Kingsley Christopher B
Diabetes, Cardiovascular, and Metabolic Diseases Division, Translational Genomics Research Institute, Phoenix, AZ, USA.
Methods Mol Biol. 2011;700:37-46. doi: 10.1007/978-1-61737-954-3_3.
Over the last decade, genetic studies have identified numerous associations between single nucleotide polymorphism (SNP) alleles in the human genome and important human diseases. Unfortunately, extending these initial associative findings to identification of the true causal variants that underlie disease susceptibility is usually not a straightforward task. Causal variant identification typically involves searching through sizable regions of genomic DNA in the vicinity of disease-associated SNPs for sequence variants in functional elements including protein coding, regulatory, and structural sequences. Prioritization of these searches is greatly aided by knowledge of the location of functional sequences in the human genome. This chapter briefly reviews several of the common approaches used to functionally annotate the human genome and discusses how this information can be used in concert with the emerging technology of next generation high-throughput sequencing to identify causal variants of human disease.
在过去十年中,基因研究已经确定了人类基因组中的单核苷酸多态性(SNP)等位基因与重要人类疾病之间的众多关联。不幸的是,将这些最初的关联研究结果扩展到确定导致疾病易感性的真正因果变异通常并非易事。因果变异的鉴定通常涉及在与疾病相关的SNP附近的大片基因组DNA区域中搜索功能元件(包括蛋白质编码、调控和结构序列)中的序列变异。人类基因组中功能序列位置的知识极大地有助于这些搜索的优先级排序。本章简要回顾了几种用于对人类基因组进行功能注释的常见方法,并讨论了如何将这些信息与新一代高通量测序的新兴技术结合使用,以识别人类疾病的因果变异。