Papers by Keyword: Single Nucleotide Polymorphism (SNP)

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Authors: Rui Bai, Xiao Li Hu, Lin Sheng Zhao, Xin Geng, Wei Ming Zhang
Abstract: To detect two single nucleotide polymorphisms (SNPs) in DNMT1 gene and the expression of corresponding region (1aa-120aa) in Wilms tumor (WT) and normal kidney tissue and analyze the association of polymorphism with clinicopathological parameters. Genomic DNA was extracted from tumor and normal tissues. PCR was used to amplify two SNP sites (rs16999593 and rs75616428 from the coding region of DNMT1). The PCR products were sequenced and genotyped. The protein expression of the first 120 aa of DNMT1 was evaluated by immunohistochemistry. Among 25 cases whose genotypes and gene frequencies studied, 18 cases of heterozygosity and 2 cases of homozygosity were identified. The region of the first 120 aa of DNMT1 was expressed in epithelial cells in normal kidney tissue, but was upregulated in tumors. The rs16999593 polymorphism might be used as a risk factor for WT diagnosis.
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Authors: Xue Bin Li, Xiao Ling Yu, Yun Rui Guo, Zhi Feng Xiang, Kun Zhao, Fei Ren
Abstract: Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.
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Authors: Xue Bin Li, Xiao Ling Yu, Xiao Jian Zhang
Abstract: Vast amount of bioinformation immerged in the past, HapMap Project had genotyped more than 3.1 million Single Nucleotide Polymorphisms (SNPs) information by 2007, a prediction equation based on SNPs was derived to calculate genomic breeding values. However, the simple mathematical function could not reflect the complex relation between genome and phenotypes. Unlike the methods of regression, artificial neural networks could perform well for optimization in complex non-linear systems; artificial neural networks have not been used to calculate genomic breeding values. In this paper, back-propagation neural network is used to simulate and predict the genomic breeding values or polygenic genotype value, and the different numbers of gene loci and hidden neurons were used to discuss the influence of the learning rate on estimating the polygenic genotype value. The result showed normalization was very important for training prediction model. After phenotype value normalized, optimum neural network for estimating the animal phenotype could be established without considering the gene number, but the optimum neural network should be selected from amount of neuronal networks with different hidden neuron number. No matter what the gene number is, as well as the number of hidden neurons is right, BP networks could be used to predict the animal phenotypes.
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