Poster No:
603
Submission Type:
Abstract Submission
Authors:
Nadine Parker1, Kevin O’Connell1, Alexey Shadrin1, Espen Hagen1, Pravesh Parekh1, Paul Thompson2, Anders Dale3, Christopher Ching2, Ole Andreassen1
Institutions:
1University of Oslo, Oslo, Norway, 2University of Southern California, Marina Del Rey, CA, 3University of California San Diego, San Diego, CA
First Author:
Co-Author(s):
Paul Thompson
University of Southern California
Marina Del Rey, CA
Anders Dale
University of California San Diego
San Diego, CA
Introduction:
Bipolar disorder (BD) is associated with brain structure and genetic variation (Hibar et al., 2016, 2018; Mullins et al., 2021). These associations can be leveraged to understand the disorder's pathophysiology, its treatment, and ultimately improve diagnostic prediction. Here we test associations between structural MRI measures and the genetic liability to BD.
Methods:
Using data from 35,660 UK Biobank participants [52.91% female, mean age(sd)=63.55 (7.53)], we determined the associations between FreeSurfer derived measures (i.e., cortical thickness, surface area, and subcortical volumes) in regions of interest (ROI) (Desikan et al., 2006; Fischl et al., 2002) and BD (Mullins et al., 2021) polygenic risk scores (PRS; using LDpred2-auto (Privé et al., 2020)). Next, using a subsample of patients from the ENIGMA BD working group [n=267, 58.05% female, mean age (sd)=34.53 (12.04)], we examined how each ROI is associated with the PRS for BD in a case-only analysis. With an additional sample of 748 healthy controls [44.39% female, mean age (sd)=33.81 (9.84)] from the ENIGMA BD group we tested the interaction between BD status and PRS in significant ROIs identified in the case-only analysis. We also tested for underlying clinical and biological factors by (1) stratifying analysis based on medication use (i.e., anti-depressants, lithium, first and second generation antipsychotics, anti-epileptics, and no medication) and (2) assessing associations with pathway-specific PRS for neural cell types using genes from post-mortem fetal (Bhaduri et al., 2021) and adult (Lake et al., 2018) brain. All linear models included age, age2, sex, scanner, genetic batch, and the first 20 genetic principal components as covariates. Models with brain volumes as outcomes also included intracranial volume as a covariate.
Results:
The large cohort level analysis, using the UK Biobank sample, revealed that the BD PRS was associated with reductions in thickness and volume but increased surface area across ROI. In our case-only analysis, positive associations were observed between the BD PRS and thickness in six ROIs in the temporal lobe. Interactions between PRS and BD status were observed for cortical thickness in three of these regions [right inferior temporal (beta (se)=-0.06 (0.02), p_fdr=0.02), left middle temporal (beta (se)=-0.06 (0.02), p_fdr=0.02), and left fusiform (beta (se)=-0.05 (0.01), p_fdr=0.03)]. The positive association for BD patients was strongest among those taking antidepressants and lithium. Additionally, among the six regions associated with the BD PRS, the adult microglia PRS was significantly associated with temporal pole thickness (beta (se)=0.35 (0.10), p_fdr =0.04) in BD.
Conclusions:
Structural brain measures are associated with the genetic liability to BD with variability when testing in large non-clinical cohorts compared to BD patients only. Part of this variability may be explained by BD treatment with differing medication effects. Additionally, microglial cells may play a role in mediating the genetically associated variations in brain structure among BD patients. These findings illustrate the importance of combining neuroimaging and genetics to improve our understanding of BD with implications for improved diagnostics.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Association Studies 2
Keywords:
Cortex
Glia
Psychiatric Disorders
Other - Polygenic Risk Score; Bipolar Disorder; Structural MRI
1|2Indicates the priority used for review
Provide references using author date format
Bhaduri, A., Sandoval-Espinosa, C., Otero-Garcia, M., Oh, I., Yin, R., Eze, U. C., Nowakowski, T. J., & Kriegstein, A. R. (2021). An Atlas of Cortical Arealization Identifies Dynamic Molecular Signatures. Nature, 598(7879), 200-204.
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Kouwe, A. van der, Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., & Dale, A. M. (2002). Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron, 33(3), 341–355.
Hibar, D. P., Westlye, L. T., Doan, N. T., Jahanshad, N., Cheung, J. W., Ching, C. R. K., Versace, A., Bilderbeck, A. C., Uhlmann, A., Mwangi, B., Krämer, B., Overs, B., Hartberg, C. B., Abé, C., Dima, D., Grotegerd, D., Sprooten, E., Bøen, E., Jimenez, E., … Andreassen, O. A. (2018). Cortical abnormalities in bipolar disorder: An MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Molecular Psychiatry, 23(4), 932–942.
Hibar, D. P., Westlye, L. T., van Erp, T. G. M., Rasmussen, J., Leonardo, C. D., Faskowitz, J., Haukvik, U. K., Hartberg, C. B., Doan, N. T., Agartz, I., Dale, A. M., Gruber, O., Krämer, B., Trost, S., Liberg, B., Abé, C., Ekman, C. J., Ingvar, M., Landén, M., … Andreassen, O. A. (2016). Subcortical volumetric abnormalities in bipolar disorder. Molecular Psychiatry, 21(12), Article 12.
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., & Zhang, K. (2018). Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nature Biotechnology.
Mullins, N., Forstner, A. J., O’Connell, K. S., et al., (2021). Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature Genetics, 53(6).
Privé, F., Arbel, J., & Vilhjálmsson, B. J. (2020). LDpred2: Better, faster, stronger. Bioinformatics, 36(22–23), 5424–5431.