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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.
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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,...
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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.
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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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Efficient association study design via power-optimized tag SNP selection.

B Han1, H M Kang, M S Seo

  • 1Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA.

Annals of Human Genetics
|August 16, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for selecting tag single nucleotide polymorphisms (SNPs) to maximize statistical power in genetic association studies. The proposed method offers increased power or reduced SNP requirements compared to existing r(2)-based approaches.

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Area of Science:

  • Human Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Genetic association studies identify disease-related genetic variations by correlating causal variants with clinical traits.
  • Linkage disequilibrium (LD) allows using a subset of single nucleotide polymorphisms (SNPs) as tag SNPs to infer information about nearby variants.
  • Current tag SNP selection often relies on optimizing pairwise correlation (r^2), which may not maximize study power.

Purpose of the Study:

  • To develop a study design framework for selecting tag SNPs that maximizes statistical power in genetic association studies.
  • To compare the power of the proposed SNP selection method against established r^2-based methods.
  • To provide an efficient method for power measurement through empirical simulation.

Main Methods:

  • Developed a novel framework for tag SNP selection focused on maximizing statistical power.
  • Employed empirical simulations using HapMap data to assess the power of the proposed method.
  • Compared the performance of the power-optimized method against the widely used r^2-based criterion.

Main Results:

  • The proposed power-optimized method significantly increases statistical power compared to r^2-based methods.
  • Fewer tag SNPs are required to achieve desired statistical power using the new method.
  • A 100k whole genome tag set designed with this method showed equivalent power to a 500k chip for the CEU population.
  • Custom follow-up studies using this method can achieve up to double the power increase with the same number of tag SNPs.

Conclusions:

  • The power-optimized tag SNP selection framework enhances the efficiency and effectiveness of genetic association studies.
  • This approach offers a superior alternative to r^2-based methods for both high-throughput genotyping product design and custom studies.
  • The method is publicly accessible via a web server for broader research application.