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Biostatistics Advance Access published online on September 19, 2006
Biostatistics, doi:10.1093/biostatistics/kxl028
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received January 11, 2006
Revised August 24, 2006
Accepted September 13, 2006

Article

Bayesian method for gene detection and mapping, using a case and control design and DNA pooling

Toby Johnson 1 *

1 School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3JT, UK and Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK

* To whom correspondence should be addressed.
Toby Johnson, E-mail: toby.johnson{at}ed.ac.uk


  Abstract

Association mapping studies aim to determine the genetic basis of a trait. A common experimental design uses a sample of unrelated individuals classified into two groups, for example cases and controls. If the trait has a complex genetic basis, consisting of many quantitative trait loci (QTLs), each group needs to be large. Each group must be genotyped at marker loci covering the region of interest; for dense coverage of a large candidate region, or a whole genome scan, the number of markers will be very large. The total amount of genotyping required for such a study is formidable. A laboratory effort efficient technique called DNA pooling could reduce the amount of genotyping required, but the data generated are less informative and require novel methods for efficient analysis.

In this paper, a Bayesian statistical analysis of the classic model of McPeek and Strahs is proposed. In contrast to previous work on this model, I assume that data are collected using DNA pooling, so individual genotypes are not directly observed, and also account for experimental errors. A complete analysis can be performed using analytical integration, a propagation algorithm for a hidden Markov model, and quadrature.

The method developed here is both statistically and computationally efficient. It allows simultaneous detection and mapping of a QTL, in a large scale association mapping study, using data from pooled DNA. The method is shown to perform well on datasets simulated under a realistic coalescent-with-recombination model, and is shown to outperform classical single point methods. The method is illustrated on data consisting of 27 markers in an 880kb region around the CYP2D6 gene.

Keywords: Association mapping; case and control study; DNA pooling; LD mapping; genetic association; QTL mapping.
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