Disease risk prediction with rare and common variants

BMC Proceedings

Proceedings

Andrew T DeWan¹  Chengqing Wu¹  Kyle M Walsh¹  Zuoheng Wang¹  Josephine Hoh¹ 

[1] Department of Epidemiology and Public Health, Yale University, 60 College Street, 06510, New Haven, CT, USA;

关键词: Support Vector Machine;    Rare Variant;    Risk Prediction;    Penalty Parameter;    Common Genetic Variant;   

DOI  :  10.1186/1753-6561-5-S9-S61

来源: Springer

【 摘 要 】

A number of studies have been conducted to investigate the predictive value of common genetic variants for complex diseases. To date, these studies have generally shown that common variants have no appreciable added predictive value over classical risk factors. New sequencing technology has enhanced the ability to identify rare variants that may have larger functional effects than common variants. One would expect rare variants to improve the discrimination power for disease risk by permitting more detailed quantification of genetic risk. Using the Genetic Analysis Workshop 17 simulated data sets for unrelated individuals, we evaluate the predictive value of rare variants by comparing prediction models built using the support vector machine algorithm with or without rare variants. Empirical results suggest that rare variants have appreciable effects on disease risk prediction.

【 授权许可】

CC BY   
© Wu et al; licensee BioMed Central Ltd. 2011