Clarifying the causes of consistent and inconsistent findings in genetics

Abstract

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family- and genome-wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.

Keywords: GWAS; causal inference; confounding; consistency; heritability; replications; selection bias.

Figure 1

(a–g) Direct acyclic graphs depicting causal relationships in genetic association analyses, in which nodes (variables) are connected with each other by arcs. Dashed arcs represent non‐causal statistical associations, while filled arcs represent causal statistical associations. Boxes represent variables which have been selected on, for example, by regression adjustment or inclusion/exclusion criteria in a study. Panels (a) and (b) represent effect modification, where the magnitude or direction of a causal effect is modified by a third variable, which acts upon a mediating mechanism. Panels (c) and (d) represent confounding, where a presumed exposure and outcome have a shared cause. Panels (e–g) depict selection bias, in which a presumed exposure and outcome both affect a third variable which is selected upon in the analysis. CNV, copy number variant; PRS, polygenic risk score; SNP, single nucleotide polymorphism.