A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations

The next phase of human genomics will involve largescale screens of populations for signi cant DNA polymorphisms, notably single nucleotide polymorphisms SNP's. Dense human SNP maps are currently under construction. However, the utility of those maps and screens will be limited by the fact that humans are diploid, and that it is presently di cult to get separate data on the two copies". Hence genotype blended SNP data will be collected, and the desired haplotype partitioned data must then be partially inferred. A particular non-deterministic inference algorithm was proposed and studied before SNP data was available, and extensively applied more recently to study the rst available SNP data. In this paper, we consider the question of whether we can obtain an e cient, deterministic variant of that method to optimize the obtained inferences. Although we have shown elsewhere that the optimization problem is NP-hard, we present here a practical approach based on integer linear pro...

• Type: Conference
• Year: 2000
• Where: ISMB
• Authors: Dan Gusfield
• Keywords: Computational Biology, Dense Human Snp, ISMB 2000, Nucleotide Polymorphisms Snp, SNP Data