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Association Studies on mtDNA and Parkinson’s Disease Population Discrimination Using the Statistical Classification

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Since mitochondrial DNA (mtDNA) follows directly maternal inheritance, the presence of common polymorphisms in mtDNA sequences can classify mtDNAs into haplo groups and sub-haplo groups. Regarding to the rapidly growth of mtDNA sequences, many bioinformatics scientists are dedicated to uncover the association between the common mtDNA polymorphisms and the complex genetic diseases. In this study we analyze the mtDNA sequences from 96 Japanese Parkinson’s disease (PD) patients and 96 Japanese normal persons. A special algorithm based on keyword tree is employed to quickly align the mtDNAs. The mitochondrial single nucleotide polymorphisms (mtSNP) are revealed from the mtDNA alignments by using the genetic characteristic of SNPs and mtDNAs. A statistical significance based locating algorithm is proposed to select the disease associated mtSNPs as the features of classification in disease association research. Sequence transforming probability is introduced in the process of sample classification to discriminate the Parkinson’s disease patients and the common persons. The experimental results indicate that Parkinson’s disease patients can be characterized by unique mtSNPs. Although several mtSNPs are different from previously reported mtSNPs, the algorithm precision of Parkinson’s disease population discrimination reaches 90%.
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Keywords: Classification; Parkinson’s disease; genetic association analysis; mitochondrial DNA; mitochondrial SNP; statistical significance

Document Type: Research Article

Publication date: 2014-11-01

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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