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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
12.4K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

62.4K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
62.4K
Significance Testing: Overview01:04

Significance Testing: Overview

10.2K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
10.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
7.0K
Genetic Screens02:46

Genetic Screens

4.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Effect of Parental-Child Age Gaps and Skipped-Generation Families on Comorbidities Related to Attention Deficit Hyperactivity Disorder: A Population-Based Case-Control Study.

Children (Basel, Switzerland)·2025
Same author

The Prevalence and Risk Analysis of Cerebral Palsy and Other Neuro-Psychological Comorbidities in Children with Low Birth Weight in Taiwan: A Nationwide Population-Based Cohort Study.

Journal of clinical medicine·2024
Same author

Spiro-Twisted Benzoxazine Derivatives Bearing Nitrile Group for All-Solid-State Polymer Electrolytes in Lithium Batteries.

Polymers·2022
Same author

A robust association test with multiple genetic variants and covariates.

Statistical applications in genetics and molecular biology·2022
Same author

Epoxy-Based Interlocking Membranes for All Solid-State Lithium Ion Batteries: The Effects of Amine Curing Agents on Electrochemical Properties.

Polymers·2021
Same author

The effect of weather, air pollution and seasonality on the number of patient visits for epileptic seizures: A population-based time-series study.

Epilepsy & behavior : E&B·2020

Related Experiment Video

Updated: Apr 27, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K

A powerful and robust test in genetic association studies.

Kuang-Fu Cheng1, Jen-Yu Lee

  • 1Biostatistics Center and School of Public Health, Taipei Medical University, Taipei, Taiwan, ROC.

Human Heredity
|June 28, 2014
PubMed
Summary

This study introduces a new, robust statistical test for gene-disease association analysis that works across all genetic models, improving variant detection for common and rare genetic variants.

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Single nucleotide polymorphism (SNP) tests are used for gene-disease association.
  • Existing tests have restrictive conditions and are difficult to verify.
  • A comprehensive analysis requires robust testing across all genetic models.

Purpose of the Study:

  • To propose a powerful and robust statistical test for gene-disease association.
  • To overcome the limitations of existing tests by removing model restrictions.
  • To enable a more comprehensive analysis of genetic variants.

Main Methods:

  • Developed a novel statistical test based on selected 2x2 tables derived from 2x3 tables.
  • The test does not assume any restrictive genetic model conditions.

More Related Videos

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

4.6K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.3K

Related Experiment Videos

Last Updated: Apr 27, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K
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

4.6K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.3K
  • Evaluated the test's performance through simulations.

    • Main Results:

    • The proposed test demonstrated high power and robustness across various allele frequencies and genetic models.
    • The test showed near-uniform power for both low- and high-frequency variants.
    • Simulations confirmed the test's effectiveness in detecting gene-disease association signals.

    Conclusions:

    • The new statistical test offers a powerful and robust approach for gene-disease association studies.
    • It overcomes limitations of existing methods by not requiring specific model assumptions.
    • The test is applicable to a wide range of genetic variants and models, enhancing genetic analysis.