Genome-wide associations study (GWAS) results represent a potentially rich source of information for etiological and treatment research that builds bridges between genome science and clinical and public health practice 1,2. Given the large number of such studies, sufficient GWAS data exist to support such translational research for a number of common chronic health conditions, including obesity 3,4. Infrastructure is in place at the start of the translational pipeline with GWAS data banked and curated in continuously updated searchable databases 3,5. Likewise, at the other end of the pipeline, evidence from translational research is evaluated to establish the clinical utility of genomic information and to issue guidelines for clinical practice 6. However, significant gaps remain in the middle of the translational pipeline and approaches are needed to support research at this juncture, where population-based samples with rich environmental and phenotypic measurements can be used to follow-up disease markers identified in GWAS. Specifically, systematic approaches are needed to sift the results of numerous association studies and distill the most promising set of markers for further investigation. These approaches must be able to
