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

13.4K
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...
13.4K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

254
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
254
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

169
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...
169
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

95
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...
95
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

190
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
190

You might also read

Related Articles

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

Sort by
Same author

Selective vagus-recurrent laryngeal nerve anastomosis guided by intraoperative neuromonitoring: evidence of lateral motor fiber clustering in the vagus nerve.

Frontiers in endocrinology·2026
Same author

Associations of cumulative exposure and two-time-point clustering of the triglyceride-cholesterol-body weight index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study.

BMC public health·2026
Same author

Early postoperative voice-change phenotypes after thyroid surgery: a prospective cohort study.

Frontiers in endocrinology·2026
Same author

POSTN<sup>+</sup> CAFs facilitate gastric cancer peritoneal metastasis by promoting ICAM-1-dependent tumor cell adhesion and CD8<sup>+</sup> T-cell exhaustion.

Frontiers in immunology·2026
Same author

Observation of localization reversal and harmonic generation in nonlinear non-Hermitian skin effect.

Nature communications·2026
Same author

Predictive Equation for Peak Heart Rate and First Ventilatory Threshold Heart Rate in Patients With Coronary Heart Disease.

Cardiology research and practice·2026

Related Experiment Video

Updated: Jun 28, 2025

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

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Peiyao Wang1, Zhaotong Lin1,2, Haoran Xue1,3

  • 1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, United States of America.

Plos Genetics
|April 22, 2024
PubMed
Summary

This study addresses collider bias in genome-wide association studies (GWAS) when adjusting for multiple heritable covariates. A new method using multivariable Mendelian randomization (MVMR) corrects this bias, improving genetic effect estimation.

More Related Videos

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

10.1K
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

19.0K

Related Experiment Videos

Last Updated: Jun 28, 2025

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
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

10.1K
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

19.0K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Epidemiology

Background:

  • Genome-wide association studies (GWAS) identify genetic loci for complex traits.
  • Adjusting for heritable covariates in GWAS can introduce collider bias.
  • Existing bias correction methods have limitations, especially with multiple covariates.

Purpose of the Study:

  • To derive an analytical expression for collider bias with multiple covariates.
  • To propose a robust multivariable Mendelian randomization (MVMR) method for bias estimation.
  • To establish the statistical properties of the new bias-corrected estimator.

Main Methods:

  • Derivation of an analytical collider bias expression for multiple covariates.
  • Application of a robust multivariable Mendelian randomization (MVMR) method (MVMR-cML).
  • Simulation studies and real data analyses using GWAS of waist-hip ratio and BMI.

Main Results:

  • The proposed MVMR-cML method effectively mitigates collider bias.
  • The new bias-corrected estimator demonstrates consistency and asymptotic normality.
  • Simulations and real data analyses confirm the method's effectiveness.

Conclusions:

  • The developed analytical framework and MVMR-cML method offer improved bias correction in GWAS.
  • This approach enhances the accuracy of estimating genetic effects in the presence of multiple covariates.
  • The findings are applicable to complex traits and diseases influenced by multiple factors.