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BMC Proc. 2011 Nov 29;5 Suppl 9:S93. doi: 10.1186/1753-6561-5-S9-S93.

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model.

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Department of Dairy Science, University of Wisconsin-Madison, 444 Animal Science Building, 1675 Observatory Drive, Madison, WI 53706-1284, USA.
Departamento de Ciências Exatas, Universidade Federal de Lavras, PO Box 3037, Lavras, MG 37200-000, Brazil.
Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, USA.
Department of Population Health Sciences, University of Wisconsin-Madison, 707 WARF Building, 610 North Walnut Street, Madison, WI 53726, USA.
Departamento de Salud Pública Universidad Industrial de Santander, Carrera 32 #29-31 Piso 3, Bucaramanga, Santander 680002, Colombia.
Contributed equally


Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.

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