A genome-wide association study of brain function across multiple cognitive domains
Poster No:
676
Submission Type:
Abstract Submission
Authors:
Lea Waller1, Renée Lipka1, Lina Mograby1, Maja Neidhart1, Firuza Rahimova2, Gina-Isabelle Henze1, Fabrizio Pizzagalli3, Carla D'Agostino3, Tomáš Paus4, Lars Nyberg5, Udo Dannlowski6, Tilo Kircher7, Thomas Lancaster8, Ahmad Hariri9, Alexander Holmes10, Sidhant Chopra11, Tribikram Thapa Rana12, Alex Fornito12, Jeggan Tiego12, Mark Bellgrove12, Ben Harrison13, Alec Jamieson13, Christopher Davey13, Yann Quide14, Pascal Aggensteiner15, Maximilian Monninger15, Nathalie Holz15, Tobias Banaschewski15, Alessandro Bertolino16, Giulio Pergola17, Roberta Passiatore17, Leonardo Sportelli17, Rosie Tatham18, Justine Gatt19, Thomas Nichols10, Paul Thompson20, Sarah Medland21, Ilya Veer22, Susanne Erk1, Henrik Walter1
Institutions:
1Charité Universitätsmedizin Berlin, Berlin, Germany, 2Humboldt-Universität zu Berlin, Berlin, Germany, 3University of Turin, Turin, Italy, 4University of Montreal, Montreal, Canada, 5Umeå University, Umeå, Sweden, 6University of Münster, Münster, Germany, 7University of Marburg, Marburg, Germany, 8Cardiff University, Cardiff, United Kingdom, 9Duke University, Durham, United States, 10University of Oxford, Oxford, United Kingdom, 11Orygen, Preston, Australia, 12Monash University, Clayton, Australia, 13University of Melbourne, Carlton, Australia, 14UNSW Sydney, Sydney, Australia, 15Central Institute of Mental Health, Mannheim, Germany, 16University of Bari, Bari, Italy, 17Lieber Institute for Brain Development, Baltimore, United States, 18University of Edinburgh, Edinburgh, United Kingdom, 19Neuroscience Research Australia (NeuRA), Randwick, Australia, 20University of Southern California, Marina del Rey, United States, 21QIMR Berghofer Medical Research Institute, Brisbane, Australia, 22University of Amsterdam, Amsterdam, Netherlands
First Author:
Co-Author(s):
Introduction:
Functional MRI during the performance of cognitive tasks is widely used to study the neurobiological basis of behavior, cognition, and emotion. Previous studies disagree on whether statistics derived from task-based fMRI are heritable – estimates range from approximately five percent (Smith et al. 2021) to more than forty percent (Blokland et al. 2011; Dickie et al. 2014). One explanation for this discrepancy is the different analytic choices made by researchers.
Here we present the largest and most diverse genome-wide association study of task-based fMRI to date that uses a single, harmonized data analysis pipeline across all contributing sites. This abstract reports the SNP-based heritability results obtained from the current sample.
We chose three domains of cognitive tasks that have been widely used in imaging genetics studies of task-based fMRI for inclusion in the study. These are emotional face viewing and emotion identification tasks that involve the presentation of faces displaying negative emotions, working memory tasks such as N-back or Sternberg paradigm, and reward tasks including Monetary Incentive Delay (MID) and card guessing paradigms. MID tasks measure both the neural correlates of reward anticipation, and those of experiencing the reward outcome of that anticipation. Card guessing paradigms only capture the latter.
Here we present the largest and most diverse genome-wide association study of task-based fMRI to date that uses a single, harmonized data analysis pipeline across all contributing sites. This abstract reports the SNP-based heritability results obtained from the current sample.
We chose three domains of cognitive tasks that have been widely used in imaging genetics studies of task-based fMRI for inclusion in the study. These are emotional face viewing and emotion identification tasks that involve the presentation of faces displaying negative emotions, working memory tasks such as N-back or Sternberg paradigm, and reward tasks including Monetary Incentive Delay (MID) and card guessing paradigms. MID tasks measure both the neural correlates of reward anticipation, and those of experiencing the reward outcome of that anticipation. Card guessing paradigms only capture the latter.
Methods:
We invited researchers with access to relevant data to contribute through the ENIGMA consortium mailing list, the consortium website, and public postings on social media. Researchers received an imaging analysis manual to process their data from scratch using HALFpipe (Waller et al. 2022), which is based on fMRIPrep (Esteban et al. 2020), and then estimate task contrasts on the individual level data.
Sites then calculated genome-wide associations on the resulting z-statistics for regions derived from multiple brain atlases. This step was performed using RAMP (Waller et al. 2024), which is based on RAREMETALWORKER (Feng et al. 2014). Sites were combined in a fixed-effects meta-analysis using METAL (Willer, Li, and Abecasis 2010).
We then calculated the SNP-based heritability from the meta-analysis results for each brain region using HDL (Ning, Pawitan, and Shen 2020). We thresholded heritability estimates at p(FDR) < 0.05.
Sites then calculated genome-wide associations on the resulting z-statistics for regions derived from multiple brain atlases. This step was performed using RAMP (Waller et al. 2024), which is based on RAREMETALWORKER (Feng et al. 2014). Sites were combined in a fixed-effects meta-analysis using METAL (Willer, Li, and Abecasis 2010).
