218-223 Insights - Schadt NS.indd

Our understanding of common human diseases and how best to treat them is hampered by the complexity of the human system in which they are manifested. Unlike simple Mendelian disorders, in which highly expressive, highly penetrant mutations make it possible to identify the causal genes within families in which traits associated with the disorders segregate, common human diseases originate from a more complex interplay between constellations of changes in DNA (both rare and common variations) and a broad range of factors such as diet, age, gender and exposure to environmental toxins. These complex arrays of interacting factors are thought to affect entire network states that in turn increase or decrease the risk of disease or affect disease severity. In the context of common human diseases, the disease states can be considered emergent properties of molecular networks, as opposed to the core biological processes associated with a disease being driven by responses to changes in a small number of genes. Integrating large-scale, high-dimensional molecular and physiological data holds promise not only for defining the molecular networks that directly respond to genetic and environmental perturbations that associate with disease but also for causally associating such networks with the physiological states associated with disease. Given what must be considered a deluge of data of many different types flooding life sciences and biomedical research today, including genome-wide single-nucleotide polymorphism (SNP) genotyping data, whole-genome transcription data, next-generation DNA sequencing data, RNA sequencing data, chromatin immunoprecipitation (ChIP) sequencing data and image data, it is now time to begin addressing how these large-scale, high-dimensional data sets can be integrated to better understand the molecular networks underlying physiological states associated with disease. Here, I review the progress made over the past few years to integrate DNA variation, molecular profiling and clinical data collected in populations in order to construct causal probabilistic networks of disease, providing a more comprehensive view of disease than can be achieved by examining the different data dimensions on their own. Particular attention is paid to describing how the predictive networks produced from this type of integrative modelling can help link molecular states to physiological ones, providing an alternative path for understanding how molecular states drive complex disease processes.