Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Genet Epidemiol. 2022 Dec;46(8):555-571. doi: 10.1002/gepi.22497. Epub 2022 Aug 4.
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
遗传异质性描述了在不同个体中通过不同的遗传机制出现相同或相似表型的现象。准确描述和解释遗传异质性对于追求精准医学的目标、发现新的疾病生物标志物以及确定治疗靶点至关重要。如果不考虑遗传异质性，可能会导致关联的遗漏和推断的错误。因此，必须审查遗传异质性对群体遗传研究设计和分析的影响，而这在文献中往往被忽视。在这篇综述中，我们首先通过提出将异质性分为“特征”、“结果”和“关联”异质性的高级分类，从流行病学和机器学习的角度来阐明它们之间的区别，从而为我们的遗传异质性方法提供了背景。我们强调了遗传异质性作为一种需要特定方法考虑的关联异质性的独特性质。然后，我们将重点放在排除在各种流行病学背景下有效检测和描述遗传异质性的挑战上。最后，我们讨论了系统异质性作为一种综合方法，用于在复杂疾病研究中使用遗传和其他高维多组学数据。
