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

摘要

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.