Maniatis Nikolas
Human Genetics Division, Southampton General Hospital, UK.
Methods Mol Biol. 2007;376:109-21. doi: 10.1007/978-1-59745-389-9_8.
Over the last few years, association mapping of disease genes has developed into one of the most dynamic research areas of human genetics. It focuses on identifying functional polymorphisms that predispose to complex diseases. Population-based approaches are concerned with exploiting linkage disequilibrium (LD) between single-nucleotide polymorphism (SNPs) and disease-predisposing loci. The utility of SNPs in association mapping is now well established and the interest in this field has been escalated by the discovery of millions of SNPs across the genome. This chapter reviews an association-mapping method that utilizes metric LD maps in LD units and employs a composite likelihood approach to combine information from all single SNP tests. It applies a model that incorporates a parameter for the location of the causal polymorphism. A proof-of-principle application of this method to a small region is given and its potential properties to large-scale datasets are discussed.
在过去几年中,疾病基因的关联图谱研究已发展成为人类遗传学中最具活力的研究领域之一。它专注于识别易导致复杂疾病的功能性多态性。基于群体的方法致力于利用单核苷酸多态性(SNP)与疾病易感位点之间的连锁不平衡(LD)。SNP在关联图谱中的效用现已得到充分确立,并且全基因组数百万个SNP的发现进一步激发了该领域的研究兴趣。本章回顾了一种关联图谱方法,该方法利用以LD单位表示的度量LD图谱,并采用复合似然方法来整合所有单个SNP测试的信息。它应用了一个包含因果多态性位置参数的模型。给出了该方法在一个小区域上的原理验证应用,并讨论了其在大规模数据集上的潜在特性。