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Published on: August 12, 2019
Discovering the shared biology of cognitive traits determined by genetic overlap
J P O F T Guimaraes1, J Bralten2, C U Greven3
1Department of Cognitive Neuroscience, Radboud University Medical Center, 6525 EN, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN, Nijmegen, The Netherlands; Department of Human Genetics, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands.
Abstract:
Investigating the contribution of biology to human cognition has assumed a bottom-up causal cascade where genes influence brain systems that activate, communicate, and ultimately drive behavior. Yet few studies have directly tested whether cognitive traits with overlapping genetic underpinnings also rely on overlapping brain systems. Here, we report a step-wise exploratory analysis of genetic and functional imaging overlaps among cognitive traits. We used twin-based genetic analyses in the human connectome project (HCP) dataset (N = 486), in which we quantified the heritability of measures of cognitive functions, and tested whether they were driven by common genetic factors using pairwise genetic correlations. Subsequently, we derived activation maps associated with cognitive tasks via functional imaging meta-analysis in BrainMap (N = 4484), and tested whether cognitive traits that shared genetic variation also exhibited overlapping brain activation. Our genetic analysis determined that six cognitive measures (cognitive flexibility, no-go continuous performance, fluid intelligence, processing speed, reading decoding and vocabulary comprehension) were heritable (0.3 < h2 < 0.5), and genetically correlated with at least one other heritable cognitive measure (0.2 < ρg < 0.35). The meta-analysis showed that two genetically-correlated traits, cognitive flexibility and fluid intelligence (ρg = 0.24), also had a significant brain activation overlap (ρperm = 0.29). These findings indicate that fluid intelligence and cognitive flexibility rely on overlapping biological features, both at the neural systems level and at the molecular level. The cross-disciplinary approach we introduce provides a concrete framework for data-driven quantification of biological convergence between genetics, brain function, and behavior in health and disease.
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