Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PLoS Genet. 2021 Apr 19;17(4):e1009455. doi: 10.1371/journal.pgen.1009455. eCollection 2021 Apr.
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
表达数量性状基因座（eQTL）研究用于了解非编码全基因组关联研究（GWAS）风险基因座的调控功能，但仅共定位并不能证明基因表达对性状的因果关系。通过两样本孟德尔随机化（MR），可以提供证据表明基因表达的干扰在给定的组织或发育环境中会引起下游 GWAS 性状的变化，从而提供了中介作用的证据。在这里，我们引入了一种新的统计方法 MRLocus，用于从具有等位基因异质性证据的 eQTL 和 GWAS 汇总数据中对基因到性状的效应进行贝叶斯估计，即包含多个因果变异。MRLocus 利用了应用于每个几乎独立 LD 的 eQTL 的共定位步骤，然后在 eQTL 之间进行 MR 分析步骤。此外，我们的方法通过分散参数来估计等位基因异质性的程度，表明每个单独的 eQTL 对下游性状的中介作用的可变程度。我们使用现有的模拟框架，针对其他用于估计基因到性状中介效应的最先进方法评估了我们的方法。在模拟中，MRLocus 在竞争方法中通常具有最高的准确性，并且在每种情况下，通过区间覆盖评估不确定性的估计更为准确。然后将 MRLocus 应用于五个候选因果基因，以调解特定 GWAS 性状，其中基因到性状的效应与先前报道的一致。我们发现，MRLocus 在一个基因座内对 eQTL 进行因果效应的估计为确定基因表达或单个调节元件的干扰将如何影响下游性状提供了有用的信息。MRLocus 方法已作为 R 包实现，可在 https://mikelove.github.io/mrlocus 上获得。
