Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands.
Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands.
BMC Genomics. 2022 Jul 31;23(1):546. doi: 10.1186/s12864-022-08771-7.
Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.
人群规模的表达谱研究可以为生物和疾病相关机制提供有价值的见解。表型特征的可用性对于研究临床效果至关重要。因此，缺失、不完整或不准确的表型信息会使分析变得具有挑战性，并阻止 RNA-seq 或其他组学数据被重复使用。一种可能的解决方案是使用预测器从分子数据中推断临床或行为表型特征。虽然已经基于不同的组学数据类型开发了这些预测器，并在各种研究中得到应用，但基于代谢组学的替代物比基于 DNA 甲基化图谱的预测器使用得更少。在这项研究中，我们使用先前训练的代谢组学预测器推断了 17 种特征，包括糖尿病状况和暴露于脂质药物。我们评估了这些代谢组学替代物是否可以作为替代报告信息，用于使用四个人群队列的表达谱数据研究各自的表型。对于 17 种特征中的大多数，代谢组学替代物在效应大小、显著关联数量、复制率和显著富集途径方面与报告的表型表现相似。代谢组学衍生替代结局的应用为重新使用多组学数据集开辟了新的可能性。在临床元数据可用性有限的研究中，可以使用这些替代物来补充缺失或不完整的信息，从而增加可用数据集的规模。此外，这些替代物的可用性可用于纠正潜在的生物学混杂。未来，进一步研究跨不同组学类型和队列使用分子预测器将是有趣的。