We then calculated the SNP-based heritability from the meta-analysis results for each brain region using HDL (Ning, Pawitan, and Shen 2020). We thresholded heritability estimates at p(FDR) < 0.05.
Results:
SNP-based analyses of seven datasets show moderate heritability across a wide range of brain regions for emotional faces and reward outcome (Figure 1). Quantile-quantile plots do not show any technical artifacts (Figure 2).
The amygdala is known to have a large effect size in the emotional faces task. However, we find greater heritability in cortical regions not commonly associated with the task. For the reward outcome contrast, we found the maximum heritability in the striatum, which is consistent with brain maps found by imaging-only studies.
We did not find significant heritability for working memory and reward anticipation, likely due to a lack of statistical power.
At time of writing, not all sites planned for inclusion in the meta-analysis have completed data analysis. We expect to increase statistical power by including these datasets.
The amygdala is known to have a large effect size in the emotional faces task. However, we find greater heritability in cortical regions not commonly associated with the task. For the reward outcome contrast, we found the maximum heritability in the striatum, which is consistent with brain maps found by imaging-only studies.
We did not find significant heritability for working memory and reward anticipation, likely due to a lack of statistical power.
At time of writing, not all sites planned for inclusion in the meta-analysis have completed data analysis. We expect to increase statistical power by including these datasets.
Conclusions:
Our results are consistent with previous findings of SNP-based heritability for the amygdala in the emotional faces task (Smith et al. 2021), but expand upon previous findings and describe additional heritable brain regions across multiple constructs. This demonstrates the feasibility and utility of genome-wide association studies for investigating individual differences in task-based fMRI. The results presented here will inform secondary analyses including genetic correlations and annotation. These may provide important insights into the relation of genes, molecules, cells, and circuits to psychological domains including negative valence, positive valence, social processes, and cognition.
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Genetics:
Genetic Association Studies 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Learning and Memory:
Working Memory
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Keywords:
Basal Ganglia
Cognition
Emotions
FUNCTIONAL MRI
Limbic Systems
Meta- Analysis
Open-Source Code
Phenotype-Genotype
Psychiatric Disorders
Other - Genome-wide association study
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
Dickie, Erin W., Amir Tahmasebi, Leon French, Natasa Kovacevic, Tobias Banaschewski, Gareth J. Barker, Arun Bokde, et al. 2014. “Global Genetic Variations Predict Brain Response to Faces.” PLOS Genetics 10 (8): e1004523. https://doi.org/10.1371/journal.pgen.1004523.
Esteban, Oscar, Rastko Ciric, Karolina Finc, Ross W. Blair, Christopher J. Markiewicz, Craig A. Moodie, James D. Kent, et al. 2020. “Analysis of Task-Based Functional MRI Data Preprocessed with fMRIPrep.” Nature Protocols 15 (7): 2186–2202. https://doi.org/10.1038/s41596-020-0327-3.
Feng, Shuang, Dajiang Liu, Xiaowei Zhan, Mary Kate Wing, and Gonçalo R. Abecasis. 2014. “RAREMETAL: Fast and Powerful Meta-Analysis for Rare Variants.” Bioinformatics 30 (19): 2828–29. https://doi.org/10.1093/bioinformatics/btu367.
Ning, Zheng, Yudi Pawitan, and Xia Shen. 2020. “High-Definition Likelihood Inference of Genetic Correlations across Human Complex Traits.” Nature Genetics 52 (8): 859–64. https://doi.org/10.1038/s41588-020-0653-y.
Smith, Stephen M., Gwenaëlle Douaud, Winfield Chen, Taylor Hanayik, Fidel Alfaro-Almagro, Kevin Sharp, and Lloyd T. Elliott. 2021. “An Expanded Set of Genome-Wide Association Studies of Brain Imaging Phenotypes in UK Biobank.” Nature Neuroscience 24 (5): 737–45. https://doi.org/10.1038/s41593-021-00826-4.
Waller, Lea, Micael Andersson, Gina-Isabelle Henze, Ilya Veer, Paul M. Thompson, Sarah E. Medland, Susanne Erk, and Henrik Walter. 2024. “RAMP: A Pipeline for Brain-Wide Genome-Wide Association Studies Based on the ENIGMA Protocols.” In 30th Organization for Human Brain Mapping Annual Meeting. https://ww6.aievolution.com/hbm2401/index.cfm?do=abs.viewAbstract&style=1&abstractID=1430.
Waller, Lea, Susanne Erk, Elena Pozzi, Yara J. Toenders, Courtney C. Haswell, Marc Büttner, Paul M. Thompson, et al. 2022. “ENIGMA HALFpipe: Interactive, Reproducible, and Efficient Analysis for Resting-State and Task-Based fMRI Data.” Human Brain Mapping 43 (9): 2727–42. https://doi.org/10.1002/hbm.25829.
Willer, Cristen J., Yun Li, and Gonçalo R. Abecasis. 2010. “METAL: Fast and Efficient Meta-Analysis of Genomewide Association Scans.” Bioinformatics 26 (17): 2190–91. https://doi.org/10.1093/bioinformatics/btq340.