AI Article Synopsis
- SNPs are crucial for understanding human genetic variation and are used in various fields like medical diagnostics and drug design, serving as high-resolution genetic markers for disease associations.
- Haplotype blocks consist of closely linked genetic variants that are often inherited together, and their structure is important for mapping disease genes through association-based methods.
- The paper presents a new algorithm for identifying haplotype blocks, which outperforms previous methods and is optimized using the Hadoop MapReduce framework, leading to improved computational efficiency with increased processor usage.
Article Abstract
Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4307292 | PMC |
| http://dx.doi.org/10.3390/ijms16011096 | DOI Listing |
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