Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Abstract

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.

Keywords: GWAS; complex disease; genetic heterogeneity; precision medicine.

Figure 1

Conceptual illustrations contrasting homogeneity and heterogeneity using example observations within features, outcomes, or associations. Panel (a) depicts age as a feature with less variability on the left and with more variability on the right. Panel (b) depicts a phenotypic outcome, again with less variability on the left and more variability on the right. Panel (c) depicts subjects (rows) and features (columns) where features can have different values (shading). On the left, the feature highlighted by the dotted box is homogeneously associated with Disease X. On the right, associative heterogeneity is represented by two different features independently associated with Disease X within different groups of subjects.

Figure 2

Examples of two independent causes of a single phenotype under four scenarios, allelic heterogeneity, locus heterogeneity, phenocopy, and more complex examples of heterogeneity. Causal factors are in black.