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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...
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%...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...

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Related Experiment Video

Updated: Jul 15, 2026

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

Testing association between disease and multiple SNPs in a candidate gene.

W James Gauderman1, Cassandra Murcray, Frank Gilliland

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. jimg@usc.edu

Genetic Epidemiology
|April 6, 2007
PubMed
Summary

This study introduces a novel principal components (PCs) analysis for genetic association testing, outperforming traditional genotype and haplotype methods. The PC approach effectively identifies disease associations using single nucleotide polymorphisms (SNPs) with greater power.

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

Published on: July 27, 2021

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Last Updated: Jul 15, 2026

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

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

Area of Science:

  • Genetics
  • Statistical genomics
  • Computational biology

Background:

  • Genotyping technologies enable the analysis of multiple single nucleotide polymorphisms (SNPs) within candidate genetic loci.
  • Existing methods for disease association testing include genotype-based and haplotype-based approaches, each with limitations.

Purpose of the Study:

  • To develop a novel, computationally efficient method for genetic association testing using principal components (PCs) analysis.
  • To compare the power of the PC approach against traditional genotype- and haplotype-based methods.

Main Methods:

  • Principal Components (PCs) analysis is used to derive combinations of SNPs that capture locus-specific correlation structures.
  • The derived PCs are then directly employed in a disease association test.
  • Simulations and real-world data (Children's Health Study) were used for evaluation.

Main Results:

  • The PC approach demonstrated comparable or superior statistical power to genotype- and haplotype-based methods in simulations.
  • In an analysis of respiratory symptoms and Glutathione-S-Transferase P1 SNPs, the PC approach yielded stronger evidence of association (p = 0.044) compared to genotype-based (p = 0.13) and haplotype-based (p = 0.052) methods.

Conclusions:

  • The PC approach offers a powerful and computationally feasible alternative for genetic association studies.
  • This method effectively leverages linkage disequilibrium information without the complexity of haplotype reconstruction.