Last updated: 2022-08-02
Checks: 6 1
Knit directory: funcFinemapping/
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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:
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. All the analyses were performed on ~1M hapmap3 SNPs.
Out of 10M SNPs (MAF>=0.005), around 2000 SNPs have predicted score bigger or smaller than 2, which can be considered strong.
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
QQ Plots - comparing SNPs within annotations against the rest
The prediction cutoff is top 15%.
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:
LDSC
The defined set of baseline annotations are coding, promoter, 3'UTR, 5'UTR, each with a 500-bp extended region.
MMSplice vs. Brain-specific MTSplice
Fig 1.1 Enrichment of SCZ risk variants in tissue-average (mmsplice) versus Hypothalamus-specific predicted variants with effects of altering splicing (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
Warning: Removed 1 rows containing missing values (geom_text).
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.
Torus Fig 2.2 Enrichment of SCZ risk variants in individual annotation from Torus.
LDSC
MMSplice vs. Heart-specific MTSplice
Fig 3.1 Enrichment of aFib risk variants in individual annotation from LDSC.
Torus Fig 3.2 Comparing enrichments results between LDSC and TORUS with the same baselines
The spliceAI related annotations were jointly run with the same baseline annotations one at a time over aFib risk variants.
tissue-average prediction from MMSplice
Fig 4.1 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] data.table_1.14.2 ggplot2_3.3.3
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[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
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[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