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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis.

Masao Ueki1, Gen Tamiya

  • 1Advanced Molecular Epidemiology Research Institute, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, Yamagata, Japan. uekimrsd@nifty.com

BMC Bioinformatics
|May 5, 2012
PubMed
Summary

This study introduces a novel method for genome-wide gene-gene interaction analysis, overcoming limitations in traditional genome-wide association studies (GWAS). The new approach efficiently identifies genetic factors contributing to complex diseases.

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Published on: July 18, 2013

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is crucial for understanding complex human diseases.
  • Traditional genome-wide association studies (GWAS) face challenges with multiple testing and the large p small n problem in SNP-SNP interaction analysis.
  • Existing methods struggle with the high dimensionality and complex correlations inherent in analyzing numerous SNP-SNP pairs.

Purpose of the Study:

  • To develop a robust method for ultrahigh-dimensional gene-gene interaction analysis in genome-wide association studies.
  • To address the limitations of multiple testing and the large p small n problem in identifying complex disease susceptibility genes.
  • To create an efficient computational tool for comprehensive genome-wide search of SNP-SNP interactions.

Main Methods:

  • Utilized the sure independence screening (SIS) method, an ultrahigh-dimensional variable selection technique, for logistic regression models.
  • Developed a novel ranking strategy incorporating dummy coding methods for effective SNP-SNP interaction variable selection.
  • Implemented the proposed methodology in a software program, EPISIS, leveraging General-purpose computing on graphics processing units (GPGPU) for accelerated computation.

Main Results:

  • The Sure Independence Screening (SIS) method effectively handles a large number of single nucleotide polymorphism (SNP)-SNP interactions.
  • The EPISIS software, utilizing General-purpose computing on graphics processing units (GPGPU), performs exhaustive SNP-SNP interaction searches within hours.
  • The proposed method demonstrated successful performance in both simulation experiments and real-world data analysis from the Wellcome Trust Case-control Consortium (WTCCC).

Conclusions:

  • The developed machine-learning-based method provides a powerful and flexible approach for genome-wide gene-gene interaction analysis.
  • This method enables the identification of diverse patterns of gene-gene interactions contributing to complex diseases.
  • The findings pave the way for more comprehensive and efficient genetic studies of human complex traits.