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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/30899


    Title: 動態模型演算法在100K SNP資料之模擬研究
    Dynamic Model Based Algorithm on 100K SNP Data:A Simulation Study
    Authors: 黃慧珍
    Hui-Chen Huang
    Contributors: 郭訓志
    蔡紋琦
    Hsun-Chih Kuo
    Wen-Chi Tsai
    黃慧珍
    Hui-Chen Huang
    Keywords: 動態模型演算法(DM)
    單一核苷酸差異(SNP)
    Dynamic Model-based algorithm (DM)
    Single Nucleotide Polymorphism (SNP)
    Date: 2005
    Issue Date: 2009-09-14
    Abstract: 研究指出，在不同人類個體的DNA序列中，只有0.1%的基因組排列是相異的，而其餘的序列則是相同的。這些相異的基因組排列則被稱為單一核苷酸(SNP)。Affymetrix公司發展出一種DNA晶片技術稱之為Affymetrix GeneChip Mapping 100K SNP set，此晶片可用來決定單一核苷酸資料的基因類型(genotype)。Affymetrix公司採用預設「動態模型演算法」(DM)來決定基因型態。本論文的研究目的是探討與示範對於DM方法中預設的S值的四種修正方式。而這四種修正的方法分別是： (1) Standardized L value，(2) Median-polished L value，(3) Median-center L value，和(4) Median-standardized L value。為了比較S值與四種改進方法，本研究藉由SNP的模擬資料來進行比較。資料的模擬是基於利用改寫過的階層式之Bolstad模型（2004），而模擬模型的參數估計是利用華人細胞株及基因資料庫中95位台灣人的100K SNP資料。根據AA模型與AB模型模擬資料的基因型態正確率，Standardized L value是最好的判斷基因型態之方法。在另一方面，因為DM方法並不是設計來決定Null模型的基因型態，因此對於Null模型模擬資料的基因型態判斷會有問題。關於Null模型的基因型態判斷，本論文提供了一些簡短的討論與建議。然而，依然需要進一步的研究探討。
    It is known there is only 0.1% in the DNA sequences that is different among human beings, and the rest of them are the same. These differences in DNA sequences are defined as SNPs (Single Nucleotide Polymorphism). The Affymetrix, Inc. had developed a DNA chip technology called Affymetrix GeneChip Mapping 100K SNP set for SNP data used to determine the genotype call. The default algorithm applied by Affymetrix, Inc. to decide genotype calls is the Dynamic Model-based (DM) algorithm. This study aimed to investigate and demonstrate four different ways to modify the basic component used in DM algorithm, namely, the S value. These four modified methods include: (1) Standardized L value, (2) Median-polished L value, (3) Median-centered L value, and (4) Median-standardized L value. In order to compare the S value with the four modified L values, a simulation study was conducted. A hierarchical version of Bolstad’s model (2004) was adopted to simulate the SNP Data. The parameters for the simulation model were estimated based on 95 Taiwanese 100K SNPs data from Taiwan Han Chinese Cell and Genome bank. The Standardized L value was proven to be the best method based on the accuracy of the genotype calls determined according to the simulated data of AA model and AB model. On the other hand, the genotype call for simulated data under Null model is problematic since the DM approach is not designed to determine the Null model. We have given some brief discussion and remarks of the genotype call for Null model. However, further research is still needed.


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    Reference: Affymetrix (2002). Statistical Algorithms Description Document. Technical report, Affymetrix.
    Affymetrix. (2004). GeneChip DNA Analysis software GDAS User’s Guide. Version 3.0, Affymetrix.
    Bolstad, B.M. (2004). Low-level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. Ph.D, dissertation, University of California, Berkely, USA.
    Casella, G., Berger, R. L., (2002). Statistical Inference. DUXBURY.
    Cutler, D. J., Zwick, M.E., Carrasquillo, M.M., Yohn, C.T., Tobin, K.P., Kashuk, C., Mathews D.J., Shah N.A., Eichler E.E., Warrington J.A., and Chakravarti A. (2001). High-throughput variation detection and genotyping using microarrays. Genome Res., 11, 1913–1925.
    Di, X., Matsuzaki, H., Webster, T. A., Hubbell, E., Liu, G., Dong, S., Bartell D., Huang J., Chiles R., Yang G., Shen M., Kulp D., Kennedy G. C., Mei R., Jones K. W. and Cawley S. (2005). Dynamic model based algorithms for screening and genotyping over 100K SNPs on oligonucleotide microarrays. Bioinformatics, Vol. 21: 1958–1963.
    Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., and Speed, T. P. (2003). Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. 4, 249-264.
    Li, C., and Wong, H. (2001). Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biology 2(8): research 0032.1–0032.11.
    Li, C., and Wong, H. (2001). Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Science USA, 98, 31-36.
    Liu, W. M., Di, X., Yang, G., Matsuzaki, H., Huang, J., Mei, R., Ryder, T. B., Webster, T. A., Dong, S., Liu, G., Jones, K. W., Kennedy, G. C. and Kulp, D. (2003). . Algorithms for large-scale genotyping microarray. Bioinformatics, vol.19(18):2397-2403
    Description: 碩士
    國立政治大學
    統計研究所
    93354011
    94
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093354011
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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