A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles.

Abstract:

:Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The semi-Bayesian approach promoted compares the probability of the observed data under disease hypotheses to the probability of the data under a null hypothesis defined by data at all the markers interrogated in a large study. This method automatically adjusts for the effects of diffuse population stratification. It is claimed that this characterization of the evidence for or against disease models may facilitate more appropriate inductions for large-scale genetic studies. Results include (i) an analytic solution for the population stratification-adjusted Bayes' factor, (ii) the relationship between sample size and Bayes' factors, (iii) an extension to an approximate Bayes' factor calculated across closely-linked sites, and (iv) an extension across multiple studies. Although this paper deals exclusively with genetic studies, it is possible to generalize the approach to treat many different large-scale experiments including studies of gene expression and proteomics.

authors

Schrodi SJ

doi

10.2202/1544-6115.1168

subject

Has Abstract

pub_date

2005-01-01 00:00:00

pages

Article31

eissn

2194-6302

issn

1544-6115

journal_volume

4

pub_type

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