In spite of the tremendous success of genome‐wide association studies (GWAS) in identifying genetic variants associated with complex traits and common diseases, many more are yet to be discovered. Hence, it is always desirable to improve the statistical power of GWAS. Paralleling with the intensive efforts of integrating GWAS with functional annotations or other omic data, we propose leveraging other published GWAS summary data to boost statistical power for a new/focus GWAS; the traits of the published GWAS may or may not be genetically correlated with the target trait of the new GWAS. Building on weighted hypothesis testing with a solid theoretical foundation, we develop a novel and effective method to construct single‐nucleotide polymorphism (SNP)‐specific weights based on 22 published GWAS data sets with various traits, detecting sometimes dramatically increased numbers of significant SNPs and independent loci as compared to the standard/unweighted analysis. For example, by integrating a schizophrenia GWAS summary data set with 19 other GWAS summary data sets of nonschizophrenia traits, our new method identified 1,585 genome‐wide significant SNPs mapping to 15 linkage disequilibrium‐independent loci, largely exceeding 818 significant SNPs in 13 independent loci identified by the standard/unweighted analysis; furthermore, using a later and larger schizophrenia GWAS summary data set as the validation data, 1,423 (out of 1,585) significant SNPs identified by the weighted analysis, compared to 705 (out of 818) by the unweighted analysis, were confirmed, while all 15 and 13 independent loci were also confirmed. Similar conclusions were reached with lipids and Alzheimer's disease (AD) traits. We conclude that the proposed approach is simple and cost‐effective to improve GWAS power.

中文翻译：

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利用遗传相关和不相关性状的现有 GWAS 摘要数据来提高新 GWAS 的功效。

尽管全基因组关联研究（GWAS）在识别与复杂性状和常见疾病相关的遗传变异方面取得了巨大成功，但还有更多的变异有待发现。因此，提高 GWAS 的统计能力始终是人们所希望的。在将 GWAS 与功能注释或其他组学数据相结合的同时，我们建议利用其他已发布的 GWAS 摘要数据来提高新的/重点 GWAS 的统计能力；已发布的 GWAS 的性状可能与新 GWAS 的目标性状在遗传上相关，也可能不相关。基于坚实的理论基础和加权假设检验，我们开发了一种新颖有效的方法，根据 22 个已发表的具有各种性状的 GWAS 数据集构建单核苷酸多态性 (SNP) 特异性权重，有时检测到显着增加的显着 SNP 数量，与标准/未加权分析相比，独立位点。例如，通过将精神分裂症 GWAS 摘要数据集与其他 19 个非精神分裂症性状 GWAS 摘要数据集整合，我们的新方法确定了 1,585 个全基因组显着 SNP，映射到 15 个连锁不平衡独立基因座，大大超过了 13 个独立基因座中的 818 个显着 SNP。通过标准/未加权分析确定；此外，使用后来更大的精神分裂症 GWAS 摘要数据集作为验证数据，通过加权分析确定了 1,423 个（共 1,585 个）显着 SNP，而未加权分析确定了 705 个（共 818 个），而所有 15 个还确认了13个独立位点。对于脂质和阿尔茨海默病（AD）特征也得出了类似的结论。我们的结论是，所提出的方法对于提高 GWAS 能力来说是简单且具有成本效益的。