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Bridge: a GUI package for genetic risk prediction.

ABSTRACT: Risk prediction models capitalizing on genetic and environmental information hold great promise for individualized disease prediction and prevention. Nevertheless, linking the genetic and environmental risk predictors into a useful risk prediction model remains a great challenge. To facilitate risk prediction analyses, we have developed a graphical user interface package, Bridge.The package is built for both designing and analyzing a risk prediction model. In the design stage, it provides an estimated classification accuracy of the model using essential genetic and environmental information gained from public resources and/or previous studies, and determines the sample size required to verify this accuracy. In the analysis stage, it adopts a robust and powerful algorithm to form the risk prediction model.The package is developed based on the optimality theory of the likelihood ratio and therefore theoretically could form a model with high performance. It can be used to handle a relatively large number of genetic and environmental predictors, with consideration of their possible interactions, and so is particularly useful for studying risk prediction models for common complex diseases.

SUBMITTER: Ye C

PROVIDER: S-EPMC3878190 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Bridge: a GUI package for genetic risk prediction.

Ye Chengyin C Lu Qing Q

BMC genetics 20131220

<h4>Background</h4>Risk prediction models capitalizing on genetic and environmental information hold great promise for individualized disease prediction and prevention. Nevertheless, linking the genetic and environmental risk predictors into a useful risk prediction model remains a great challenge. To facilitate risk prediction analyses, we have developed a graphical user interface package, Bridge.<h4>Results</h4>The package is built for both designing and analyzing a risk prediction model. In t ...[more]

PMID: 24359333

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Project description:BackgroundAtrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke.MethodsTo determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from <1×10-3 to <1×10-8 in a prior independent genetic association study.ResultsIncident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13-1.46; P=1.5×10-4) to 1.67 (25 variants; 95% confidence interval, 1.47-1.90; P=9.3×10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39-4.58; P=2.7×10-3). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20-4.40; P=0.01).ConclusionsComprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

Dataset Information

Bridge: a GUI package for genetic risk prediction.

Publications

Bridge: a GUI package for genetic risk prediction.

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