Motivated by, but not limited to, association analyses of multiple genetic variants, we propose here a summary statistics-based regression framework. The proposed method requires only variant-specific summary statistics, and it unifies earlier methods based on individual-level data as special cases. The resulting score test statistic, derived from a linear mixed-effect regression model, inherently transforms the variant-specific statistics using the precision matrix to improve power for detecting sparse alternatives. Furthermore, the proposed method can incorporate additional variant-specific information with ease, facilitating omic-data integration. We study the asymptotic properties of the proposed tests under the null and alternatives, and we investigate efficient P-value calculation in finite samples. Finally, we provide supporting empirical evidence from extensive simulation studies and two applications.

中文翻译：

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基于集合的关联测试：使用汇总统计的回归洞察

受多个遗传变异的关联分析的启发，但不限于，我们在此提出了一个基于汇总统计的回归框架。所提出的方法仅需要特定于变体的汇总统计数据，并且将早期基于个体级别数据的方法统一为特殊情况。从线性混合效应回归模型中得出的得分测试统计数据本质上是使用精度矩阵转换特定于变量的统计数据，以提高检测稀疏替代方案的能力。此外，所提出的方法可以轻松地合并其他特定于变体的信息，从而促进组学数据集成。我们研究了在零和替代方案下所提出的测试的渐近特性，我们研究了有效的P- 有限样本中的值计算。最后，我们提供了来自广泛模拟研究和两个应用的支持性经验证据。