Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Probability Laws

Probability Laws

Pedigree Analysis

Pedigree Analysis

Causality in Epidemiology

Causality in Epidemiology

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...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by

Same author

Genome-wide association studies of infant and toddler temperament in European and multi-ancestry populations.

Nature human behaviour·2026

Same author

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions.

Communications medicine·2026

Same author

Getting to GRIPS with MR-Egger: Modelling directional pleiotropy independently of allele coding.

PLoS genetics·2025

Same author

Genome topology analysis and transcriptomics of human osteoclasts reveals enhancer-promoter interactions at loci for bone traits and diseases.

JBMR plus·2025

Same author

Genome-wide association study of neuropathic pain phenotypes implicates loci involved in neural cell adhesion, channels, collagen matrix formation, and immune regulation.

Pain·2025

Same author

Genome-wide association meta-analysis of age at onset of walking in over 70,000 infants of European ancestry.

Nature human behaviour·2025

Search research articles

Related Experiment Video

Updated: Jun 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Estimating Causal Effects on a Disease Progression Trait Using Bivariate Mendelian Randomisation.

Siyang Cai¹, Frank Dudbridge¹

¹Department of Population Health Sciences, University of Leicester, Leicester, UK.

Genetic Epidemiology

|October 24, 2024

Summary

This study introduces methods to correct collider bias in multivariable Mendelian Randomisation (MR) analyses. Applying this to smoking and Crohn's disease prognosis found no causal link, highlighting the importance of bias adjustment in genetic epidemiology.

Keywords:

Crohn's disease GWAS causality index event bias instrumental variable selection bias

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

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

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

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Jun 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

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

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

Published on: June 21, 2018

Area of Science:

Epidemiology
Genetic Epidemiology
Statistical Genetics

Background:

Mendelian Randomisation (MR) uses genetic variants to infer causal relationships between risk factors and outcomes.
Multivariable MR extends this to multiple risk factors but can be susceptible to collider bias.
Collider bias can arise when conditioning on a variable associated with both the exposure and outcome, potentially distorting causal estimates.

Purpose of the Study:

To highlight the critical issue of collider bias in multivariable MR when the outcome is disease progression.
To propose a generalized instrument effect regression and corrected weighted least squares (CWLS) method to adjust for collider bias in MR.
To assess the causal effect of smoking initiation and cessation on Crohn's disease prognosis, accounting for potential bias.

Main Methods:

Development of a generalized instrument effect regression and CWLS adjustment method for multivariable MR.
Application of the method to assess the causal effects of smoking initiation and cessation on Crohn's disease prognosis.
Evaluation of the assumptions and utility of the proposed bias adjustment technique.

Main Results:

The proposed generalized instrument effect regression and CWLS adjustment can reduce collider bias in multivariable MR.
Illustrative application to smoking and Crohn's disease prognosis found no evidence of a causal effect of smoking initiation or cessation on disease prognosis.
The study underscores the necessity of addressing collider bias in MR studies, particularly those involving disease progression.

Conclusions:

Collider bias is a significant concern in multivariable MR, especially for disease progression outcomes.
The generalized instrument effect regression and CWLS method provides a robust approach to mitigate this bias.
There is no evidence supporting a causal effect of smoking initiation or cessation on Crohn's disease prognosis when accounting for collider bias.