Bayesian graphical models for genomewide association studies
Verzilli, Claudio J., Stallard, Nigel and Whittaker, John C.. (2006) Bayesian graphical models for genomewide association studies. American Journal of Human Genetics, Volume 79 (Number 1). pp. 100-112. ISSN 0002-9297Full text not available from this repository.
Official URL: http://dx.doi.org/10.1086/505313
As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single- locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single- nucleotide polymorphisms. Analysis is quick (< 5 min), and we are able to localize the causative site to a very short interval.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH426 Genetics|
|Divisions:||Faculty of Medicine > Warwick Medical School|
|Journal or Publication Title:||American Journal of Human Genetics|
|Official Date:||July 2006|
|Number of Pages:||13|
|Page Range:||pp. 100-112|
|Access rights to Published version:||Restricted or Subscription Access|
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