  Haplotypes, the global patterns of DNA sequence variation, have important
implications for identifying complex traits. Recently, blocks of limited
haplotype diversity have been discovered in human chromosomes, intensifying the
research on modelling the block structure as well as the transitions or
co-occurrence of the alleles in these blocks as a way to compress the
variability and infer the associations more robustly. The haplotype block
structure analysis is typically complicated by the fact that the phase
information for each SNP is missing, i.e., the observed allele pairs are not
given in a consistent order across the sequence. The techniques for
circumventing this require additional information, such as family data, or a
more complex sequencing procedure. In this paper we present a hierarchical
statistical model and the associated learning and inference algorithms that
simultaneously deal with the allele ambiguity per locus, missing data, block
estimation, and the complex trait association. While the blo structure may
differ from the structures inferred by other methods, which use the pedigree
information or previously known alleles, the parameters we estimate, including
the learned block structure and the estimated block transitions per locus,
define a good model of variability in the set. The method is completely
datadriven and can detect Chron's disease from the SNP data taken from the
human chromosome 5q31 with the detection rate of 80% and a small error
variance.
