An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset
The scripts are for our work which employs a MR framework to infer cell type-specific causal relationships between gene expression and brain-associated complex traits, using eQTL data from eight cell types and large-scale GWASs of 123 imaging-derived phenotypes (IDPs) and 26 brain disorders and behaviors (DBs).
These scripts depend on the following software and R packages:
- R v4.2.0
- GCTA v1.94.0
- SMR v1.3.1
- MetaXcan
- data.table v1.14.4
- tidyr v1.3.0
- PMR v1.0
- doParallel v1.0.17
- foreach v1.5.2
- gsmr v1.1.0
- gridExtra v2.3
- tidyverse v2.0.0
- AnnotationHub v3.6.0
- org.Hs.eg.db v3.18.0
- clusterProfiler v4.10.0
- dplyr v1.1.4
- ggplot2 v3.4.4
- stats v4.3.2
- ggsankey v0.0.9
- pheatmap v1.0.12
- ggpubr V0.4.0
- colorsapce v2.0.2
- stringr v1.4.0
- stringi v1.4.6
- export v0.3.0
- reshape2 v1.4.4
- corrplot v0.84.0
Here, we organized the custom codes of the computational analyses in our study into 3 parts as shown below.
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Part 1. Transcriptome-wide association study (TWAS) analysis. We conducted TWAS for the GWAS summary statistics of 123 IDPs and 26 DBs to investigate the potential links between gene expression levels and these brain-associated complex traits.
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Part 2. MR analysis. We performed two-sample MR analysis for inferring the causal effects of cell type-specific gene expression on brain-associated complex traits using four methods including SMR, Wald radio, PMR-Egger and GSMR. We also performed bidirectional two-sample MR analysis for inferring the relationships between the above DBs and IDPs using PMR-Egger, GSMR, and methods in R package TwoSampleMR.
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Part 3. Follow-up analysis. We performed follow-up analysis which included merging MR results, identifying putative causal cell type-specific eQTL target genes (eGenes) for IDPs and DBs, characterizing the shared causal eGenes among IDPs and DBs, replication in enternal datasets, characterizing the potential causal biological pathways amongst them, and exploring their gene expression patterns using external single-cell data.
If you have any questions, please contact Anyi Yang (yanganyi_angie@163.com) or Xingzhong Zhao (naturescarl@gmail.com).
If you use the results in your study, please cited