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Haplotypes, the global patterns of DNA sequence variation, have importantimplications for identifying complex traits. Recently, blocks of limitedhaplotype diversity have been discovered in human chromosomes, intensifying theresearch on modelling the block structure as well as the transitions orco-occurrence of the alleles in these blocks as a way to compress thevariability and infer the associations more robustly. The haplotype blockstructure analysis is typically complicated by the fact that the phaseinformation for each SNP is missing, i.e., the observed allele pairs are notgiven in a consistent order across the sequence. The techniques forcircumventing this require additional information, such as family data, or amore complex sequencing procedure. In this paper we present a hierarchicalstatistical model and the associated learning and inference algorithms thatsimultaneously deal with the allele ambiguity per locus, missing data, blockestimation, and the complex trait association. While the blo structure maydiffer from the structures inferred by other methods, which use the pedigreeinformation or previously known alleles, the parameters we estimate, includingthe learned block structure and the estimated block transitions per locus,define a good model of variability in the set. The method is completelydatadriven and can detect Chron's disease from the SNP data taken from thehuman chromosome 5q31 with the detection rate of 80% and a small errorvariance.

Joint discovery of haplotype blocks and complex trait associations from SNP sequences