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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.
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Infinium Assay for Large-scale SNP Genotyping Applications
13:33

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Published on: November 19, 2013

Chapter 10: Mining genome-wide genetic markers.

Xiang Zhang1, Shunping Huang, Zhaojun Zhang

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Ohio, United States of America.

Plos Computational Biology
|January 10, 2013
PubMed
Summary

Genome-wide association studies (GWAS) identify genetic factors for traits. This chapter reviews computational methods for large-scale GWAS, including single-locus, epistasis, and machine learning approaches, addressing current challenges and future research directions.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with phenotypic traits.
  • The scale of genetic data presents significant computational and statistical hurdles.
  • Numerous computational strategies have been developed to address these challenges in large-scale GWAS.

Purpose of the Study:

  • To provide a comprehensive overview of computational approaches used in genome-wide association studies.
  • To detail methods for single-locus analysis, epistasis detection, and machine learning applications in GWAS.
  • To discuss the limitations of existing methods and outline future research avenues.

Main Methods:

  • Review of established single-locus analysis techniques.
  • Exploration of computational methods for detecting gene-gene interactions (epistasis).
  • Discussion of emerging machine learning algorithms applied to GWAS data.

Main Results:

  • Categorization of computational GWAS approaches into single-locus, epistasis, and machine learning methods.
  • Highlighting the strengths and weaknesses of each approach.
  • Identification of key challenges in current computational GWAS.

Conclusions:

  • Computational methods are essential for navigating the complexities of large-scale GWAS.
  • Integrating diverse computational strategies, including machine learning, can enhance the discovery of genetic factors.
  • Further development is needed to overcome computational and statistical limitations in GWAS.