Methods to maximize the utility of common fund functional genomic data in multi-ethnic genetic studies

摘要 吸烟和饮酒是人类多种疾病的主要可改变和可遗传的危险因素。 阐明吸烟和饮酒成瘾的遗传基础对公共卫生至关重要。过去几 近年来，吸烟和饮酒成瘾的遗传学研究取得了重大进展。的帮助下 大数据集和先进的分析方法，我们已经确定了超过400个相关的基因座在欧洲样本中， 祖先作为下一步，我们将扩大我们的研究，包括非欧洲人口的样本，以便 进一步授权发现和阐明遗传结构。 大多数鉴定的GWAS基因座是非编码的。了解它们的功能的关键第一步是 识别目标基因。全转录组关联研究（TWAS）被提议用于关联调节变体 to target目标genes基因.在其原始形式中，TWAS整合了来自匹配祖先的eQTL和GWAS数据。作为多- 种族研究变得越来越普遍，已经表明欧洲eQTL与非欧洲eQTL的直接整合， 欧洲GWAS将导致功率损失，结果也可能难以解释。大多数 共同基金功能基因组数据（例如，GTEx和4DN）主要来自欧洲血统。它 目前尚不清楚它们是否仍然对多族裔研究有用，如果有用，如何有效利用它们。 在这里，我们提出了一系列的方法创新，结合联合收割机GTEx数据，表观遗传和3D基因组数据 和其他非欧洲的功能基因组数据，以提高基因表达预测的准确性， 组织类型和祖先对于给定的基因表达模型，我们还将提出方法来执行可证明的 多种族遗传研究中的最佳TWAS。这些建议的方法，如果成功，将打开大门使用 共同基金的数据在下一代遗传研究的复杂性状在不同的人群。相比 到极其昂贵的数据生成，这些方法开发项目具有成本效益， 最大化共同基金数据集的效用。

ABSTRACT Smoking and drinking are major modifiable and heritable risk factors for a myriad of human diseases. Elucidating the genetic basis for smoking and drinking addiction will be critical for public health. In the past few years, the genetic studies of smoking and drinking addiction have made significant progress. With the help of large datasets and advanced analytical methods, we have identified >400 associated loci in samples of European ancestry. As a next step, we will expand our study to include samples of non-European populations, in order to further empower discovery and elucidate the genetic architecture. A majority of the identified GWAS loci are non-coding. A critical first step to understand their function is to identify the target gene. Transcriptome-wide association study (TWAS) was proposed to link regulatory variants to target genes. In its original form, TWAS integrates eQTL and GWAS data from the matched ancestry. As multi- ethnic studies become more prevalent, it has been shown that direct integration of European eQTL with non- European GWAS would lead to loss of power and the results may be difficult to interpret as well. A majority of Common Funds functional genomic data (e.g., GTEx and 4DN) were primarily from European ancestry. It remains unclear whether they remain useful in multi-ethnic studies and if so, how to effectively utilize them. Here we propose a series of methodological innovations to combine GTEx data, epigenetic and 3D genomes data and other non-European functional genomic data to improve the gene expression prediction accuracy across tissue types and ancestries. For a given gene expression model, we will also propose methods to perform provably optimal TWAS in multi-ethnic genetic studies. These proposed methods, if successful, will open doors to use Common Funds data in the next generation genetic studies of complex traits in diverse populations. Compared to extremely expensive data generation, these method development projects are cost effective and could be highly impactful for maximizing the utility of Common Funds datasets.