| Title: | Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression |
| Journal: | Nature Communications |
| Published: | 7 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38849352/ |
| DOI: | https://doi.org/10.1038/s41467-024-49263-4 |
| URL: | https://www.nature.com/articles/s41467-024-49263-4.pdf |
Publication 11897
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.</p>
12 Keywords
- Brain
- Causality
- Gene Expression
- Gene Expression Regulation
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Mendelian Randomization Analysis
- Obesity
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
- Single-Cell Analysis
8 Authors
- Ruo-Han Hao
- Tian-Pei Zhang
- Feng Jiang
- Jun-Hui Liu
- Shan-Shan Dong
- Meng Li
- Yan Guo
- Tie-Lin Yang
1 Application
| Application ID | Title |
|---|---|
| 46387 | Integration of regulatory elements and multi-trait genomic and phenomic data to improve the accuracy of risk prediction of human complex diseases/traits |
Enabling scientific discoveries that improve human health