Goldfeder Rachel L, Wall Dennis P, Khoury Muin J, Ioannidis John P A, Ashley Euan A
Am J Epidemiol. 2017 Oct 15;186(8):1000-1009. doi: 10.1093/aje/kww224.
Most human diseases have underlying genetic causes. To better understand the impact of genes on disease and its implications for medicine and public health, researchers have pursued methods for determining the sequences of individual genes, then all genes, and now complete human genomes. Massively parallel high-throughput sequencing technology, where DNA is sheared into smaller pieces, sequenced, and then computationally reordered and analyzed, enables fast and affordable sequencing of full human genomes. As the price of sequencing continues to decline, more and more individuals are having their genomes sequenced. This may facilitate better population-level disease subtyping and characterization, as well as individual-level diagnosis and personalized treatment and prevention plans. In this review, we describe several massively parallel high-throughput DNA sequencing technologies and their associated strengths, limitations, and error modes, with a focus on applications in epidemiologic research and precision medicine. We detail the methods used to computationally process and interpret sequence data to inform medical or preventative action.
大多数人类疾病都有潜在的遗传原因。为了更好地理解基因对疾病的影响及其对医学和公共卫生的意义,研究人员一直在探索确定单个基因序列的方法,然后是所有基因的序列,现在则是完整的人类基因组序列。大规模平行高通量测序技术,即将DNA剪切成较小片段、进行测序,然后通过计算重新排序和分析,能够实现对完整人类基因组的快速且经济高效的测序。随着测序成本持续下降,越来越多的个体进行了基因组测序。这可能有助于在人群层面更好地进行疾病分型和特征描述,以及在个体层面进行诊断和制定个性化的治疗与预防方案。在本综述中,我们描述了几种大规模平行高通量DNA测序技术及其相关的优势、局限性和错误模式,重点关注其在流行病学研究和精准医学中的应用。我们详细介绍了用于通过计算处理和解释序列数据以指导医疗或预防行动的方法。