Haiman Christopher A, Stram Daniel O
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Methods Mol Med. 2008;141:37-54. doi: 10.1007/978-1-60327-148-6_3.
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.
我们对人类基因组变异认识的进步以及高通量基因分型技术的迅速发展，使得研究大部分由常见变异导致的与可观察表型相关的人类遗传多样性成为可能。在过去几年中，公共单核苷酸多态性（SNP）数据库已经成熟，像国际人类基因组单体型图计划（International HapMap Project）所产生的全基因组SNP实证数据，已经展示了选择和测试信息性标记（“标签SNP”）的实用性和效率，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人类基因组单体型图资源和Haploview程序来处理和分析来自人类基因组单体型图的遗传数据，评估SNP之间的连锁不平衡关系，并选择在疾病关联研究中进行检测的标签SNP。
