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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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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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Boosting signals in gene-based association studies via efficient SNP selection.

Cen Wu1, Yuehua Cui

  • 1Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, Rm C432, East Lansing, MI 48824, USA. Tel.: +1-517-432-7098; Fax: +1-517-432-1405; cui@stt.msu.edu.

Briefings in Bioinformatics
|January 18, 2013
PubMed
Summary

Selecting disease-informative single-nucleotide polymorphisms (diSNPs) improves set-based association studies for complex diseases. Our information-theory method efficiently identifies diSNPs, reducing false positives and negatives in genetic analyses.

Keywords:
entropygene-centric associationmutual informationset-based association

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Computational Biology

Background:

  • Set-based association studies analyze groups of genes or pathways to interpret complex disease signals.
  • These methods are valuable for variants with moderate or joint effects, often missed by single-marker analysis.
  • Current set-based analyses can suffer from signal dilution due to noisy variants, leading to false positives/negatives.

Purpose of the Study:

  • To develop an efficient method for selecting disease-informative single-nucleotide polymorphisms (diSNPs).
  • To enhance the power and accuracy of set-based association studies by pre-selecting relevant variants.
  • To address the challenge of signal dilution caused by non-informative variants in genetic association analyses.

Main Methods:

  • Proposed an efficient diSNP selection method utilizing information theory.
  • Selected variants based on their relative information contribution to disease status.
  • Distinguished this approach from conventional tag SNP selection methods.

Main Results:

  • Demonstrated the effectiveness of pre-selecting diSNPs in set-based association analysis.
  • Showcased improved power and reduced false positives/negatives through extensive simulation studies.
  • Validated the method's utility with real-world genetic data analysis.

Conclusions:

  • The proposed information-theory-based diSNP selection is crucial for robust set-based association studies.
  • This approach effectively mitigates signal dilution and enhances the detection of complex disease associations.
  • Pre-selection of diSNPs offers a significant advantage for interpreting genetic contributions to complex diseases.