Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model.

ABSTRACT:

Background

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. Specifically, existing methods do not test allele-specific expression (ASE) of a gene as a whole and variation in ASE within a gene across exons separately and simultaneously.

Results

We propose a generalized linear mixed model to close these gaps, incorporating variations due to genes, single nucleotide polymorphisms (SNPs), and biological replicates. To improve reliability of statistical inferences, we assign priors on each effect in the model so that informatio

SUBMITTER: Xie J 

PROVIDER: S-EPMC6819473 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature