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Frequency-dependent Selection

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.

Hardy-Weinberg Principle

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.

Mutation, Gene Flow, and Genetic Drift

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).

Chi-square Analysis

Chi-square Analysis

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.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: May 8, 2026

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Causal association between genetically predicted circulating immune cell counts and frailty: a two-sample Mendelian

Xiao-Guang Guo¹, Ya-Juan Zhang², Ya-Xin Lu³

¹Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Frontiers in Immunology

|February 7, 2024

Summary

Genetically predicted higher eosinophil counts causally increase frailty risk, independent of other immune cells. Asthma and rheumatoid arthritis may mediate this link, suggesting targeted interventions for frailty prevention.

Keywords:

Mendelian randomization eosinophil count frailty index immune cell inflammation

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Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

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Published on: September 20, 2024

Area of Science:

Immunology
Gerontology
Genetic Epidemiology

Background:

The relationship between immune function and frailty is recognized but causal links remain unclear.
Previous observational studies show disputed associations between immune cell counts and frailty.
This study investigates the causal association between genetically predicted immune cell counts and frailty.

Purpose of the Study:

To determine the causal effect of genetically predicted circulating immune cell counts on frailty.
To identify potential mediating pathways in the association between immune cells and frailty.

Main Methods:

Two-sample Mendelian randomization (MR) study using genetic variants for six immune cell subtypes.
Frailty data assessed via frailty index (FI) from a large cohort.
Univariate, reverse, multivariate, and two-step MR analyses were performed.

Main Results:

Elevated genetically predicted eosinophil count was significantly associated with higher frailty index (FI).
This association remained significant in multivariate MR, indicating an independent causal effect.
Asthma and rheumatoid arthritis were identified as significant partial mediators (31.7% and 6.4% mediated, respectively).

Conclusions:

Genetically predicted eosinophil count has an independent causal effect on frailty.
Asthma and rheumatoid arthritis may partially mediate the causal pathway from eosinophils to frailty.
Further research into eosinophil function is crucial for developing frailty interventions.