Dataset Information

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.

ABSTRACT:

Background

Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.

Methodology

We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).

Results

We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18)ConclusionIn a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).

SUBMITTER: Tam CH

PROVIDER: S-EPMC3869744 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.

Tam Claudia H T CH Ho Janice S K JS Wang Ying Y Lam Vincent K L VK Lee Heung Man HM Jiang Guozhi G Lau Eric S H ES Kong Alice P S AP Fan Xiaodan X Woo Jean L F JL Tsui Stephen K W SK Ng Maggie C Y MC So Wing Yee WY Chan Juliana C N JC Ma Ronald C W RC

PloS one 20131220 12

<h4>Background</h4>Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.<h4>Methodology</h4>We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 hea ...[more]

PMID: 24376643

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Project description:Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For predefined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true- and false-positive rates. We advocate the use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid predefined risk categories. However, it experiences many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.

Dataset Information

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.

Background

Methodology

Results

Publications

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.

Similar Datasets

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