Unsupervised Haplotype Reconstruction and LD Blocks Discovery in a Hidden Markov Framework

In the last years haplotype reconstruction and haplotype blocks discovery, i.e., the estimation of patterns of linkage disequilibrium (LD) in the haplotypes, riveted the attention of the computer scientists due to the involved strong computational aspects. Such tasks are usually faced separately; recently, statistical generative techniques permitted to solve them jointly. Following this trend, we propose a generative framework based on hidden Markov processes, equipped with two novel inference strategies. The first strategy estimates finely haplotypes, while the second provides a quantitative measure to estimate LD blocks boundaries. Comparative real data results validate the proposed framework.