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Abstract:
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High-throughput sequencing experiments, which can determine allele origins, have been used to assess genome-wide allele-specific expression. Despite the amount of data generated from high-throughput experiments, statistical methods are often too simplistic to understand the complexity of gene expression. Existing methods do not test allele-specific expression of a gene as a whole and variation in allele-specific expression within a gene across exons separately and simultaneously. We propose a generalized linear mixed model to close these gaps, incorporating variations due to genes, SNPs and biological replicates. To improve reliability of inferences, we assign priors on each effect in the model. We utilize the Bayes factor to test the hypothesis of allele-specific gene expression for each gene and variations across SNPs within a gene. We apply our method to four tissue types from large offspring syndrome, and uncover intriguing predictions of regulatory allele-specific expression across exons and across tissue types. Simulation studies indicate the proposed method exhibits improved control of the false discovery rate and improved power over existing methods.
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