Critical to the understanding of the genetic basis for complex diseases is the modeling of human variation. Most of this variation can be characterized by single nucleotide polymorphisms (SNPs) which are mutations at a single nucleotide position. To characterize the genetic variation between different people, we must determine an individual's haplotype or which nucleotide base occurs at each position of these common SNPs for each chromosome. In this paper, we present results for a highly accurate method for haplotype resolution from genotype data. Our method leverages a new insight into the underlying structure of haplotypes which shows that SNPs are organized in highly correlated "blocks". In a few recent studies (see Daly et al. (2001); Patil et al. (2001) ), considerable parts of the human genome were partitioned into blocks, such that the majority of the sequenced genotypes have one of about four common haplotypes in each block. Our method partitions the SNPs into blocks and for each block, we predict the common haplotypes and each individual's haplotype. We evaluate our method over biological data. Our method predicts the common haplotypes perfectly and has a very low error rate (less than ¢ ¡ over the data from Daly et al. (2001) .) when taking into account the predictions for the uncommon haplotypes. Our method is extremely efficient compared to previous methods, such as PHASE and HAPLOTYPER. Its efficiency allows us to find the block partition of the haplotypes, to cope with missing data and to work with large data sets. Availability: The algorithm is available via webserver at
In particular, Halperin and Eskin observed that the distribution of haplotypes in a block is uneven, and four main patterns of haplotypes constitute 97% of the haplotypes in each block REF .
Haplotype reconstruction from genotype data using imperfect phylogeny
Bioinformatics
Computer Science Biology Medicine