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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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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.
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Expected Frequencies in Goodness-of-Fit Tests01:19

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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).
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Identifying Statistically Significant Differences: The F-Test01:14

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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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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.
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F Distribution01:19

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The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
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Chi-square Analysis02:46

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The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
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Evolution of quantitative traits with background selection.

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Related Experiment Video

Updated: Mar 24, 2026

A Quantitative Fitness Analysis Workflow
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The B -value calculator: expected diversity under background selection.

Jacob I Marsh1, Austin T Daigle1,2,3, Parul Johri1,2,4

  • 1Department of Biology, University of North Carolina, Chapel Hill, NC 27599.

Biorxiv : the Preprint Server for Biology
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

Background selection (BGS) shapes genomic diversity. We developed Bvalcalc, a Python tool, to analytically estimate B-values, crucial for understanding evolutionary processes and detecting selection across genomes.

Keywords:
B-valuebackground selectiondemographic inferencegenetic diversity

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Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Genomics

Background:

  • Background selection (BGS) is a significant evolutionary force impacting genomic diversity.
  • Accurate estimation of B-values (diversity under BGS vs. neutrality) is vital for genomic inference.

Purpose of the Study:

  • To develop an efficient analytical method for calculating B-values.
  • To provide a user-friendly tool for estimating genomic diversity under BGS.

Main Methods:

  • Extended and integrated existing theory to analytically estimate B-values without selective interference.
  • Developed Bvalcalc, a Python command-line tool for genome-wide B-value calculation at single base-pair resolution.
  • Incorporated modules for recombination maps, gene conversion, self-fertilization, population size changes, and unlinked effects.

Main Results:

  • Bvalcalc enables efficient analytical calculation of expected B-values across the genome.
  • The tool accounts for various evolutionary factors including recombination and population structure.
  • Validated Bvalcalc against simulations and generated B-maps for human, fruit fly, and Arabidopsis.

Conclusions:

  • Bvalcalc provides a robust method for estimating genomic diversity shaped by background selection.
  • The tool facilitates null model development for demographic inference and selection detection.
  • Bvalcalc is publicly available, supporting broader research in population genetics and evolutionary genomics.