SNP_finemapping_results
Last updated: 2022-08-17
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Knit directory: funcFinemapping/
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Functional fine-maping
The general procedures for functional fine-mapping are (1) compute the causal probability for each variant from association statsitics and annotations (TORUS) and (2) perform fine-mapping with prior knowledge obtained in the first step (SuSiE).
SpliceAI-predicted variant effects on splicing
Enrichment results
Annotations
- spliceAI predictions are not tissue-specific.
- The predicted delta score of a variant can be interpreted as the probrability of the variant being splice-altering.
Procedure
- Build binary annotation by denoting delta score>=0.05 as 1 and detal score < 0.05 as 0.
- Run TORUS jointly over spliceAI-predictions, m6A sites in heart, CM_specific OCR and the baselines.
- Use the derived prior probability for each SNP to further perform fine mapping with SuSiE. We used 10% of UK Biobank samples to build LD reference matrix, which was further integrated with GWAS summary statsistics to compute for PIPs at different assumptions of number of causal variants per LD block.
Torus enrichment estimates 
Fine-mapping results
Sanity check: -log10(p-values) against susie PIPs
No red flags for the fine-mapping run using both uniform and functional priors at L=1. Most SNPs with high PIPs have low p-values. 
When run at L=2, we see few spuriously prioritized SNPs with non-significant p-values and a number of SNPs with elevated PIPs at less significant p-values. A larger fraction of SNPs with significant p-values were prioritized with functional priors.
Compare PIPs between uniform and functional priors
A larger fraction of SNPs lie above the diagonal line, indicating higher PIPs assigned to SNPs with functional priors. Same trend for both L=1 and L=2.
Compare the sizes of credible sets
Uniform priors
plot_cs_size(uniform.L1)
run with functional prior at L=1 
run with functional prior at L=2 
With functional priors run at L=2, we observe smaller credible sets and less resolved cases.
Distributions of spliceAI scores for SNPs in credible sets
L = 1
###load spliceAI scores###
colnames(annot.L1)[which(colnames(annot.L1)=="rsID")]<-"SNP"
annot.L1<-left_join(annot.L1, scores[, c("SNP", "spliceAI_varPred")], by="SNP")
annot.L1[,"susie_pip.unif"]<-uniform.L1[,"susie_pip"]
annot.cs<-annot.L1[annot.L1$cs==1, ]
hist(annot.cs$spliceAI_varPred[annot.cs$spliceAI_varPred>0], main="", xlab="SNPs in credible sets with non-zero spliceAI scores")
The majority of SNPs in credible sets have zero spliceAI scores. Here we plotted the distribution of non-zero spliceAI scores for SNPs in credible sets.
snp pval zscore locus cs_size susie_pip
386899 13:113833499:A:G:rs486407 1.488e-07 -5.238095 1323 71 0.08457434
455599 17:7453919:T:C:rs12940684 7.014e-08 -5.430556 1490 7 0.20622422
462263 17:38062217:T:C:rs2305479 3.276e-10 6.318182 1506 17 0.43238643
121351 3:111589772:A:C:rs1282932 8.805e-15 -7.800000 346 22 0.75659812
358139 12:57119236:A:G:rs3214051 1.398e-10 6.391304 1214 18 0.23236096
49118 2:61405795:T:G:rs2600665 7.965e-08 -5.382353 173 104 0.02351085
379733 12:133142411:C:T:rs6560884 4.974e-07 -5.032609 1261 10 0.46202750
213142 6:88032402:A:C:rs2257153 1.419e-09 -6.014493 690 180 0.02215107
462198 17:38028634:T:G:rs11557467 1.237e-10 6.454545 1506 17 0.41703764
472083 17:76757296:A:C:rs8076588 4.457e-08 5.447761 1527 20 0.55177380
Coding_UCSC_d Promoter_UCSC_d UTR_3_UCSC_d UTR_5_UCSC_d spliceAI_varPred
386899 0 0 0 0 0.19
455599 0 1 0 0 0.18
462263 1 1 0 1 0.18
121351 0 0 0 0 0.17
358139 1 1 0 1 0.14
49118 1 1 0 1 0.13
379733 0 0 0 0 0.12
213142 0 1 0 0 0.08
462198 1 0 0 0 0.08
472083 0 0 0 0 0.08Examine the results for L=2 
Compared with the run at L=1, the fine-mapping results at L=2 overall gives smaller credible sets. For example, 10 SNPs in the locus 1261 were captured in one credible set at L=1, but 3 SNPs were captured at L=2. Notably, since the coverage cutoff was set to be the same, the secondary signals may not be captured. We may need to lower the cutoff for capturing the secondary signals.
