Last updated: 2024-09-24

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Settings

6 modalities from Munro

Weight processing:

PredictDB:

all the PredictDB are converted from FUSION weights

drop_strand_ambig = TRUE,
scale_by_ld_variance = F (FUSION converted weights)
load_predictdb_LD = F,

Parameter estimation and fine-mapping

niter_prefit = 5,
niter = 30(default),
L: determined by uniform susie,
group_prior_var_structure = “shared_type”,
maxSNP = 20000,
min_nonSNP_PIP = 0.5,

weights from predictdb

Weight processing:

PredictDB (eqtl, sqtl)

drop_strand_ambig = TRUE,
scale_by_ld_variance = T
load_predictdb_LD = F,

Parameter estimation and fine-mapping

group_prior_var_structure = “shared_type”,
filter_L = TRUE,
filter_nonSNP_PIP = FALSE,
min_nonSNP_PIP = 0.5,
min_abs_corr = 0.1,

mem: 100g 5cores

Results

Four weights

predictdb eQTL + sQTL + Munro rsQTL + apaQTL

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 849089 rows [131, 132,
133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, ...].

2024-09-24 12:41:29 INFO::Annotating fine-mapping result ...
2024-09-24 12:41:29 INFO::Map molecular traits to genes
2024-09-24 12:41:29 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-24 12:41:35 INFO::Add gene positions
2024-09-24 12:41:35 INFO::Add SNP positions

2024-09-24 12:41:49 INFO::Limit gene results to credible sets

Eight weights

predictdb eQTL + sQTL + Munro 6 modalities

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 872421 rows [243, 244,
245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
261, 262, ...].

2024-09-24 12:42:26 INFO::Annotating fine-mapping result ...
2024-09-24 12:42:26 INFO::Map molecular traits to genes
2024-09-24 12:42:27 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-24 12:42:29 INFO::Add gene positions
2024-09-24 12:42:29 INFO::Add SNP positions

2024-09-24 12:42:40 INFO::Limit gene results to credible sets

Comparing results from 2 settings

[1] "all genes discovered by 4 weights setting were overlapped with 8 weights setting"

Unique genes reported by 8 weights setting

Neither of these two settings reported TNFRSF6B.

[1] "Locus plot -- 4 weights setting"

[1] "The estimated L = 2"

2024-09-24 12:42:42 INFO::Limit to protein coding genes
2024-09-24 12:42:42 INFO::focal id: intron_20_63695854_63696760|Colon_Transverse_sQTL
2024-09-24 12:42:42 INFO::focal molecular trait: TNFRSF6B Colon_Transverse sQTL
2024-09-24 12:42:42 INFO::Range of locus: chr20:63558727-64328976
2024-09-24 12:42:43 INFO::focal molecular trait QTL positions: 63697746
2024-09-24 12:42:43 INFO::Limit PIPs to credible sets

[1] "Locus plot -- 8 weights setting"

[1] "The estimated L = 2"

2024-09-24 12:42:47 INFO::Limit to protein coding genes
2024-09-24 12:42:47 INFO::focal id: intron_20_63695854_63696760|Colon_Transverse_sQTL
2024-09-24 12:42:47 INFO::focal molecular trait:
2024-09-24 12:42:47 INFO::Range of locus: chr20:63558727-64328976
2024-09-24 12:42:47 INFO::focal molecular trait QTL positions:
2024-09-24 12:42:47 INFO::Limit PIPs to credible sets

[1] "weights for "
[1] "ENSG00000243509:chr20:63695854:63696760:clu_44474_+|Colon_Transverse_sQTL"
              weight
rs6011040 -0.0211776
rs8957    -0.0255221
[1] "weights for "
[1] "ENSG00000243509.4|Colon_Transverse_pred_eQTL"
               weight
rs41298344 -0.1634486
rs55765053  0.0848684
[1] "weights for "
[1] "intron_20_63697191_63697328|Colon_Transverse_pred_sQTL"
              weight
rs55765053 0.2576402
[1] "weights for "
[1] "intron_20_63697522_63698280|Colon_Transverse_pred_sQTL"
                weight
rs74748720 -0.01402964
[1] "weights for "
[1] "intron_20_63695854_63696760|Colon_Transverse_pred_sQTL"
              weight
rs6062496 -0.0549018

The LD for the high pip SNPs in 4 weights setting and the sQTLs in 8 weights setting. The SNPs in row1 and row2 (column1 and column2) are the high pip SNPs in 4 weights setting.

