Prediction Methods for Inherited Disease

Recent improvements in the accessibility of high-throughput genotyping have brought a great deal of attention to disease association studies[6]. It is believed that more accurate disease association is achieved with inferred haplotypes rather than with directly available genotypes. The main goal of disease association analysis is to identify gene variations or, in general, haplotypes which contribute to the risk of a particular disease. There are basically two main steps in disease association: (i) the population haplotyping and (ii) the haplotypes associating with diseases. We propose scalable methods for haplotyping family trio data and combinatorial methods for predicting susceptibility for complex diseases. We validated these methods by using a leave-one-out framework to estimate the predictive power of different predicting algorithms.