Gene transcription is regulated with distinct sets of regulatory factors at multiple levels. Transcriptional and post-transcriptional regulation constitute two major regulation modes of gene expression to either activate or repress the initiation of transcription and thereby control the number of proteins synthesized during translation. Disruptions of the proper regulation patterns at transcriptional and post-transcriptional levels are increasingly recognized as causes of human diseases. Consequently, identifying the differential gene expression at transcriptional and post-transcriptional levels respectively is vital to identify potential disease-associated and/or causal genes and understand their roles in the disease development. Here, we proposed a novel method with a linear mixed model that can identify a set of differentially expressed genes at transcriptional and post-transcriptional levels. The simulation and real data analysis showed our method could provide an accurate way to identify genes subject to aberrant transcriptional and post-transcriptional regulation and reveal the potential causal genes that contributed to the diseases.
Keywords: causal gene; gene expression; post-transcriptional regulation; regulation pattern; transcriptional regulation.