利用HapMap和标记snp。

Christopher A Haiman, Daniel O Stram
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引用次数: 18

摘要

我们对人类基因组变异的理解的进步和高通量基因分型技术的快速改进,使得研究大多数人类遗传多样性成为可能,这些多样性是由于与可观察表型相关的共同变异造成的。在过去的几年里,公共SNP数据库已经成熟,全基因组SNP数据,如国际HapMap项目产生的经验数据,已经显示了选择和测试信息标记(“标签SNP”)的效用和效率,这些标记利用了由于连锁不平衡(LD)导致的附近多态性之间的冗余。在本章中,我们将演示如何使用HapMap资源和Haploview程序来处理和分析来自HapMap的遗传数据,评估snp之间的LD关系,并选择标记snp在疾病关联研究中进行检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing HapMap and tagging SNPs.

Advancements in our understanding of variation in the human genome and rapid improvements in high-throughput genotyping technology have made it feasible to study most of the human genetic diversity that is due to common variations in relation to observable phenotypes. Over the past few years, public SNP databases have matured and empirical genome-wide SNP data, such as that generated by the International HapMap Project, have shown the utility and efficiency of selecting and testing informative markers ("tag SNPs") that exploit redundancies among nearby polymorphisms due to linkage disequilibrium (LD). In this chapter, we will demonstrate how to use the HapMap resource and the Haploview program to process and analyze genetic data from HapMap, to evaluate LD relations between SNPs, and to select tagging SNPs to be examined in disease association studies.

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