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(1) Séminaire(s)


Mar. 11/07/2017 10:45 K71, Bâtiment K, RdC

ALVAREZ Ignacio (Iowa State University | UdelaR)
Hierarchical Bayesian analysis of allele-specific gene expression data.


Diploid organisms have two copies of each gene (alleles) that can be separately transcribed. The
RNA abundance of any particular allele is known as allele-specific expression (ASE). When two
alleles have sequences of polymorphisms in transcribed regions, ASE can be studied with RNA-
seq read count data. Reads counts that can be unambiguously attributed to a specific allele are
correlated with allele's expression.

We present statistical methods for modeling ASE and detecting genes with differential allele
expression. A hierarchical overdispersed count regression model is used to deal with ASE counts from a single
hybrid genotype. The model accommodates gene-specific overdispersion, it has an internal
measure of the reference allele bias, controls the allele effect sparsity using shrinkage
distributions, and include sample effects. The hierarchical model is extended to incorporate more
genotypes (the hybrid and its inbred parental lines) and other types of gene expression (total
RNA-seq and ASE). Using the extended model we study the relationship between specific allelic
gene-specific patterns and the so-called hybrid vigor.

Fully Bayesian inference is obtained using fbseq package that implements a parallel
strategy to make the computational times reasonable. Simulation and real data analysis suggest
the proposed model is a practical and powerful tool for the study of differential allele expression.

Pour plus d'informations, merci de contacter Cugliari J.