Post-transcriptional regulation

Last updated: 2022-08-02

Checks: 5 2

Knit directory: funcFinemapping/

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~/projects/funcFinemapping/data/qqplot_SNPs_high_spliceAI_scores_allergy.png	data/qqplot_SNPs_high_spliceAI_scores_allergy.png
~/projects/funcFinemapping/data/qqplot_SNPs_high_spliceAI.png	data/qqplot_SNPs_high_spliceAI.png
~/projects/funcFinemapping/docs/assets/splicing/compare_MAFs_spliceAI_vs_bkg_hm3.png	docs/assets/splicing/compare_MAFs_spliceAI_vs_bkg_hm3.png

Repository version: 1df183a

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Unstaged changes:
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    Modified:   analysis/index.Rmd
    Modified:   analysis/ldsc_PTR_results.Rmd
    Deleted:    output/AAD/Caldero2019_disjoint_snps.sumstats
    Modified:   output/AAD/allergy/Caldero2019_disjoint_snps.sumstats
    Modified:   output/AAD/allergy/Ulirsch2019/GMP_merge_compare.est
    Modified:   output/AAD/allergy/Wang2020_indiv.est
    Modified:   output/AAD/allergy/Wang2020_joint.results
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    Deleted:    output/AAD/asthma/zhang2021_cell_type_overlaps.txt
    Deleted:    output/AAD/asthma/zhang2021_peaks_per_celltype.txt
    Modified:   output/AAD/asthma_adult/Ulirsch2019/CD4_compare.est
    Modified:   output/AAD/asthma_adult/Ulirsch2019/CD8_compare.est
    Deleted:    output/AAD/asthma_adult/Ulirsch2019/GMP_merge_compare.est
    Modified:   output/AAD/asthma_adult/Wang2020_indiv.est
    Modified:   output/AAD/asthma_adult/Wang2020_joint.results
    Modified:   output/AAD/asthma_child/Ulirsch2019/GMP_merge_compare.est
    Modified:   output/AAD/asthma_child/Wang2020_indiv.est
    Modified:   output/AAD/asthma_child/Wang2020_joint.results
    Deleted:    output/asthma/Caldero2019_diffDA_annot_percent.txt
    Deleted:    output/asthma/Caldero2019_stimuDA_annot_percent.txt
    Deleted:    output/asthma/celltype_specific_adult_lungs_torus.est
    Deleted:    output/asthma/diffe_adult_blood_torus.est
    Deleted:    output/asthma/joint_lung_vs_blood_immune_diff_torus.est
    Deleted:    output/asthma/joint_lung_vs_blood_immune_stimu_torus.est
    Deleted:    output/asthma/lung_clusters_dict.txt
    Deleted:    output/asthma/lung_clusters_info.txt
    Deleted:    output/asthma/stimu_adult_blood_torus.est
    Deleted:    output/asthma/zhang2021_annot_percent.txt
    Deleted:    output/asthma/zhang2021_cell_type_overlaps.txt
    Deleted:    output/asthma/zhang2021_peaks_per_celltype.txt

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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 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.

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.

MAF Baselines jointly run with spliceAI at 0.05 cutoff

Fig 1.3 Comparing the enrichment of risk variants across MAF bins obtained from LDSC.

We see an overall trend that disease risk variants with MAF>5% were more enriched in high MAF bins than the low ones. It is likely due to higher power of predicting splicing effects on SNPs with high allele frequency, as LDSC runs on the chi-square of association z scores.

Torus with no baseline

Fig 1.4 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 Test SNPs: ~1M hapmap SNPs
Baseline annotations: coding, promoter, 3' UTR, and 5'UTR, each with a 500bp extended region as baseline annotations.

Fig 2.2 Comparing enrichments results between LDSC and TORUS with the same baseline set

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, the enrichment estimate from LDSC is much higher than the one from Torus. Meanwhile, its standard error is much lower compared to those at higher cutoffs.

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

The selected baseline set: coding, promoter, 3' UTR and 5' UTR, each with 500-bp extended regions.

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.

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.

spliceAI predicted effects and MAFs

Some characteristics of spliceAI scores:

Out of ~10M SNPs catelogued in 1kg phase 3, 31K (0.3%) SNPs have non-zero predicted spliceAI scores.
The minimum non-zero spliceAI score is 0.01.

Comparing MAF of SNPs above or below threshold

Compared against SNPs with non-zero spliceAI scores

Compared against all other SNPs

Fig Comparing MAF density between SNPs with spliceAI scores above and below the threshold

With increasing threshold, we see the density goes lower at low MAF bins and higher at mid/high MAF bins.

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