Predicting splicing effects with MTSplice

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Last updated: 2022-09-13

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Knit directory: funcFinemapping/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/PTR_splicing_mtsplice.Rmd) and HTML (docs/PTR_splicing_mtsplice.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	ab4948d	Jing Gu	2022-09-13	update MTSplice enrichment results
html	171a0db	Jing Gu	2022-08-17	PTR_mtsplice
html	10deffe	Jing Gu	2022-08-15	PTR_mtsplice
html	aa0b52d	Jing Gu	2022-08-10	PTR_mtsplice
html	03e831f	Jing Gu	2022-08-02	PTR_splicing
html	abda215	Jing Gu	2022-08-02	PTR_mtsplice

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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

compare predictions

Table of prediction cutoffs

Understand the distribution of predicted scores wrt. MAFs

Examine MAF distribution for SNPs above or below threshold

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

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
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 5% Hypothalamus-specific MTSplice predictions

Warning: Removed 1 rows containing missing values (geom_text).

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Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
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

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
aa0b52d	Jing Gu	2022-08-10
03e831f	Jing Gu	2022-08-02
abda215	Jing Gu	2022-08-02

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 2% to top 5%.

Joint run with other annotations

Sequentially adding annotation one at a time with the MTSplice prediction and the baseline set.

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
aa0b52d	Jing Gu	2022-08-10

Fig 3.2 Enrichment of SCZ risk variants jointly run with baselines and annotations of interest added one after another 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

AFib

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
abda215	Jing Gu	2022-08-02

Fig 4.1a-d Enrichment of aFib risk variants jointly run with baselines with LDSC.

Top 5% MTSplice predictions for left ventricle and atrial appendage heart shows higher enrichment of risk variants with large standard errors.

SCZ

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
aa0b52d	Jing Gu	2022-08-10

LDL/HDL

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Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15
aa0b52d	Jing Gu	2022-08-10

Asthma

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
aa0b52d	Jing Gu	2022-08-10

Evaluate features with Torus

Torus - with the baseline set SNPs with MAF>=0.05

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13
171a0db	Jing Gu	2022-08-17
10deffe	Jing Gu	2022-08-15

Fig 4.2 Torus individual test on 8M aFib GWAS SNPs

The Torus run shows top 5% of predictions average across tissues or specific to blood or aorta artery show significant enrichment.

Torus joint analysis
TBD - top 5% predictions

Fig 4.2 Torus joint run on aFib GWAS SNPs with MAF>=5%

The Torus run shows top 5% of predictions average across tissues or specific to blood or aorta artery show significant enrichment.

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Fig 4.3 Torus joint run on COA GWAS SNPs with MAF>=5%

SplcieAI at above 0.05 threshold shows enrichment after conditional on other annotations and baselines.

Torus - Joint analysis with other annotations

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

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.

Enrichment for tissue-agnostic predictions across multiple traits

Torus

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Past versions of unnamed-chunk-26-2.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Fig 5.1 Examine the enrichment of MMSplice-predicted SNPs that may alter splicing across traits

All traits do not display signal of enrichment except for AOA and COA.

LDSC results across multiple traits

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Fig 5.2 Examine the enrichment of MMSplice predicted SNPs conditional on spliceAI predictions via LDSC

PTR enrichment conditional on transcriptional features

scz

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Fig 6.1 Conditional on transcriptional effects, the enrichment of PTR features remains.

asthma

allergy

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

aFib

Past versions of unnamed-chunk-34-1.png

Version	Author	Date
ab4948d	Jing Gu	2022-09-13

Session information

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

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         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.0.9       gridExtra_2.3     data.table_1.14.2 ggplot2_3.3.6    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     pillar_1.7.0     compiler_4.2.0   bslib_0.3.1     
 [5] later_1.3.0      jquerylib_0.1.4  git2r_0.30.1     highr_0.9       
 [9] workflowr_1.7.0  tools_4.2.0      digest_0.6.29    gtable_0.3.0    
[13] jsonlite_1.8.0   evaluate_0.15    lifecycle_1.0.1  tibble_3.1.7    
[17] pkgconfig_2.0.3  rlang_1.0.2      DBI_1.1.2        cli_3.3.0       
[21] rstudioapi_0.13  yaml_2.3.5       xfun_0.30        fastmap_1.1.0   
[25] withr_2.5.0      stringr_1.4.0    knitr_1.39       generics_0.1.2  
[29] fs_1.5.2         vctrs_0.4.1      sass_0.4.1       tidyselect_1.1.2
[33] grid_4.2.0       rprojroot_2.0.3  glue_1.6.2       R6_2.5.1        
[37] fansi_1.0.3      rmarkdown_2.14   farver_2.1.0     purrr_0.3.4     
[41] magrittr_2.0.3   whisker_0.4      scales_1.2.0     promises_1.2.0.1
[45] ellipsis_0.3.2   htmltools_0.5.2  assertthat_0.2.1 colorspace_2.0-3
[49] httpuv_1.6.5     labeling_0.4.2   utf8_1.2.2       stringi_1.7.6   
[53] munsell_0.5.0    crayon_1.5.1