Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example
Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment...
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| Published in | Frontiers in genetics Vol. 5; p. 254 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
01.08.2014
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| Abstract | Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.
We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.
The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.
The proposed method facilitates the standardized incorporation of a GRS in risk assessment. |
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| AbstractList | Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.PURPOSEGenetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.MATERIALS AND METHODSWe illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.RESULTSThe addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.The proposed method facilitates the standardized incorporation of a GRS in risk assessment.CONCLUSIONThe proposed method facilitates the standardized incorporation of a GRS in risk assessment. Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration. The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. The proposed method facilitates the standardized incorporation of a GRS in risk assessment. Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials and Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration. Results: The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment. Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials & Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor SNPs are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment. |
| Author | Ioannidis, John P. A. Assimes, Themistocles L. Goldstein, Benjamin A. Salfati, Elias Knowles, Joshua W. |
| AuthorAffiliation | 1 Department of Medicine, Stanford University School of Medicine Stanford, CA, USA 2 Department of Health Research and Policy, Stanford University School of Medicine Stanford, CA, USA 3 Department of Statistics, Stanford University School of Humanities and Sciences Stanford, CA, USA |
| AuthorAffiliation_xml | – name: 3 Department of Statistics, Stanford University School of Humanities and Sciences Stanford, CA, USA – name: 2 Department of Health Research and Policy, Stanford University School of Medicine Stanford, CA, USA – name: 1 Department of Medicine, Stanford University School of Medicine Stanford, CA, USA |
| Author_xml | – sequence: 1 givenname: Benjamin A. surname: Goldstein fullname: Goldstein, Benjamin A. – sequence: 2 givenname: Joshua W. surname: Knowles fullname: Knowles, Joshua W. – sequence: 3 givenname: Elias surname: Salfati fullname: Salfati, Elias – sequence: 4 givenname: John P. A. surname: Ioannidis fullname: Ioannidis, John P. A. – sequence: 5 givenname: Themistocles L. surname: Assimes fullname: Assimes, Themistocles L. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25136350$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1161/ATVBAHA.113.301218 10.1038/nature11632 10.1016/j.ygeno.2011.04.005 10.1097/01.CCM.0000275267.64078.B0 10.1038/gim.2013.131 10.1038/gim.2012.45 10.1161/circulationaha.113.007680 10.1161/01.cir.0000437741.48606.98 10.1038/ng.2480 10.1161/circ.106.25.3143 10.1038/gim.2013.88 10.1001/jama.2013.284657 10.1016/j.atherosclerosis.2012.05.035 10.1371/journal.pone.0040922 10.1001/jama.2010.503 10.1161/CIRCGENETICS.112.962746 10.1177/0962280213497434 10.2337/dc12-0884 10.1038/ng.2354 10.1161/CIRCULATIONAHA.113.007648 10.1016/j.ygeno.2011.08.007 10.1038/gim.2013.72 10.1161/circulationaha.106.672402 10.1016/S1474-4422(09)70275-3 10.1097/EDE.0000000000000018 |
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| Copyright | Copyright © 2014 Goldstein, Knowles, Salfati, Ioannidis and Assimes. 2014 |
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| Keywords | coronary heart disease genetic risk scores electronic health records personalized medicine |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Applied Genetic Epidemiology, a section of the journal Frontiers in Genetics. Edited by: Helena Kuivaniemi, Geisinger Health System, USA Reviewed by: Qing Lu, Michigan State University, USA; Braxton D. Mitchell, University of Maryland School of Medicine, USA |
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| Snippet | Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic... Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of... Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of... |
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| SubjectTerms | biomarkers Cardiovascular Diseases Coronary Disease Electronic Health Records genetic risk score (GRS) Genetics risk prediction |
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| Title | Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example |
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