教師資料查詢 | 類別: 期刊論文 | 教師: 王彥雯 WANG, CHARLOTTE (瀏覽個人網頁)

標題:Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies.
學年104
學期1
出版(發表)日期2015/08/24
作品名稱Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies.
作品名稱(其他語言)
著者Charlotte Wang; Wen-Hsin Kao; Chuhsing Kate Hsiao
單位
出版者
著錄名稱、卷期、頁數PLoS ONE 10(8), pp.e0135918
摘要The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.
關鍵字
語言英文(美國)
ISSN1932-6203
期刊性質國外
收錄於SCI;
產學合作
通訊作者Chuhsing Kate Hsiao
審稿制度
國別美國
公開徵稿
出版型式,電子版
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