Influence of Sample Size on Genetic Mapping Using Back-Propagation Artificial Neural Networks
Genetic mapping is the localization of genes underlying phenotypes on the basis of corrdation with DNA variation, without the need for prior hypotheses about biological function. Although International HapMap Project had genotyped more than 3 million SNPs by 2007, genetic mapping uses classical genetic techniques to determine sequence features within a genome, it requires the use of DNA from lots of families, and is a very lame and labor intensive method. To research the influence of sample size on Genetic mapping, Back-Propagation Artificial Neural Network was used to simulate and predicate Influence of Sample Size on genetic mapping. The results showed that the sample size could affect mapping precise and accuracy, the precise and accuracy were substantially Improved along with the enlargement of sample size. The gene selection could affect mapping precise and accuracy like that the sample size did, and the mapping precise and accuracy were related to the map distance from estimated gene to tag gene. The larger the map distance estimated gene was, the smaller the precise and accuracy were. Selection could only affect the precise and accuracy of estimated genes with a certain selection rate of different recombined genotypes, and could not affect the other genes locating, even for their nearby gene. If we don locate the selected gene, the selected population could also be used for genetic mapping. These suggested that, for enhancing the mapping precise and accuracy, a large sample size and different map tag gene was needed.
Genetic mapping map distance artificial neural networks learning rate
Xue-Bin LI Xiao-Ling YU Kun ZHAO Zhi-Feng XIANG Xiao-Jian ZHANG
Henan Institute of Science and Technolog Xinxiang 453003, P. R. China
国际会议
2009 International Workshop on Information Security and Application(2009 信息安全与应用国际研讨会)
青岛
英文
291-294
2009-11-21（万方平台首次上网日期，不代表论文的发表时间）