Computational Haplotype Analysis: An overview of computational methods in genetic variation study

One of the major interests of current genomics research is disease-gene association, that is, identifying which DNA variation or a set of DNA variations is highly associated with a specific disease. In particular, single nucleotide polymorphisms (SNPs), which are the most common form of DNA variation on the human genome, and a set of SNPs on one chromosome, referred to as a haplotype, are at the forefront of the disease-gene association studies. In general, when haplotype information is used for studying disease-gene association, it is called haplotype analysis. Numerous studies have shown that haplotype analysis can successfully identify the DNA variations relevant to several common and complex human diseases. However, despite its advantages over other approaches, the use of haplotype analysis has been limited due to the high cost and long operation time of bio-molecular methods for obtaining the haplotype information. To address this limitation, two computational procedures, namely, Haplotype Phasing and Tag SNP Selection have been incorporated in haplotype analysis, and now provide the most practical framework for conducting large-scale association studies. In this depth paper, we introduce an overview of computational haplotype analysis, survey the existing approaches for Haplotype Phasing and Tag SNP Selection, and discuss their open problems. Given the current state of the field, as presented in this survey, we plan to conduct further research in the area of Tag SNP Selection. Chapter