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Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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.
William S. Gosset (1876–1937) of the Guinness...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Mutation, Gene Flow, and Genetic Drift01:09

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

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Related Experiment Video

Updated: Jun 10, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference.

Jørgen Ødegård1, Theo H E Meuwissen, Bjørg Heringstad

  • 1Nofima Marin, NO-1432 As, Norway. jorgen.odegard@nofima.no

Genetics, Selection, Evolution : GSE
|July 24, 2010
PubMed
Summary
This summary is machine-generated.

A new Gibbs sampling algorithm corrects biased heritability estimates in animal threshold models for binary traits. This method improves genetic parameter estimation and speeds up analysis, offering an advantage for various animal models.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Related Experiment Videos

Last Updated: Jun 10, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

  • Quantitative Genetics
  • Statistical Genetics
  • Animal Breeding

Background:

  • Standard animal threshold models often yield biased heritability estimates for binary traits with single observations per animal.
  • Existing models like sire or sire-dam models are unsuitable when individual records on parents are available.

Purpose of the Study:

  • To develop a novel Gibbs sampling algorithm for accurate estimation of genetic (co)variance components within animal threshold models.
  • To address the limitations of current models in genetic analysis of binary traits.

Main Methods:

  • Classified individuals as 'informative' or 'non-informative' based on Mendelian sampling deviations.
  • Inferred genetic (co)variance components from informative individuals, analogous to sire-dam models when parent records are absent.
  • Sampled breeding values using the full relationship matrix, similar to standard animal models.

Main Results:

  • The new algorithm produced accurate parameter estimates, comparable to sire-dam models, unlike standard models which failed on simulated data.
  • Demonstrated significantly faster Markov chain mixing properties for genetic parameters compared to standard methods.
  • Successfully estimated genetic variance without drifting towards infinity, a common issue with standard models.

Conclusions:

  • The developed Gibbs sampling algorithm effectively resolves bias issues in animal threshold model analyses for binary traits.
  • The algorithm enhances Gibbs sampler efficiency for genetic parameters, beneficial for linear and non-linear animal models.
  • Provides a robust method for estimating genetic parameters in complex trait analyses.