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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,...
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...
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%...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A simulation study to assess a variable selection method for selecting single nucleotide polymorphisms associated

Huwaida S Rabie1, Ian W Saunders

  • 1Phenomics and Bioinformatics Research Centre, School of Mathematics and Statistics, University of South Australia, Mawson Lakes, Australia. huwaida.rabie@unisa.edu.au

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 13, 2012
PubMed
Summary
This summary is machine-generated.

GeneRaVE software effectively identifies disease-associated single nucleotide polymorphisms (SNPs) in genome-wide association studies. This multivariate approach outperforms traditional single SNP analyses, reducing false positives and improving genetic discovery.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) involve genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs).
  • High potential for false positives necessitates robust methods for selecting relevant SNPs.
  • Current methods often evaluate SNPs individually, overlooking multi-SNP interactions.

Purpose of the Study:

  • To assess the performance of the GeneRaVE software for variable selection in SNP data.
  • To evaluate the potential improvement offered by multi-SNP approaches over single SNP analyses.
  • To identify sets of SNPs associated with disease in case-control studies.

Main Methods:

  • Simulations were conducted using datasets where a haplotype of three SNPs was associated with disease.
  • GeneRaVE was applied as a multivariate variable selection method.
  • Performance was compared against single SNP analysis using chi-squared tests with multiple testing correction.

Main Results:

  • GeneRaVE successfully identified disease-related SNPs in simulations.
  • The software outperformed single SNP analysis in identifying relevant SNPs.
  • In a large dataset application, GeneRaVE detected known disease-associated SNPs missed by single SNP methods.

Conclusions:

  • GeneRaVE is a valuable tool for variable selection in GWAS, offering advantages over single SNP approaches.
  • Multivariate SNP analysis methods like GeneRaVE can improve the accuracy and power of genetic association studies.
  • This approach aids in reducing false positives and discovering complex genetic associations with diseases.