The Personalized Medicine Research Project (PMRP), is one of the study sites the eMERGE network, a consortium funded by the National Human Genome Research Institute and the National Institute of General Medical Sciences to perform genome wide association studies of selected phenotypes. The studies are performed using phenotypes defined from the electronic medical record and banked DNA samples. Each site in the network has a primary phenotype of interest, and in PMRP the primary phenotype was cataract, diagnosed in 3,947 participants. This PMRP sub-group is enriched for older adults and has a mean age of 72 years (age range 52 to 90 years). High-density lipoproteins are a second, separate phenotype of interest for the PMRP.
DNA samples for the 3,947 PMRP participants were genotyped on the Illumina HD Human660W-Quad BeadChip platform. Therefore, for each of these participants, genotype data are available for approximately 660,000 single nucleotide polymorphisms. These variants cover approximately 90% of common genomic variation at r2=0.8 for individuals of European ancestry.
Of the 3,947 PMRP participants with cataracts, 2,271 also completed a questionnaire that was part of the PhenX Toolkit project. Questionnaires in the PhenX Toolkit are designed to facilitate combining data from a variety of studies and are intended to aid in the integration of genetic and epidemiological research. In response to the PhenX Toolkit questionnaire, PMRP participants provided data on alcohol use, smoking, sun exposure, hand use preference, depression, and stroke history.
Each site in the eMERGE network is using computer algorithms to identify phenotypes of interest from the electronic medical record. Phenotypes for which algorithms have been developed are listed on the eMERGE network website. The algorithms are implemented and validated by members of the network and are available through collaboration with network sites. As part of this effort, PMRP electronic medical record data for adults aged 50 years and older were used to create a case-finding algorithm for type 2 diabetes mellitus. The following citation describes the methods for development of this algorithm:
Wilke RA, Berg RL, Peissig PL, Kitchner TE, Sijercic B, McCarty CA, McCarty DJ. Use of an electronic medical record for the identification of research subjects with diabetes mellitus. CLIN MED RES 2007;5:1-7.
PubMed ID: 17456828