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

Alexander S Hatoum; Andrew E Reineberg; Philip A Kragel; Tor D Wager; Naomi P Friedman

doi:10.1159/000527224

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

Complex Psychiatry. 2022 Dec 1;8(3-4):63-79. doi: 10.1159/000527224. eCollection 2023 Jan-Dec.

Authors

Alexander S Hatoum^{1

2}, Andrew E Reineberg¹, Philip A Kragel³, Tor D Wager⁴, Naomi P Friedman^{1

3}

Affiliations

¹ Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, Colorado, USA.
² Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA.
³ Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, Colorado, USA.
⁴ Department of Psychological and Brain Sciences, Dartmouth University, Hanover, New Hampshire, USA.

Abstract

Introduction: Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly nonsignificant. 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 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.

Method: 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 UK Biobank. 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.

Results: We found that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable (h2 = 0.155-0.181) than single brain regions (h2 = 0.038-0.118) and captured about half of the genetic variance in IQ (rG = 0.422-0.576), while rGs with single brain measures were smaller and nonsignificant. 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.

Conclusion: Multisystem predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

Keywords: Endophenotype; GWAS; Genome-wide association study; Intelligence; Multivariate MRI; Predictive modeling.

Abstract

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