Annotating SNPs within credible sets
snp pval zscore locus cs_size susie_pip
1 13:113833499:A:G:rs486407 1.488e-07 -5.238095 1323 71 0.08457434
2 13:113833499:A:G:rs486407 1.488e-07 -5.238095 1323 71 0.08457434
3 17:7453919:T:C:rs12940684 7.014e-08 -5.430556 1490 7 0.20622422
4 17:7453919:T:C:rs12940684 7.014e-08 -5.430556 1490 7 0.20622422
5 17:38062217:T:C:rs2305479 3.276e-10 6.318182 1506 17 0.43238643
6 17:38062217:T:C:rs2305479 3.276e-10 6.318182 1506 17 0.43238643
7 3:111589772:A:C:rs1282932 8.805e-15 -7.800000 346 22 0.75659812
8 12:57119236:A:G:rs3214051 1.398e-10 6.391304 1214 18 0.23236096
9 12:57119236:A:G:rs3214051 1.398e-10 6.391304 1214 18 0.23236096
10 12:57119236:A:G:rs3214051 1.398e-10 6.391304 1214 18 0.23236096
11 12:133142411:C:T:rs6560884 4.974e-07 -5.032609 1261 10 0.46202750
12 6:88032402:A:C:rs2257153 1.419e-09 -6.014493 690 180 0.02215107
13 6:88032402:A:C:rs2257153 1.419e-09 -6.014493 690 180 0.02215107
14 6:88032402:A:C:rs2257153 1.419e-09 -6.014493 690 180 0.02215107
15 6:88032402:A:C:rs2257153 1.419e-09 -6.014493 690 180 0.02215107
16 17:38028634:T:G:rs11557467 1.237e-10 6.454545 1506 17 0.41703764
17 17:76757296:A:C:rs8076588 4.457e-08 5.447761 1527 20 0.55177380
18 6:87887985:C:A:rs7767449 2.433e-09 5.984848 690 180 0.03200709
spliceAI_varPred category gene_name
1 0.19 introns PCID2
2 0.19 splice_junctions PCID2
3 0.18 introns TNFSF12
4 0.18 introns TNFSF12-TNFSF13
5 0.18 exons GSDMB
6 0.18 splice_junctions GSDMB
7 0.17 introns PHLDB2
8 0.14 exons NACA
9 0.14 UTRs NACA
10 0.14 splice_junctions NACA
11 0.12 introns FBRSL1
12 0.08 introns GJB7
13 0.08 introns SMIM8
14 0.08 splice_junctions SMIM8
15 0.08 splice_junctions SMIM8
16 0.08 exons ZPBP2
17 0.08 introns CYTH1
18 0.07 introns ZNF292Examine locus 1323
Check the distribution of p-values and PIPs
The snp rs486407 was prioritized among this locus, which contains a large number of SNPs. This SNP has the highest prediction scores, which is expected as it locates in a splice junction. It is predicted to alter the variant at 6bp upstream by increasing its use as a splice donor by 19%. This SNPs lie in between exon 13 and 14 of gene PCID2, and has been found to be a splice junction in one humen ETS. PCID2 encodes for a component of the TREX-2 complex, which regulates mRNA export from the nucleus. 
