Since the seminal work of Prentice and Pyke (1979), the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case-control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case-control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common-random-effect assumption, which assumes a common random effect for all subjects. We develop the locally most powerful aggregation test based on the retrospective likelihood under an independent-random-effect assumption, which allows the genetic effect to vary among subjects. In contrast to the fact that disease prevalence information cannot be used to improve efficiency for the estimation of odds ratio parameters in logistic regression models, we show that it can be utilized to enhance the testing power in genetic association studies. Extensive simulations demonstrate the advantages of the proposed method over the existing ones. A real genome-wide association study is analyzed for illustration. This article is protected by copyright. All rights reserved.

中文翻译：

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与病例对照数据的遗传关联的回顾性评分测试与前瞻性评分测试

自 Prentice 和 Pyke (1979) 的开创性工作以来，前瞻性逻辑似然已成为回顾性收集病例对照数据的标准分析方法，特别是用于测试单个遗传标记与遗传病例中疾病结果之间的关联。对照研究。在对影响相对较小的多个遗传标记的研究中，特别是那些具有罕见变异的遗传标记，已经开发了基于相同预期可能性的各种聚合方法，以整合所有考虑的标记之间的微妙关联证据。许多常用的检验是从共同随机效应假设下的预期似然得出的，该假设假设所有受试者都有共同的随机效应。我们基于独立随机效应假设下的回顾性可能性开发了本地最强大的聚合测试，这允许遗传效应在受试者之间发生变化。与不能使用疾病流行信息来提高逻辑回归模型中优势比参数估计效率的事实相反，我们表明它可用于增强遗传关联研究中的测试能力。大量的模拟证明了所提出的方法优于现有方法的优点。分析了一项真正的全基因组关联研究以进行说明。本文受版权保护。版权所有。与不能使用疾病流行信息来提高逻辑回归模型中优势比参数估计效率的事实相反，我们表明它可用于增强遗传关联研究中的测试能力。大量的模拟证明了所提出的方法优于现有方法的优点。分析了一项真正的全基因组关联研究以进行说明。本文受版权保护。版权所有。与不能使用疾病流行信息来提高逻辑回归模型中优势比参数估计效率的事实相反，我们表明它可用于增强遗传关联研究中的测试能力。大量的模拟证明了所提出的方法优于现有方法的优点。分析了一项真正的全基因组关联研究以进行说明。本文受版权保护。版权所有。本文受版权保护。版权所有。本文受版权保护。版权所有。