Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high‐quality genotyped data to identify variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost‐effective approach to increase the power of association testing. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, and so forth. Hence, based on the insight of iECAT, we propose a novel score‐based test that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We assess the existence of batch effect at a variant by comparing control samples of internal and external sources. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age‐related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium and Michigan Genomics Initiative. Through the incorporation of the score test approach, we extend the use of iECAT to adjust for covariates and improve power, further honing the statistical methods needed to identify disease‐causing variants within the human genome.

中文翻译：

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通过整合外部控制来增加关联测试能力的新分数测试

基因分型和测序技术的最新进展使遗传关联研究能够利用高质量的基因分型数据来识别占疾病风险很大一部分的变异。使用外部对照（其基因组已经过基因分型并可公开获得）可能是一种提高关联测试能力的经济有效的方法。最近一直在努力整合外部控制，同时调整可能的批次效应，例如将外部控制集成到关联测试 (iECAT) 中。然而，原始 iECAT 测试无法针对年龄、性别等协变量进行调整。因此，基于 iECAT 的洞察力，我们提出了一种新的基于分数的测试，它允许协变量调整并构建收缩分数统计，该统计是仅使用内部样本并使用内部和外部控制样本的分数统计的加权和。我们通过比较内部和外部来源的对照样本来评估变体中批次效应的存在。我们通过模拟研究表明，我们的方法在控制 I 类错误率的同时提高了原始 iECAT 的能力。我们利用来自国际 AMD 基因组学联盟和密歇根基因组学倡议的数据，展示了我们的方法在年龄相关性黄斑变性 (AMD) 关联研究中的应用。通过结合分数测试方法，我们扩展了 iECAT 的使用以调整协变量并提高功效，