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

Multiple Allele Traits01:49

Multiple Allele Traits

32.4K
The Concept of Multiple Allelism
32.4K
Epistasis Analysis01:09

Epistasis Analysis

4.9K
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...
4.9K
Polygenic Traits01:18

Polygenic Traits

58.2K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
58.2K
Polygenic Traits01:18

Polygenic Traits

7.1K
7.1K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

5.8K
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...
5.8K
Genetic Screens02:46

Genetic Screens

4.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis.

Human mutation·2011
Same author

A case of intimal sarcoma of the pulmonary artery successfully treated with chemotherapy.

International journal of clinical oncology·2011
Same author

Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer.

Nature genetics·2011
Same author

Energetic salts based on dipicrylamine and its amino derivative.

Chemistry (Weinheim an der Bergstrasse, Germany)·2011
Same author

Biophysical properties of slow potassium channels in human embryonic stem cell derived cardiomyocytes implicate subunit stoichiometry.

The Journal of physiology·2011
Same author

Natural variation of folate content and composition in spinach (Spinacia oleracea) germplasm.

Journal of agricultural and food chemistry·2011
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Apr 23, 2026

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging
09:37

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging

Published on: July 14, 2016

7.7K

Testing genetic association by regressing genotype over multiple phenotypes.

Kai Wang1

  • 1Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America.

Plos One
|September 16, 2014
PubMed
Summary
This summary is machine-generated.

Analyzing multiple phenotypes jointly for complex disorders can be powerful. However, this study shows the proportional odds model may be less powerful than univariate methods for genetic association studies.

More Related Videos

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

4.6K
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

9.2K

Related Experiment Videos

Last Updated: Apr 23, 2026

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging
09:37

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging

Published on: July 14, 2016

7.7K
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

4.6K
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

9.2K

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Complex disorders often exhibit multiple phenotypes, necessitating joint analysis for increased statistical power.
  • Existing methods, like regressing genotypes on multiple phenotypes using a proportional odds model, offer a joint analysis approach.
  • The proportional odds model is a recent technique for analyzing multiple phenotypes simultaneously in genetic studies.

Purpose of the Study:

  • To derive an explicit expression for the score test statistic and its non-centrality parameter for the proportional odds model.
  • To evaluate the performance of the proportional odds model in joint phenotype analysis.
  • To compare the power of the proportional odds model with traditional univariate methods for genetic association studies.

Main Methods:

  • Derivation of the score test statistic and its non-centrality parameter for the proportional odds model.
  • Conducting simulation studies mirroring those in Galesloot et al. (2014) to assess statistical performance.
  • Theoretical analysis and simulations to compare power between joint and univariate trait analyses.

Main Results:

  • An explicit formula for the score test statistic and its non-centrality parameter was derived.
  • Simulation studies confirmed the theoretical findings regarding the model's performance.
  • The proportional odds model, while useful for multiple phenotypes, demonstrated reduced power compared to univariate methods for single traits.

Conclusions:

  • The proportional odds model provides a framework for joint analysis of multiple phenotypes in complex disorders.
  • Researchers should be aware that this joint analysis approach may yield less power than univariate methods for specific trait analyses.
  • An R package, iGasso, has been developed for implementing the proposed score statistic, facilitating its use in genetic research.