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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...

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

ATOM: a powerful gene-based association test by combining optimally weighted markers.

Mingyao Li1, Kai Wang, Struan F A Grant

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. mingyao@mail.med.upenn.edu

Bioinformatics (Oxford, England)
|December 17, 2008
PubMed
Summary

We developed a new gene-based association test, ATOM, to effectively analyze genetic data from large studies. This method enhances the power to detect associations between genes and traits, improving genetic discovery.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Last Updated: Jun 27, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Large-scale genetic studies generate vast amounts of data, posing analytical challenges.
  • Identifying associations between genes/regions and traits requires effective utilization of genetic information.

Purpose of the Study:

  • To introduce a novel gene-based association test called ATOM (Association Test by combining Optimally Weighted Markers).
  • To enhance the power of detecting genetic associations by optimally weighting markers within genomic regions.

Main Methods:

  • ATOM combines genetic information from multiple single nucleotide polymorphisms (SNPs) within a gene or region.
  • Optimal marker weights are derived analytically and can be estimated from reference databases like HapMap.
  • The method is scalable for analyzing all SNPs in candidate genes and pathways.

Main Results:

  • ATOM demonstrated increased power to detect genetic associations compared to existing multi-marker tests in simulations.
  • Analysis of the FTO gene in a childhood obesity genome-wide association study confirmed ATOM's effectiveness.

Conclusions:

  • ATOM provides a powerful and scalable approach for gene-based association testing.
  • The method improves the ability to identify genetic associations, aiding in the understanding of complex traits.