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一种新型统计算法,用于提高HapMap数据在非HapMap人群中设计基因组关联研究的效用。

A novel statistical algorithm for enhancing the utility of HapMap data to design genomic association studies in non-HapMap populations.

作者信息

Sarkar-Roy Neeta, Mondal Debabrata, Bhattacharya Paramita, Majumder Partha

机构信息

TCG-ISI Centre for Population Genomics, Institute of Molecular Medicine, Kolkata, India.

出版信息

Int J Data Min Bioinform. 2011;5(6):706-16. doi: 10.1504/ijdmb.2011.045418.

Abstract

The HapMap database should be effectively used in designing disease association studies in non-HapMap populations. The efficiency of portability of tagSNPs from HapMap to non-HapMap populations is widely variable. A new algorithm is proposed for selecting SNPs from HapMap for use in non-HapMap populations by simultaneously considering and combining data on allele frequencies and linkage-disequilibrium values in the four HapMap populations. Empirical comparison and validation of the algorithm are provided by using Tagger, available HapMap data and data from an Indian population. The proposed method is shown to be efficient and effective. A software implementing this algorithm is freely available.

摘要

HapMap数据库应有效地用于设计非HapMap人群的疾病关联研究。标签单核苷酸多态性(tagSNPs)从HapMap转移到非HapMap人群的效率差异很大。本文提出了一种新算法,通过同时考虑和整合四个HapMap人群中等位基因频率和连锁不平衡值的数据,从HapMap中选择单核苷酸多态性(SNPs)用于非HapMap人群。利用Tagger、现有的HapMap数据和来自印度人群的数据,对该算法进行了实证比较和验证。结果表明,该方法是高效且有效的。实现该算法的软件可免费获取。

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