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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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...
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,...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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)...

You might also read

Related Articles

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

Sort by
Same author

Repurposing advanced DMEM/F-12 cell culture medium to mitigate glycerol toxicity in poultry semen.

British poultry science·2026
Same author

Validation of the traditional Chinese version of the Menopausal Rating Scale with WHOQOL-BREF.

Climacteric : the journal of the International Menopause Society·2015
Same author

Association of older paternal age with earlier onset among co-affected schizophrenia sib-pairs.

Psychological medicine·2015
Same author

The impact of plasma Epstein-Barr virus DNA and fibrinogen on nasopharyngeal carcinoma prognosis: an observational study.

British journal of cancer·2014
Same author

Role of 15-F2t-isoprostane in intestinal injury induced by intestinal ischemia/reperfusion in rats.

Free radical research·2014
Same author

The G-protein-coupled estrogen receptor agonist G-1 suppresses proliferation of ovarian cancer cells by blocking tubulin polymerization.

Cell death & disease·2013
Same journal

Utility of Urine-Derived Cells for Characterizing Aberrant Splicing Caused by a Novel Deep Intronic L1CAM Variant.

Annals of human genetics·2026
Same journal

Distribution of HLA-DRB1 Alleles and Genotypes With Respect to Plasma Anti-SARS-CoV-2 IgG Titres Among COVID-19-Vaccinated Bangladeshi Adults.

Annals of human genetics·2026
Same journal

FIGLA Novel Variant c.385-9G>A Affects RNA Splicing in a Minigene Assay.

Annals of human genetics·2026
Same journal

Epigenetic Shifts in MTNR1A, MTNR1B and Fn14 and Their Links to Preeclampsia Risk.

Annals of human genetics·2026
Same journal

Hip Bone Marrow Adiposity as a Risk Factor for Alzheimer's Disease: Insights From Mendelian Randomization Analysis.

Annals of human genetics·2026
Same journal

A Novel Biallelic REL Frameshift Variant p.(Tyr9Ilefs*2) Causing Immunodeficiency-92 With Profound c-Rel Deficiency.

Annals of human genetics·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 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

A Bayesian spatial multimarker genetic random-effect model for fine-scale mapping.

M-Y Tsai1, C K Hsiao, S-H Wen

  • 1Institute of Statistics and Information Science, College of Science, National Changhua University of Education.

Annals of Human Genetics
|June 25, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel genetic random effects model to improve disease gene localization by accounting for marker correlations. The proposed model offers more precise estimates and better performance than traditional single-locus analyses.

More Related Videos

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

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Related Experiment Videos

Last Updated: Jul 4, 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

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

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Linkage disequilibrium (LD) analysis commonly uses multiple markers to pinpoint disease gene locations.
  • Simultaneous contributions of multiple markers to disease etiology are often overlooked in traditional methods.

Purpose of the Study:

  • To propose a genetic random effects model that incorporates spatial structures to account for marker dependence.
  • To enhance the accuracy of disease gene localization by modeling correlations between loci.

Main Methods:

  • Developed a genetic random effects model integrating spatial relationships (Relative Distance Function - RDF and Exponential Decay Function - EDF).
  • Employed Bayesian inference with Markov chain Monte Carlo (MCMC) sampling for parameter estimation.
  • Validated the model using two real datasets and simulation studies.

Main Results:

  • The proposed spatial correlation models outperformed single-locus analysis in disease gene localization.
  • The Relative Distance Function (RDF) model provided more precise disease locus estimates, especially with dense markers.
  • Simulation studies confirmed unbiased genetic parameter estimates and improved confidence interval coverage with the spatial correlation structure.

Conclusions:

  • The novel genetic random effects model effectively captures marker dependence through spatial structures.
  • This approach offers a significant improvement over single-locus methods for disease gene mapping.
  • The model provides more reliable and precise localization of disease-associated genes.