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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.1
- 수록면
- 138 - 148 (11page)
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The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.
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참고문헌 (57)
참고문헌 신청
Manolio TA / 2009 / Finding the missing heritability of complex diseases / Nature 461:747~753
Wang WY / 2005 / Genome-wide association studies: theoretical and practical concerns / Nat Rev Genet 6:109~118
Evans DM / 2009 / Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk / Hum Mol Genet 18:3525~3531
International Schizophrenia Consortium / 2009 / Common polygenic variation contributes to risk of schizophrenia and bipolar disorder / Nature 460:748~752
Davies RW / 2010 / Improved prediction of cardiovascular disease based on a panel of single nucleotide polymorphisms identified through genome-wide association studies / Circ Cardiovasc Genet 3:468~474
Wang WY / 2005 / Genome-wide association studies: theoretical and practical concerns / Nat Rev Genet 6:109~118
Evans DM / 2009 / Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk / Hum Mol Genet 18:3525~3531
International Schizophrenia Consortium / 2009 / Common polygenic variation contributes to risk of schizophrenia and bipolar disorder / Nature 460:748~752
Davies RW / 2010 / Improved prediction of cardiovascular disease based on a panel of single nucleotide polymorphisms identified through genome-wide association studies / Circ Cardiovasc Genet 3:468~474
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