ArticlePloS one2014
The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data.Scientific reports · 2025Article
- Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score.Antioxidants (Basel, Switzerland) · 2022Article
- Genomic prediction in plants: opportunities for ensemble machine learning based approaches.F1000Research · 2022Article
- "H" for Heterogeneity in the Algorithm for Type 2 Diabetes Management.Current diabetes reports · 2020Review
- An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.Machine learning · 2020Article
- Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations.Frontiers in genetics · 2020Article
- A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.BMC medical genomics · 2019Article
- Machine Learning SNP Based Prediction for Precision Medicine.Frontiers in genetics · 2019Review
- Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data.Communications biology · 2019Article
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
- Genetic risk score of common genetic variants for impaired fasting glucose and newly diagnosed type 2 diabetes influences oxidative stress.Scientific reports · 2018Article
- Prediction of cholesterol ratios within a Korean population.Royal Society open science · 2018Article
- Genetic-risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10 year follow-up of a population-based cohort study.Journal of human genetics · 2016Article
- Whole genome prediction and heritability of childhood asthma phenotypes.Immunity, inflammation and disease · 2016Article
- Development of a genetic marker set to diagnose aspirin-exacerbated respiratory disease in a genome-wide association study.The pharmacogenomics journal · 2015Article
- Polymorphisms of the peroxisome proliferator-activated receptor-γ (rs1801282) and its coactivator-1 (rs8192673) are associated with obesity indexes in subjects with type 2 diabetes mellitus.Cardiovascular diabetology · 2015Article
- Genomic-based tools for the risk assessment, management, and prevention of type 2 diabetes.The application of clinical genetics · 2015Review
- Genetic-based prediction of disease traits: prediction is very difficult, especially about the future.Frontiers in genetics · 2014Review
- Genomics in Patient Care and Workforce Decisions in High-Level Isolation Units: A Survey of Healthcare Workers.Health securityArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (p--value 2:09 x 10(-11)). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.
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