Collapsed haplotype pattern method for linkage analysis of next-generation sequence data

用于下一代测序数据连锁分析的折叠单倍型模式方法

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Abstract

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.

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