Genomics of white matter network efficiency unravels shared genetic mechanism with intelligence
Poster No:
678
Submission Type:
Abstract Submission
Authors:
Weijie Huang1, Xinyi Dong1, Ni Shu1
Institutions:
1Beijing Normal University, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
Intelligence is an important behavioural trait. It is one of the most stable cognitive abilities across the lifespan and one of the best predictors of education achievement, occupational status, mental and physical health and longevity (Plomin & von Stumm, 2018). The etiology of individual differences in human intelligence has always been the focus in the field of cognitive neuroscience (Deary et al., 2022). Based on the findings of sMRI and fMRI, several network theories of intelligence has been proposed, such as the P-FIT (Jung & Haier, 2007) and Multiple Demand Network (Camilleri et al., 2018). Both theories suggest that intelligence arises from the interaction and collaboration between certain brain regions, including the dorso-medial and lateral prefrontal cortex, insula and parietal cortex [3-4]. Previous studies have primarily focused on the relationship between structural (Karama et al., 2009) and functional changes (Basten et al., 2015) in these brain regions and individual differences in intelligence, while overlooking the impact of white matter connections between brain regions - the anatomical basis for inter-regional information transmission - on intelligence.
Methods:
In this study, We used a human brain connectome approach to measure the information transfer efficiency of the white matter structural network based on diffusion MRI data, and explored the phenotypic and genetic associations between the information transfer efficiency of the white matter network and intelligence by combining with genome data. Firstly, we used genome-wide association analysis to identify the genetic architecture of white matter network. Then we performed subsequent analyses including genetic correlation, colocalization analysis, Mendelian randomization analysis, mediation model and machine learning method to explore the shared genetic architecture between efficiency of the white matter network and intelligence. Figure 1 provides an overview of the study design and analyses.
·Figure 1. Overview of the study design and analyses.
Results:
We identified the genetic architecture of white matter network, which is mainly associated with the genetic variation on chromosome 6,12 and 17. In addition, we found the significant phenotypically and genetically correlation (rp = 0.12, P = 1.24 × 10–83; rg = 0.30, P = 0.01) and the shared genetic locus between network efficiency with intelligence. Mendelian randomization analysis further proved causal relationship from efficiency of white matter network to intelligence. Thirdly, we found the shared genetic architecture between white matter network and intelligence is heterogeneous across the whole brain in terms of nodal efficiency (Figure 2). Lastly, through mediation analysis, we identified an indirect pathway from intelligence polygenic scores to intelligence via white matter network efficiency (ab = 0.006, 95% CI = [0.004 to 0.007]). Through machine learning methods, we found that combining white matter network nodal efficiency with polygenic scores for nodal efficiency predicts intelligence scores better than using any single type of data alone.
·Figure 2. Genetic loci associated with nodal efficiency of WM network and intelligence. (a) The regions sharing variants with intelligence are highlighted.
Conclusions:
These findings not only support previous network theories of intelligence but also provide further evidence from a genetic perspective for understanding the neurobiological mechanisms underlying intelligence.
Genetics:
Genetic Association Studies 1
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Keywords:
White Matter
Other - neuroimaging genetics, structural connectome, intelligence, genetic correlation,
1|2Indicates the priority used for review
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Provide references using APA citation style.
Camilleri, J. A., Müller, V. I., Fox, P., Laird, A. R., Hoffstaedter, F., Kalenscher, T., & Eickhoff, S. B. (2018). Definition and characterization of an extended multiple-demand network. NeuroImage, 165, 138–147. https://doi.org/10.1016/j.neuroimage.2017.10.020
Deary, I. J., Cox, S. R., & Hill, W. D. (2022). Genetic variation, brain, and intelligence differences. Molecular Psychiatry, 27(1), 335–353. https://doi.org/10.1038/s41380-021-01027-y
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. The Behavioral and Brain Sciences, 30(2), 135–154; discussion 154-187. https://doi.org/10.1017/S0140525X07001185
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., & Evans, A. C. (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37(2), 145–155. https://doi.org/10.1016/j.intell.2008.09.006
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews. Genetics, 19(3), 148–159. https://doi.org/10.1038/nrg.2017.104