Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France.
ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.
Environ Int. 2021 Aug;153:106509. doi: 10.1016/j.envint.2021.106509. Epub 2021 Mar 25.
Challenges in the assessment of the health effects of the exposome, defined as encompassing all environmental exposures from the prenatal period onwards, include a possibly high rate of false positive signals. It might be overcome using data dimension reduction techniques. Data from the biological layers lying between the exposome and its possible health consequences, such as the methylome, may help reducing exposome dimension. We aimed to quantify the performances of approaches relying on the incorporation of an intermediary biological layer to relate the exposome and health, and compare them with agnostic approaches ignoring the intermediary layer. We performed a Monte-Carlo simulation, in which we generated realistic exposome and intermediary layer data by sampling with replacement real data from the Helix exposome project. We generated a Gaussian outcome assuming linear relationships between the three data layers, in 2381 scenarios under five different causal structures, including mediation and reverse causality. We tested 3 agnostic methods considering only the exposome and the health outcome: ExWAS (for Exposome-Wide Association study), DSA, LASSO; and 3 methods relying on an intermediary layer: two implementations of our new oriented Meet-in-the-Middle (oMITM) design, using ExWAS and DSA, and a mediation analysis using ExWAS. Methods' performances were assessed through their sensitivity and FDP (False-Discovery Proportion). The oMITM-based methods generally had lower FDP than the other approaches, possibly at a cost in terms of sensitivity; FDP was in particular lower under a structure of reverse causality and in some mediation scenarios. The oMITM-DSA implementation showed better performances than oMITM-ExWAS, especially in terms of FDP. Among the agnostic approaches, DSA showed the highest performance. Integrating information from intermediary biological layers can help lowering FDP in studies of the exposome health effects; in particular, oMITM seems less sensitive to reverse causality than agnostic exposome-health association studies.
评估涵盖从胎儿期开始的所有环境暴露的外显子组健康效应所面临的挑战包括可能出现高假阳性信号。这可以通过使用数据降维技术来克服。来自外显子组与其可能的健康后果（如甲基组）之间的生物层的数据可以帮助减少外显子组的维度。我们旨在量化依赖于包含中间生物层来关联外显子组和健康的方法的性能，并将其与忽略中间层的无偏方法进行比较。我们进行了一项蒙特卡罗模拟，通过从 Helix 外显子组项目中用替换真实数据进行抽样，生成了真实的外显子组和中间层数据。我们假设三个数据层之间存在线性关系，生成了高斯结果，在五种不同因果结构下的 2381 种情况下，包括中介和反向因果关系。我们测试了 3 种仅考虑外显子组和健康结果的无偏方法：ExWAS（外显子组全基因组关联研究）、DSA、LASSO；以及 3 种依赖中间层的方法：两种我们新的定向Meet-in-the-Middle（oMITM）设计的实现，分别使用 ExWAS 和 DSA，以及使用 ExWAS 的中介分析。通过敏感性和 FDP（错误发现率）来评估方法的性能。基于 oMITM 的方法通常比其他方法具有更低的 FDP，可能是以敏感性为代价；在反向因果关系结构和某些中介情景下，FDP 尤其较低。oMITM-DSA 实现比 oMITM-ExWAS 具有更好的性能，特别是在 FDP 方面。在无偏方法中，DSA 表现出最高的性能。整合中间生物层的信息可以帮助降低外显子组健康效应研究中的 FDP；特别是，oMITM 似乎比无偏外显子组与健康关联研究对反向因果关系更不敏感。
