Poster No:
675
Submission Type:
Abstract Submission
Authors:
Zhuopin Sun1,2, Jiru Han1,2, Zachary Gerring1,2, Victoria Jackson1,2, Thiago Rezende3,4, Ian Harding5,6, Melanie Bahlo1,2
Institutions:
1Walter and Eliza Hall Institute of Medical Research, Parkville, Australia, 2Department of Medical Biology, The University of Melbourne, Parkville, Australia, 3Department of Neurology, University of Campinas, Campinas, Brazil, 4Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil, 5QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 6School of Translational Medicine, Monash University, Melbourne, Australia
First Author:
Zhuopin Sun
Walter and Eliza Hall Institute of Medical Research|Department of Medical Biology, The University of Melbourne
Parkville, Australia|Parkville, Australia
Co-Author(s):
Jiru Han
Walter and Eliza Hall Institute of Medical Research|Department of Medical Biology, The University of Melbourne
Parkville, Australia|Parkville, Australia
Zachary Gerring
Walter and Eliza Hall Institute of Medical Research|Department of Medical Biology, The University of Melbourne
Parkville, Australia|Parkville, Australia
Victoria Jackson
Walter and Eliza Hall Institute of Medical Research|Department of Medical Biology, The University of Melbourne
Parkville, Australia|Parkville, Australia
Thiago Rezende
Department of Neurology, University of Campinas|Brazilian Institute of Neuroscience and Neurotechnology
Campinas, Brazil|Campinas, Brazil
Ian Harding, Ph.D.
QIMR Berghofer Medical Research Institute|School of Translational Medicine, Monash University
Brisbane, Queensland|Melbourne, Australia
Melanie Bahlo
Walter and Eliza Hall Institute of Medical Research|Department of Medical Biology, The University of Melbourne
Parkville, Australia|Parkville, Australia
Introduction:
The spinal cord is a crucial communication pathway between the brain and the rest of the body, containing important networks for movement and sensation (Cohen-Adad & Wheeler-Kingshott, 2014). Genetically, a small cohort twin study suggested high heritability of the C2 spinal cord cross-sectional area (CSA) (h2=0.9) (Dahlberg et al., 2020), and genotype-specific spinal cord damage was reported in rare motor neuron diseases like spinocerebellar ataxias (Rezende et al., 2024). Despite its importance, the genetic architecture of the spinal cord remains unexplored in large-scale studies. This study aims to investigate the genetic underpinnings of upper spinal cord morphometric phenotypes by leveraging the neuroimaging and genomic data in the UK Biobank (UKB).
Methods:
T1-weighted brain MRI data of a total of n=46,217 UKB participants were processed via the Enigma Spinal Cord pipeline (Enigma-SC), leveraging the Spinal Cord Toolbox (SCT) (De Leener et al., 2017) alongside a deep learning method for optimised upper spinal cord vertebral labelling. This pipeline involves the following key steps : (1) spinal cord segmentation, (2) automated vertebral labelling of the C1-C3 spinal levels using a pre-trained nnUNet v2 model (Isensee et al., 2021) (3) registration of the spinal cord to the PAM50 template (4) computation of spinal cord shape metrics including CSA and eccentricity. We performed quality assessment of the labelled spinal cord segments by visually inspecting the quality control (QC) reports generated by the SCT. Vertebral-level specific QC was conducted to exclude samples meeting any of the following criteria: (1) images with artifacts or poor image quality, (2) inaccurate segmentation, or (3) inaccurate vertebral labelling.
Genome-wide association studies (GWAS) were conducted on the derived spinal cord phenotypes (C1-C3) using the REGENIE (Mbatchou et al., 2021). Participants with unusual heterozygosity, high missingness rates, sex mismatches, or familial relatedness were excluded and further restricted to individuals of European ancestry (Pan-UKBB). Variants with a minor allele frequency below 0.05, imputation quality scores less than 0.8, or those failing Hardy-Weinberg equilibrium tests (P < 1e−6) were also excluded. The analyses controlled for age, sex, intracranial volume, genetic batch, scanning site, and the first ten genetic principal components. A preliminary subset of 12,807 samples was used for the analysis. Functionally annotation was done on independent significant SNPs, defined as those with an r² ≤ 0.6 within a 1 Mb window, using FUMA (version 1.6.1) (Watanabe et al., 2020).
