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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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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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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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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Research on single nucleotide polymorphisms interaction detection from network perspective.

Lingtao Su1, Guixia Liu1, Han Wang2

  • 1College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.

Plos One
|March 13, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces Interaction Gain (IG), a novel method to detect low-order and high-order Single Nucleotide Polymorphism (SNP) interactions and main-effect SNPs crucial for understanding complex diseases. The method demonstrates superior performance and accuracy in simulations and real-world data analysis.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-Wide Association Studies (GWAS) identify Single Nucleotide Polymorphisms (SNPs) associated with complex diseases.
  • Existing methods struggle to comprehensively explain disease susceptibility due to limitations in detecting SNP interactions, especially when marginal effects are weak or absent.
  • There is a need for methods that can detect both low-order and high-order SNP interactions, as well as main-effect SNPs, with acceptable computational complexity.

Purpose of the Study:

  • To develop a novel method for detecting low-order and high-order SNP interactions and main-effect SNPs.
  • To improve the understanding of genetic contributions to complex diseases by accounting for intricate SNP relationships.
  • To provide a computationally efficient and accurate tool for genetic interaction analysis.

Main Methods:

  • Introduction of Interaction Gain (IG), a pairwise (low-order) interaction detection method that does not require disease models and utilizes parallel computing.
  • Detection of high-order SNP interactions by identifying closely connected functional modules within a network constructed from IG results.
  • Validation using diverse simulated datasets and four Wellcome Trust Case Control (WTCCC) real datasets.

Main Results:

  • The proposed methods accurately detected low-order and high-order SNP interactions.
  • Disease-associated main-effect Single Nucleotide Polymorphisms (SNPs) were reliably identified.
  • The methods demonstrated superior performance and accuracy compared to existing approaches in both simulated and real datasets.

Conclusions:

  • The developed methods offer a significant advancement in identifying complex genetic interactions underlying complex diseases.
  • Accurate detection of both low-order and high-order SNP interactions and main-effect SNPs is crucial for a comprehensive understanding of disease susceptibility.
  • This research provides more reliable SNP interaction detection, advancing complex disease research.