Methodological Contributions in Brain-Imaging Genetics: A Review, Simulation Study, and Experimental Analysis

Brain-imaging genetics is focused on understanding the genetic underpinnings of complex cognitive traits using imaging endophenotypes that reflect brain structure or function. This field has seen much methodological development over the past decade, but has not reached a consensus on “best” techniques for analyzing combined genetic, brain-imaging, and behavioral datasets. Historically, a lack of large and diverse datasets has limited method exploration. Recently, after years of collec- tion, new biobanks are releasing large-scale datasets aimed at understanding the biology of complex cognitive traits. Namely, the Child Mind Institute’s Healthy Brain Network (CMI HBN) has re- cently released genotypic, structural brain-imaging, and academic cognitive assessments for up to 4,868 children. We performed a three-part study to analyze this novel dataset. We first present a narrative review that captures the current state of brain-imaging genetic methodology. Next, we select leading methods from the field and compare their performance in a simulated setting. We find that a regularized multiple multivariate regression strategy, the elastic net, is most suited to analyzing simulated brain-imaging genetic data under a range of assumptions. We then apply the elastic net to the CMI HBN dataset exploring complex academic skills. Our analysis identifies sev- eral genes and imaging indicators associated with reading ability. Many of these findings align with previously established associations between the identified genes and reading or similar academic achievement measures. Besides our genetic findings, a key contribution of this work is a flexible and data-driven pipeline for analyzing any brain-imaging genetic datasets like the CMI HBN. Our analytical strategy can serve as a valuable resource for future brain-imaging genetic studies and facilitate the identification of key factors involved in academic skills.