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

14.5K
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...
14.5K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

133
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
133
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.2K
Epistasis Analysis01:09

Epistasis Analysis

5.3K
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...
5.3K
Cluster Sampling Method01:20

Cluster Sampling Method

12.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.9K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

917
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
917

You might also read

Related Articles

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

Sort by
Same author

Cardiovascular Disease Subtypes and Alzheimer's Disease: Phenotypic and Genetic Associations in the UK Biobank and All of Us Research Program.

Journal of the American Heart Association·2026
Same author

Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics.

Research square·2026
Same author

Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning integrating gated SPECT.

Nuclear medicine communications·2025
Same author

Chaihu Shugan powder restores fatty acid synthesis to alleviate insulin resistance in metabolic syndrome by regulating the LXRα/SREBP-1 signaling pathway.

Frontiers in pharmacology·2024
Same author

Efficacy and safety of guanxinshutong capsule combined with western medicine on stable angina pectoris: a systematic review and meta-analysis.

Frontiers in pharmacology·2024
Same author

A novel method for multiple phenotype association studies based on genotype and phenotype network.

PLoS genetics·2024

Related Experiment Video

Updated: Sep 25, 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

3.8K

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

Meida Wang1, Shuanglin Zhang1, Qiuying Sha1

  • 1Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America.

Plos One
|April 28, 2022
PubMed
Summary

A new computationally efficient method, ceCLC, improves joint analysis in genome-wide association studies (GWAS). It enhances statistical power for detecting genetic variants linked to complex diseases by efficiently combining phenotype data.

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

10.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K

Related Experiment Videos

Last Updated: Sep 25, 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

3.8K
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

10.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Joint analysis of multiple phenotypes in genome-wide association studies (GWAS) increases power to detect genetic variants for complex diseases.
  • Existing methods like Clustering Linear Combination (CLC) are computationally demanding due to simulation requirements for determining optimal phenotype clusters.

Purpose of the Study:

  • To develop a computationally efficient method (ceCLC) for the joint analysis of multiple phenotypes in GWAS.
  • To improve upon the limitations of the CLC method by reducing computational burden while maintaining statistical power.

Main Methods:

  • The ceCLC method utilizes the Cauchy combination test to integrate p-values from CLC statistics across various cluster numbers.
  • This approach approximates a standard Cauchy distribution, enabling direct p-value calculation without simulations.

Main Results:

  • ceCLC effectively controls type I error rates across diverse simulation scenarios.
  • The method demonstrates superior or comparable statistical power to existing approaches in simulations and real-world data analysis (COPDGene).

Conclusions:

  • ceCLC offers a computationally efficient and powerful alternative for joint multi-phenotype analysis in GWAS.
  • The method is robust and suitable for identifying genetic associations with complex traits.