Examine locus 1261
The SNP rs6560884 was proritized than other SNPs with functional prior at L=1 and further prioritized at L=2 due to its predicted splicing effects (spliceAI=0.12).
Based on spliceAI prediction, this SNP increases the probability of the variant at 2 bp downstream used as a splice donor by 12%. This variant is located in the intron region of Fibrosin Like 1 gene (FBRL1), which functions to enable RNA binding activity. 
Check the distribution of p-values and PIPs
genome browser
Examine locus 1490
Check the distribution of p-values and PIPs
Two SNPs in this locus were prioritized, which are rs9899183 & rs12940684. Though the first one has higher PIP due to higher z score, the second one were predicted to alter the variant at 5bp downstream by increasing its use as a splice acceptor by 18%. These two SNPs are located in the intron regions between exon 1 and exon2 of gene TNFSF12, which encodes for a cytokine that belongs to the TNF superfamily. 
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
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[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] RColorBrewer_1.1-2 GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[4] IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.36.1
[7] ggplot2_3.3.3 bigsnpr_1.9.11 bigstatsr_1.5.6
[10] susieR_0.12.16 dplyr_1.0.4 data.table_1.14.2
loaded via a namespace (and not attached):
[1] Biobase_2.50.0 MatrixGenerics_1.2.1
[3] sass_0.3.1 jsonlite_1.7.2
[5] foreach_1.5.1 bslib_0.2.4
[7] assertthat_0.2.1 mixsqp_0.3-43
[9] highr_0.8 Rsamtools_2.6.0
[11] GenomeInfoDbData_1.2.4 yaml_2.2.1
[13] pillar_1.5.0 lattice_0.20-41
[15] glue_1.6.1 digest_0.6.27
[17] promises_1.2.0.1 XVector_0.30.0
[19] colorspace_2.0-2 cowplot_1.1.1
[21] htmltools_0.5.1.1 httpuv_1.5.5
[23] Matrix_1.4-0 plyr_1.8.6
[25] XML_3.99-0.5 pkgconfig_2.0.3
[27] bigparallelr_0.3.2 zlibbioc_1.36.0
[29] purrr_0.3.4 scales_1.1.1
[31] later_1.1.0.1 BiocParallel_1.24.1
[33] git2r_0.28.0 tibble_3.0.6
[35] generics_0.1.0 farver_2.1.0
[37] ellipsis_0.3.2 SummarizedExperiment_1.20.0
[39] withr_2.4.3 cli_3.2.0
[41] magrittr_2.0.1 crayon_1.4.1
[43] evaluate_0.14 bigassertr_0.1.5
[45] fs_1.5.0 fansi_1.0.2
[47] doParallel_1.0.16 tools_4.0.4
[49] lifecycle_1.0.0 matrixStats_0.58.0
[51] stringr_1.4.0 plyranges_1.10.0
[53] munsell_0.5.0 bigsparser_0.6.0
[55] DelayedArray_0.16.3 irlba_2.3.3
[57] Biostrings_2.58.0 compiler_4.0.4
[59] jquerylib_0.1.3 rlang_1.0.1
[61] grid_4.0.4 RCurl_1.98-1.6
[63] iterators_1.0.13 rstudioapi_0.13
[65] bitops_1.0-6 labeling_0.4.2
[67] rmarkdown_2.7 gtable_0.3.0
[69] codetools_0.2-18 flock_0.7
[71] DBI_1.1.1 reshape_0.8.8
[73] R6_2.5.1 GenomicAlignments_1.26.0
[75] rtracklayer_1.50.0 knitr_1.31
[77] utf8_1.2.2 workflowr_1.6.2
[79] rprojroot_2.0.2 stringi_1.5.3
[81] Rcpp_1.0.8 vctrs_0.3.8
[83] tidyselect_1.1.1 xfun_0.21