Instrumenting the health care enterprise for discovery research in the genomic era

Abstract

Tens of thousands of subjects may be required to obtain reliable evidence relating disease characteristics to the weak effects typically reported from common genetic variants. The costs of assembling, phenotyping, and studying these large populations are substantial, recently estimated at three billion dollars for 500,000 individuals. They are also decade-long efforts. We hypothesized that automation and analytic tools can repurpose the informational byproducts of routine clinical care, bringing sample acquisition and phenotyping to the same high-throughput pace and commodity price-point as is currently true of genome-wide genotyping. Described here is a demonstration of the capability to acquire samples and data from densely phenotyped and genotyped individuals in the tens of thousands for common diseases (e.g., in a 1-yr period: N = 15,798 for rheumatoid arthritis; N = 42,238 for asthma; N = 34,535 for major depressive disorder) in one academic health center at an order of magnitude lower cost. Even for rare diseases caused by rare, highly penetrant mutations such as Huntington disease (N = 102) and autism (N = 756), these capabilities are also of interest.A common thread in the recent flurry of studies relating characteristics of complex diseases to the generally weak effects of individual genetic variants is that very large numbers of subjects are needed to obtain reproducible results-closer to 200,000 individuals (Manolio et al. 2006) than the few thousand typical of recent publications. The costs of assembling, phenotyping, and studying these huge populations are estimated at three billion dollars for 500,000 individuals (Spivey 2006). Reciprocally, studying rare diseases often requires searching through very large populations, and sufficient sample sizes are hard to achieve. Coincidentally, the United States spends over two trillion dollars in healthcare per year (Catlin et al. 2008), and of those costs, the total investment in information technology (IT) is at least seven billion dollars per year (Girosi et al. 2005). The stimulus package recently enacted by the U.S. Congress includes a very significant increase in spending on electronic health records, prompting interest in the secondary use of the data gathered in such records. Yet there is widespread, often justified skepticism about our ability to use routinely collected electronic health records (EHRs) for research-quality phenotype data, given the well-known biases and coarse-grained nature of billing/claims diagnoses and procedures (Safran 1991;Jollis et al. 1993). By the same measure, the consistency of phenotypic definitions in large genome-wide association studies (GWAS), especially when they consist of the aggregation of several existing studies, and the consequent effect upon these study results, has been questioned (Ioannidis 2007;Wojczynski and Tiwari 2008;Buyske et al. 2009).To meet these challenges, we have undertaken a series of institutional experiments that collectively demonstrate that automated systems for mining of EHRs are essentia...

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