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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
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Postassociation cleaning using linkage disequilibrium information.

Buhm Han1, Brian M Hackel, Eleazar Eskin

  • 1Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA.

Genetic Epidemiology
|December 25, 2010
PubMed
Summary
This summary is machine-generated.

A new method called Post-Association Cleaning (PAC) identifies spurious genetic associations after quality control. This approach uses linkage disequilibrium (LD) to improve the reliability of genetic association studies.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genetic association studies employ quality control (QC) filters to remove unreliable markers.
  • Despite QC, spurious associations can persist, potentially leading to false conclusions.
  • Existing methods for identifying spurious associations often lack a formal statistical framework.

Purpose of the Study:

  • To introduce a novel Post-Association Cleaning (PAC) approach to identify spurious genetic associations.
  • To develop a systematic, quantitative framework for detecting spurious associations using linkage disequilibrium (LD).
  • To explore the potential of PAC in rescuing potentially valid associations from QC-excluded data.

Main Methods:

  • Proposed a PAC filter leveraging linkage disequilibrium (LD) information.
  • Developed a likelihood ratio-based statistical framework (LD-PAC) for quantitative assessment of spurious associations.
  • Validated the LD-PAC method through simulations and application to real-world genetic data.

Main Results:

  • Simulations demonstrated that LD-PAC achieves a high detection rate (84%) for spurious associations.
  • The LD-PAC method successfully identified a novel candidate association for type 1 diabetes within QC-excluded markers from WTCCC data.
  • The identified locus was subsequently validated by a later meta-analysis, highlighting the utility of LD-PAC.

Conclusions:

  • The LD-PAC method offers a robust approach to complement existing QC procedures in genetic association studies.
  • LD-PAC can effectively identify and mitigate spurious associations, enhancing the reliability of genetic findings.
  • This method holds promise for uncovering significant genetic loci, even among markers initially excluded by QC filters.