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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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Methods and Tools in Genome-wide Association Studies.

Anja C Gumpinger1,2, Damian Roqueiro3,4, Dominik G Grimm3,4

  • 1Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. anja.gumpinger@bsse.ethz.ch.

Methods in Molecular Biology (Clifton, N.J.)
|November 14, 2018
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify genetic links to traits and diseases using large datasets. This work reviews essential statistical methods and computational tools for effective GWAS analysis.

Keywords:
GWAS toolsGenome-wide association studiesGenomic inflationLinkage disequilibriumMeta-analysisMissing heritabilityMultilocus mappingMultiple hypothesis correctionPhenotypesPopulation structure correctionUnivariate mapping

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Human traits and disease susceptibility are influenced by genetic factors.
  • Advances in genotyping technology have enabled large-scale genetic datasets.
  • Genome-wide association studies (GWAS) are crucial for identifying genetic associations with traits.

Purpose of the Study:

  • To review widely used statistical methods and computational tools for genome-wide association studies.
  • To explain the assumptions and peculiarities of genetic data analysis in GWAS.
  • To introduce common software packages for various GWAS tasks with application examples.

Main Methods:

  • Statistical modeling (frequentist and Bayesian approaches).
  • Analysis of high-dimensional genetic data.
  • Computational optimization techniques for genetic association studies.

Main Results:

  • Diverse statistical models are employed in GWAS.
  • Computational challenges in GWAS are addressed by specialized software.
  • A comprehensive overview of GWAS methodologies and tools is presented.

Conclusions:

  • Effective GWAS requires understanding statistical models and data characteristics.
  • Numerous software tools facilitate the complex analysis of genetic association studies.
  • This review provides a guide to methods and tools for conducting genome-wide association studies.