Highly Scalable Genotype Phasing by Entropy Minimization

A Single Nucleotide Polymorphism (SNP) is a position in the genome at which two or more of the possible four nucleotides occur in a large percentage of the population. SNPs account for most of the genetic variability between individuals, and mapping SNPs in the human population has become the next high-priority in genomics after the completion of the Human Genome project. In diploid organisms such as humans, there are two non-identical copies of each autosomal chromosome. A description of the SNPs in a chromosome is called a haplotype. At present, it is prohibitively expensive to directly determine the haplotypes of an individual, but it is possible to obtain rather easily the conflated SNP information in the so called genotype. Computational methods for genotype phasing, i.e., inferring haplotypes from genotype data, have received much attention in recent years as haplotype information leads to increased statistical power of disease association tests. However, existing algorithms have impractical running time for phasing large genotype datasets such as those generated by the international HapMap project. In this paper we propose a highly scalable algorithm based on entropy minimization. Our algorithm is capable of phasing genotype data coming from either unrelated individuals or families consisting of a child and one or both parents. Experimental results show that our algorithm achieves a phasing accuracy close to that of best existing methods while being several orders of magnitude faster