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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

725
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
725
Multiple Allele Traits01:49

Multiple Allele Traits

39.2K
The Concept of Multiple Allelism
39.2K
Multiple Allele Traits01:49

Multiple Allele Traits

15.2K
15.2K
X-linked Traits01:19

X-linked Traits

60.0K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
60.0K
X-linked Traits01:19

X-linked Traits

8.0K
8.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

8.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Neurofeedback as an Adjunct to Pharmacotherapy in OCD: A Randomized Controlled Trial on Symptom Reduction.

Applied psychophysiology and biofeedback·2026
Same author

Examining the relationship between internet addiction and the willingness to continue living, mediated by life satisfaction and negative suicidal ideation, with depression as a mediator.

International journal of adolescent medicine and health·2025
Same author

Zero-Inflated Count Regression Models in Solving Challenges Posed by Outlier-Prone Data; an Application to Length of Hospital Stay.

Archives of academic emergency medicine·2024
Same author

Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure.

Journal of preventive medicine and hygiene·2023
Same author

Balance-energy of resting state network in obsessive-compulsive disorder.

Scientific reports·2023
Same author

Pattern of weight gain in pregnant women in slum areas of Hamadan using multilevel ordinal regression.

BMC public health·2023

Related Experiment Video

Updated: Apr 16, 2026

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

17.0K

Two-part zero-inflated negative binomial regression model for quantitative trait loci mapping with count trait.

Abbas Moghimbeigi1

  • 1Modeling of Noncommunicable Disease Research Canter, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Journal of Theoretical Biology
|March 3, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a two-part zero-inflated negative binomial (ZINB) model for quantitative trait locus (QTL) mapping of count data. This approach effectively addresses over-dispersion and excess zeros in genetic studies, improving association detection.

Keywords:
Cholesterol gallstoneExtra zeroNegative binomial regression modelQTL mapping

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

40.1K
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

5.1K

Related Experiment Videos

Last Updated: Apr 16, 2026

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

17.0K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

40.1K
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

5.1K

Area of Science:

  • Genetics
  • Biostatistics
  • Statistical Genetics

Background:

  • Standard Poisson regression models are commonly used for quantitative trait locus (QTL) mapping of count traits.
  • Count traits frequently exhibit over-dispersion, deviating from the Poisson distribution assumptions.
  • Zero-inflated models like ZIP, ZIGP, and ZINB offer alternatives for over-dispersed count data in QTL analysis.

Purpose of the Study:

  • To address challenges in QTL mapping of over-dispersed count traits with excess zeros.
  • To apply a two-part zero-inflated negative binomial (ZINB) model for enhanced QTL mapping.
  • To investigate the association between gallstone formation and genetic marker genotypes using this model.

Main Methods:

  • Utilized a two-part zero-inflated negative binomial (ZINB) model.
  • Employed the Expectation-Maximization (EM) algorithm with the Newton-Raphson method for parameter estimation.
  • Applied the model to detect associations between gallstone formation and marker genotypes.

Main Results:

  • The two-part ZINB model was successfully applied for QTL mapping.
  • The model demonstrated utility in detecting associations between gallstone formation and specific genotypes.
  • Genetic variables added to the negative binomial component influenced the excess zero data.

Conclusions:

  • The two-part ZINB model provides a robust framework for QTL mapping of count traits, especially those with over-dispersion and excess zeros.
  • This methodology enhances the ability to detect genetic associations relevant to complex traits like gallstone formation.
  • The study highlights the importance of appropriate statistical modeling for accurate genetic analyses.