Goal 3: Advance Translational Research

Maximizing Previous Investment in Existing Cohorts

Everyone would like to see integration of genomic, metabolomic, epigenomic, proteomic, transcriptomic, etc. data analyzed in the context of clinical disease, environmental influences, and even end-organ effects (lung versus heart or blood as an example). Rarely can this occur on small cohorts, but rarely are funds available to take maximum use of existing large cohorts and the samples and information collected within them. Multi-million dollar cohorts with multi-million dollar sample storage requirements deserve multi-million dollar investment in sample analysis and data integration (what good is a sample stored in a freezer). How can we fund these efforts and generate a mechanism whereby samples are not left unanalyzed.

Tags (Keywords associated with the idea)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)?: Critical Challenge (CC)

Details on the impact of addressing this CQ or CC:

The impact would be huge as it would leverage already extremely expensive cohorts to maximum potential, allowing for exploration into clinical subphenotyping, disease mechanisms, personalized medicine, surrogate endpoints, biomarker exploration, etc. Maximizine output on previous investment is the clearest impact, since even simple analysis in a large number of samples adds up to a very hefty sum. Additionally, data from samples becomes more valuable with longitudinal follow-up of available subjects.

Feasibility and challenges of addressing this CQ or CC:

The challenges include the expense of analysis in large cohorts and the ability to attract and fund high level biostatistical faculty at top-notch institutions and get them engaged fully in the problem. Biostatisticians of high caliber will not engage without funding and without an ability to “train” students using the data and explore their own research interests within the context of the overall clinical problem. Funding mechanisms that are large (to allow for deep phenotyping of cohort samples on multiple platforms in multiple sample types) and that seek to generate solid and ongoing collaborations between the data generators and the data analyzers must emerge.

Name of idea submitter and other team members who worked on this idea: Wanda K. O’Neal, PhD


22 net votes
34 up votes
12 down votes
Idea No. 132