The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power allow building and processing large collections of these data types. However, the super-rapid data generation is counterweighted by a slow-pace for data integration methods development. Most currently available integrative analytic tools pertain to pairing omics data and focus on between-data source relationships, making strong assumptions about within-data source architectures. A limited number of initiatives exist aiming to find the most optimal ways to analyze multiple, possibly related, omics databases, and fully acknowledge the specific characteristics of each data type. A thorough understanding of the underlying assumptions of integrative methods is needed to draw sound conclusions afterwards. In this chapter, the authors discuss how the field of “integromics” has evolved and give pointers towards essential research developments in this context.
A Hybrid Approach to Selecting Susceptible Single Nucleotide Polymorphisms for Complex Disease Analysis
