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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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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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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Hui-Yi Lin1, Dung-Tsa Chen2, Po-Yu Huang3

  • 1Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.

Bioinformatics (Oxford, England)
|January 1, 2017
PubMed
Summary

The SNP Interaction Pattern Identifier (SIPI) enhances genetic association studies by testing numerous biologically relevant SNP-SNP interactions. SIPI demonstrates higher power than existing methods and identifies key interactions associated with prostate cancer aggressiveness.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genetic association studies face bottlenecks in identifying complex SNP-SNP interactions.
  • Existing statistical methods for testing SNP-SNP interactions are underdeveloped, limiting discovery.

Purpose of the Study:

  • To introduce the SNP Interaction Pattern Identifier (SIPI) for comprehensive testing of SNP-SNP interactions.
  • To evaluate SIPI's performance against established methods in detecting genetic interactions.

Main Methods:

  • SIPI tests 45 biologically meaningful interaction patterns for binary outcomes, considering non-hierarchical models and inheritance modes.
  • Comparative analysis with Multifactor Dimensionality Reduction (MDR), AA_Full, Geno_Full, and SNPassoc.

Main Results:

  • SIPI exhibited higher statistical power in detecting interactions compared to other methods in simulations.
  • Application to prostate cancer data identified significant SNP pairs (EGFR-EGFR, EGFR-MMP16, EGFR-CSF1) associated with aggressiveness.

Conclusions:

  • SIPI offers a more powerful and robust approach for identifying biologically relevant SNP-SNP interactions.
  • The method overcomes limitations of unstable interaction patterns and enhances genetic association studies.