检测外显子组序列数据中的多个因果罕见变异。
Detecting multiple causal rare variants in exome sequence data.
机构信息
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
出版信息
Genet Epidemiol. 2011;35 Suppl 1(Suppl 1):S18-21. doi: 10.1002/gepi.20644.
Abstract
Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants. Weaknesses of the exome sequence data and the relative strengths and weaknesses of the five approaches are discussed.
摘要
测序技术的最新进展带来了机遇和挑战，在实际样本量下，检测单个因果罕见变异的统计能力有限。为了克服这一问题，第 17 届遗传分析研讨会第 1 组的参与者试图开发出一种以生物学意义上有意义的方式来检测多个因果罕见变异的综合信号的方法。参与者使用基因、基因组位置邻近性或遗传途径作为组合来自多个变体的信息的基本单元。讨论了外显子组序列数据的弱点以及五种方法的相对优势和弱点。
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