Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

中文翻译：

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加权选择概率在多表型关联研究中优先考虑敏感稀有变异并应用于大豆遗传数据集。

具有多种性状或疾病的罕见变异关联研究引起了广泛关注，因为如果多个表型结果与相同的罕见变异相关，则罕见变异的关联信号可以增强。大多数识别与多种表型相关的罕见变异的现有统计方法都是基于群体测试，其中一次测试一个预先指定的遗传区域。然而，这些方法并不是为了在遗传区域内定位易受影响的罕见变异而设计的。在本文中，我们提出了新的统计方法，当对遗传区域的群体测试识别出与多个表型的统计关联时，优先考虑该遗传区域内的罕见变异。它计算单个稀有变体的加权选择概率 (WSP)，并根据其 WSP 从最大到最小对它们进行排名。在模拟研究中，我们证明，当多个表型彼此相关时，所提出的方法在真阳性选择方面优于其他统计方法。我们还将其应用于具有 13 个高度相关氨基酸的大豆单核苷酸多态性 (SNP) 数据，其中我们在 19 号染色体上发现了一些潜在易受影响的罕见变异。