The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data has facilitated the identification of rare variant associated with quantitative traits. However, existing statistical methods depend on certain assumptions and thus lacking uniform power. The present study focuses on mapping rare variant associated with quantitative traits. In the present study, we proposed a two-stage strategy to identify rare variant of quantitative traits using phenotype extreme selection design and Kullback-Leibler distance, where the first stage was association analysis and the second stage was fine mapping. We presented a statistic and a linkage disequilibrium measure for the first stage and the second stage, respectively. Theory analysis and simulation study showed that (1) the power of the proposed statistic for association analysis increased with the stringency of the sample selection and was affected slightly by non-causal variants and opposite effect variants, (2) the statistic here achieved higher power than three commonly used methods, and (3) the linkage disequilibrium measure for fine mapping was independent of the frequencies of non-causal variants and simply dependent on the frequencies of causal variants. We conclude that the two-stage strategy here can be used effectively to mapping rare variant associated with quantitative traits.

中文翻译：

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通过Kullback-Leibler距离识别人口极端样本中数量性状的稀有变异

测序技术的飞速发展以及大量序列数据的同时获得使用，促进了与数量性状相关的稀有变异的鉴定。但是，现有的统计方法取决于某些假设，因此缺乏统一的功效。本研究的重点是绘制与数量性状相关的稀有变异。在本研究中，我们提出了一种两阶段策略，即使用表型极端选择设计和Kullback-Leibler距离来识别数量性状的稀有变异，其中第一阶段是关联分析，第二阶段是精细映射。我们分别介绍了第一阶段和第二阶段的统计量和连锁不平衡量度。理论分析和模拟研究表明：（1）所提出的用于关联分析的统计量的功效随着样本选择的严格性而增加，并且受到非因果变量和相反效应变量的影响较小；（2）此处的统计量获得了更高的功效而不是三种常用方法；（3）精细映射的连锁不平衡度独立于非因果变量的频率，而仅取决于因果变量的频率。我们得出的结论是，这里的两阶段策略可以有效地用于绘制与数量性状相关的稀有变异。（2）此处的统计数据比三种常用方法具有更高的功效，并且（3）精细映射的连锁不平衡度量独立于非因果变量的频率，而仅取决于因果变量的频率。我们得出的结论是，这里的两阶段策略可以有效地用于绘制与数量性状相关的稀有变异。（2）此处的统计数据比三种常用方法具有更高的功效，并且（3）精细映射的连锁不平衡度量独立于非因果变量的频率，而仅取决于因果变量的频率。我们得出的结论是，这里的两阶段策略可以有效地用于绘制与数量性状相关的稀有变异。