Last updated: 2022-08-10

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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 compare 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%              0.3789613

Understand the distribution of predicted scores wrt. MAFs

MAFs distribution

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

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

Version Author Date
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 5% to top 15%.
Individual run with the baseline set
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.
Torus - with the baseline set
Fig 4.3 Torus individual run with the baseline set For all three types of predictions, we see a decrease in enrichment estimates.
Torus - Joint analysis with other annotations
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 predictions

LDSC 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
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
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 [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] gridExtra_2.3     data.table_1.14.2 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      
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 [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 
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