Department of Genome Sciences, University of Washington School of Medicine, Seattle WA 98195, USA.
Hum Mol Genet. 2010 Oct 15;19(R2):R119-24. doi: 10.1093/hmg/ddq390. Epub 2010 Sep 15.
Massively parallel sequencing has enabled the rapid, systematic identification of variants on a large scale. This has, in turn, accelerated the pace of gene discovery and disease diagnosis on a molecular level and has the potential to revolutionize methods particularly for the analysis of Mendelian disease. Using massively parallel sequencing has enabled investigators to interrogate variants both in the context of linkage intervals and also on a genome-wide scale, in the absence of linkage information entirely. The primary challenge now is to distinguish between background polymorphisms and pathogenic mutations. Recently developed strategies for rare monogenic disorders have met with some early success. These strategies include filtering for potential causal variants based on frequency and function, and also ranking variants based on conservation scores and predicted deleteriousness to protein structure. Here, we review the recent literature in the use of high-throughput sequence data and its analysis in the discovery of causal mutations for rare disorders.
大规模平行测序技术能够快速、系统地大规模识别变体。这反过来又加速了基因发现和分子水平疾病诊断的步伐，并有可能彻底改变方法，特别是对于孟德尔疾病的分析。使用大规模平行测序技术，研究人员能够在没有连锁信息的情况下，在连锁区间的背景下，甚至在全基因组范围内检测变体。目前的主要挑战是区分背景多态性和致病性突变。最近为罕见单基因疾病开发的策略取得了一些早期成功。这些策略包括基于频率和功能过滤潜在的因果变异，以及基于保守评分和预测对蛋白质结构的有害性对变异进行排序。在这里，我们回顾了最近在使用高通量测序数据及其在罕见疾病因果突变发现中的分析方面的文献。
