Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes
Genomics & Informatics
;
: 138-148, 2016.
Artigo
em Inglês
| WPRIM
| ID: wpr-172207
ABSTRACT
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Modelos Logísticos
/
Curva ROC
/
Técnicas de Apoio para a Decisão
/
Medição de Risco
/
Área Sob a Curva
/
Diabetes Mellitus Tipo 2
/
Diagnóstico
/
Estudo de Associação Genômica Ampla
/
Conjunto de Dados
Tipo de estudo:
Estudo diagnóstico
/
Estudo de etiologia
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Inglês
Revista:
Genomics & Informatics
Ano de publicação:
2016
Tipo de documento:
Artigo
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