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Haplotype Inference by Entropy Minimization

The completion of the Human Genome Project and the identification of millions of SNPs in the human population has opened the way for large-scale association studies between genetic variation and susceptibility to common diseases. Constructing a haplotype map of the human population has become the next major target in genomics (http://www.hapmap.org/). The population haplotyping problem (PHP) is to infer the haplotypes from the genotypes of a large population; see [1, 2, 3] for recent surveys on computational methods for solving this problem. Recently, Halperin and Karp [4] introduced a novel approach to PHP based on the entropy minimization principle, which suggests that most common haplotypes are evenly distributed in large populations. They also proposed a simple greedy entropy minimization algorithm for missing allele imputation and haplotype inference. In this poster we show that a local improvement entropy minimization algorithm yields significantly higher phasing accuracy than the algorithm of [4], coming close to the phasing accuracy of the widely used but much slower PHASE algorithm [5].