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

Epistasis Analysis01:09

Epistasis Analysis

5.7K
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...
5.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.3K
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...
15.3K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.1K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.1K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

7.4K
Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
7.4K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

360
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...
360
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Epigenome-wide association study meta-analysis of wellbeing.

Clinical epigenetics·2026
Same author

Immunoaffinity-Based Protocol to Enrich Nervous System Cell-, Lung Alveolar Cell-, and Hepatocyte-Derived Extracellular Vesicles From Human Plasma.

Journal of extracellular biology·2026
Same author

Social determinants of vulnerability to nitrogen oxide- and sulfur dioxide-related bone damage among postmenopausal women in the United States.

Frontiers in public health·2026
Same author

TESTING FOR THE CAUSAL MEDIATION EFFECTS OF MULTIPLE MEDIATORS USING THE KERNEL MACHINE DIFFERENCE METHOD IN GENOME-WIDE EPIGENETIC STUDIES.

The annals of applied statistics·2026
Same author

Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease.

Nature communications·2026
Same author

Early-life liquefied petroleum gas cooking intervention and lung function in Guatemalan children: A randomized clinical trial.

Annals of the American Thoracic Society·2026

Related Experiment Video

Updated: Jan 16, 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

10.6K

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.

Haoyu Yang1, Zhonghua Liu2, Ruoyu Wang1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, USA.

Annual Review of Statistics and Its Application
|September 26, 2025
PubMed
Summary

Causal mediation analysis integrates various data types for health and social sciences. This review covers advancements in single/multiple mediator and exposure analyses, focusing on high-dimensional statistical inference.

Keywords:
causal inferencecomposite null hypothesisfalse discovery ratelarge-scale mediation analysismultiple exposuresmultiple testing

More Related Videos

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.3K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.3K

Related Experiment Videos

Last Updated: Jan 16, 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

10.6K
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.3K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.3K

Area of Science:

  • Biostatistics
  • Genomics
  • Epidemiology

Background:

  • Causal mediation analysis is increasingly vital in health and social sciences.
  • Integrating exposure, genomic, and phenotype data requires robust analytical frameworks.

Purpose of the Study:

  • To review recent developments in causal mediation analysis.
  • To focus on advancements in statistical inference for high-dimensional mediation analysis.
  • To compare existing methods using simulation studies and real-world data.

Main Methods:

  • Review of statistical inference methods for causal mediation analysis.
  • Simulation studies comparing methods for single and multiple mediators/exposures.
  • Application to the Normative Aging Study data (smoking, DNA methylation, lung function).

Main Results:

  • Recent advancements address single and multiple mediator/exposure scenarios.
  • High-dimensional mediation analysis presents challenges in testing composite null hypotheses.
  • Simulation studies provide comparative performance across various scenarios.

Conclusions:

  • Causal mediation analysis is a powerful framework for complex data integration.
  • Statistical inference, particularly in high-dimensional settings, is a key area of development.
  • Further research is needed to refine methods and address remaining challenges.