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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Epistasis01:39

Epistasis

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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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Genome-wide Association Studies-GWAS01:11

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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.
GWAS does not require the identification of the target gene involved in...
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SNPInt-GPU: Tool for Epistasis Testing with Multiple Methods and GPU Acceleration.

Lars Wienbrandt1, Jan Christian Kässens2, David Ellinghaus2

  • 1Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany. l.wienbrandt@ikmb.uni-kiel.de.

Methods in Molecular Biology (Clifton, N.J.)
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

SNPInt-GPU is a new software tool for statistical epistasis testing, offering multiple methods and GPU acceleration. It provides efficient analysis of genetic interactions for research.

Keywords:
BOOSTEntropyGPUInformation gainInteraction testLogistic regressionMutual informationPLINKStatistical epistasisThird-order test

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Statistical epistasis testing is crucial for understanding complex genetic traits.
  • Existing methods may lack computational efficiency for large-scale genomic datasets.
  • The development of specialized software is needed to accelerate these analyses.

Purpose of the Study:

  • To introduce SNPInt-GPU, a novel software for statistical epistasis testing.
  • To provide a versatile tool supporting various testing methodologies and hardware acceleration.
  • To offer detailed guidance on installation, usage, and result interpretation.

Main Methods:

  • Implementation of logistic regression, BOOST, log-linear regression, mutual information (MI), and information gain (IG) for pairwise epistasis.
  • Inclusion of MI and IG for third-order epistasis testing.
  • Support for GPU acceleration via Nvidia CUDA and optional linkage disequilibrium (LD) r^2 score calculation.

Main Results:

  • SNPInt-GPU offers a comprehensive suite of statistical epistasis testing methods.
  • The software demonstrates computational efficiency through GPU acceleration.
  • It provides flexibility for users with or without dedicated GPU hardware.

Conclusions:

  • SNPInt-GPU enhances the capability for performing complex genetic interaction analyses.
  • The software is publicly available, promoting wider adoption in genetic research.
  • It facilitates preliminary quality control and analysis of epistasis testing results.