Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment. By varying the number of...

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Published in:	Genetics in medicine Vol. 19; no. 3; pp. 322 - 329
Main Authors:	Läll, Kristi, Mägi, Reedik, Morris, Andrew, Metspalu, Andres, Fischer, Krista
Format:	Journal Article
Language:	English
Published:	New York Elsevier Inc 01.03.2017 Nature Publishing Group US Elsevier Limited Nature Publishing Group
Subjects:	631/208/205/2138 631/208/2489/144 631/208/727/2000 692/699/2743/137/773 Alleles Biomedical and Life Sciences Biomedicine Body Mass Index Cohort Studies Diabetes Mellitus, Type 2 - genetics Diabetes Mellitus, Type 2 - prevention & control Diabetes Mellitus, Type 2 - therapy Female Genetic Predisposition to Disease genetic risk genetic risk score Genetic Testing - methods Genome-Wide Association Study Genotype Human Genetics Humans Laboratory Medicine Male Middle Aged Original original-research-article Polymorphism, Single Nucleotide precision medicine Precision Medicine - methods Prospective Studies Risk Factors risk prediction type 2 diabetes genetic risk score precision medicine type 2 diabetes risk prediction genetic risk
ISSN:	1098-3600, 1530-0366, 1530-0366
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Abstract	Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment. By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases). The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31–5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211–0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed. The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors. Genet Med19 3, 322–329.
AbstractList	Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment.PURPOSEUsing effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment.By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases).METHODSBy varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases).The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed.RESULTSThe best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed.The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329.CONCLUSIONThe proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329. Purpose: Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment. Methods: By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases). Results: The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31–5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211–0.444) . In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed. Conclusion: The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors. Genet Med 19 3, 322–329. Purpose:Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment.Methods:By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases).Results:The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed.Conclusion:The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329. Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment. By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases). The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31–5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211–0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed. The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors. Genet Med19 3, 322–329. Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment. By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases). The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed. The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329.
Author	Mägi, Reedik Fischer, Krista Morris, Andrew Läll, Kristi Metspalu, Andres
Author_xml	– sequence: 1 givenname: Kristi surname: Läll fullname: Läll, Kristi organization: Estonian Genome Center, University of Tartu, Tartu, Estonia – sequence: 2 givenname: Reedik surname: Mägi fullname: Mägi, Reedik organization: Estonian Genome Center, University of Tartu, Tartu, Estonia – sequence: 3 givenname: Andrew surname: Morris fullname: Morris, Andrew organization: Estonian Genome Center, University of Tartu, Tartu, Estonia – sequence: 4 givenname: Andres surname: Metspalu fullname: Metspalu, Andres organization: Estonian Genome Center, University of Tartu, Tartu, Estonia – sequence: 5 givenname: Krista surname: Fischer fullname: Fischer, Krista email: krista.fischer@ut.ee organization: Estonian Genome Center, University of Tartu, Tartu, Estonia
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27513194$$D View this record in MEDLINE/PubMed
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Keywords	genetic risk score precision medicine type 2 diabetes risk prediction genetic risk
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R: A language and environment for statistical computing, 2008. – volume: 14 start-page: 507 year: 2013 end-page: 515 ident: bb0035 article-title: Pitfalls of predicting complex traits from SNPs publication-title: Nat Rev Genet – volume: 26 start-page: 725 year: 2003 end-page: 731 ident: bb0095 article-title: The diabetes risk score: a practical tool to predict type 2 diabetes risk publication-title: Diabetes Care – volume: 81 start-page: 559 year: 2007 end-page: 575 ident: bb0065 article-title: PLINK: a tool set for whole-genome association and population-based linkage analyses publication-title: Am J Hum Genet – reference: .Accessed 9 December, 2015. – reference: . Accessed 1 January 2015. – volume: 111 start-page: E5272 year: 2014 end-page: E5281 ident: bb0050 article-title: Measuring missing heritability: inferring the contribution of common variants publication-title: Proc Natl Acad Sci USA – volume: 46 start-page: 234 year: 2014 end-page: 244 ident: bb0090 article-title: Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility publication-title: Nat Genet – reference: Novo Nordisk A/S. Changing diabetes barometer: diabetes—a chronic disease. – volume: 44 start-page: 981 year: 2012 end-page: 990 ident: bb0060 article-title: Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes publication-title: Nat Genet – volume: 54 start-page: 2811 year: 2011 end-page: 2819 ident: bb0025 article-title: Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study publication-title: Diabetologia – volume: 