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Japanese Alzheimer's disease and other complex disorders diagnosis based on mitochondrial SNP haplogroups.

Takasaki S - Int J Alzheimers Dis (2012)

Bottom Line: The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients.As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations.The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians.

View Article: PubMed Central - PubMed

Affiliation: Toyo University, Izumino 1-1-1, Ora-gun Itakuracho, Gunma 374-0193, Japan.

ABSTRACT
This paper first explains how the relations between Japanese Alzheimer's disease (AD) patients and their mitochondrial SNP frequencies at individual mtDNA positions examined using the radial basis function (RBF) network and a method based on RBF network predictions and that Japanese AD patients are associated with the haplogroups G2a and N9b1. It then describes a method for the initial diagnosis of Alzheimer's disease that is based on the mtSNP haplogroups of the AD patients. The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients. As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations. The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians.

No MeSH data available.


Related in: MedlinePlus

RBF network representation of the relations between individual mtSNPs and the AD patients. The input layer is the set of mtSNP sequences represented numerically (A, G, C, and T are converted to 1, 2, 3, and 4). The hidden layer classifies the input vectors into several clusters according to the similarities of individual input vectors. The determination of the output layer depends on which analysis is carried out. In the case of AD patients, 1 corresponds to AD patients and 0 corresponds to seven other classes of people. The other classes of people (PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, non-obese young males, and obese young males) are carried out in a similar way. Xi is the ith input vector, TN is the maximum number of vectors (in this example, TN = 523 (64 × 7 + 75  (112 × 2/3)), TSNP is the maximum number of mtSNPs (in this example, TSNP = 562), Mm is the location vector, m is the number of basis functions, μ is the basis function, σ is the standard deviation, wi is the ith weighting variable, and f(X) is the weighted sum function.
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fig1: RBF network representation of the relations between individual mtSNPs and the AD patients. The input layer is the set of mtSNP sequences represented numerically (A, G, C, and T are converted to 1, 2, 3, and 4). The hidden layer classifies the input vectors into several clusters according to the similarities of individual input vectors. The determination of the output layer depends on which analysis is carried out. In the case of AD patients, 1 corresponds to AD patients and 0 corresponds to seven other classes of people. The other classes of people (PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, non-obese young males, and obese young males) are carried out in a similar way. Xi is the ith input vector, TN is the maximum number of vectors (in this example, TN = 523 (64 × 7 + 75  (112 × 2/3)), TSNP is the maximum number of mtSNPs (in this example, TSNP = 562), Mm is the location vector, m is the number of basis functions, μ is the basis function, σ is the standard deviation, wi is the ith weighting variable, and f(X) is the weighted sum function.

Mentions: The RBF network shown in Figure 1 was learned from the training set as the mtSNPs of the AD patients were regarded as correct and the mtSNPs of other seven classes of people (i.e., PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, obese young males, and non-obese young males) were regarded as incorrect. The mtSNP classifications for the other seven classes were carried out in the same way as that for the AD patients (Figure 1).


Japanese Alzheimer's disease and other complex disorders diagnosis based on mitochondrial SNP haplogroups.

Takasaki S - Int J Alzheimers Dis (2012)

RBF network representation of the relations between individual mtSNPs and the AD patients. The input layer is the set of mtSNP sequences represented numerically (A, G, C, and T are converted to 1, 2, 3, and 4). The hidden layer classifies the input vectors into several clusters according to the similarities of individual input vectors. The determination of the output layer depends on which analysis is carried out. In the case of AD patients, 1 corresponds to AD patients and 0 corresponds to seven other classes of people. The other classes of people (PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, non-obese young males, and obese young males) are carried out in a similar way. Xi is the ith input vector, TN is the maximum number of vectors (in this example, TN = 523 (64 × 7 + 75  (112 × 2/3)), TSNP is the maximum number of mtSNPs (in this example, TSNP = 562), Mm is the location vector, m is the number of basis functions, μ is the basis function, σ is the standard deviation, wi is the ith weighting variable, and f(X) is the weighted sum function.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3405642&req=5

fig1: RBF network representation of the relations between individual mtSNPs and the AD patients. The input layer is the set of mtSNP sequences represented numerically (A, G, C, and T are converted to 1, 2, 3, and 4). The hidden layer classifies the input vectors into several clusters according to the similarities of individual input vectors. The determination of the output layer depends on which analysis is carried out. In the case of AD patients, 1 corresponds to AD patients and 0 corresponds to seven other classes of people. The other classes of people (PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, non-obese young males, and obese young males) are carried out in a similar way. Xi is the ith input vector, TN is the maximum number of vectors (in this example, TN = 523 (64 × 7 + 75  (112 × 2/3)), TSNP is the maximum number of mtSNPs (in this example, TSNP = 562), Mm is the location vector, m is the number of basis functions, μ is the basis function, σ is the standard deviation, wi is the ith weighting variable, and f(X) is the weighted sum function.
Mentions: The RBF network shown in Figure 1 was learned from the training set as the mtSNPs of the AD patients were regarded as correct and the mtSNPs of other seven classes of people (i.e., PD patients, T2D patients, T2D patients with angiopathy, centenarians, semi-supercentenarians, obese young males, and non-obese young males) were regarded as incorrect. The mtSNP classifications for the other seven classes were carried out in the same way as that for the AD patients (Figure 1).

Bottom Line: The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients.As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations.The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians.

View Article: PubMed Central - PubMed

Affiliation: Toyo University, Izumino 1-1-1, Ora-gun Itakuracho, Gunma 374-0193, Japan.

ABSTRACT
This paper first explains how the relations between Japanese Alzheimer's disease (AD) patients and their mitochondrial SNP frequencies at individual mtDNA positions examined using the radial basis function (RBF) network and a method based on RBF network predictions and that Japanese AD patients are associated with the haplogroups G2a and N9b1. It then describes a method for the initial diagnosis of Alzheimer's disease that is based on the mtSNP haplogroups of the AD patients. The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients. As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations. The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians.

No MeSH data available.


Related in: MedlinePlus