scholarly journals Risk prediction using genome-wide association studies

2010 ◽  
Vol 34 (7) ◽  
pp. 643-652 ◽  
Author(s):  
Charles Kooperberg ◽  
Michael LeBlanc ◽  
Valerie Obenchain
Keyword(s):  
Risk Prediction ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities

2012 ◽  
Vol 33 (12) ◽  
pp. 1708-1718 ◽  
Author(s):  
Florian Mittag ◽  
Finja Büchel ◽  
Mohamad Saad ◽  
Andreas Jahn ◽  
Claudia Schulte ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Risk Prediction ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Support Vector ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Vector Machines

scholarly journals Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies

2013 ◽  
Vol 45 (4) ◽  
pp. 400-405 ◽  
Author(s):  
Nilanjan Chatterjee ◽  
Bill Wheeler ◽  
Joshua Sampson ◽  
Patricia Hartge ◽  
Stephen J Chanock ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Could personalised risk prediction for type 2 diabetes using polygenic risk scores direct prevention, enhance diagnostics, or improve treatment?

2020 ◽  
Vol 5 ◽  
pp. 206
Author(s):  
Mathilde Boecker ◽  
Alvina G. Lai
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide

Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.

scholarly journals Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence

2013 ◽  
Vol 19 (4) ◽  
pp. 708-721 ◽  
Author(s):  
Jia Yan ◽  
Fazil Aliev ◽  
Bradley T. Webb ◽  
Kenneth S. Kendler ◽  
Vernell S. Williamson ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Candidate Gene ◽  
Risk Prediction ◽  
Genetic Information ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide
Sign in / Sign up

Export Citation Format

Share Document