Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records

Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamenta...

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Main Authors: Sinnott, Jennifer A. (Author), Dai, Wei (Author), Liao, Katherine P. (Author), Shaw, Stanley Y. (Author), Ananthakrishnan, Ashwin N. (Author), Gainer, Vivian S. (Author), Karlson, Elizabeth W. (Author), Churchill, Susanne (Author), Szolovits, Peter (Contributor), Murphy, Shawn N. (Author), Kohane, Isaac (Author), Plenge, Robert (Author), Cai, Tianxi (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Springer-Verlag, 2016-02-02T01:14:14Z.
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