A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

Abstract

With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions’ variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

A comparative evaluation of factor‐ and component‐based structural equation modelling approaches under (in)correct construct representations

“…The regression method (Thomson, 1934) was used for the estimation of factor scores. We confirmed that our MATLAB code for GSCA and PLSPM produced the same solutions as GSCA Pro (version 1.1.4) (Hwang, Cho, & Choo, 2021a) and SmartPLS 3.0 (Ringle, Wende, & Becker, 2015). The maximum number of iterations was set to 10,000 for all SEM approaches.…”

“…Furthermore, we showed that in its most general form, the GSCA model is not a structural model but an index‐generating model using certain rules, given the covariance matrix of indicators. Under this index‐generating model, GSCA can be seen as a statistical approach for creating composite indexes and testing their relationships with other variables based on prior knowledge (Hwang, Cho, Jin, et al., 2021). From this modelling perspective, a component is no longer an independent entity that causes other variables to vary at the population level, simply corresponding to a composite index of indicators derived based on a given theory‐driven model.…”

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“…While GSCA is a relatively recent approach compared to other SEM techniques, it has gained acceptance and recognition within the research community. It has been successfully applied in various fields, including social sciences, business, and psychology, to examine complex relationships and identify latent constructs (Hwang & Takane, 2014;Hwang et al, 2021;Jung et al, 2021).…”