Polygenic type 2 diabetes prediction at the limit of common variant detection

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in You...

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Published in:	Diabetes (New York, N.Y.) Vol. 63; no. 6; p. 2172
Main Authors:	Vassy, Jason L, Hivert, Marie-France, Porneala, Bianca, Dauriz, Marco, Florez, Jose C, Dupuis, Josée, Siscovick, David S, Fornage, Myriam, Rasmussen-Torvik, Laura J, Bouchard, Claude, Meigs, James B
Format:	Journal Article
Language:	English
Published:	United States 01.06.2014
Subjects:	Adolescent Adult Aged Black or African American - genetics Child Child, Preschool Diabetes Mellitus, Type 2 - epidemiology Diabetes Mellitus, Type 2 - genetics Environmental Exposure Female Follow-Up Studies Genetic Predisposition to Disease Genetic Variation Genome-Wide Association Study Genotype Humans Insulin Resistance - genetics Male Middle Aged Obesity - complications Obesity - genetics Polymorphism, Single Nucleotide Proportional Hazards Models Prospective Studies Risk Factors Sedentary Behavior White People - genetics
ISSN:	1939-327X, 1939-327X
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Abstract	Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.
AbstractList	Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants. Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.
Author	Siscovick, David S Bouchard, Claude Porneala, Bianca Rasmussen-Torvik, Laura J Meigs, James B Fornage, Myriam Hivert, Marie-France Florez, Jose C Dauriz, Marco Dupuis, Josée Vassy, Jason L
Author_xml	– sequence: 1 givenname: Jason L surname: Vassy fullname: Vassy, Jason L organization: Harvard Medical School, Boston, MASection of General Internal Medicine, VA Boston Healthcare System, Boston, MADivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA – sequence: 2 givenname: Marie-France surname: Hivert fullname: Hivert, Marie-France organization: Harvard Medical School, Boston, MADepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MADivision of Endocrinology, Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada – sequence: 3 givenname: Bianca surname: Porneala fullname: Porneala, Bianca organization: General Medicine Division, Massachusetts General Hospital, Boston, MA – sequence: 4 givenname: Marco surname: Dauriz fullname: Dauriz, Marco organization: Harvard Medical School, Boston, MAGeneral Medicine Division, Massachusetts General Hospital, Boston, MADivision of Endocrinology and Metabolic Diseases, Department of Medicine, University of Verona Medical School and Hospital Trust of Verona, Verona, Italy – sequence: 5 givenname: Jose C surname: Florez fullname: Florez, Jose C organization: Harvard Medical School, Boston, MADiabetes Research Center (Diabetes Unit), and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MAProgram in Medical and Population Genetics, Broad Institute, Cambridge, MA – sequence: 6 givenname: Josée surname: Dupuis fullname: Dupuis, Josée organization: Department of Biostatistics, Boston University School of Public Health, Boston, MANational Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA – sequence: 7 givenname: David S surname: Siscovick fullname: Siscovick, David S organization: Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA – sequence: 8 givenname: Myriam surname: Fornage fullname: Fornage, Myriam organization: Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX – sequence: 9 givenname: Laura J surname: Rasmussen-Torvik fullname: Rasmussen-Torvik, Laura J organization: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL – sequence: 10 givenname: Claude surname: Bouchard fullname: Bouchard, Claude organization: Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA – sequence: 11 givenname: James B surname: Meigs fullname: Meigs, James B email: jmeigs@partners.org organization: Harvard Medical School, Boston, MAGeneral Medicine Division, Massachusetts General Hospital, Boston, MA jmeigs@partners.org
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SubjectTerms	Adolescent Adult Aged Black or African American - genetics Child Child, Preschool Diabetes Mellitus, Type 2 - epidemiology Diabetes Mellitus, Type 2 - genetics Environmental Exposure Female Follow-Up Studies Genetic Predisposition to Disease Genetic Variation Genome-Wide Association Study Genotype Humans Insulin Resistance - genetics Male Middle Aged Obesity - complications Obesity - genetics Polymorphism, Single Nucleotide Proportional Hazards Models Prospective Studies Risk Factors Sedentary Behavior White People - genetics
Title	Polygenic type 2 diabetes prediction at the limit of common variant detection
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