ArticleGenomics & informatics2016
Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes.
Article in Genomics & informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Association of Single Nucleotide Polymorphisms in KCNA10 and SLC13A3 Genes with the Susceptibility to Chronic Kidney Disease of Unknown Etiology in Central Indian Patients.Biochemical genetics · 2023Article
- Gene-Nutrient Interactions in Obesity:International journal of molecular sciences · 2023Article
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.NAR genomics and bioinformatics · 2021Article
- Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.Genetic epidemiology · 2020Article
- Gene-gene interaction: the curse of dimensionality.Annals of translational medicine · 2019Review
- Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes.Genomics & informatics · 2019Article
- Genome-wide association analysis identifies multiple loci associated with kidney disease-related traits in Korean populations.PloS one · 2018Article
- An ensemble-based likelihood ratio approach for family-based genomic risk prediction.Journal of Zhejiang University. Science. BArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.