Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-05-28

AUTHORS

Hiroyuki Mishima, Andrew C Lidral, Jun Ni

ABSTRACT

BackgroundGenetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies.ResultsAnalysis of both consecutive and combinational window haplotypes was conducted by the FBAT (Laird et al., 2000) and Unphased (Dudbridge, 2003) programs. The dataset consisted of 26 loci from 277 extended families (1484 persons). Using the Rocks Linux cluster with 22 compute-nodes, FBAT jobs performed about 14.4–15.9 times faster, while Unphased jobs performed 1.1–18.6 times faster compared to the accumulated computation duration.ConclusionExecution of exhaustive haplotype analysis using non-parallel software packages on a Linux-based system is an effective and efficient approach in terms of cost and performance. More... »

PAGES

s10

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-9-s6-s10

DOI

http://dx.doi.org/10.1186/1471-2105-9-s6-s10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044920081

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18541045


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