Instrumenting the health care enterprise for discovery research in the genomic era
Author(s) -
Shawn N. Murphy,
Susanne Churchill,
Lynn Bry,
Henry C. Chueh,
Scott T. Weiss,
Ross Lazarus,
Qing Zeng,
A. K. Dubey,
Vivian S. Gainer,
Michael Mendis,
John Glaser,
Isaac S. Kohane
Publication year - 2009
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.094615.109
Subject(s) - biology , disease , genotyping , pace , autism , health care , genomics , genome , computational biology , data science , genetics , computer science , genotype , medicine , psychiatry , geodesy , economic growth , economics , gene , geography
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.
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