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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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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...
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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,...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Unbiased relatedness estimation in structured populations.

Jinliang Wang1

  • 1Institute of Zoology, Zoological Society of London, London NW1 4RY, United Kingdom. jinliang.wang@ioz.ac.uk

Genetics
|January 8, 2011
PubMed
Summary

Estimating genetic relatedness using genetic markers can be biased when populations have structures. A direct approach using subpopulation allele frequencies is more accurate than indirect methods, even with small samples.

Area of Science:

  • Population genetics
  • Quantitative genetics
  • Conservation genetics

Background:

  • Estimating genetic relatedness is crucial for various biological research fields.
  • Existing relatedness estimators assume large, random-mating populations, often violated in real-world scenarios.
  • Population structures, due to geographic or social factors, can significantly impact genetic relatedness estimations.

Purpose of the Study:

  • To investigate two distinct approaches for estimating genetic relatedness between individuals within a subpopulation.
  • To evaluate the accuracy and bias of these approaches under conditions with population structure.
  • To identify potential modifications for improving the accuracy of relatedness estimators.

Main Methods:

  • Simulations were employed to compare two relatedness estimation approaches: indirect (using whole population allele frequencies) and direct (using subpopulation allele frequencies).

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  • The indirect approach was analyzed both with and without accounting for population structure using Wright's F(st).
  • The performance of estimators was assessed using small sample sizes for allele frequency and relatedness estimation.

    • Main Results:

    • Widely used relatedness estimators show upward bias when employing the indirect approach without accounting for population structure.
    • Modifying indirect estimators with Wright's F(st) reduces bias but results in inferior accuracy compared to the direct approach.
    • The direct approach, utilizing subpopulation allele frequencies, demonstrates superior accuracy and robustness, even with limited sample data.

    Conclusions:

    • The direct approach for estimating genetic relatedness within subpopulations is more reliable than indirect methods when population structure is present.
    • Accounting for population structure can mitigate bias in indirect estimators, but they remain less accurate.
    • Researchers should favor the direct approach for accurate genetic relatedness estimation in structured populations.