We notice that, the 2 high pip SNPs are in LD themselves. And they are in LD with rs6011040 and rs8957, the sQTL for ENSG00000243509:chr20:63695854:63696760:clu_44474_+|splicing_Colon_Transverse, whose susie pip = 7.516698e-11 in 8 weights setting.

RS_number	rs6089961	rs202143810	rs41298344	rs55765053	rs6062496	rs74748720	rs6011040	rs8957
rs6089961	1.0	0.963	0.164	0.021	0.364	0.009	0.592	0.447
rs202143810	0.963	1.0	0.152	0.02	0.35	0.008	0.569	0.445
rs41298344	0.164	0.152	1.0	0.004	0.071	0.002	0.103	0.128
rs55765053	0.021	0.02	0.004	1.0	0.055	0.003	0.034	0.028
rs6062496	0.364	0.35	0.071	0.055	1.0	0.023	0.611	0.486
rs74748720	0.009	0.008	0.002	0.003	0.023	1.0	0.014	0.012
rs6011040	0.592	0.569	0.103	0.034	0.611	0.014	1.0	0.791
rs8957	0.447	0.445	0.128	0.028	0.486	0.012	0.791	1.0

However, the earlier 2 SNPs still have high pip

sQTL (predictdb) only

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 837367 rows [89, 90, 91,
92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
...].

2024-09-24 12:43:27 INFO::Annotating fine-mapping result ...
2024-09-24 12:43:27 INFO::Map molecular traits to genes
2024-09-24 12:43:27 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-24 12:43:29 INFO::Add gene positions
2024-09-24 12:43:29 INFO::Add SNP positions

2024-09-24 12:43:34 INFO::Limit gene results to credible sets

For the earlier example, TNFRSF6B.

[1] "Locus plot -- sQTL setting"

[1] "The estimated L = 1"

2024-09-24 12:43:36 INFO::Limit to protein coding genes
2024-09-24 12:43:36 INFO::focal id: intron_20_63695854_63696760|Colon_Transverse_sQTL
2024-09-24 12:43:36 INFO::focal molecular trait: TNFRSF6B Colon_Transverse sQTL
2024-09-24 12:43:36 INFO::Range of locus: chr20:63558727-64328976
2024-09-24 12:43:36 INFO::focal molecular trait QTL positions: 63697746
2024-09-24 12:43:36 INFO::Limit PIPs to credible sets

TNFRSF6B and different settings

weights	pre-estimated L	genes with PIP > 0.8	SNPs with PIP > 0.8
predictdb eQTL + sQTL	2	/	rs6089961, rs202143810
predictdb eQTL + sQTL	set L=1 manually	TNFRSF6B	/
predictdb eQTL + sQTL	set L=10 manually	ARFRP: combined pip > 0.8	rs6089961，rs202143810
predictdb sQTL	1	TNFRSF6B	/
predictdb eQTL + sQTL + Munro rsQTL + apaQTL	2	/	rs6089961, rs202143810
predictdb eQTL + sQTL + Munro 6 modalities	2	/	rs6089961, rs202143810
Munro 6 modalities, region merge	3	/	rs6089961, rs202143810

IBD GWAS only, uniform prior

L=1~10, rs6062496 is the only SNP with PIP > 0.8 & in CS. The PIP for this SNP is 1. For the other two SNPs, rs6089961 and rs202143810, the highest PIP is 0.002.

