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

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...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

You might also read

Related Articles

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

Sort by
Same author

Hydrogen-Bond-Directed Assembly of Polyanionic Cluster [Mo<sub>2</sub>O<sub>5</sub>(IO<sub>3</sub>)<sub>4</sub>]<sup>2-</sup> for Nonlinear Optical Crystal Design.

Inorganic chemistry·2026
Same author

Effectiveness and safety of probiotics in treating knee osteoarthritis: an updated systematic review and meta-analysis of randomized controlled trials.

Frontiers in medicine·2026
Same author

Cerebrovascular thrombosis during pediatric ALL therapy: a case series highlighting temporal association with PEG-asparaginase exposure.

Frontiers in pediatrics·2026
Same author

Diversity of stem endophytes communities from wild asparagus resources and the biocontrol potential on Phomopsis asparagi.

BMC microbiology·2026
Same author

Disruption of BnPHT5;1b gene enhances arsenate tolerance through increased arsenate retention in shoot cell walls of Brassica napus.

Journal of hazardous materials·2026
Same author

Validating the accuracy of the YuWell YE680B automated upper-arm blood pressure monitor in pediatric and adult populations according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (ISO 81060-2:2018/Amd.1:2020/Amd.2:2024).

Blood pressure monitoring·2026

Related Experiment Video

Updated: May 29, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Gene- or region-based association study via kernel principal component analysis.

Qingsong Gao1, Yungang He, Zhongshang Yuan

  • 1Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China.

BMC Genetics
|August 30, 2011
PubMed
Summary
This summary is machine-generated.

The new Kernel Principal Component Analysis-Logistic Regression Test (KPCA-LRT) method improves genetic association studies by avoiding multicollinearity, enhancing power for detecting disease-related genetic variations, especially with lower relative risks.

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

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: May 29, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Gene- or region-based methods are popular in Genome-Wide Association Studies (GWAS) for detecting associations between multiple SNPs and diseases.
  • Kernel Principal Component Analysis-Logistic Regression Test (KPCA-LRT) has been used for gene expression data classification.
  • Existing kernel-based logistic regression models for association studies can suffer from multicollinearity, reducing statistical power.

Purpose of the Study:

  • To propose a novel KPCA-LRT model to overcome multicollinearity issues in genetic association studies.
  • To enhance the power of detecting associations between genetic variations and diseases.

Main Methods:

  • Developed a KPCA-LRT model to project nonlinear SNP data into a linear feature space while avoiding multicollinearity.
  • Compared the performance of KPCA-LRT against Principal Component Analysis-Logistic Regression Test (PCA-LRT) and single-locus tests.

Main Results:

  • KPCA-LRT demonstrated superior power compared to PCA-LRT across various sample sizes, significance levels, and relative risks.
  • The method was particularly effective at the genewide level (1E-5) and for lower relative risks (e.g., RR = 1.2, 1.3).
  • Application to rheumatoid arthritis data showed KPCA-LRT outperformed single-locus and PCA-LRT methods.

Conclusions:

  • KPCA-LRT is a powerful and valid gene- or region-based method for GWAS data analysis.
  • The method shows particular promise for analyses involving lower relative risks and lower significance levels.