The classic definition of a phenotype is the physical manifestation of genotype. However, with the increase knowledge of epigenetic, environmental and molecular alterations, the word phenotype has evolved and gained multiple definitions. The ability to develop genotypes of phenotypes on multiple levels – molecular, function, morphologic, or a combination of these three – facilitates personalized medicine.
One of the barriers to development of successful therapeutics is that many patients with common musculoskeletal symptoms (pain, immobility) from disparate diseases that have different mechanisms may be similarly treated, diluting the effectiveness of their treatment. The design and conduct of clinical trials requires optimized inclusion and exclusion criteria to select patients that share common pathophysiology responsive to the treatment tested, yet generalizable enough to implement in clinical practice.
At the other end of the spectrum of clinical research, the use of omics to understand molecular or genetic causes of diseases requires appropriate biospecimens for analysis. Starting with well-developed phenotypes will improve the ability to identify novel targets through omic technology that will have enhanced clinical applicability. Increasing the specificity of phenotypes is critical for precision medicine. If researchers cannot define the group of patients with a particular molecular or genetic cause of disease, then they cannot target that molecular or genetic mechanism. Big data, whether in the form of electronic health records, published literature, or omics databases is the foundation of well-defined phenotypes that allow the advancement of medical cures. he Indiana Center for Musculoskeletal Health Clinical Research Core leverage this data and campus resources to enhance clinical research in musculoskeletal disorders.
Using two complementary approaches, this core characterizes computer phenotypes that optimizes clinical trial design, clinical outcomes research, biomarker development, and therapeutic discovery. The first approach uses Electronic Health Records (HER) and Medical Informatics to phenotype musculoskeletal disorders. Data managers assist investigators to define, validate and refine computable phenotypes by accessing multiple clinical data sources in central Indiana, organized and integrated by the Regenstrief Institute. The second approach integrates and harmonizes published phenotypes for musculoskeletal disorders and resulting clinical symptoms through medical literature based text mining and knowledge discovery.
Recent technological advancements have expanded our ability to analyze human blood and tissue beyond genomic, to proteomics, transcriptomics (including microRNAs) and metabolomics. Genetic studies of complex traits are unlikely to explain fully the resulting phenotype. Fully understanding systems biology and identify regulatory pathways requires the integration of various omics as predictor variables to allow the creation of a molecular phenotype that can then be associated with a genotype or used as a biomarker. Developing novel methods to integrate the various omics analyses and the result is a more holistic understanding of the pathological conditions leading to musculoskeletal dysfunction. Additionally, the analyses elucidate novel metabolic pathways for drug targeting and circulating biomarkers to use for diagnostic or monitoring purposes.