Predicting splicing effects with MTSplice
Last updated: 2022-08-10
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Knit directory: funcFinemapping/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 03e831f | Jing Gu | 2022-08-02 | PTR_splicing |
| html | abda215 | Jing Gu | 2022-08-02 | PTR_mtsplice |
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
MMSplice/MTSplice
A CNN method that aims to provide tissue-average or tissue-specific combined with the average predictions on how likely a given variant can alter splicing patterns based upon its sequence alone.
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.
Notably, we ran Torus without being limited to 1M hapmap SNPs. However, all analyses with LDSC were limited to 1M hapmap SNPs.
Results
Characteristics for MTSplice predictions
Out of 10M SNPs (MAF>=0.005), around 2000 SNPs have predicted score bigger than 2 or smaller than -2, which can be considered strong.
Compare MMSplice and MTSplice predictions 
Table of prediction cutoffs
delta_logit_psi Hypothalamus...Brain Atrial.Appendage...Heart
85% 0.1231559 0.3461778 0.1902997
90% 0.1763513 0.4321360 0.2414605
95% 0.3011078 0.5868945 0.3530906
Left.Ventricle...Heart
85% 0.2160257
90% 0.2698549
95% 0.3789613Understand the distribution of predicted scores wrt. MAFs
MAFs distribution
QQ Plots - comparing SNPs within annotations and the rest
Color scheme: * red - tissue specific * blue - mmsplice * black - background
Top-left: SCZ gwas
Top-right and Down-left: aFib gwas; AA - Atrial Appendage; LV -Left ventrical
QQ plots for both SCZ and aFib traits
Enrichment analysis on SCZ GWAS
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
Evaluate features with LDSC
The defined set of baseline annotations for running LDSC are coding, promoter, 3’UTR, 5’UTR, each with a 500-bp extended region.
MMSplice vs. Brain-specific MTSplice
| Version | Author | Date |
|---|---|---|
| abda215 | Jing Gu | 2022-08-02 |
Fig 1.1 Enrichment of SCZ risk variants in tissue-average (mmsplice) versus Hypothalamus-specific predictions for altering splicing patterns (mtsplice) via LDSC.
The variants with predicted score within top 5%, 10% and 15% were compared for their enrichment of risk variants between the two methods.Examine baseline annotations
test annotation:top 10% Hypothalamus-specific MTSplice predictions
Warning: Removed 1 rows containing missing values (geom_text).
| Version | Author | Date |
|---|---|---|
| abda215 | Jing Gu | 2022-08-02 |
Fig 2.1 Baseline enrichment of SCZ risk variants from LDSC.
Conditional on coding, introns, promoters and UTR annotations, MTSplice predictions specific to Hypothalams shows significant enrichment with SCZ risk variants. The enrichment estimate of MTSplice prediction was not affected much by whether or not including introns.Evaluate features with Torus
Individual run at different cutoffs 
Fig 3.1 Enrichment of SCZ risk variants in individual annotation from Torus.
No baselines was provided for this run of analysis. For each of the two features, we ran torus at different cutoffs, from top 5% to top 15%.
Fig 3.2 Enrichment of SCZ risk variants jointly run with baseline annotation via Torus.
The top 5% predictions were selected based on the best performance from the individual run. The enrichment for brain tissue-specific prediction remains conditional on baseline annotations.Joint run with other annotations
Sequentially adding annotation one at a time with the MTSplice prediction and the baseline set.
Fig 3.3 Enrichment of SCZ risk variants jointly run with other annotations and baselines via Torus.
After jointly run with adult brain OCRs, iPSC derived OCRs, differentially methylated sites across brain regions,and brain m6A sites, we observed slightly decrease in the enrichment of mtsplice predictions. The biggest decrease occurs when brain differentially methylated regions were added.Enrichment analysis on AFib GWAS
Evaluate features with LDSC
MMSplice vs. Heart-specific MTSplice
Fig 4.1 Enrichment of aFib risk variants in individual annotation from LDSC.
Tissue-specific predictions show much higher fold enrichment with risk variants than the tissue average ones.Evaluate features with Torus
No baselines
The enrichment results from LDSC were replicated in the torus run, probably due to different number of SNPs being tested. 
| Version | Author | Date |
|---|---|---|
| abda215 | Jing Gu | 2022-08-02 |
Fig 4.2 Torus individual test on 8M aFib GWAS SNPs
The Torus run across differnt types predictions shows similar results.
Fig 4.3 Torus individual run with the baseline set
For all three types of predictions, we see a decrease in enrichment estimates.
Fig 4.4 Torus joint run with other types of annotations for AA-specific MTSplice predictions
With CM-specific OCRs and m6A sites, AA-MTSplice predictions no longer shows significant enrichment.
Fig 4.5 Torus joint run with other types of annotations for LV-specific MTSplice predictions
Similar trend as AA-specific MTSplice predictionsLDSC results across multiple traits
tissue-average prediction from MMSplice
Fig 5.1 LDSC Enrichment results across traits
Enrichment estimates each with a 95% confidence interval for MMSplice/MTSplice predictions on splicing effects across various traits.
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
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attached base packages:
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