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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Identifying interacting SNPs using Monte Carlo logic regression.

Charles Kooperberg1, Ingo Ruczinski

  • 1Division of Public Health Services, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. clk@fhcrc.org

Genetic Epidemiology
|November 9, 2004
PubMed
Summary
This summary is machine-generated.

Monte Carlo logic regression identifies potential single-nucleotide polymorphism (SNP) interactions for disease association studies. This method explores SNP combinations, aiding in discovering genetic pathways and disease-associated haplotypes.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Single-nucleotide polymorphism (SNP) association studies frequently investigate interactions.
  • Identifying interacting SNPs can reveal disease-associated haplotypes or genetic pathways.
  • Current datasets are often too small to definitively detect SNP interactions.

Purpose of the Study:

  • To introduce Monte Carlo logic regression (MCLR) as an exploratory tool for identifying SNP interactions.
  • To generate collections of potentially disease-associated SNP interactions for further investigation.
  • To address the challenge of small datasets and competing interaction models in association studies.

Main Methods:

  • MCLR combines Markov chain Monte Carlo and logic regression.
  • Logic regression constructs predictors as Boolean combinations of binary covariates like SNPs.
  • The method tabulates frequently occurring SNP interaction patterns rather than averaging models.

Main Results:

  • The MCLR method was applied to a heart disease study with 779 participants and 89 SNPs.
  • A simulation study was conducted to evaluate the performance of MCLR.
  • The study aims to identify SNP interactions warranting further investigation.

Conclusions:

  • MCLR provides a novel approach for exploring SNP interactions in genetic association studies.
  • The method is valuable for generating hypotheses about genetic contributions to disease.
  • Further investigation of identified SNP patterns can enhance understanding of disease etiology.