The rapidly growing amount of genomic sequence data being generated and made 
publicly available necessitate the development of new data storage and archiving 
methods. The vast amount of data being shared and manipulated also create new 
challenges for network resources. Thus, developing advanced data compression 
techniques is becoming an integral part of data production and analysis. The 
HapMap project is one of the largest public resources of human single-nucleotide 
polymorphisms (SNPs), characterizing over 3 million SNPs genotyped in over 1000 
individuals. The standard format and biological properties of HapMap data 
suggest that a dedicated genetic compression method can outperform generic 
compression tools. We propose a compression methodology for genetic data by 
introducing HapZipper, a lossless compression tool tailored to compress HapMap 
data beyond benchmarks defined by generic tools such as gzip, bzip2 and lzma. We 
demonstrate the usefulness of HapZipper by compressing HapMap 3 populations to 
<5% of their original sizes. HapZipper is freely downloadable from 
https://bitbucket.org/pchanda/hapzipper/downloads/HapZipper.tar.bz2.