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Bayesian Genome-wide TWAS Method to Leverage both

机译：贝叶斯基因组宽的TWA方法杠杆兼作

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摘要

Transcriptome-wide association studies (TWASs) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans-expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian genome-wide TWAS (BGW-TWAS) method that leverages both cis- and trans-eQTL information for a TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis- and trans-eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWASs achieved higher power compared to existing TWAS methods that do not assess trans-eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N = ∼3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of ZC3H12B and Alzheimer dementia (AD) (p value = 5.42 × 10−13), neurofibrillary tangle density (p value = 1.89 × 10−6), and global measure of AD pathology (p value = 9.59 × 10−7). These associations for ZC3H12B were completely driven by trans-eQTL. Additionally, the GReX of KCTD12 was found to be significantly associated with β-amyloid (p value = 3.44 × 10−8) which was driven by both cis- and trans-eQTL. Four of the top driven trans-eQTL of ZC3H12B are located within APOC1, a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N = ∼54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1, as well as ZC3H12B.
机译：转录组 - 范围的关联研究（双转弯）已被广泛用于整合基因表达和遗传数据以研究复杂性状。由于计算负担，现有的TWA方法不评估已知大多数基因的重要表达变异的远处反式表达定量性状基因座（EQT1）。我们提出了一种贝叶斯基因组 - 宽的TWA（BGW-TWA）方法，用于搭配TWA的CIS-和Trans-EQTL信息。我们的BGW-TWA方法基于贝叶斯变量选择回归，这不仅会占目标基因的CIS和Trans-eqtL，而且还通过使用标准EQTL分析的汇总统计来实现有效的计算。我们的模拟研究表明，与不评估Trans-EQTL信息的现有TWA方法相比，BGW-Twass实现了更高的功率。我们进一步将BWG-TWA应用于单独的GWAS数据（n =〜3.3k），其鉴定了ZC312b和阿尔茨海默痴呆（Ad）的遗传调节基因表达（GREX）之间的显着关联（P值= 5.42×10-13 ），神经纤维缠结密度（P值= 1.89×10-6），以及AD病理学的全局测量（P值= 9.59×10-7）。 ZC3H12B的这些关联由Trans-EQTL完全驱动。另外，发现KCTD12的GEX与由CIS-和TRANS-eQT1驱动的β-淀粉样蛋白（P值= 3.44×10-8）显着相关。 ZC3H12B的四个顶部驱动的Trans-EQTL位于Apoc1内，是AD和血脂的已知主要风险基因。另外，通过将BGW-TWA与AD（n =〜54k）的摘要级别数据应用，我们鉴定了13个重要基因，包括已知的GWAS风险基因HLA-DRB1和APOC1，以及ZC3H12B。

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