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

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Probability Laws01:49

Probability Laws

Overview

You might also read

Related Articles

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

Sort by
Same author

AI-enhanced Wrist-Hand US Image Acquisition: Development and Initial Clinical Evaluation.

Radiology·2026
Same author

Biodistribution and Semiquantitative Analysis of <sup>99m</sup>Tc-HYNIC-PSMA-11in Prostate Cancer Patients: A Retrospective Study.

Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, India·2025
Same author

Optimizing Low-Dose [18F]FDG-PET/CT Scans: Ensuring Quality Amid Radiotracer Availability Challenges - Insights from a Peripheral Tertiary Care Center.

Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, India·2025
Same author

Genome-wide association analyses identify distinct genetic architectures for age-related macular degeneration across ancestries.

Nature genetics·2024
Same author

Identifying X-chromosome variants associated with age-related macular degeneration.

Human molecular genetics·2024
Same author

Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.

Human molecular genetics·2024
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

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

Bayesian intervals for linkage locations.

Ritwik Sinha1, Robert P Igo, Shiv K Saini

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106-7281, USA.

Genetic Epidemiology
|February 6, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian method for intermediate fine mapping of disease genes using affected sibling pair data. This approach offers more precise and efficient genetic interval estimation compared to existing statistical methods.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Related Experiment Videos

Last Updated: Jun 25, 2026

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

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Intermediate fine mapping aims to refine disease-associated chromosomal regions identified in initial linkage studies.
  • Existing methods like LOD-support intervals and bootstrap can lack precision or efficiency in confidence interval construction.
  • Accurate genetic interval estimation is crucial for identifying disease susceptibility genes.

Purpose of the Study:

  • To propose a novel Bayesian method for constructing statistically precise and valid confidence intervals for disease gene locations.
  • To evaluate the performance of the proposed Bayesian method using affected sibling pair data.
  • To compare the efficiency of the Bayesian approach against existing intermediate fine mapping techniques.

Main Methods:

  • A Bayesian framework was employed, treating susceptibility gene location as a parameter with a uniform prior.
  • A Metropolis-Hastings algorithm was utilized to sample from the posterior distribution.
  • Highest posterior density intervals were constructed to estimate disease gene locations.

Main Results:

  • The proposed Bayesian method maintains correct coverage levels for confidence intervals.
  • Simulation studies and analysis of a rheumatoid arthritis dataset demonstrated improved efficiency compared to current methods.
  • The Bayesian intervals provide statistically precise and valid chromosomal regions for fine mapping.

Conclusions:

  • The novel Bayesian method offers a more efficient and accurate approach for intermediate fine mapping of disease genes.
  • This method enhances the precision of genetic interval estimation, aiding in susceptibility gene identification.
  • The findings suggest a valuable new tool for genetic research in complex diseases.