用于下一代测序数据连锁分析的塌缩单倍型模式方法。
Collapsed haplotype pattern method for linkage analysis of next-generation sequence data.
作者信息
Wang Gao T, Zhang Di, Li Biao, Dai Hang, Leal Suzanne M
机构信息
Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
出版信息
Eur J Hum Genet. 2015 Dec;23(12):1739-43. doi: 10.1038/ejhg.2015.64. Epub 2015 Apr 15.
Recent advances in next-generation sequencing (NGS) make it possible to directly sequence genomes and exomes of individuals with Mendelian diseases and screen sequence data for causal variants. With the reduction in cost of NGS, DNA samples from entire families can be sequenced and linkage analysis can be performed directly using NGS data. Inspired by 'burden' tests, which are used for complex trait rare variant association studies, we developed the collapsed haplotype pattern (CHP) method for linkage analysis. Using data from several deafness genes we demonstrate that the CHP method is substantially more powerful than analyzing individual variants. Unlike applying NGS data filtering approaches, the CHP method provides statistical evidence of a gene's involvement in disease etiology and is also less likely to exclude causal variants in the presence of phenocopies and/or reduced penetrance. The CHP method was implemented in the SEQLinkage software package, which can perform linkage analysis on NGS data or can generate data compatible with many linkage analysis programs, reviving them for use in NGS era.
新一代测序（NGS）技术的最新进展使得直接对患有孟德尔疾病的个体的基因组和外显子组进行测序，并筛选序列数据中的致病变异成为可能。随着NGS成本的降低，可以对整个家族的DNA样本进行测序，并直接使用NGS数据进行连锁分析。受用于复杂性状罕见变异关联研究的“负荷”检验的启发，我们开发了用于连锁分析的压缩单倍型模式（CHP）方法。利用来自多个耳聋基因的数据，我们证明CHP方法比分析单个变异的效力要高得多。与应用NGS数据过滤方法不同，CHP方法提供了基因参与疾病病因的统计证据，并且在存在表型模拟和/或外显率降低的情况下，也不太可能排除致病变异。CHP方法已在SEQLinkage软件包中实现，该软件包可以对NGS数据进行连锁分析，也可以生成与许多连锁分析程序兼容的数据，使其在NGS时代得以重新使用。
Zotero 插件