SNPs are fundamental roles for various applications including medical diagnostic, phylogenies and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Genetic variants that are near each other tend to be inherited together; these regions of linked variants are known as haplotypes. Recently, genetics researches revealed that SNPs within certain haplotype blocks induce only a few distinct common haplotypes in the majority of the population. The existence of haplotype block structure has serious implications for association-based methods for the mapping of disease genes. This paper proposes a parallel haplotype block partition and SNPs selection method under a diversity function by using the Hadoop MapReduce framework. The experiment shows that the proposed MapReduce-paralleled combinatorial algorithm performs well on the real-world data obtained in from the HapMap data set; the computation efficiency can be significantly improved proportional to the number of processors being used. © 2011 Springer-Verlag.
CITATION STYLE
Hung, C. L., Lin, Y. L., Hua, G. J., & Hu, Y. C. (2011). CloudTSS: A TagSNP selection approach on cloud computing. In Communications in Computer and Information Science (Vol. 261 CCIS, pp. 525–534). https://doi.org/10.1007/978-3-642-27180-9_64
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