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

Polygenic Traits01:18

Polygenic Traits

11.5K
11.5K
Polygenic Traits01:18

Polygenic Traits

70.7K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
70.7K
Probability Laws01:49

Probability Laws

45.2K
Overview
45.2K
Quadratic Models01:23

Quadratic Models

336
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
336
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
Multiple Allele Traits01:49

Multiple Allele Traits

39.0K
The Concept of Multiple Allelism
39.0K

You might also read

Related Articles

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

Sort by
Same author

State Alcohol Policies Most Strongly Associated With College Students' Drinking, Driving Risks, and Alcohol-Related Harm.

American journal of preventive medicine·2026
Same author

A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data.

ArXiv·2026
Same author

Genetic associations with longevity in a Calabrian cohort: an exploratory genome-wide study.

GeroScience·2026
Same author

Metabolomic signatures of extreme old age: findings from the New England Centenarian Study.

GeroScience·2026
Same author

Relations between depression and cannabis use among college students in evolving state cannabis policy environments.

Drug and alcohol dependence·2026
Same author

Identification of novel plasma proteomic biomarkers of Dupuytren disease.

PloS one·2026

Related Experiment Video

Updated: Apr 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits.

Harold Bae1, Thomas Perls2, Martin Steinberg3

  • 1Department of Biostatistics, Boston University School of Public Health.

Bayesian Analysis
|June 2, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework to efficiently select the best genetic model for association studies. The method simultaneously fits five common genetic models, improving computational efficiency and accuracy in genetic analysis.

Keywords:
Bayesian model selectionGWASadditiveco-dominantdominantmarginal likelihoodparameterizationrecessive

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Apr 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genetic association studies commonly employ five distinct models: genotypic, additive, dominant, co-dominant, and recessive.
  • Selecting the most appropriate genetic model is crucial for accurate association analysis.
  • Existing methods may lack computational efficiency or comprehensive model comparison.

Purpose of the Study:

  • To develop a unified Bayesian framework for selecting the most likely genetic model.
  • To enhance computational efficiency by simultaneously fitting multiple genetic models.
  • To provide a robust method for genetic model selection in association studies.

Main Methods:

  • A polynomial parameterization of genetic data is utilized to fit five genetic models concurrently.
  • A closed-form expression for the marginal likelihood is derived for normally distributed data.
  • The proposed Bayesian framework is evaluated using both simulated and real genome-wide datasets.

Main Results:

  • The proposed Bayesian framework offers a coherent and computationally efficient approach to genetic model selection.
  • Simultaneous fitting of multiple models streamlines the analysis process.
  • Performance evaluation demonstrates the method's efficacy on simulated and real-world genetic data.

Conclusions:

  • The developed Bayesian framework provides a superior method for selecting the most appropriate genetic model in association studies.
  • This approach enhances analytical efficiency and accuracy in genetic data interpretation.
  • The method is broadly applicable to genome-wide association studies for improved biological insights.