Post-transcriptional regulation

Last updated: 2022-07-02

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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

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

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)

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.

Torus 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.

Other traits - AFib

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 drown in the baseline annotations.

QQ plots for comparing p-values

Comparing p-values of SNPs with high spliceAI scores against 1M genome-wide SNPs

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 with predicted spliceAI scores.
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 by LDSC.

Across multiple traits

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     jsonlite_1.7.2   
[13] evaluate_0.14     lifecycle_1.0.0   tibble_3.0.6      gtable_0.3.0     
[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    scales_1.1.1      promises_1.2.0.1  ellipsis_0.3.2   
[45] htmltools_0.5.1.1 assertthat_0.2.1  colorspace_2.0-2  httpuv_1.5.5     
[49] labeling_0.4.2    utf8_1.2.2        stringi_1.5.3     munsell_0.5.0    
[53] crayon_1.4.1