchical modeling for multilocus selection intensity estimation.
  • Modeled neutral and selected variability within loci using k-allele models.

Main Results:

  • Successfully estimated selection intensity under overdominance using Bayesian approaches.
  • Demonstrated the utility of the hierarchical model for multilocus analysis.
  • Applied the methods to worldwide Human Leukocyte Antigen (HLA) data.

Conclusions:

  • The proposed Bayesian methods offer a powerful new tool for selection intensity inference.
  • The framework is applicable to complex genetic systems like the HLA loci.
  • Advances understanding of evolutionary forces shaping genetic variation.