Jove
Visualize
Contact Us

Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

5.0K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.5K
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...
13.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

199
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
199
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

103
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
103
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

170
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
170

You might also read

Related Articles

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

Sort by
Same author

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes.

bioRxiv : the preprint server for biology·2026
Same author

Cardiovascular Disease Subtypes and Alzheimer's Disease: Phenotypic and Genetic Associations in the UK Biobank and All of Us Research Program.

Journal of the American Heart Association·2026
Same author

Hypoxia-induced downregulation of cAMP drives Ganoderic acid biosynthesis and restricts biofilm development in Ganoderma lucidum.

Food research international (Ottawa, Ont.)·2026
Same author

Multifunctional Nanoplatform for Synergistic Cancer Therapy via Enhanced Immunogenic Cell Death and Immune Modulation.

ACS applied materials & interfaces·2026
Same author

Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics.

Research square·2026
Same author

A Biomimetic Lubricant Captures Hyaluronic Acid In Situ to Regenerate Cartilage: From Bench to Bedside.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
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 Experiment Video

Updated: Jul 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer

Hongjing Xie1, Xuewei Cao1, Shuanglin Zhang1

  • 1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States.

Bioinformatics (Oxford, England)
|November 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Multi-Layer Network with Omnibus (MLN-O) method for analyzing genome-wide associations with multiple, unbalanced phenotypes. MLN-O effectively controls type I error rates and enhances power, identifying more significant single nucleotide polymorphisms (SNPs) in real-world biobank data.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
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

3.7K

Related Experiment Videos

Last Updated: Jul 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
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

3.7K

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying relationships between phenotypes and single nucleotide polymorphisms (SNPs).
  • Large biobanks often contain binary phenotypes that are highly unbalanced, leading to inflated type I error rates in standard association tests.
  • Joint analysis of multiple phenotypes is challenging due to data imbalance and the need for robust statistical methods.

Purpose of the Study:

  • To develop a novel method, Multi-Layer Network with Omnibus (MLN-O), for joint analysis of multiple, unbalanced phenotypes in GWAS.
  • To improve the control of type I error rates and increase the statistical power of association tests in large biobanks.
  • To provide a computationally efficient approach for identifying significant SNPs associated with complex trait sets.

Main Methods:

  • Constructing a Multi-Layer Network (MLN) using individuals with at least one case status across all phenotypes.
  • Employing a community detection algorithm to cluster related phenotypes based on the MLN.
  • Applying a score test for individual merged phenotypes within clusters and an Omnibus test for overall association with a SNP.

Main Results:

  • Extensive simulations demonstrate that MLN-O effectively controls type I error rates and outperforms existing methods in terms of power.
  • Application to UK Biobank data, specifically for musculoskeletal and connective tissue diseases, revealed MLN-O identified more significant SNPs compared to other methods.
  • The MLN-O approach proved robust and powerful in analyzing real-world, imbalanced phenotype data.

Conclusions:

  • MLN-O offers a statistically sound and powerful approach for joint phenotype-SNP association analysis in large-scale biobanks with unbalanced data.
  • The method's ability to control type I errors and enhance power makes it a valuable tool for genetic discovery.
  • MLN-O successfully identified novel genetic associations in a real-world dataset, highlighting its practical utility in complex trait genetics.