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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Springer-Verlag
2016
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| Online Access: | http://hdl.handle.net/1721.1/101048 https://orcid.org/0000-0001-8411-6403 |
| _version_ | 1826207310593130496 |
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| author | Sinnott, Jennifer A. Dai, Wei Liao, Katherine P. Shaw, Stanley Y. Ananthakrishnan, Ashwin N. Gainer, Vivian S. Karlson, Elizabeth W. Churchill, Susanne Szolovits, Peter Murphy, Shawn N. Kohane, Isaac Plenge, Robert Cai, Tianxi |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Sinnott, Jennifer A. Dai, Wei Liao, Katherine P. Shaw, Stanley Y. Ananthakrishnan, Ashwin N. Gainer, Vivian S. Karlson, Elizabeth W. Churchill, Susanne Szolovits, Peter Murphy, Shawn N. Kohane, Isaac Plenge, Robert Cai, Tianxi |
| author_sort | Sinnott, Jennifer A. |
| collection | MIT |
| description | 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 fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case–control status, and this estimated case–control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies. |
| first_indexed | 2024-09-23T13:47:20Z |
| format | Article |
| id | mit-1721.1/101048 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:47:20Z |
| publishDate | 2016 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/1010482022-10-01T17:10:41Z Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records Sinnott, Jennifer A. Dai, Wei Liao, Katherine P. Shaw, Stanley Y. Ananthakrishnan, Ashwin N. Gainer, Vivian S. Karlson, Elizabeth W. Churchill, Susanne Szolovits, Peter Murphy, Shawn N. Kohane, Isaac Plenge, Robert Cai, Tianxi Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Szolovits, Peter 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 fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case–control status, and this estimated case–control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies. 2016-02-02T01:14:14Z 2016-02-02T01:14:14Z 2014-07 2014-02 Article http://purl.org/eprint/type/JournalArticle 0340-6717 1432-1203 http://hdl.handle.net/1721.1/101048 Sinnott, Jennifer A., Wei Dai, Katherine P. Liao, Stanley Y. Shaw, Ashwin N. Ananthakrishnan, Vivian S. Gainer, Elizabeth W. Karlson, et al. “Improving the Power of Genetic Association Tests with Imperfect Phenotype Derived from Electronic Medical Records.” Human Genetics 133, no. 11 (July 26, 2014): 1369–1382. https://orcid.org/0000-0001-8411-6403 en_US http://dx.doi.org/10.1007/s00439-014-1466-9 Human Genetics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer-Verlag PMC |
| spellingShingle | Sinnott, Jennifer A. Dai, Wei Liao, Katherine P. Shaw, Stanley Y. Ananthakrishnan, Ashwin N. Gainer, Vivian S. Karlson, Elizabeth W. Churchill, Susanne Szolovits, Peter Murphy, Shawn N. Kohane, Isaac Plenge, Robert Cai, Tianxi Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title | Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title_full | Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title_fullStr | Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title_full_unstemmed | Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title_short | Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| title_sort | improving the power of genetic association tests with imperfect phenotype derived from electronic medical records |
| url | http://hdl.handle.net/1721.1/101048 https://orcid.org/0000-0001-8411-6403 |
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