A Set of Dynamic Programming Algorithms for Haplotype Block Partitioning and Tag SNP Selection via Haplotype Data or Genotype Data

Recent studies have revealed a haplotype block structure for human genome such that it can be decomposed into large blocks with high linkage disequilibrium (LD) and relatively limited haplotype diversity, separated by short regions of low LD. One of the practical implications of this observation is that only a small number of tag SNPs can be chosen for mapping genes responsible for human complex diseases, which can significantly reduce genotyping effort without much loss of power. In this paper, we summarize the dynamic programming algorithms developed for haplotype block partitioning and tag SNP selection, with a focus on algorithmic consideration. Extensions of the algorithms for use to genotype data from unrelated individuals as well as genotype data from general pedigrees are considered. Finally, we discuss the implications of haplotype blocks and tag SNPs in association studies to search for complex disease genes.