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

Randomized Experiments01:13

Randomized Experiments

6.7K
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...
6.7K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

144
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...
144
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
Law of Independent Assortment02:03

Law of Independent Assortment

54.2K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
54.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
Chi-square Analysis02:46

Chi-square Analysis

37.4K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
37.4K

You might also read

Related Articles

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

Sort by
Same author

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same author

Author Correction: Challenges and future directions for Mendelian randomization.

Nature genetics·2026
Same author

Challenges and future directions for Mendelian randomization.

Nature genetics·2026
Same author

Disentangling the Effect of BMI on Hepatocellular Carcinoma From Cirrhosis With Multivariable Mendelian Randomization.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

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

Communications medicine·2026
Same author

High Body Mass Index as a Causal Risk Factor for Vascular-Related Dementia: A Mendelian Randomization Study.

The Journal of clinical endocrinology and metabolism·2026
Same journal

Genetic survey of biomarkers at early and mid-pregnancy identifies pregnancy-specialized immune regulation.

PLoS genetics·2026
Same journal

Argonaute proteins orchestrate Meiotic Sex Chromosome Inactivation and timing of the spermatogenic transcriptional program.

PLoS genetics·2026
Same journal

Genome wide association study meta-analysis of neuropathologic lesions of Alzheimer's disease and related dementias in a multi-site autopsy cohort.

PLoS genetics·2026
Same journal

Microtubule stiffening by the doublecortin-domain protein ZYG-8 contributes to mitotic spindle orientation during zygote division in Caenorhabditis elegans.

PLoS genetics·2026
Same journal

Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.

PLoS genetics·2026
Same journal

Nuclear ubiquitin-conjugating enzyme TrUbc4 and F-box protein TrFwd1-mediated modification of Cre1 in Trichoderma reesei establishes a regulatory mechanism for carbon catabolite repression.

PLoS genetics·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

Estimating and visualising multivariable Mendelian randomization analyses within a radial framework.

Wes Spiller1,2, Jack Bowden3,4, Eleanor Sanderson1,2

  • 1Population Health Sciences, University of Bristol, Bristol, United Kingdom.

Plos Genetics
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Radial Multivariable Mendelian Randomization (MVMR) to visualize causal effects and identify pleiotropic variants. The new method helps reduce bias in genetic association studies by enabling outlier removal.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
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

9.7K

Related Experiment Videos

Last Updated: Jun 5, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
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

9.7K

Area of Science:

  • Statistical genetics
  • Causal inference
  • Epidemiology

Background:

  • Mendelian randomization (MR) estimates causal effects using genetic variants.
  • Multivariable MR (MVMR) extends this to multiple exposures.
  • Pleiotropic variants can bias MR and MVMR analyses, violating core assumptions.

Purpose of the Study:

  • To develop a visualization tool for MVMR analyses.
  • To enable identification and removal of pleiotropic variants in MVMR.
  • To improve the accuracy of causal effect estimation in MVMR.

Main Methods:

  • Propose a radial formulation of MVMR.
  • Adapt the Galbraith radial plot for MVMR.
  • Develop an R package (RMVMR) for implementation and outlier removal.

Main Results:

  • Demonstrate effectiveness through simulations and applied analyses.
  • Visualize and remove outliers affecting multiple exposures.
  • Show that outlier removal reduces bias under various pleiotropy scenarios.
  • Applied Radial MVMR to estimate lipid fractions' effect on coronary heart disease (CHD).

Conclusions:

  • Radial MVMR effectively visualizes causal effect estimates.
  • Provides valuable diagnostic information for MVMR assumptions.
  • Facilitates robust causal inference in complex genetic studies.