Through genome-wide association studies, researchers have identified hundreds of genetic risk factors predisposing individuals to many complex diseases. Conducting these large scale genetic studies requires large patient cohorts that can be difficult to develop and be costly and time consuming.
Researchers at Brigham and Women’s Hospital (BWH) have demonstrated that there is the potential for using the clinical data from electronic health records to link to biological specimens to do genetic research. The study findings were published online in the “American Journal of Human Genetics.”
In this study, the researchers used software developed by the NIH funded “National Center for Biomedical Computing, Informatics for Integrating Biology and the Bedside” (i2b2) program. The researchers used i2b2 to link anonymous and de-identified biological specimens from a biobank along with corresponding de-identified EHRs to investigate genetic risk factors for an autoimmune disease—rheumatoid arthritis (RA).
The study included experts from MGH, Children’s Hospital Boston, MIT, Broad Institute, Harvard School of Public Health, and the Harvard Medical School, with funding provided in part by NLM.
In another research study, i2bi2 is helping researchers at MGH study obesity. The research on this subject is being conducted by Lee M Kaplan MD, PhD, Director, of the MGH Weight Center and his team. The team is looking at the knowledge already at hand but knows that the understanding of human obesity is still rudimentary.
One reason is that body weight and composition are affected by a multitude of factors including physiological, genetic, psychological, environmental, and developmental influences. Moreover, obesity can be influenced by age of onset, body fat distribution, associated eating behaviors, food preferences, energy expenditure responses, and associated (co-morbid) diseases.
The research project will use large cohorts of patients with obesity that can be identified from the large Partners HealthCare patient data repository with capabilities to efficiently mine data from the EMR. The plan is to identify specific clinical phenotypes that alone or in combination can predict important clinical outcomes and/or responses to therapeutic interventions.
The i2b2 team will work together to sub-stratify the patient cohorts and by using complex queries and statistical approaches try to determine clinically meaningful and statistically distinct subtypes. By using sophisticated query tools to extract data from large populations in combination with well established statistical methods, the team hopes to significantly accelerate the definition and characterization of clinically relevant obesity subtypes. The team is hopeful, that the research will accelerate the development of effective treatments and provide preventive strategies for obesity and its complications.
