Mutation detection by TaqMan-allele specific amplification: application to molecular diagnosis of glycogen storage disease type Ia and medium-chain acyl-CoA dehydrogenase deficiency.
ABSTRACT We have devised an allele-specific amplification method with a TaqMan fluorogenic probe (TaqMan-ASA) for the detection of point mutations. Pairwise PCR amplification using two sets of allele-specific primers in the presence of a TaqMan probe was monitored in real time with a fluorescence detector. Difference in amplification efficiency between the two PCR reactions was determined by "threshold" cycles to differentiate mutant and normal alleles without post-PCR processing. The method measured the efficiency of amplification rather than the presence or absence of end-point PCR products, therefore allowing greater flexibility in designing allele-specific primers and an ample technical margin for allelic discrimination. We applied the TaqMan-ASA method to detect a prevalent 727G>T mutation in Japanese patients with glycogen storage disease type Ia and a common 985A>G mutation in Caucasian patients with medium-chain acyl-CoA dehydrogenase deficiency. The method can be automated and may be applicable to the DNA diagnosis of various genetic diseases.
Article: Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma.[show abstract] [hide abstract]
ABSTRACT: Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease. To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs. ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients.BMC Bioinformatics 10/2004; 5:120. · 2.75 Impact Factor