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

12.9K
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...
12.9K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.1K
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,...
15.1K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

11.6K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
11.6K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

129
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
129
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

143
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
143

You might also read

Related Articles

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

Sort by
Same author

RNF2 mediates pulmonary fibroblasts activation and proliferation by regulating mTOR and p16-CDK4-Rb1 signaling pathway.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2022
Same author

Neural stem cell‑derived exosomes transfer miR‑124‑3p into cells to inhibit glioma growth by targeting FLOT2.

International journal of oncology·2022
Same author

Guidewire simulation of endovascular intervention: A systematic review.

The international journal of medical robotics + computer assisted surgery : MRCAS·2022
Same author

Targeting attack hypergraph networks.

Chaos (Woodbury, N.Y.)·2022
Same author

Prophylactic and Therapeutic HPV Vaccines: Current Scenario and Perspectives.

Frontiers in cellular and infection microbiology·2022
Same author

Creating a Thermostable β-Glucuronidase Switch for Homogeneous Immunoassay by Disruption of Conserved Salt Bridges at Diagonal Interfaces.

Biochemistry·2022
Same journal

Characterization of genomic diversity in bacteriophages infecting Rhodococcus.

PloS one·2026
Same journal

Effectiveness of the Responding to Experienced and Anticipated Discrimination (READ) training on reducing stigma for medical students in Tunisia.

PloS one·2026
Same journal

Cell-cell junction gene signatures as subtype-specific prognostic biomarkers in breast cancer.

PloS one·2026
Same journal

GC-MS based tentative identification of γ-sitosterol from Brassica nigra seeds and evaluation of its anticancer potential: An integrated in vitro and in silico study.

PloS one·2026
Same journal

Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.

PloS one·2026
Same journal

Negotiating knowledge: The role of network hedging in the production of high-impact science.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 7, 2026

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

Weighted SNP set analysis in genome-wide association study.

Hui Dai1, Yang Zhao, Cheng Qian

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

Plos One
|October 8, 2013
PubMed
Summary
This summary is machine-generated.

Weighted principal component analysis (wPCA) enhances the power of genome-wide association studies (GWAS) for identifying genetic variants, especially those with low minor allele frequencies (MAF). This method outperforms traditional PCA and kernel machine approaches in detecting disease-associated SNPs.

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

41.3K
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

20.2K

Related Experiment Videos

Last Updated: May 7, 2026

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
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

41.3K
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

20.2K

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to disease risk.
  • Single locus association analysis has limitations; SNP set analysis offers improved power.
  • Kernel machine methods and principal component analysis (PCA) are used for SNP set analysis.

Purpose of the Study:

  • To enhance the power of SNP set analysis in GWAS, particularly for variants with low minor allele frequencies (MAF).
  • To compare the performance of weighted principal component analysis (wPCA) against PCA and logistic kernel machine (LKM) methods.
  • To evaluate methods under varying linkage disequilibrium (LD) structures and numbers of causal SNPs.

Main Methods:

  • Developed and implemented weighted principal component analysis (wPCA) as an extension of PCA.
  • Conducted comparative analyses using wPCA, logistic kernel machine based test (LKM), and PCA.
  • Utilized simulation studies with varying MAF, LD structures, and numbers of causal SNPs.
  • Applied the methods to a real GWAS dataset for non-small cell lung cancer in the Han Chinese population.

Main Results:

  • wPCA demonstrated superior power compared to PCA and other kernel machine functions when causal SNPs had low MAF.
  • Performance was robust across different LD structures and varying numbers of causal SNPs.
  • Analysis of the non-small cell lung cancer dataset showed wPCA and weighted IBS outperformed linear kernel, IBS kernel, and PCA.

Conclusions:

  • Weighted PCA (wPCA) is a powerful method for SNP set analysis in GWAS, especially for low MAF variants.
  • wPCA offers improved detection power over traditional PCA and kernel machine methods.
  • The findings suggest wPCA is a valuable tool for genetic association studies, enhancing the identification of disease-related genetic variants.