Calibrated Bayes Factors in Assessing Genetic Association Models

J. G. Liao, Duanping Liao and Arthur Berg

The American Statistician, 2016, vol. 70, issue 3, 250-256

Abstract: Three competing genetic models—additive, dominant, and recessive—are often considered in genetic association analysis. We propose and develop a calibrated Bayes approach for comparing these competing models that has the desired property of giving equal support to the three models when no genetic association is present. The naïve approach with noncalibrated priors is shown to produce misleading Bayes factors. The method is fully developed with simulation studies, real data analyses, and an efficient algorithm based on an asymptotic approximation. An illuminating connection to the Kullback–Leibler divergence is also established. The proposed calibrated prior can serve as a reference prior for a genetic association study or as a common baseline prior for comparing Bayes analyses of different datasets.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2015.1109548 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:70:y:2016:i:3:p:250-256

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2015.1109548

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().