Preprint on cell types associated with brain-related traits

My research on identifying cell types that are associated with brain-related traits is available as a preprint (link: https://www.biorxiv.org/content/10.64898/2025.12.05.692533v1)

Background My post-doc work is funded by the BRAINSCAPES consortium, which is a grant aimed at unraveling the biological mechanims underlying brain-related disorders. In complex traits, multiple genetic variants contribute to give rise to the phenotype but each one has very little effect size. It is therefore impossible to target any specific variant or gene. Instead, we hypothesize that perhaps the trait-associated genetic variants/genes might affect the same biological mechanism, pathway, tissue, or cell types. The underlying principal of my project is to leverage trait-associated genetic variants identified from genome-wide association studies (GWAS) and single-cell RNAseq to prioritize cell types that are most likely to be important to diseases.

In recent years there has been an explosion of the availability of single-cell RNAseq datasets. Even though there are many GWAS-to-cell-type computational methods (>10) that have been developed throughout the years, one limitation in implementing of these methods is the computational burden to process these datasets. Further, if a researcher is interested in a specific region of the brain, it was not straightforward to find the relevant single-cell RNAseq datasets and processed them for GWAS-to-cell-type purposes.

Goal Given these limitations, we set out to create a central repository where we curated and processed single-cell RNAseq datasets from different regions of the human brain and at different developmental timepoint. We then applied this database to gain insights into which categories of brain cell types are associated with brain-related traits

Summary of findings

• We confirmed previous findings such as the association between microglia and Alzheimer disease in the entorhinal cortex.
• We found novel evidence for the involvement of specific cell types in disease, such as astrocytes being implicated in alcohol-related phenotypes.

How to use our results

• The processed single-cell RNAseq data from this study has been deposited to FUMA (https://fuma.ctglab.nl/) since version 1.8.2.
• You can run the FUMA cell type pipeline on FUMA. However, the implemented workflow on FUMA cell type currently does not take into account the complex structure of the brain datasets. Instead, you can download the processed data available in the Download tab from FUMA, and implement the workflow as described here: https://github.com/tanyaphung/FUMA_Celltype_cmd
• To help with the ease of selecting datasets for GWAS-to-cell-type, I also created an Rshiny app: https://tanyaphung.shinyapps.io/scrna_category_rshiny/