Post-transcriptional regulation
Last updated: 2022-07-15
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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 | 461f8f1 | Jing Gu | 2022-07-15 | modify baseline annotations |
| html | a6ed615 | Jing Gu | 2022-07-15 | modify baseline annotations |
| html | 2511e16 | Jing Gu | 2022-07-09 | update PTR annotations |
| html | bb03726 | Jing Gu | 2022-07-09 | update PTR annotations |
| html | 5ed2d28 | Jing Gu | 2022-07-09 | PTR annotations |
| html | 675a05f | Jing Gu | 2022-07-05 | PTR annotations |
| html | 7f557c4 | Jing Gu | 2022-07-05 | PTR annotations |
| 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 LDSC.Then we jointly assessed a set of annotations.
For the early attempts, I ran torus and 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 LDSC analysis, around 1M hapmap3 SNPs were tested across all traits.
Results
Enrichment analysis on SCZ GWAS
To make it comparable, I ran LDSC and Torus on the same set of test SNPs, which are around 1 million SNPs from hapmap3.
Legends for the plots:
- y-axis - annotations(% of SNPs within annotations)
- x-axis - fold of enrichment
- label on the plot - percent of SNP heritability or enrichment p-value
- dashed line - no enrichment
LDSC
modified baselines from m6A paper:
genic features, LD, MAF, background selection, nucleotide diversity, promoter, recombination rate.
annotations: spidex, spliceai, fetal brain m6a, neuronal OCR, differentially methylated regions in brains (DMR)
Fig 1.1 Enrichment of SCZ risk variants in each annotation from LDSC.
All the annotations were binary. For instance, spliceai_binary"X" represents if a GWAS SNP is predicted to have a spliceAI score equal or higher than X. Spidex_top"Y" means if a GWAS SNP is predicted to have a spidex score among the top Y percent. The rest of the annotations are bed files and each GWAS SNP was asked whether it lies in the peak regions.
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.
Warning: Removed 1 rows containing missing values (geom_text).
Fig 1.2 Baseline enrichment of SCZ risk variants from LDSC.
The baseline models used in this analysis was from the m6A science paper. We observed that coding and 3'UTR regions show significant enrichment, but the overall contribution to SNP h2g is small(<5%). While introns and their planking regions were slighty enriched, they contribute to ~50% of SNP heritability.
Torus 
Fig 1.3 Enrichment of SCZ risk variants in individual annotation from Torus.
There is no baseline annotation jointly tested for each torus run. The first plot was generated using 1M hapmap SNPs, while the second one was from running torus over ~6M SNPs using our pipeline.
With the same set of test SNPs, the enrichment estimates are similarly low between torus and LDSC for SCZ. The enrichment signals seen in the previous runs for torus were gone, likely due to the background difference.
Enrichment analysis on AFib GWAS
Fig 2.1 Enrichment of aFib risk variants in individual annotation from Torus.
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 LDSC results. The possible reason might be the signal was drowned out in the baseline annotations.
Comparing LDSC and Torus with the same baseline
Baseline annotations: coding and the flanking regions 
Fig 2.2 Comparing enrichments results between LDSC and TORUS with the same baselines
The spliceAI related annotations were jointly run with the same baseline annotations one at a time over aFib risk variants.
With the same coding baseline, LDSC estimates show more dependence on the number of SNPs within annotations. At 0.01 threshold, enrichment estimates from LDSC are much higher than those from Torus. At 0.05 threshold, LDSC gives much larger standard deviation.
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.
LDSC results across multiple traits
| Version | Author | Date |
|---|---|---|
| 7f557c4 | Jing Gu | 2022-07-05 |
Fig 3.1 LDSC Enrichment results across traits
Enrichment estimates each with a 95% confidence interval for PTR annotations across various traits.
LDSC results with different baselines
To know the impact of varying baselines on the enrichment estimation of spliceAI features, we focused on the binary annotation of spliceAI scores >=0.05 and then added baseline annotations sequentially.
| Version | Author | Date |
|---|---|---|
| a6ed615 | Jing Gu | 2022-07-15 |
Fig 4.1 Enrichment results for spliceAI scores >=0.05 under different baselines
The full set of baseline annotations is from m6A paper, which include genomic annotation, MAF, nucleotide diversity, background selection and CpG content. Different genomic annotations from coding to UTR were added sequentially, one at a time. The labeled numbers refer to the enrichment p-value output from LDSC.
Jointly run with coding sequences, spliceAI scores remains to be enriched in several traits like scz and aFib. The enrichment p-value is not significant likely due to low sample size. Across traits, there seems to be a bigger drop in estimates when including introns or MAF.
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
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