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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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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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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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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A robust GWSS method to simultaneously detect rare and common variants for complex disease.

Chung-Feng Kao1, Jia-Rou Liu2, Hung Hung3

  • 1Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.

Plos One
|April 17, 2015
PubMed
Summary

A new generalized weighted-sum statistic (GWSS) effectively detects disease-associated variants, including rare ones. This method improves upon existing approaches by simultaneously considering common and rare variants for robust genetic association analysis.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Next-generation sequencing advances enable detection of disease-associated variants, especially low-frequency ones.
  • Existing methods often fail to simultaneously assess the independent effects of common and rare variants on disease risk.

Purpose of the Study:

  • To propose a novel statistical method, the generalized weighted-sum statistic (GWSS), for detecting disease associations.
  • To simultaneously analyze common and rare variants in case-control studies.

Main Methods:

  • Developed the generalized weighted-sum statistic (GWSS) framework.
  • Aggregated rare variant information using a weighted sum approach, considering variant signal direction and strength.
  • Utilized permutations to determine empirical p-values for test statistics.

Main Results:

  • GWSS demonstrated superior performance compared to existing methods across various simulation scenarios.
  • The VDWSS-t variant of GWSS showed robustness against opposite association directions, varying strengths, and minor allele frequency distributions.
  • Application to Genetic Analysis Workshop 17 data yielded results consistent with simulations, confirming method efficacy.

Conclusions:

  • The GWSS is a promising tool for identifying disease-associated loci by effectively detecting associations with both common and rare variants.
  • Recommended for use in re-sequencing studies aiming to identify disease loci.