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Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk
Open accessAbstract
OBJECTIVES—Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type 2 diabetes variants to differentiate between type 2 diabetic case and control subjects. RESEARCH DESIGN AND METHODS—We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC). RESULTS—Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally increased this to 0.80. CONCLUSIONS—Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However, the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.
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Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011
Tuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., Keinänen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200105033441801 · Cited by 10,873
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,601
Lippa, N. C., & Sanderson, S. C. (2013) · Human Heredity · doi:10.1159/000353712 · Cited by 6,078
Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531
Self-citationPan, X.-R., Li, G.-W., Hu, Y.-H., Wang, J.-X., Yang, W.-Y., An, Z.-X., Hu, Z.-X., Juan-Lin, Xiao, J.-Z., Cao, H.-B., et al. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.537 · Cited by 4,320
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes
Grant, S. F. A., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Manolescu, A., Sainz, J., Helgason, A., Stefansson, H., Emilsson, V., Helgadottir, A., et al. (2006) · Nature Genetics · doi:10.1038/ng1732 · Cited by 2,287
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Chen, L., Magliano, D. J., & Zimmet, P. Z. (2012) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2011.183 · Cited by 2,227
Houle, D., Govindaraju, D. R., & Omholt, S. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2897 · Cited by 1,279
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McCarthy, M. I. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0906948 · Cited by 938
Self-citationGenotype score in addition to common risk factors for prediction of type 2 diabetes
Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., Manning, A. K., Florez, J. C., Wilson, P. W. F., D'Agostino, R. B., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0804742 · Cited by 759
Toperoff, G., Aran, D., Kark, J. D., Rosenberg, M., Dubnikov, T., Nissan, B., Wainstein, J., Friedlander, Y., Levy-Lahad, E., Glaser, B., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/ddr472 · Cited by 349
Metadata
- Authors
- Lango, H., the U.K. Type 2 Diabetes Genetics Consortium, Palmer, C. N. A., Morris, A. D., Zeggini, E., Hattersley, A. T., McCarthy, M. I., Frayling, T. M., & Weedon, M. N.
- Venue
- Diabetes
- Publisher
- American Diabetes Association
- Published
- 2008-11-01
- Volume
- 57
- Issue
- 11
- Pages
- 3129-3135
- ISSN
- 0012-1797 (ISSN-L)
- 1939-327X
- OpenAlex
- W2159656713
- Cited by
- 294
- References
- 47
- FWCI
- 28.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Lango, H., the U.K. Type 2 Diabetes Genetics Consortium, Palmer, C. N. A., Morris, A. D., Zeggini, E., Hattersley, A. T., McCarthy, M. I., Frayling, T. M., & Weedon, M. N. (2008). Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk. Diabetes. https://doi.org/10.2337/db08-0504
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