MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.
MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.
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DOI：
10.1371/journal.pgen.1009455
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发表时间：
2021-04
期刊：
影响因子：
4.5
通讯作者：
Love MI
中科院分区：
文献类型：
--
作者：
Zhu A;Matoba N;Wilson EP;Tapia AL;Li Y;Ibrahim JG;Stein JL;Love MI
表达数量性状基因座(EQTL)的研究被用来了解非编码全基因组关联研究(GWAS)风险基因座的调节功能，但仅靠共定位并不能证明影响性状的基因表达之间的因果关系。两样本孟德尔随机化(MR)可以提供中介证据，即在给定组织或发育背景下基因表达的扰动将导致下游GWAS性状的变化。在这里，我们介绍了一种新的统计方法，MRLocus，用于从eQTL和Gwas的具有等位基因异质性证据的基因座的总结数据中，即包含多个因果变量，来贝叶斯估计基因对性状的效应。MRLocus利用对每个几乎与Ld无关的eQTL应用共定位步骤，然后对eQTL进行MR分析步骤。此外，我们的方法包括通过离散参数估计等位基因异质性的程度，表明每个单独的eQTL对下游性状的可变中介效应。我们的方法与其他最先进的估计基因到性状中介效应的方法进行了比较，使用了一个现有的模拟框架。在模拟中，MRLocus往往是竞争方法中精度最高的，并且在每种情况下都可以通过区间覆盖来评估不确定性，从而提供更准确的估计。然后将MRLocus应用于五个候选因果基因，以中介特定的GWA型性状，其中基因对性状的效应与先前报道的一致。我们发现，MRLocus对一个基因座内eQTL之间因果效应的估计为确定基因表达或单个调控元件的扰动将如何影响下游性状提供了有用的信息。MRLocus方法以R包的形式实现，可在https://mikelove.github.io/mrlocus.上获得全基因组关联研究已经确定了许多与复杂性状和疾病相关的基因座。如果基因在性状或疾病中起中介作用，则表达数量性状基因座(EQTL)可能有助于解释GWAs关联的机制。表现出等位基因异质性的基因座，即包含多个因果变量的基因座，提供了调查eQTL和GWAS之间效应是否一致和成比例的机会；如果基因是性状的部分中介，则不同eQTL变体上的效应的符号和大小应该反映在Gwas关联中。在eQTL研究中，这样的孟德尔随机化(MR)分析因eQTL研究中的适度样本量和连锁不平衡(LD)而变得复杂，导致eQTL和GWAs的估计效应大小的复杂模式。我们开发了一个统计模型，MRLocus，包括两个步骤：选择eQTL SNPs作为遗传座位MR分析的工具，并在考虑工具不确定性的情况下估计基因对性状的中介效应。在仿真实验中，与其他同类方法相比，该方法具有更高的精度和更好的不确定性度量，并与文献中的候选因果基因-性状对的估计进行了比较。
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. Genome-wide association studies (GWAS) have identified many loci associated with complex traits and diseases. Expression quantitative trait loci (eQTL) may help to explain mechanisms of GWAS associations, if the gene has a role as a mediator of the trait or disease. Loci that exhibit allelic heterogeneity, that is, loci containing multiple causal variants, offer the opportunity to investigate whether effects are concordant and proportional across eQTL and GWAS; if the gene is a partial mediator of the trait, the sign and size of the effects across distinct eQTL variants should be reflected in GWAS associations. Such a Mendelian Randomization (MR) analysis of individual loci is complicated by moderate sample sizes in eQTL studies and linkage disequilibrium (LD), resulting in complex patterns of estimated effect sizes for eQTL and GWAS. We develop a statistical model, MRLocus, with two steps: selection of eQTL SNPs to act as instruments in the MR analysis of a genetic locus, and estimation of the gene-to-trait mediation effect taking instrument uncertainty into account. In simulation, the method has higher accuracy and better uncertainty measures compared to other competing methods, and we compare its estimates on candidate causal gene-trait pairs from literature.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
DOI：
10.1126/science.1260793
发表时间：
2015-02-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Battle A;Khan Z;Wang SH;Mitrano A;Ford MJ;Pritchard JK;Gilad Y
通讯作者：
Gilad Y
DOI：
10.1097/ede.0000000000000559
发表时间：
2017-01
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Burgess S;Bowden J;Fall T;Ingelsson E;Thompson SG
通讯作者：
Thompson SG
DOI：
10.1093/biostatistics/kxy027
发表时间：
2020-01-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Berzuini C;Guo H;Burgess S;Bernardinelli L
通讯作者：
Bernardinelli L
影响因子：
--
作者：
Codina-Fauteux, Valerie-Anne;Beaudoin, Melissa;Lettre, Guillaume
通讯作者：
Lettre, Guillaume