Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition

Abstract

Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly non-significant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling (CBPM) has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N=3,000 individuals; and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N=13,092 individuals from the UKBiobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single "candidate" brain areas. We find that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable than single brain regions (h 2 =.155-.181) and capture about half of the genetic variance in IQ (rG=.422-.576), while rGs with single brain measures were smaller and non-significant. For the different approaches, LASSO and Ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. We conclude that multi-system predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

Distributed functional connectivity predicts neuropsychological test performance among older adults

“…Also, the predictive model that selects sparse features (higher α regularization) showed poorer predictive accuracy. Consistent with previous studies, accurate individualized prediction of test performance requires a fine‐grained combination of neural features rather than highly selective neural features (Cui & Gong, 2018; Gao et al, 2019; Hatoum et al, 2019).…”

Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition

“…We would like to thank Cold Spring Harbor Laboratory for hosting the preprint of this manuscript [60].…”

Emerging phenotyping strategies will advance our understanding of psychiatric genetics

“…In a recent GWAS of circadian rhythm, wearable devices were used to gather objective measures of sleep timing, duration and quality 45 . More recently, structural connectivity from fMRI was proposed as endophenotype for IQ 46 The approach we are proposing will be orthogonal to the efforts of the PGC because RDoC traits and endophenotypes "split" diagnostic categories into discrete units of analysis. The SUD field provides a good example of how a complex disorder can be split into smaller, more biologically meaningful units.…”