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A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data

机译:连锁分析和稀有变异关联的统一测试用于谱系序列数据的分析

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摘要

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.
机译:相关个体的高通量测序已成为研究人类疾病的重要工具。但是,由于技术复杂性和缺乏可用的工具,大多数基于谱系的测序研究依赖于次优分析的临时组合。在这里,我们介绍了谱系-VAAST(pVAAST),这是一种为谱系中的高通量序列数据设计的疾病基因识别工具。 pVAAST使用基于序列的模型来执行变体和基于基因的连锁分析。链接信息然后在统一的统计框架中与功能预测和稀有变异病例-病例关联信息结合在一起。在模拟中,pVAAST优于连锁和稀有变异关联测试,并从具有显性,隐性和从头遗传模式的三个人类谱系的全基因组序列数据中鉴定出致病基因。该方法对于不完全的外显力和基因座异质性是鲁棒的,并且适用于多种遗传特征。 pVAAST在单个谱系中的单基因,高渗透性表型与涉及数百个谱系的高度多基因,常见表型的研究中均保持着强大的实力。

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