Genome-wide association studies (GWASs) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes and functional annotation from external databases to prioritize the most plausible causal single-nucleotide polymorphisms (SNPs) in each trait-associated locus. Our proposed method improves upon previous fine-mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine-mapping studies helps to distinguish between variants in high LD and to identify promising targets for follow-up studies.

中文翻译：

---

ANNORE：具有功能注释的遗传精细定位

全基因组关联研究 (GWAS) 已成功识别出与许多复杂性状有关的人类基因组位点。然而，这项研究设计的局限性使得很难确定特定的因果变异或关联的生物学机制。我们提出了一种新方法 AnnoRE，它使用 GWAS 汇总统计、基因型之间的局部相关结构和来自外部数据库的功能注释来优先考虑每个性状相关基因座中最合理的因果单核苷酸多态性 (SNP)。我们提出的方法通过从全基因组汇总统计中估计功能注释的影响，改进了以前的精细映射方法，允许包含许多注释类别。通过随机效应实施具有差异收缩的多元回归模型，我们避免对每个基因座的因果 SNP 数量进行简化假设。将该方法应用于体重指数的大型 GWAS 荟萃分析，确定了六个基因座，这些基因座具有支持一种或多种变异的重要证据。在另外 24 个基因座中，一个或两个变体在该区域中优先于其他变体。在遗传精细定位研究中使用功能注释有助于区分高 LD 中的变异，并为后续研究确定有希望的目标。