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Abstract:
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In the past few years, there has been a remarkable development in high-throughput omics technologies such as those that are relevant in genomics, epigenomics, and transcriptomics. These advancements have promoted the rise of data integration strategies to support a better understanding of biological systems and to eventually establish new associations between genes, biological functions, and a wide range of diseases. However, to date, little is known about the causal relationships among these multi-omic data despite the belief that understanding the links between them might offer entirely new avenues. The purpose of this study is to develop a statistical methodology to shed light on such relationships, which are anticipated to offer more biologically interpretable inferences and therefore can drive new scientific insights. Simulated and real data sets are utilized to demonstrate the performance of the proposed method under realistic settings.
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