Papers by Keyword: Molecular Marker

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Authors: Bo Jiang, Xuan Zhou, Song Jin, Cun Yu Li, Ji Lin Li, Yu Xin Li, Yan Ming Zhang
Abstract: Crop genetic diversity is crucial for the environment, for feeding humanity and for sustainable agriculture development, which is providing genetic barriers against different biotic and abiotic stresses; however, it is being lost at an alarming rate. Fortunately, more and more people are conscious of the conservation and sustainable use of genetic resources goes far beyond avoiding the extinction of species, and the objective must be to conserve and use as much diversity as possible within each species. There is now a need for an integrated strategy for the conservation and management of crop genetic diversity and the organization of related information at several levels, for instance, at the highest level, it is necessary for entire agro-ecosystem, and also applies to the gene pools of individual crops at the interspecies level as well as at the intervarietal levels. This paper assesses the estimates methods on different genetic diversity in crop, introduces the status of crop genetic diversity, and prospects the significant conservation of crop genetic diversity for sustainable agriculture in the future.
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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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Authors: Zhou Xuan, Hong Dao Zhang, Zheng Hong Li, Cheng Zhang, Ji Lin Li, Yan Ming Zhang
Abstract: Plants are fundamental to life, being the basis of our food production and an essential part of the global ecosystem on which life on earth depends. Plant genetic resources include primitive forms of cultivated plant species and landraces, modern cultivars, breeding lines and genetic stocks, weedy types and related wild species, which provide the building blocks that, allow classical plant breeders and biotechnologists to develop new commercial varieties and other biological products. Detection and analysis of genetic variation can help us to understand the molecular basis of various biological phenomena in plants. Molecular markers for the detection and exploitation of DNA polymorphism is one of the most significant developments in the field of molecular genetics. The presence of various types of molecular markers, and differences in their principles, methodologies, and applications require careful consideration in choosing one or more of such methods. This article describes the advances of molecular marker in present, introduces the molecular basis in development of plant genetic resources and perspectives the important role of molecular marker in development of plant genetic resources in the future.
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