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Abstract:
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Allele specific expression (ASE) in diploid genomes refers to the phenomenon that the two alleles of a gene express substantially differently. To understand diseases associated with aberrations in ASE, we develop a new and powerful algorithm for detecting ASE regions in a healthy control group and regions of ASE alterations in a case group compared to the control. Specifically, we develop a bivariate Bayesian hidden Markov model (HMM) and an expectation-maximization inferential procedure. Our proposed algorithm gains advantages over existing methods by addressing their limitations and by recognizing the complexity of biology. First, the bivariate Bayesian HMM detects ASEs for different mRNA isoforms due to alternative splicing and RNA variants. Second, it models spatial correlations among genomic observations, unlike existing methods that often assume independence. At last, the bivariate HMM draws inferences simultaneously for control and case samples, which maximizes the utilization of available information in data. Real data analysis and simulation studies that mimic real data sets are shown to illustrate the improved performance and practical utility of our method.
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