Autoři: Anqi Zhu aff001; Nana Matoba aff002; Emma P. Wilson aff002; Amanda L. Tapia aff001; Yun Li aff001; Joseph G. Ibrahim aff001; Jason L. Stein aff002; Michael I. Love aff001 Působiště autorů: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America aff001; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America aff002; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America aff003; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America aff004 Vyšlo v časopise: MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 17(4): e1009455. doi:10.1371/journal.pgen.1009455 Kategorie: Research Article doi: https://doi.org/10.1371/journal.pgen.1009455
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.
Arteries – Blood – Gene expression – Genetic loci – Genome-wide association studies – Heredity – Quantitative trait loci – Simulation and modeling
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