Comparing predictdb & Munro: predictdb eQTL + sQTL + Munro rsQTL + apaQTL VS all 8 weights – updated functions
XSun
2024-09-18
Last updated: 2024-09-19
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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| File | Version | Author | Date | Message |
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| Rmd | 41a6670 | XSun | 2024-09-18 | update |
| html | 41a6670 | XSun | 2024-09-18 | update |
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
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 814514 rows [131, 132,
133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, ...].2024-09-19 13:20:57 INFO::Annotating fine-mapping result ...
2024-09-19 13:20:57 INFO::Map molecular traits to genes
2024-09-19 13:20:58 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-19 13:21:07 INFO::Add gene positions
2024-09-19 13:21:08 INFO::Add SNP positions2024-09-19 13:21:28 INFO::Limit gene results to credible setsEight weights
predictdb eQTL + sQTL + Munro 6 modalities
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 852856 rows [243, 244,
245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
261, 262, ...].2024-09-19 13:22:12 INFO::Annotating fine-mapping result ...
2024-09-19 13:22:12 INFO::Map molecular traits to genes
2024-09-19 13:22:13 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-19 13:22:17 INFO::Add gene positions
2024-09-19 13:22:17 INFO::Add SNP positions2024-09-19 13:22:29 INFO::Limit gene results to credible setsComparing 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
We notice TNFRSF6B PIP was from predictdb sQTL, it should be discovered in the 4 weights setting. And this gene has some known roles in IBD.
https://pubmed.ncbi.nlm.nih.gov/23965943/
https://jbiomedsci.biomedcentral.com/articles/10.1186/s12929-017-0347-7
https://www.nature.com/articles/s41598-021-88489-w
[1] "Locus plot -- 4 weights setting"[1] "The estimated L = 3"2024-09-19 13:22:31 INFO::Limit to protein coding genes
2024-09-19 13:22:31 INFO::focal id: intron_20_63701898_63702136|splicing_Colon_Transverse
2024-09-19 13:22:31 INFO::focal molecular trait: ARFRP1 Colon_Transverse sQTL
2024-09-19 13:22:31 INFO::Range of locus: chr20:63558727-64328976
2024-09-19 13:22:32 INFO::focal molecular trait QTL positions: 63701763
2024-09-19 13:22:32 INFO::Limit PIPs to credible sets
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
[1] "Locus plot -- 8 weights setting"[1] "The estimated L = 3"2024-09-19 13:22:37 INFO::Limit to protein coding genes
2024-09-19 13:22:37 INFO::focal id: intron_20_63695854_63696760|splicing_Colon_Transverse
2024-09-19 13:22:37 INFO::focal molecular trait: TNFRSF6B Colon_Transverse_pred sQTL_pred
2024-09-19 13:22:37 INFO::Range of locus: chr20:63558727-64328976
2024-09-19 13:22:37 INFO::focal molecular trait QTL positions: 63697746
2024-09-19 13:22:37 INFO::Limit PIPs to credible sets
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
[1] "weights for "
[1] "ENSG00000243509:chr20:63695854:63696760:clu_44474_+|splicing_Colon_Transverse"
weight
rs6011040 -0.0211776
rs8957 -0.0255221
[1] "weights for "
[1] "ENSG00000243509.4|expression_Colon_Transverse"
weight
rs41298344 -0.1634486
rs55765053 0.0848684
[1] "weights for "
[1] "intron_20_63697191_63697328|splicing_Colon_Transverse"
weight
rs55765053 0.2576402
[1] "weights for "
[1] "intron_20_63697522_63698280|splicing_Colon_Transverse"
weight
rs74748720 -0.01402964
[1] "weights for "
[1] "intron_20_63695854_63696760|splicing_Colon_Transverse"
weight
rs6062496 -0.0549018The 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
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 792906 rows [89, 90, 91,
92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
...].2024-09-19 13:23:26 INFO::Annotating fine-mapping result ...
