◾ Integrating Omics Data in Big Data Analysis

Abstract

Understanding the genetic basis of complex traits has been an ongoing quest for many researchers. e availability of rich omics data has made possible to derive global molecular insights into health and disease. Historically and currently, many investigators have ventured to probe each type of omics data independently to look for relationships with biological processes. Using these methods, some of the pieces of the puzzleof complex-trait genetic architecture and basic biological pathways have been successfully untangled. However, much of the genetic etiology of complex traits and biological networks remains unexplained, which could be partly due to the focus on restrictive single-data-type study designs. Recognizing this limitation, integrated omics data analyses have been used increasingly. is integrated omics approach can achieve a more thorough and informative interrogation of genotype-phenotype associations than an analysis that uses only a single data type. Combining multiple data types can compensate for missing or unreliable information in any single data type, and multiple sources of evidence pointing to the same gene or pathway are less likely to lead to false positives. Importantly, the complete biological model is only likely to be discovered if the dierent levels of omics data are considered in an analysis. In this chapter, we will highlight some successful applications of integrated omics data analysis, synopsize most important strategies in integrated omics data analysis, and demonstrate one special example of such integrated omics data analysis.