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

14.0K
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...
14.0K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.6K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.6K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
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).
2.6K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

300
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
300
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
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...
1.8K

You might also read

Related Articles

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

Sort by
Same author

GEiPRS: a fast and powerful machine learning method for polygenic risk score prediction by leveraging genotype-environment interactions.

Briefings in bioinformatics·2026
Same author

Comprehensive analysis of mitophagy-related genes reveals prognostic signatures in breast cancer: based on immune landscapes and treatment target predict.

Frontiers in immunology·2026
Same author

PETScan: score-based genome-wide association analysis of RNA-Seq and ATAC-Seq data.

Bioinformatics (Oxford, England)·2026
Same author

Combined developmental toxicity of organophosphorus flame retardant TCEP and lead on zebrafish.

Ecotoxicology and environmental safety·2026
Same author

Epigenetic Age Acceleration and Hearing Function in US Older Adults.

Ear and hearing·2026
Same author

Treponema pallidum lipoprotein Tp0768 promotes H3K18 Lactylation modification to target PTK2 and enhance endothelial permeability.

International immunopharmacology·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Aug 21, 2025

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

10.2K

Robust genetic model-based SNP-set association test using CauchyGM.

Yeonil Kim1, Yueh-Yun Chi2, Judong Shen1

  • 1Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA.

Bioinformatics (Oxford, England)
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces CauchyGM, a novel genetic association test for genome-wide association studies (GWAS). CauchyGM improves power by integrating multiple genetic models, identifying significant genes in pharmacogenomic data.

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

3.8K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.1K

Related Experiment Videos

Last Updated: Aug 21, 2025

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

10.2K
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

3.8K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.1K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) commonly use single genetic models, potentially reducing power when true mechanisms are unknown.
  • Complex traits often involve unknown genetic architectures, necessitating flexible association testing methods.

Purpose of the Study:

  • To develop an integrative association test for SNP-set level analysis that accommodates unknown genetic models.
  • To enhance statistical power in GWAS by inferring and combining information across various inheritance patterns.

Main Methods:

  • Proposed Cauchy combination Genetic Model-based association test (CauchyGM) using a generalized linear model framework.
  • Developed an omnibus test (CauchyGM-O) combining CauchyGM with SKAT and burden tests for diverse signal patterns.
  • Utilized Cauchy Combination Test to aggregate correlated P-values across SNPs and models.

Main Results:

  • CauchyGM and CauchyGM-O maintain type I error rates at genome-wide significance.
  • Both methods demonstrate substantial power improvements over existing approaches in simulations.
  • Applied to pharmacogenomic GWAS data, identifying novel genome-wide significant genes.

Conclusions:

  • CauchyGM and CauchyGM-O offer powerful and flexible tools for genetic association analysis.
  • These methods are valuable for dissecting complex traits and pharmacogenomic associations.
  • The R package CauchyGM is available for public use.