Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
Am J Hum Genet. 2021 Jan 7;108(1):25-35. doi: 10.1016/j.ajhg.2020.11.012. Epub 2020 Dec 11.
Colocalization analysis has emerged as a powerful tool to uncover the overlapping of causal variants responsible for both molecular and complex disease phenotypes. The findings from colocalization analysis yield insights into the molecular pathways of complex diseases. In this paper, we conduct an in-depth investigation of the promise and limitations of the available colocalization analysis approaches. Focusing on variant-level colocalization approaches, we first establish the connections between various existing methods. We proceed to discuss the impacts of various controllable analytical factors and uncontrollable practical factors on outcomes of colocalization analysis through realistic simulations and real data examples. We identify a single analytical factor, the specification of prior enrichment levels, which can lead to severe inflation of false-positive colocalization findings. Meanwhile, the combination of many other analytical and practical factors all lead to diminished power. Consequently, we recommend the following strategies for the best practice of colocalization analysis: (1) estimating prior enrichment level from the observed data and (2) separating fine-mapping and colocalization analysis. Our analysis of 4,091 complex traits and the multi-tissue expression quantitative trait loci (eQTL) data from the GTEx (v.8) suggests that colocalizations of molecular QTLs and causal complex trait associations are widespread. However, only a small proportion can be confidently identified from currently available data due to a lack of power. Our findings set a benchmark for current and future integrative genetic association analysis applications.
共定位分析已成为揭示导致分子和复杂疾病表型的因果变异重叠的有力工具。共定位分析的结果为复杂疾病的分子途径提供了深入的见解。在本文中，我们深入研究了现有共定位分析方法的前景和局限性。我们专注于变异水平的共定位方法，首先建立了各种现有方法之间的联系。然后，我们通过真实模拟和真实数据示例讨论了各种可控分析因素和不可控实际因素对共定位分析结果的影响。我们确定了一个单一的分析因素，即先验富集水平的规范，这可能导致假阳性共定位发现的严重膨胀。同时，许多其他分析和实际因素的组合都会降低功效。因此，我们为共定位分析的最佳实践推荐以下策略：（1）从观察到的数据中估计先验富集水平，（2）分离精细映射和共定位分析。我们对来自 GTEx（v.8）的 4091 个复杂特征和多组织表达定量性状基因座（eQTL）数据的分析表明，分子 QTLs 和因果复杂性状关联的共定位是广泛存在的。然而，由于缺乏功效，目前可用的数据只能确认一小部分。我们的发现为当前和未来的综合遗传关联分析应用设定了基准。
