Post-transcriptional regulation
Last updated: 2022-07-05
Checks: 5 2
Knit directory: funcFinemapping/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/annotations_PTR.Rmd) and HTML (docs/annotations_PTR.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | af0fffa | Jing Gu | 2022-07-03 | add PTR annotations |
| html | 1da45e4 | Jing Gu | 2022-07-02 | add PTR annotation |
Backgrounds
Post-transcriptional regulatory (PTR) processes have been implicated in development and diseases, however, it is largely unknown how genetic variations are mediated through PTR processes. We propose to annotate GWAS variants using both experimental measurements and computational predictions. With this prior knowledge, we can further identify most likely causal variants through fine-mapping and then link them to genes.
Several post-transcriptonal features will be explored:
- alternative splicing
- RNA modification: m6A
- RNA binding
- Polyadenylation
Methods
SNP effect predictions
The predictions of variant effects on post-transcriptional regulation were performed on 10 million SNPs after some QC criteria, from 1000 genome phase 3 project.
Enrichment analysis
We first tested annotations one at a time using both TORUS and S-LDSC.Then we jointly assessed a set of annotations.
For the early attempts, I ran torus and S-LDSC on different numbers of test SNPs. As an example, there are 6M SNPs for SCZ, but 8M SNPs for aFIb after the same filtering steps. However, for S-LDSC analysis, around 1M hapmap3 SNPs were tested across all traits.
Results
Enrichment analysis on SCZ GWAS
To make it comparable, I run S-LDSC and Torus on the same set of test SNPs, which are around 1 million SNPs from hapmap3.
Legends for the plots:
- x-axis - annotations
- y-axis - fold of enrichment
- label on the plot - percent of narrow-sense heritability
- dashed line - no enrichment
S-LDSC
modified baselineLD model： genic features (introns excluded), LD, MAF, background selection, nucleotide diversity, promoter, recombination rate. annotations: spidex, spliceai, fetal brain m6a, neuronal OCR, differentially methylated regions in brains (DMR)
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
Examine baseline annotations
- baseline annotations from m6A paper: baseline_gene_MAF_LD
- test annotation:spliceai score >=0.05
The intron region annotations explain ~50% of SNP heritability. To not interfere with our testing of splicing effects, I removed intron related annotations from baselines.
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
Torus 
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
With the same set of test SNPs, the enrichment estimates are similarly low between torus and LDSC. The enrichment signals seen in the previous runs for torus were gone, likely due to the background difference.
Enrichment analysis on AFib GWAS
Comparing LDSC and Torus with the same baselines
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
For aFib, we see significant enrichment of variants with spliceAI scores >=0.05. Approximately, they contribute to 2.4% of SNP heritability based on taking a product of percent of variants within annotation and enrichment estimate. However, we did not see such enrichment in S-LDSC results. The possible reason might be the signal was drowned out in the baseline annotations.
With the same baseline annotations, Torus produces lower but still significant enrichment estimates for the SNPs with spliceai >=0.05. However, we see no enrichment in LDSC results based on the enrichment estimates and p-values.
QQ plots for comparing p-values
Comparing p-values of SNPs with high spliceAI scores against genome-wide SNPs with spliceAI scores >=0.
black: 1M genome-wide SNPs with spliceAI scores >=0
blue: GWAS SNPs with spliceAI scores >=0.05
Schizophrenia
QQ plots
Afib
QQ plots
Allergy
QQ plots
For SCZ, the p-values of GWAS SNPs with spliceiAI scores above the threshold deviate from the null in a similar way as compared to genome-wide SNPs.
For AFib and allergy, we observed that p-values of SNPs with high spliceAI scores deviated more from the null compared to the genome-wide SNPs. However, this enrichemnt signal was gone after adjusting for baseline annotations with LDSC.
Across multiple traits
| Version | Author | Date |
|---|---|---|
| 467bc60 | Jing Gu | 2022-07-03 |
test annotations jointly 
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 pillar_1.5.0 compiler_4.0.4 bslib_0.2.4
[5] later_1.1.0.1 jquerylib_0.1.3 git2r_0.28.0 highr_0.8
[9] workflowr_1.6.2 tools_4.0.4 digest_0.6.27 gtable_0.3.0
[13] jsonlite_1.7.2 evaluate_0.14 lifecycle_1.0.0 tibble_3.0.6
[17] pkgconfig_2.0.3 rlang_1.0.1 DBI_1.1.1 cli_3.2.0
[21] rstudioapi_0.13 yaml_2.2.1 xfun_0.21 withr_2.4.3
[25] dplyr_1.0.4 stringr_1.4.0 knitr_1.31 generics_0.1.0
[29] fs_1.5.0 vctrs_0.3.8 sass_0.3.1 tidyselect_1.1.1
[33] rprojroot_2.0.2 grid_4.0.4 glue_1.6.1 R6_2.5.1
[37] fansi_1.0.2 rmarkdown_2.7 farver_2.1.0 purrr_0.3.4
[41] magrittr_2.0.1 whisker_0.4 scales_1.1.1 promises_1.2.0.1
[45] ellipsis_0.3.2 htmltools_0.5.1.1 assertthat_0.2.1 colorspace_2.0-2
[49] httpuv_1.5.5 labeling_0.4.2 utf8_1.2.2 stringi_1.5.3
[53] munsell_0.5.0 crayon_1.4.1