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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...
Genetic Screens02:46

Genetic Screens

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 result in visible changes...
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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...

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Related Experiment Video

Updated: Jun 3, 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

Variable set enrichment analysis in genome-wide association studies.

Wei Yang1, Lisa de las Fuentes, Victor G Dávila-Román

  • 1Division of Biostatistics, Washington University School of Medicine, St Louis, MO 63110, USA.

European Journal of Human Genetics : EJHG
|March 24, 2011
PubMed
Summary

Variable Set Enrichment Analysis (VSEA) improves upon gene-set enrichment analysis for complex diseases. New gene scoring methods reduce bias from gene size, enhancing genome-wide association study power.

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Last Updated: Jun 3, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex diseases like hypertension are multifactorial, involving numerous genetic variants with small effect sizes.
  • Genome-wide association studies (GWAS) analyze single-nucleotide polymorphisms (SNPs) to detect aggregated genetic effects.
  • Existing gene-set enrichment analysis (GSEA) methods have limitations, including sensitivity to gene size and linkage disequilibrium.

Purpose of the Study:

  • To introduce Variable Set Enrichment Analysis (VSEA) and novel gene scoring methods for GWAS.
  • To develop gene scoring approaches less dependent on gene size and definition.
  • To enhance the detection of aggregated genetic effects in complex diseases.

Main Methods:

  • Proposed Variable Set Enrichment Analysis (VSEA) using new gene score methods.
  • Treated groups of variables (SNPs or other variants) as base units for summarizing gene scores.
  • Conducted simulation studies to model complex multiloci interactions and analyzed VSEA power.

Main Results:

  • The new gene score methods generally outperformed existing GSEA extensions for GWAS.
  • VSEA demonstrated improved performance in detecting aggregated genetic effects across various scenarios.
  • Simulations showed substantial performance gains with the new methods in specific cases.

Conclusions:

  • VSEA offers a more robust approach to analyzing aggregated genetic effects in GWAS.
  • The developed gene scoring methods provide a practical utility for complex disease research.
  • Implementation in an R package facilitates the application of VSEA in real-world GWAS settings.