A combinatorial method for predicting genetic susceptibility to complex diseases.
Recent improvements in the accessibility of high-throughput genotyping have brought a great deal of attention to disease association and susceptibility studies. This paper explores possibility of applying combinatorial methods to disease susceptibility prediction. The proposed combinatorial methods as well as standard statistical methods are applied to publicly available genotype data on Crohn's disease and autoimmune disorders for predicting susceptibility to these diseases. The quality of susceptibility prediction algorithm is assessed using leave-one-out and leave-many-out tests - the disease status of one or several individuals is predicted and compared to the their actual disease status which is initially made unknown to the algorithm. The best prediction rate achieved by the proposed algorithms is 77.78% for Crohn's disease and 64.99% for autoimmune disorders, respectively.
Mao
Weidong
W
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. E-Mail: wmao@cs.gsu.edu.
He
Jingwu
J
Brinza
Dumitru
D
Zelikovsky
Alex
A