MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK.
Am J Hum Genet. 2021 Jun 3;108(6):983-1000. doi: 10.1016/j.ajhg.2021.04.010. Epub 2021 May 1.
We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.
我们提出了 EPISPOT，这是一个完全联合的框架，利用大量的表观遗传注释作为变体级信息来增强分子数量性状基因座 (QTL) 作图。由于采用了专门设计的贝叶斯推理算法，EPISPOT 可以容纳顺式和反式作用的功能信息，包括 QTL 热点效应。它有效地将数千个遗传变异和分子性状的同时 QTL 分析与无假设的生物可解释注释选择相结合，这些注释直接有助于 QTL 效应。这种统一的、基于表观基因组的学习方法提高了统计能力，并揭示了未被发现的命中的调控基础；因此，EPISPOT 标志着朝着提高对反式作用遗传变异和热点的挑战性检测和功能解释迈出了重要一步。我们通过模拟真实数据条件的模拟和单核细胞表达 QTL 研究说明了 EPISPOT 的优势，该研究确认了已知的热点，并找到了其他信号以及可能的作用机制。特别是，通过强调来自 >150 个表观遗传注释的单核细胞 DNase-I 敏感性位点的作用，我们阐明了靠近溶菌酶基因的主要热点的中介效应和细胞类型特异性。我们的方法避免了一次注释一次进行优先级 cis 和 trans QTL 命中的耗时且功能不足的富集分析任务，并且针对任何转录组、蛋白质组或代谢组 QTL 问题进行了定制。通过实现原则上的基于表观基因组的 QTL 作图，EPISPOT 有助于更好地理解遗传调控的功能。
