The genetic architecture of rhythm functional connectivity
Poster No:
986
Submission Type:
Abstract Submission
Authors:
Yasmina Mekki1, Jennifer Below1, Reyna Gordon1
Institutions:
1Vanderbilt university medical center, Nashville, TN
First Author:
Co-Author(s):
Introduction:
Understanding genetically associated brain individual differences underlying the human capacity to perceive and synchronize to musical rhythm will provide insights into its neural mechanisms. Prior studies linked genetic variation to the ability to move in time with a musical beat and revealed a complex, and polygenic genetic architecture underlying human rhythm. At the brain level, its genetic architecture remains largely unknown. Neural activity measured during task performance is the standard approach to study rhythm processing. However, a growing body of evidence suggests that there is a close correspondence between resting state networks and known cognitive task activation maps (smith et al., 2009, Cole et al., 2014, Tavor et al., 2016). Our aim is to take advantage of resting-state functional MRI data to understand how the brain supports rhythm by unveiling the genetic factors that might contribute to it.
Methods:
We used individuals from the UK Biobank cohort with both resting-state functional MRI and genotyping data. We excluded participants with unusual heterozygosity, high missingness, and sex mismatches. We further restricted our analyses to individuals with white British ancestry in order to avoid any possible confounding effects related to ancestry. This resulted in 31,768 individuals (mean age = 55.31, 16,507 females) passing the sample QC. Using PLINK v1.9, we excluded variants with minor allele frequency < 0.01, and imputation quality INFO scores < 0.8. Multiallelic variants were also removed. 53 Regions of interest (ROIs) were defined as the intersection between rhythm networks (informed by Kasdan et al. 2022) and the combination of parcellations from AICHA and Diedrichsen cerebellar atlases. We constructed a rhythm functional connectome for each individual using a shrunk estimate of partial correlation between each pair of the defined ROIs, resulting in 1,378 functional connectivities (FCs) for each individual. These FCs were pre-residualised controlling for covariates including sex, genotype array type, age, recruitment site, and first ten genetic principal components, then normalized using a rank-based inverse-normal transformation. We estimated the SNP-based heritability of the FCs using GCTA (v1.93.0beta) and performed a multivariate genome-wide association study (mGWAS) using MOSTest (Van der meer et al., 2020).
Results:
SNP-based heritability analysis showed that 146 out of the 1,378 FCs are heritable (pFDR<0.05). We investigated which genetic variants contribute to the heritable rhythm-related FCs by performing a mGWAS. There were 22 significant loci (genomic threshold p<5e-8) associated with different aspects of the rhythm network. We investigated the shared genetic underlying both brain rhythm network mGWAS and behavioral rhythm GWAS (Niarchou et al., 2022) and found a significant genetic correlation (ρ=0.18, se=±0.05, p=1.93e-14). An extensive functional annotation performed highlighted a significant functional enrichment of genes involved in embryonic brain expression.
·Manhattan Plot for Multivariate GWAS Analysis: Exploring 146 Heritable Rhythm Functional Connectivities in 31,768 Participants. The Red Dashed Line Marks the Genome-Wide Significance Threshold p=5e-8.
Conclusions:
This preliminary work represents a step forward towards understanding how genes influence the neurofunctional basis of human rhythm skills, complementing behavioral results. By using resting-state fMRI data, we tried to contribute to alternative task-free approaches to study behavioral traits such as rhythm and its genetic underpinnings.
Genetics:
Genetic Association Studies 2
Higher Cognitive Functions:
Music 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Multivariate Approaches
Keywords:
Other - Rhythm; multivariate GWAS; UK Biobank; resting state fMRI
1|2Indicates the priority used for review
Provide references using author date format
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238-251.
Tavor, I., Jones, O. P., Mars, R. B., Smith, S. M., Behrens, T. E., & Jbabdi, S. (2016). Task-free MRI predicts individual differences in brain activity during task performance. Science, 352(6282), 216-220.
Kasdan, A. V., Burgess, A. N., Pizzagalli, F., Scartozzi, A., Chern, A., Kotz, S. A., ... & Gordon, R. L. (2022). Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review. Neuroscience & Biobehavioral Reviews, 136, 104588.
van der Meer, D., Frei, O., Kaufmann, T., Shadrin, A. A., Devor, A., Smeland, O. B., ... & Dale, A. M. (2020). Understanding the genetic determinants of the brain with MOSTest. Nature communications, 11(1), 3512.
Niarchou, M., Gustavson, D. E., Sathirapongsasuti, J. F., Anglada-Tort, M., Eising, E., Bell, E., ... & Gordon, R. L. (2022). Genome-wide association study of musical beat synchronization demonstrates high polygenicity. Nature Human Behaviour, 6(9), 1292-1309.