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

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

locale:
[1] C

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2         
 [3] AnnotationFilter_1.20.0   GenomicFeatures_1.48.3   
 [5] AnnotationDbi_1.58.0      Biobase_2.56.0           
 [7] GenomicRanges_1.48.0      GenomeInfoDb_1.39.9      
 [9] IRanges_2.30.0            S4Vectors_0.34.0         
[11] BiocGenerics_0.42.0       forcats_0.5.1            
[13] stringr_1.5.1             dplyr_1.1.4              
[15] purrr_1.0.2               readr_2.1.2              
[17] tidyr_1.3.0               tibble_3.2.1             
[19] ggplot2_3.5.1             tidyverse_1.3.1          
[21] data.table_1.14.2         ctwas_0.4.14             

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3            rjson_0.2.21               
  [3] ellipsis_0.3.2              rprojroot_2.0.3            
  [5] XVector_0.36.0              locuszoomr_0.2.1           
  [7] fs_1.5.2                    rstudioapi_0.13            
  [9] farver_2.1.0                DT_0.22                    
 [11] ggrepel_0.9.1               bit64_4.0.5                
 [13] lubridate_1.8.0             fansi_1.0.3                
 [15] xml2_1.3.3                  codetools_0.2-18           
 [17] logging_0.10-108            cachem_1.0.6               
 [19] knitr_1.39                  jsonlite_1.8.0             
 [21] workflowr_1.7.0             Rsamtools_2.12.0           
 [23] broom_0.8.0                 dbplyr_2.1.1               
 [25] png_0.1-7                   compiler_4.2.0             
 [27] httr_1.4.3                  backports_1.4.1            
 [29] assertthat_0.2.1            Matrix_1.5-3               
 [31] fastmap_1.1.0               lazyeval_0.2.2             
 [33] cli_3.6.1                   later_1.3.0                
 [35] htmltools_0.5.2             prettyunits_1.1.1          
 [37] tools_4.2.0                 gtable_0.3.0               
 [39] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [41] rappdirs_0.3.3              Rcpp_1.0.12                
 [43] cellranger_1.1.0            jquerylib_0.1.4            
 [45] vctrs_0.6.5                 Biostrings_2.64.0          
 [47] rtracklayer_1.56.0          crosstalk_1.2.0            
 [49] xfun_0.41                   rvest_1.0.2                
 [51] lifecycle_1.0.4             irlba_2.3.5                
 [53] restfulr_0.0.14             XML_3.99-0.14              
 [55] zlibbioc_1.42.0             zoo_1.8-10                 
 [57] scales_1.3.0                gggrid_0.2-0               
 [59] hms_1.1.1                   promises_1.2.0.1           
 [61] MatrixGenerics_1.8.0        ProtGenerics_1.28.0        
 [63] parallel_4.2.0              SummarizedExperiment_1.26.1
 [65] LDlinkR_1.2.3               yaml_2.3.5                 
 [67] curl_4.3.2                  memoise_2.0.1              
 [69] sass_0.4.1                  biomaRt_2.54.1             
 [71] stringi_1.7.6               RSQLite_2.3.1              
 [73] highr_0.9                   BiocIO_1.6.0               
 [75] filelock_1.0.2              BiocParallel_1.30.3        
 [77] rlang_1.1.2                 pkgconfig_2.0.3            
 [79] matrixStats_0.62.0          bitops_1.0-7               
 [81] evaluate_0.15               lattice_0.20-45            
 [83] labeling_0.4.2              GenomicAlignments_1.32.0   
 [85] htmlwidgets_1.5.4           cowplot_1.1.1              
 [87] bit_4.0.4                   tidyselect_1.2.0           
 [89] magrittr_2.0.3              R6_2.5.1                   
 [91] generics_0.1.2              DelayedArray_0.22.0        
 [93] DBI_1.2.2                   withr_2.5.0                
 [95] haven_2.5.0                 pgenlibr_0.3.3             
 [97] pillar_1.9.0                KEGGREST_1.36.3            
 [99] RCurl_1.98-1.7              mixsqp_0.3-43              
[101] modelr_0.1.8                crayon_1.5.1               
[103] utf8_1.2.2                  BiocFileCache_2.4.0        
[105] plotly_4.10.0               tzdb_0.4.0                 
[107] rmarkdown_2.25              progress_1.2.2             
[109] readxl_1.4.0                grid_4.2.0                 
[111] blob_1.2.3                  git2r_0.30.1               
[113] reprex_2.0.1                digest_0.6.29              
[115] httpuv_1.6.5                munsell_0.5.0              
[117] viridisLite_0.4.0           bslib_0.3.1

Comparing predictdb & Munro: predictdb eQTL + sQTL + Munro rsQTL + apaQTL VS all 8 weights – updated functions – thin method V1

XSun

2024-09-23