NeuroImageNeurosciencePeer-reviewed journal article
Discovering the shared biology of cognitive traits determined by genetic overlap
Joao GuimaraesiD, Janita BralteniD, Corina U. GreveniD, Barbara FrankeiD, Emma SprooteniD, Christian F. BeckmanniD
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 (card sorting, 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<{rho}g<0.35). The meta-analysis showed that two genetically-correlated traits, card sorting and fluid intelligence ({rho}g=0.24), also had a significant brain activation overlap ({rho}perm=0.29). These findings indicate that fluid intelligence and executive functioning 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.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 7
- FWCI
- 0.88
- Field percentile
- top 19%
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