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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.
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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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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A scalable, knowledge-based analysis framework for genetic association studies.

James W Baurley1, David V Conti

  • 1Bioinformatics Research Group, Bina Nusantara University, Jakarta, Indonesia. baurley@gmail.com.

BMC Bioinformatics
|October 25, 2013
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Summary
This summary is machine-generated.

We developed PEAK, a scalable Bayesian algorithm for genetic analysis. PEAK efficiently handles numerous variables, improving Markov-Chain Monte Carlo (MCMC) efficiency and identifying gene-gene interactions associated with childhood asthma.

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Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genetics

Background:

  • Marginal association testing may miss complex genetic relationships like gene-gene interactions.
  • Bayesian methods offer advantages for modeling multiple variables and incorporating prior biological evidence.
  • Markov-Chain Monte Carlo (MCMC) is computationally intensive and difficult to parallelize for large-scale variable selection.

Purpose of the Study:

  • To introduce PEAK, a scalable algorithm enhancing MCMC efficiency for Bayesian variable selection.
  • To address the challenge of analyzing a large number of genetic variants and their complex interactions.
  • To leverage parallel computing and biological databases for improved genetic association studies.

Main Methods:

  • Developed the PEAK algorithm, utilizing a graph-based approach to partition variable space.
  • Implemented PEAK to manage over 500,000 candidate variables, improving MCMC sampling efficiency.
  • Applied PEAK to a childhood asthma case-control study with 2,521 genetic variants.

Main Results:

  • PEAK successfully improved MCMC efficiency and identified true simulated causal variables, including a gene-gene interaction.
  • Analysis of childhood asthma data revealed significant associations for variants in ERBB4, OXR1, and BCL2.
  • An informative graph derived from Gene Ontology for oxidative stress was used.

Conclusions:

  • PEAK provides a flexible and efficient framework for Bayesian variable selection with numerous candidate variables.
  • The algorithm accommodates informative or symmetric graphs, aiding in the analysis of gene-gene interactions and model space management.
  • PEAK is adaptable to various study designs and analyses, including pathway and rare-variant studies, through modifications to its likelihood and proposal functions.