Enhancing the Informativeness and Replicability of Imaging Genomics Studies

Abstract

Imaging genomics is a new field of investigation that seeks to gain insights into the impact of human genetic variation on the structure, chemistry, and function of neural systems in health and disease. Because publications in this field have increased over the past decade, increasing concerns have been raised about false-positive results entering the literature. Here, we provide an overview of the field of imaging genomic and genetic approaches and discuss factors related to research design and analysis that can enhance the informativeness and replicability of these studies. We conclude that imaging genetic studies can provide important insights into the role of human genetic variation on neural systems and circuits, both in the context of normal quantitative variation and in relation to neuropsychiatric disease. We also argue that demonstrating genetic association to imaging-derived traits is subject to the same constraints as any other genetic study, including stringent type I error control. Adequately powered studies are necessary; however, there are currently limited data available to allow precise estimates of effect sizes for candidate gene studies. Independent replication is necessary before a result can be considered definitive, and for studies with small sample sizes it is necessary before publication. Increased transparency of methods and enhanced data sharing will further enhance replicability.

Keywords: Clinical confounds; Imaging genomics; Multiple comparisons; Replication; Sample size; Statistical power.

Figure 1

Power calculations for an independent sample t-test for an effect size of 0.5 and different height thresholds typically used in imaging genetic studies. The simulations assume equal sample sizes for each of the groups. For typically used height thresholds and this effect size having 50-100 subjects in each group will generally have only moderate power to reject the null hypothesis.