2024-09-19 13:23:26 INFO::Map molecular traits to genes
2024-09-19 13:23:26 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-19 13:23:28 INFO::Add gene positions
2024-09-19 13:23:29 INFO::Add SNP positions2024-09-19 13:23:34 INFO::Limit gene results to credible setsFor the earlier example, TNFRSF6B.
[1] "Locus plot -- sQTL setting"[1] "The estimated L = 3"2024-09-19 13:23:41 INFO::Limit to protein coding genes
2024-09-19 13:23:41 INFO::focal id: intron_20_63695854_63696760|splicing_Colon_Transverse
2024-09-19 13:23:41 INFO::focal molecular trait: TNFRSF6B Colon_Transverse sQTL
2024-09-19 13:23:41 INFO::Range of locus: chr20:63558727-64328976
2024-09-19 13:23:41 INFO::focal molecular trait QTL positions: 63697746
2024-09-19 13:23:41 INFO::Limit PIPs to credible sets
| Version | Author | Date |
|---|---|---|
| 41a6670 | XSun | 2024-09-18 |
TNFRSF6B and different settings
| weights | pre-estimated L | genes with PIP > 0.8 | SNPs with PIP > 0.8 |
|---|---|---|---|
| predictdb eQTL + sQTL | 3 | / | rs6089961, rs202143810 |
| predictdb eQTL + sQTL | set L=1 manually | TNFRSF6B | / |
| predictdb eQTL + sQTL | set L=10 manually | ARFRP: combined pip > 0.8 | rs202143810 |
| predictdb sQTL | 3 | TNFRSF6B | rs6089961, rs202143810 |
| predictdb eQTL + sQTL + Munro rsQTL + apaQTL | 3 | / | rs6089961, rs202143810 |
| predictdb eQTL + sQTL + Munro rsQTL + apaQTL | set L=10 manually | ARFRP: combined pip > 0.8 | rs6089961, rs202143810 |
| predictdb eQTL + sQTL + Munro 6 modalities | 3 | TNFRSF6B | rs6089961, rs202143810 |
| Munro 6 modalities, region merge | 4 | / | rs6089961, rs202143810 |
predictdb sQTL, uniform prior
| L | PIP_TNFRSF6B | CS_TNFRSF6B | PIP_rs6062496(sQTL) | CS_rs6062496(sQTL) | PIP_rs6089961 | CS_rs6089961 | PIP_rs202143810 | CS_rs202143810 |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.98 | 1 | 0.02 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0.87 | 1 | 1 | 2 |
| 3 | 0.96 | 1 | 0 | 0 | 0.87 | 3 | 1 | 2 |
| 4 | 0 | 0 | 0 | 0 | 0.97 | 1 | 1 | 2 |
| 5 | 0.99 | 3 | 0.01 | 0 | 0.97 | 3 | 1 | 2 |
| 6 | 0 | 0 | 0 | 0 | 0.99 | 1 | 1 | 2 |
| 7 | 0.99 | 1 | 0.01 | 0 | 0.99 | 3 | 1 | 2 |
| 8 | 0 | 0 | 0 | 0 | 0.99 | 1 | 1 | 2 |
| 9 | 0.99 | 1 | 0.01 | 0 | 1 | 3 | 1 | 2 |
| 10 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 |
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 whisker_0.4
[99] KEGGREST_1.36.3 RCurl_1.98-1.7
[101] mixsqp_0.3-43 modelr_0.1.8
[103] crayon_1.5.1 utf8_1.2.2
[105] BiocFileCache_2.4.0 plotly_4.10.0
[107] tzdb_0.4.0 rmarkdown_2.25
[109] progress_1.2.2 readxl_1.4.0
[111] grid_4.2.0 blob_1.2.3
[113] git2r_0.30.1 reprex_2.0.1
[115] digest_0.6.29 httpuv_1.6.5
[117] munsell_0.5.0 viridisLite_0.4.0
[119] bslib_0.3.1