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

摘要 吸烟和饮酒是多种人类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众健康至关重要。在过去的几年里 多年来，吸烟和饮酒成瘾的遗传学研究取得了重大进展。在…的帮助下 大型数据集和先进的分析方法，我们已经在欧洲人的样本中确定了&gt；400个相关基因座 血统。作为下一步，我们将扩大我们的研究范围，包括非欧洲人群的样本，以便 进一步促进发现和阐明基因结构。 大多数已鉴定的GWAS基因座都是非编码的。要了解它们的功能，关键的第一步是 确定目标基因。转录组广泛的关联研究(TWAS)被建议将调节变体联系起来 以基因为目标。在其原始形式中，TWAs整合了来自匹配祖先的eQTL和GWAs数据。作为多个- 种族研究变得更加普遍，已经表明欧洲eQTL与非EQTL直接整合 欧洲GWA将导致停电，其结果可能也很难解释。大多数人 共同基金的功能基因组数据(如GTEx和4DN)主要来自欧洲血统。它 目前尚不清楚它们在多种族研究中是否仍然有用，如果是的话，如何有效地利用它们。 在这里，我们提出了一系列方法创新，将GTEx数据、表观遗传学数据和3D基因组数据结合起来 和其他非欧洲功能基因组数据，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，我们还将提出可证明执行的方法 在多种族遗传研究中的最佳TWAs。这些拟议的方法，如果成功，将打开使用的大门 共同基金数据用于下一代对不同种群中复杂性状的遗传研究。相比较 对于极其昂贵的数据生成，这些方法开发项目是具有成本效益的，并且可能非常 对于最大限度地发挥共同基金数据集的效用具有重要影响。

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.