Haplotype reconstruction from genotype data using imperfect phylogeny. Bioinformatics

Critical to the understanding of the genetic basis for com-plex diseases is the modeling of human variation. Most of this variation can be characterized by single nucleotide poly-morphisms (SNPs) which are mutations at a single nucleotide position. To characterize the genetic variation between differ-ent people, we must determine an individual’s haplotype or which nucleotide base occurs at each position of these com-mon 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 that shows that SNPs are organized in highly correlated ‘blocks’. In a few recent studies, considerable parts of the human genome were partitioned into blocks, such that the majority of the sequenced genotypes have one of about four common hap-lotypes in each block. Our method partitions the SNPs into blocks, and for each block, we predict the common haplo-types and each individual’s haplotype.We evaluate our method over biological data. Our method predicts the common haplo-types perfectly and has a very low error rate (<2 % over the data) when taking into account the predictions for the uncom-mon haplotypes. Our method is extremely efficient compared with previous methods such as PHASE and HAPLOTYPER. Its efficiency allows us to find the block partition of the haplo-types, to cope with missing data and to work with large datasets. Availability: The algorithm is available via a Web server a