Mendelian Randomization Studies of Adolescent Brain Morphometry and Parkinson’s Disease
Poster No:
859
Submission Type:
Abstract Submission
Authors:
Lang Liu1, Konstantin Senkevich2, Alain Dagher3, Ziv Gan-Or1
Institutions:
1McGill University, Montreal, QC, 2McGill University, Montreal, Quebec, 3Montreal Neurological Institute and Hospital, McGill University, Montreal, QC
First Author:
Co-Author(s):
Introduction:
Significant genetic correlations have been found between Parkinson's disease (PD) and volumes of multiple subcortical structures, including the putamen and brainstem (García-Marín et al., 2023). And a putative causal relationship has been suggested between larger putamen and increased risk of PD by conducting Mendelian randomization (MR). The previous research focuses on adult and elderly populations. There is currently limited research on the long-term impact of adolescent brain development on the risk of PD. In rare instance, Parkinson's-like symptoms can be found in adolescence and such early-onset parkinsonism in adolescence is genetically heterogeneous (Morales-Briceño et al., 2020). And the detected genetic mutations are sometimes associated with altered brain imaging phenotypes. It remains unclear whether the variability observed in brain structure during adolescence is associated with vulnerability to PD. Therefore, we aim to identify if there is an early genetic predisposition to PD by influencing the brain developmental changes in the adolescent stage.
Methods:
The Adolescent Brain Cognitive Development (ABCD) Study release 4.0 was used as the main cohort in this project (Casey et al., 2018). After performing quality control and following exclusion criteria: non-European ancestry, mismatch between genetic and self-reported sex as well as relation to another participant closer than cousin, there were 4696 participants remaining in the analysis. Genome wide association studies (GWAS) were conducted on imaging-derived phenotypes (thickness, surface area and volume) from 25 subcortical regions. Age, sex and the first 10 genetic principal components were included as covariates. Heritability and genetic correlation were calculated using LDSC (linkage disequilibrium score regression) (Bulik-Sullivan et al., 2015). The power to detect genetic association can indeed be limited when conducting GWAS on a relatively small sample size. And genetic correlation across the various brain regions have been reported for cortical thickness, cortical surface and subcortical volume (Eyler et al., 2010; Hofer et al., 2020). Thus, a tool called multi-trait analysis of GWAS (MTAG) was implemented to leverage the genetic correlation between traits to boost the power of GWAS (Turley et al., 2018). There are two major confounding factors in Mendelian randomization: pleiotropy and sample structure . To account for these two confounding factors, we implemented a unified approached named MR Accounting for Pleiotropy and Sample Structure (MR-APSS). The p-value threshold for instrument variable in the MR was set at 5e-05 as the default threshold. P values of the resulting causal effect derived from MR-APSS were corrected by the false discovery rate approach.
Results:
There was no significant causal effect detected by MR-APSS between subcortical volume and PD, as described in Figure 1.
·Figure 1. the causal effects of subcortical volume on Parkinson’s disease, derived from Mendelian randomization – Accounting for Pleiotropy and Sample Structure (MR-APSS).
Conclusions:
In the adolescent stage, change in the subcortical structures may not causally affect the risk of developing PD in the later stage of lifetime. And GWAS was derived from participants with age ranging from 9 to 10 years old. The genetic predisposition to PD in brain developmental changes at age of 9-10 might be not displayed. It is also possible that our MR analysis was restricted by the statistical power of GWAS since the cohort had a relatively small sample size. Next steps will be incorporating scanning data from multiple visits together to increase the sample size and therefore increase the statistical power of our analysis.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Genetics:
Genetic Association Studies 1
Genetic Modeling and Analysis Methods
Lifespan Development:
Early life, Adolescence, Aging
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Computational Neuroscience
Data analysis
Neurological
Structures
Other - Adolescence
1|2Indicates the priority used for review
Provide references using author date format
Casey, B. J.(2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54.
Eyler, L. T.(2010). Genetic patterns of correlation among subcortical volumes in humans: Results from a magnetic resonance imaging twin study. Human Brain Mapping, 32(4), 641–653.
García-Marín, L. M. (2023). Shared molecular genetic factors influence subcortical brain morphometry and Parkinson’s disease risk. Npj Parkinson’s Disease, 9(1), Article 1. https://doi.org/10.1038/s41531-023-00515-y
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Hofer, E. (2020). Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-18367-y
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