64 start-page: 1830 year: 2015 end-page: 1840 ident: bb0085 article-title: Sixty-five common genetic variants and prediction of type 2 diabetes publication-title: Diabetes – volume: 42 start-page: 565 year: 2010 end-page: 569 ident: bb0055 article-title: Common SNPs explain a large proportion of the heritability for human height publication-title: Nat Genet – volume: 8 start-page: e1002973 year: 2012 ident: bb0100 article-title: Comparison of family history and SNPs for predicting risk of complex disease publication-title: PLoS Genet – reference: MahajanAGoMJZhangWGenome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibilityNat Genet2014462342441:CAS:528:DC%2BC2cXitFanuro%3D10.1038/ng.2897 – reference: Mladovsky P, Allin S, Masseria C, et al. Health in the European Union: Trends and Analysis, 2009. http://www.euro.who.int/en/home. Accessed 10 January 2015. – reference: PoulsenPKyvikKOVaagABeck-NielsenHHeritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin studyDiabetologia1999421391451:CAS:528:DyaK1MXhtFajsbk%3D10.1007/s001250051131 – reference: ZengPZhaoYQianCStatistical analysis for genome-wide association studyJ Biomed Res20152928529726243515 – reference: PencinaMJD’AgostinoRBSrSteyerbergEWExtensions of net reclassification improvement calculations to measure usefulness of new biomarkersStat Med201130112110.1002/sim.4085 – reference: TalmudPJCooperJAMorrisRWUCLEB ConsortiumSixty-five common genetic variants and prediction of type 2 diabetesDiabetes201564183018401:CAS:528:DC%2BC2MXotVajtL4%3D10.2337/db14-1504 – reference: WrayNRYangJHayesBJPriceALGoddardMEVisscherPMPitfalls of predicting complex traits from SNPsNat Rev Genet2013145075151:CAS:528:DC%2BC3sXpsV2mu70%3D10.1038/nrg3457 – reference: PurcellSNealeBTodd-BrownKPLINK: a tool set for whole-genome association and population-based linkage analysesAm J Hum Genet2007815595751:CAS:528:DC%2BD2sXhtVSqurrL10.1086/519795 – reference: Cox DR.Tests of Separate Families of HypothesesProc Fourth Berkeley Symp Math Stat Probab19611105123 – reference: LyssenkoVLaaksoMGenetic screening for the risk of type 2 diabetes: worthless or valuable?Diabetes Care201336 Suppl 2S120S12610.2337/dcS13-2009 – reference: TuomilehtoJLindströmJErikssonJGFinnish Diabetes Prevention Study GroupPrevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose toleranceN Engl J Med2001344134313501:STN:280:DC%2BD3M3kvFejsA%3D%3D10.1056/NEJM200105033441801 – reference: R Development Core Team. R: A language and environment for statistical computing, 2008 . http://www.r-project.org.Accessed 9 December, 2015. – reference: DoCBHindsDAFranckeUErikssonNComparison of family history and SNPs for predicting risk of complex diseasePLoS Genet20128e10029731:CAS:528:DC%2BC38XhsFOrsLbI10.1371/journal.pgen.1002973 – reference: AlmgrenPLehtovirtaMIsomaaBBotnia Study GroupHeritability and familiality of type 2 diabetes and related quantitative traits in the Botnia StudyDiabetologia201154281128191:CAS:528:DC%2BC3MXht12js7rM10.1007/s00125-011-2267-5 – reference: LindströmJTuomilehtoJThe diabetes risk score: a practical tool to predict type 2 diabetes riskDiabetes Care20032672573110.2337/diacare.26.3.725 – reference: GolanDLanderESRossetSMeasuring missing heritability: inferring the contribution of common variantsProc Natl Acad Sci USA2014111E5272E52811:CAS:528:DC%2BC2cXhvFKmu77F10.1073/pnas.1419064111 – reference: YangJBenyaminBMcEvoyBPCommon SNPs explain a large proportion of the heritability for human heightNat Genet2010425655691:CAS:528:DC%2BC3cXns1GisL8%3D10.1038/ng.608 – reference: MorrisAPVoightBFTeslovichTMWellcome Trust Case Control Consortium; Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) ConsortiumLarge-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetesNat Genet2012449819901:CAS:528:DC%2BC38XhtFOgsLfP10.1038/ng.2383 – reference: Novo Nordisk A/S. Changing diabetes barometer: diabetes—a chronic disease. http://www.changingdiabetesbarometer.com/about-diabetes.aspx. Accessed 1 January 2015. – ident: 10.1038/gim.2016.103_bb0030 – ident: 10.1038/gim.2016.103_bb0080 – ident: 10.1038/gim.2016.103_bb0060 – ident: 10.1038/gim.2016.103_bb0040 – volume: 1 start-page: 105 year: 1961 ident: 10.1038/gim.2016.103_bb0075 article-title: Tests of Separate Families of Hypotheses publication-title: Proc Fourth Berkeley Symp Math Stat Probab – ident: 10.1038/gim.2016.103_bb0085 – ident: 10.1038/gim.2016.103_bb0025 – ident: 10.1038/gim.2016.103_bb0035 – ident: 10.1038/gim.2016.103_bb0020 – ident: 10.1038/gim.2016.103_bb0090 – ident: 10.1038/gim.2016.103_bb0065 – ident: 10.1038/gim.2016.103_bb0050 – ident: 10.1038/gim.2016.103_bb0055 – ident: 10.1038/gim.2016.103_bb0100 – ident: 10.1038/gim.2016.103_bb0010 – ident: 10.1038/gim.2016.103_bb0015 – ident: 10.1038/gim.2016.103_bb0045 – ident: 10.1038/gim.2016.103_bb0070 – ident: 10.1038/gim.2016.103_bb0095
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Snippet	Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2... Purpose: Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with... Purpose:Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with...
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SubjectTerms	631/208/205/2138 631/208/2489/144 631/208/727/2000 692/699/2743/137/773 Alleles Biomedical and Life Sciences Biomedicine Body Mass Index Cohort Studies Diabetes Mellitus, Type 2 - genetics Diabetes Mellitus, Type 2 - prevention & control Diabetes Mellitus, Type 2 - therapy Female Genetic Predisposition to Disease genetic risk genetic risk score Genetic Testing - methods Genome-Wide Association Study Genotype Human Genetics Humans Laboratory Medicine Male Middle Aged Original original-research-article Polymorphism, Single Nucleotide precision medicine Precision Medicine - methods Prospective Studies Risk Factors risk prediction type 2 diabetes
Title	Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores
URI	https://dx.doi.org/10.1038/gim.2016.103 https://link.springer.com/article/10.1038/gim.2016.103 https://www.ncbi.nlm.nih.gov/pubmed/27513194 https://www.proquest.com/docview/1874423331 https://www.proquest.com/docview/1859715721 https://pubmed.ncbi.nlm.nih.gov/PMC5506454
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