Results:
Our analysis revealed significant genetic loci (p < 5e-8) associated with spinal cord eccentricity and CSA across C1-C3 levels. We identified 3, 5, and 1 independent significant variants associated with C1, C2, and C3 CSA, respectively. We found 29, 44, and 33 independent significant variants associated with C1, C2, and C3 eccentricity, respectively. A subset of the genes implicated by CSA associated signals, especially at the C1 level, were previously reported to be associated with subcortical brain volumes. For instance, the lead SNP for CSA near the SGTB gene was previously linked to brainstem volume (Elvsåshagen et al., 2020). By contrast, the genetic associations for eccentricity appeared to be distinct and did not overlap with genes previously linked to other brain volume measures. These significant genetic loci identified, which include both shared and level-specific associations across C1-C3, provide valuable insights into the underlying genetic architecture of the spinal cord.
Conclusions:
This GWAS study represents the first to uncover genetic loci associated with spinal cord shape metrics, providing new insights into the genetic architecture of spinal cord. Our findings, combining imaging and genetic evidence, highlight the importance of further investigating the spinal cord's involvement in human health.
Genetics:
Genetic Association Studies 1
Genetics Other 2
Keywords:
Aging
Development
Phenotype-Genotype
Segmentation
Spinal Cord
STRUCTURAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
Spinal Cord Toolbox
Provide references using APA citation style.
Cirulli, E. T., Kasperaviciūte, D., Attix, D. K., Need, A. C., Ge, D., Gibson, G., & Goldstein, D. B. (2010). Common genetic variation and performance on standardized cognitive tests. European Journal of Human Genetics: EJHG, 18(7), 815–820.
Cohen-Adad, J., & Wheeler-Kingshott, C. (2014). Quantitative MRI of the Spinal Cord. Academic Press.
De Leener, B., Fonov, V. S., Collins, D. L., Callot, V., Stikov, N., & Cohen-Adad, J. (2018). PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. NeuroImage, 165, 170–179.
Elvsåshagen, T., Bahrami, S., van der Meer, D., Agartz, I., Alnæs, D., Barch, D. M., Baur-Streubel, R., Bertolino, A., Beyer, M. K., Blasi, G., Borgwardt, S., Boye, B., Buitelaar, J., Bøen, E., Celius, E. G., Cervenka, S., Conzelmann, A., Coynel, D., Di Carlo, P., … Kaufmann, T. (2020). The genetic architecture of human brainstem structures and their involvement in common brain disorders. Nature Communications, 11(1), 4016.
Enigma-SC. Github. Retrieved December 10, 2024, from https://github.com/art2mri/Enigma-SC
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211.
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., Habegger, L., Ferreira, M., Baras, A., Reid, J., Abecasis, G., Maxwell, E., & Marchini, J. (2021). Computationally efficient whole-genome regression for quantitative and binary traits. Nature Genetics, 53(7), 1097–1103.
Rezende, T. J. R., Adanyaguh, I., Barsottini, O. G. P., Bender, B., Cendes, F., Coutinho, L., Deistung, A., Dogan, I., Durr, A., Fernandez-Ruiz, J., Göricke, S. L., Grisoli, M., Hernandez-Castillo, C. R., Lenglet, C., Mariotti, C., Martinez, A. R. M., Massuyama, B. K., Mochel, F., Nanetti, L., … Harding, I. H. (2024). Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study. Journal of Neurology, Neurosurgery, and Psychiatry, 95(7), 682–690.
Solstrand Dahlberg, L., Viessmann, O., & Linnman, C. (2020). Heritability of cervical spinal cord structure. Neurology. Genetics, 6(2), e401.
Watanabe, K., Mirkov, M. U., de Leeuw, C. A., van den Heuvel, M. P., & Posthuma, D. (2020). Author Correction: Genetic mapping of cell type specificity for complex traits. Nature Communications, 11(1), 1718.
No