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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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Comparing Copy Number Variations and SNPs02:26

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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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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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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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection.

Gizem Caylak1, Oznur Tastan2, A Ercument Cicek1,3

  • 1Computer Engineering Department, Bilkent University, Ankara, Turkey.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 4, 2020
PubMed
Summary

This study introduces a novel algorithm to improve the precision of identifying gene interactions in genome-wide association studies (GWAS). The method prioritizes diverse and complementary genetic regions, significantly enhancing the discovery of disease-related genetic variants.

Keywords:
complementationdiversificationepistasis test prioritizationpopulation coversubmodular optimization

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants associated with diseases but explain only a fraction of heritability.
  • Epistatic interactions between multiple genetic loci are crucial for understanding complex genetic diseases but are computationally challenging to detect.
  • Current epistasis test prioritization algorithms lack precision, hindering the identification of significant gene-gene interactions.

Purpose of the Study:

  • To develop a novel epistasis test prioritization algorithm that improves the precision and efficiency of identifying epistatic interactions.
  • To enhance phenotype prediction by selecting diverse and complementary sets of single nucleotide polymorphisms (SNPs).
  • To reduce the computational burden and statistical challenges associated with detecting epistasis in large-scale genetic studies.

Main Methods:

  • Developed an algorithm that optimizes a submodular set function to select diverse and complementary genomic regions.
  • Ranked SNP pairs based on their co-coverage of the case cohort within selected diverse regions.
  • Incorporated prioritization of SNPs from regulatory/coding regions to further boost performance.

Main Results:

  • Achieved a substantial improvement in precision, increasing it from 0.003 to 0.652, while maintaining the significance of identified SNP pairs.
  • Reduced the number of epistasis tests required by 25-fold.
  • Decreased computational runtime by 4-fold compared to state-of-the-art methods.
  • Demonstrated performance improvement up to 0.8 by prioritizing SNPs from regulatory/coding regions.

Conclusions:

  • The proposed algorithm significantly enhances the precision and efficiency of detecting epistatic interactions in GWAS.
  • Selecting diverse and complementary genomic regions is a promising strategy for improving phenotype prediction and understanding genetic architecture.
  • Prioritizing SNPs in regulatory/coding regions offers a potential avenue for further improving the performance of epistasis detection algorithms.