How accurate can genetic predictions be?
Background Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central Ltd.
2013
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| Online Access: | http://hdl.handle.net/1721.1/76313 |
| _version_ | 1811083233061765120 |
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| author | Dreyfuss, Jonathan M. Levner, Daniel Galagan, James E. Church, George M. Ramoni, Marco F. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Dreyfuss, Jonathan M. Levner, Daniel Galagan, James E. Church, George M. Ramoni, Marco F. |
| author_sort | Dreyfuss, Jonathan M. |
| collection | MIT |
| description | Background Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified. |
| first_indexed | 2024-09-23T12:29:16Z |
| format | Article |
| id | mit-1721.1/76313 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:29:16Z |
| publishDate | 2013 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/763132022-10-01T09:19:44Z How accurate can genetic predictions be? Dreyfuss, Jonathan M. Levner, Daniel Galagan, James E. Church, George M. Ramoni, Marco F. Harvard University--MIT Division of Health Sciences and Technology Ramoni, Marco F. Background Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified. 2013-01-18T19:07:29Z 2013-01-18T19:07:29Z 2012-07 2011-11 2013-01-02T16:07:57Z Article http://purl.org/eprint/type/JournalArticle 1471-2164 http://hdl.handle.net/1721.1/76313 Dreyfuss, Jonathan M et al. “How Accurate Can Genetic Predictions Be?” BMC Genomics 13.1 (2012): 340. Web. en http://dx.doi.org/10.1186/1471-2164-13-340 BMC Genomics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Jonathan M Dreyfuss et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd. BioMed Central Ltd |
| spellingShingle | Dreyfuss, Jonathan M. Levner, Daniel Galagan, James E. Church, George M. Ramoni, Marco F. How accurate can genetic predictions be? |
| title | How accurate can genetic predictions be? |
| title_full | How accurate can genetic predictions be? |
| title_fullStr | How accurate can genetic predictions be? |
| title_full_unstemmed | How accurate can genetic predictions be? |
| title_short | How accurate can genetic predictions be? |
| title_sort | how accurate can genetic predictions be |
| url | http://hdl.handle.net/1721.1/76313 |
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