Poster No:
858
Submission Type:
Abstract Submission
Authors:
Zuriel Ceja1, Luis M. Garcia Marin1, Jill Rabinowitz2, Miguel Renteria3
Institutions:
1QIMR Berghofer Medical Research Institute, Brisbane, QLD, 2Department of Mental Health Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 3Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, AK
First Author:
Zuriel Ceja
QIMR Berghofer Medical Research Institute
Brisbane, QLD
Co-Author(s):
Jill Rabinowitz
Department of Mental Health Johns Hopkins Bloomberg School of Public Health
Baltimore, MD
Miguel Renteria
Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute
Brisbane, AK
Introduction:
Introduction: Suicide accounts for one in every 100 deaths in the world, and it is the fourth leading death cause for individuals aged between 15 and 29. Genetic studies have identified several genomic regions associated with suicide attempt (SA) risk. These putative risk genes are related to multiple domains, including epigenetics and cellular stress response, and are expressed in the brain and pituitary gland tissues. Understanding the complex biological basis of SA risk might enable the development of targeted interventions and improve public health strategies. This study leverages GWAS summary-based data to estimate genetic correlations between SA and brain morphometry measurements, aiming to produce novel insights into potential causal relationships.
Methods:
Methods: We used GWAS summary statistics comprising 43,871 SA cases and 915,025 controls; participants included individuals of European ancestry from The International Suicide Genetics Consortium (ISGC) and The Million Veteran Program (MVP). We then examined genetic correlations between SA and intracranial volume (ICV) and nine subcortical brain volumes (i.e. caudate nucleus, hippocampus, brainstem, ventral diencephalon, thalamus, globus pallidus, putamen, nucleus accumbens, and amygdala). We used LD score regression (LDSC) and GWAS-pairwise (GWAS-PW) to identify shared genomic features to elucidate their interplay with SA. Lastly, we performed functional annotation and gene-based association analyses to gain insights into the biological mechanisms underlying the observer associations.
Results:
Results: We identified significant genetic correlations between SA and specific brain regions. After adjusting for multiple testing, only the genetic correlation between SA and intracranial volume (ICV) was significant (rG = -0.10, p-value = 0.0019). GWAS-Pairwise identified ten genomic segments involving SA and at least one of the eight brain structures via the same genetic variants. The thalamus shared the largest number of shared segments (7 segments), followed by the putamen (2 segments) and caudate (1 segment). Lastly we performed functional annotation observing the enrichment of 7 genes (BTN3A2, HIST1H1B, HIST1H2BL, HIST1H2BN, HIST1H2AJ, HIST1H4L, and OR2B2) in the thalamus followed by the putamen and caudate, each sharing 2 (PPP4R1 and DCC) and 1 (DCC) enriched genes respectively with SA.
Conclusions:
Conclusion: Our findings provide additional evidence for a genetic link between SA and intracranial volume (ICV). We found a negative correlation, meaning a reduced ICV may be associated with an increased risk of SA. This finding offers novel insights into the potential for genetic factors to concurrently influence brain structural changes and SA risk.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Genetics:
Genetic Association Studies 1
Keywords:
Computational Neuroscience
Informatics
Psychiatric
Psychiatric Disorders
Sub-Cortical
Thalamus
Other - Suicide
1|2Indicates the priority used for review
Provide references using author date format
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