Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

394
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
394
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

743
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
743
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.6K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.6K
Moment of Inertia01:14

Moment of Inertia

19.6K
The comparability between linear and angular velocities, linear and angular accelerations, and the kinematic equations of translational and rotational motion can be extended to the concept of inertia.
If a rigid body is rotating about an axis but is not in translational motion, its translational kinetic energy is zero. However, since each particle undergoes rotational motion, it possesses non-zero velocity and kinetic energy. Thus, the kinetic energy of the rigid body, which is the sum of the...
19.6K
Principal Moments of Area01:14

Principal Moments of Area

1.7K
In mechanics, the product of inertia and moments of inertia of area help to calculate the stability and performance of various structures and components. The coordinate transformation relations are used to calculate the moments and products of inertia for an area about the inclined axes. Further, the moments and products of inertia with respect to the principal axes can be determined using the moments and products of inertia about the inclined axes.
The principal moment of inertia axes are the...
1.7K
Moment-Area Theorems01:17

Moment-Area Theorems

716
The Moment-Area Theorem is crucial in structural engineering for analyzing beam bending, particularly in applications like building floor supports. This theorem utilizes the geometric properties of the elastic curve, which depicts how a beam deforms under load, to simplify the calculations of deflections and slopes.
The theorem is divided into two parts. The first part connects the angle between tangents at any two points on the beam's elastic curve to the area under a curve derived by...
716

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhancement of hidden Markov model analyses for improved inference of archaic introgression in modern humans.

Molecular biology and evolution·2026
Same author

Clinical Intervention Following Detection of Incident Diabetes Within a Large Psychiatric Service System: A Chart Review Study.

Acta psychiatrica Scandinavica·2026
Same author

A transdiagnostic sleep intervention for outpatients with sleep problems and comorbid mental health disorders, data from a Danish quality assurance project.

Nordic journal of psychiatry·2025
Same author

Estimating Gene Conversion Tract Length and Rate From PacBio HiFi Data.

Molecular biology and evolution·2025
Same author

Estimating evolutionary and demographic parameters via ARG-derived IBD.

PLoS genetics·2025
Same author

Genetic liability to bipolar disorder and body mass index: A bidirectional two-sample Mendelian randomization study.

Bipolar disorders·2022

Related Experiment Video

Updated: Jan 29, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.6K

A general framework for moment-based analysis of genetic data.

Maria Simonsen Speed1,2, David Joseph Balding3, Asger Hobolth4

  • 1Bioinformatics Research Centre, Aarhus University, 8000, Aarhus, Denmark. maria@birc.au.dk.

Journal of Mathematical Biology
|February 9, 2019
PubMed
Summary
This summary is machine-generated.

The Dirichlet model is a common approximation for allele fractions in population genetics but is often inaccurate. This study shows that alternative models are better suited for various mutation processes.

Keywords:
Allele fractionBeta–DirichletDiffusionDirichletDistribution of allele fractionsEvolutionary historyHierarchical BetaMomentsMulti-allelic Wright–FisherMutation processesPyramid

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

535

Related Experiment Videos

Last Updated: Jan 29, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

535

Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • The Dirichlet (Balding-Nichols) model is a long-standing approximation for allele fraction distributions in multi-allelic populations.
  • Previous work has noted limitations of the Dirichlet model, particularly its inability to account for positive correlations among alleles.
  • A systematic investigation into the validity of the Dirichlet distribution across different mutation models has been lacking.

Purpose of the Study:

  • To provide a comprehensive overview of modeling allele fraction data under various multi-allelic mutation structures.
  • To systematically evaluate the accuracy of the Dirichlet model and explore alternative models for allele fraction distributions.
  • To propose new models that better capture allele fraction dynamics under different mutation processes.

Main Methods:

  • Simulating allele fractions using a diffusion approximation of the multi-allelic Wright-Fisher process with mutation.
  • Applying a moment-based analysis to compare model performance.
  • Investigating the suitability of the Dirichlet model and proposing alternative distributions like Beta-Dirichlet and Hierarchical Beta models.

Main Results:

  • The optimal model for allele fraction distribution is contingent upon the specific mutation process.
  • The Dirichlet model is a good approximation only for pure drift, Jukes-Cantor, and parent-independent mutation processes with low mutation rates.
  • Alternative models, including Beta-Dirichlet and Hierarchical Beta models, are necessary for other mutation processes.

Conclusions:

  • The Dirichlet model's applicability is limited to specific, simple mutation scenarios.
  • New models, such as the Pyramidal Hierarchical Beta model, are developed for more complex mutation processes like generalized time-reversible and single-step mutations.
  • Accurate modeling of allele fractions requires selecting models tailored to the underlying evolutionary and mutational dynamics.