Skip to main content
An official website of the United States government
Government Funding Lapse
Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.

The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov.

Updates regarding government operating status and resumption of normal operations can be found at opm.gov.

Email

REBET

A Subregion-based Burden Test for Simultaneous Identification of Susceptibility Loci and Subregions in Rare-variant Association Studies

In rare-variant association studies, aggregating rare and/or low frequency variants, may increase statistical power for detection of the underlying susceptibility gene or region. However, it’s unclear which variants, or class of them, in a gene contribute most to the association. We proposed a subregion-based burden test (REBET) to simultaneously select susceptibility genes and identify important underlying subregions. The subregions are predefined based on shared common biologic characteristics, such as the protein domain or possible functional impact.  Based on a subset-based approach considering local correlations between combinations of test statistics of sub-regions, REBET is able to properly control the type I error rate while adjusting for multiple comparisons in a computationally efficient manner. Simulation studies show that REBET can achieve power competitive to alternative methods when rare variants cluster within subregions. In two case studies, REBET is able to identify known disease susceptibility genes, and more importantly pinpoint the unreported most susceptible subregions, which represent protein domains essential for gene function.

Download Software:

Reference

Zhu B, Mirabello L, Chatterjee N. A subregion-based burden test for simultaneous identification of susceptibility loci and subregions in rare-varient association studies. Genet Epidemiol 2018. 

Support:

  • Updated:
Email