Comparing predictdb & Munro: predictdb eQTL + sQTL + Munro rsQTL + apaQTL VS all 8 weights
XSun
2024-09-09
Last updated: 2024-09-12
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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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
| Version | Author | Date |
|---|---|---|
| a581199 | XSun | 2024-09-09 |
2024-09-12 17:09:22 INFO::Annotating ctwas finemapping result ...
2024-09-12 17:09:32 INFO::add gene_name and gene_type
2024-09-12 17:09:36 INFO::split PIPs for traits mapped to multiple genes
2024-09-12 17:09:36 INFO::use gene mid positions
2024-09-12 17:09:36 INFO::add SNP positionsEight weights
predictdb eQTL + sQTL + Munro 6 modalities
| Version | Author | Date |
|---|---|---|
| a581199 | XSun | 2024-09-09 |
2024-09-12 17:09:57 INFO::Annotating ctwas finemapping result ...
2024-09-12 17:10:01 INFO::add gene_name and gene_typeWarning in left_join(., gene_annot, by = "gene_id", multiple = "all"): Detected an unexpected many-to-many relationship between `x` and `y`.
i Row 185 of `x` matches multiple rows in `y`.
i Row 3411 of `y` matches multiple rows in `x`.
i If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.2024-09-12 17:10:02 INFO::split PIPs for traits mapped to multiple genes
2024-09-12 17:10:02 INFO::use gene mid positions
2024-09-12 17:10:02 INFO::add SNP positionsComparing 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"2024-09-12 17:10:08 INFO::focal gene: TNFRSF6B
2024-09-12 17:10:08 INFO::focal id: ENSG00000243509.4|expression_Colon_Transverse
2024-09-12 17:10:08 INFO::plot locus range: chr20 63558727,64328976
2024-09-12 17:10:09 INFO::TNFRSF6B Colon_Transverse eQTL QTLs
2024-09-12 17:10:09 INFO::QTL positions: 63662692,63697127
[1] "Locus plot -- 8 weights setting"[1] "The estimated L = 3"2024-09-12 17:10:13 INFO::focal gene: TNFRSF6B
2024-09-12 17:10:13 INFO::focal id: intron_20_63695854_63696760|splicing_Colon_Transverse
2024-09-12 17:10:13 INFO::plot locus range: chr20 63558727,64328976
2024-09-12 17:10:13 INFO::TNFRSF6B Colon_Transverse_pred sQTL_pred QTLs
2024-09-12 17:10:13 INFO::QTL positions: 63697746
[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 |
|---|---|---|
| 6c40cbe | XSun | 2024-09-12 |
2024-09-12 17:10:31 INFO::Annotating ctwas finemapping result ...
2024-09-12 17:10:36 INFO::add gene_name and gene_type
2024-09-12 17:10:36 INFO::split PIPs for traits mapped to multiple genes
2024-09-12 17:10:36 INFO::use gene mid positions
2024-09-12 17:10:36 INFO::add SNP positionsFor the earlier example, TNFRSF6B.
[1] "Locus plot -- sQTL setting"[1] "The estimated L = 3"2024-09-12 17:10:39 INFO::focal gene: TNFRSF6B
2024-09-12 17:10:39 INFO::focal id: intron_20_63695854_63696760|splicing_Colon_Transverse
2024-09-12 17:10:39 INFO::plot locus range: chr20 63558727,64328976
2024-09-12 17:10:39 INFO::TNFRSF6B Colon_Transverse sQTL QTLs
2024-09-12 17:10:39 INFO::QTL positions: 63697746
| Version | Author | Date |
|---|---|---|
| 6c40cbe | XSun | 2024-09-12 |
TNFRSF6B and different settings
rs202143810
| 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 |
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 logging_0.10-108
[23] ctwas_0.4.11
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] cachem_1.0.6 knitr_1.39
[19] jsonlite_1.8.0 workflowr_1.7.0
[21] Rsamtools_2.12.0 broom_0.8.0
[23] dbplyr_2.1.1 png_0.1-7
[25] compiler_4.2.0 httr_1.4.3
[27] backports_1.4.1 assertthat_0.2.1
[29] Matrix_1.5-3 fastmap_1.1.0
[31] lazyeval_0.2.2 cli_3.6.1
[33] later_1.3.0 htmltools_0.5.2
[35] prettyunits_1.1.1 tools_4.2.0
[37] gtable_0.3.0 glue_1.6.2
[39] GenomeInfoDbData_1.2.8 rappdirs_0.3.3
[41] Rcpp_1.0.12 cellranger_1.1.0
[43] jquerylib_0.1.4 vctrs_0.6.5
[45] Biostrings_2.64.0 rtracklayer_1.56.0
[47] crosstalk_1.2.0 xfun_0.41
[49] rvest_1.0.2 lifecycle_1.0.4
[51] irlba_2.3.5 restfulr_0.0.14
[53] XML_3.99-0.14 zlibbioc_1.42.0
[55] zoo_1.8-10 scales_1.3.0
[57] gggrid_0.2-0 hms_1.1.1
[59] promises_1.2.0.1 MatrixGenerics_1.8.0
[61] ProtGenerics_1.28.0 parallel_4.2.0
[63] SummarizedExperiment_1.26.1 LDlinkR_1.2.3
[65] yaml_2.3.5 curl_4.3.2
[67] memoise_2.0.1 sass_0.4.1
[69] biomaRt_2.54.1 stringi_1.7.6
[71] RSQLite_2.3.1 highr_0.9
[73] BiocIO_1.6.0 filelock_1.0.2
[75] BiocParallel_1.30.3 rlang_1.1.2
[77] pkgconfig_2.0.3 matrixStats_0.62.0
[79] bitops_1.0-7 evaluate_0.15
[81] lattice_0.20-45 labeling_0.4.2
[83] GenomicAlignments_1.32.0 htmlwidgets_1.5.4
[85] cowplot_1.1.1 bit_4.0.4
[87] tidyselect_1.2.0 magrittr_2.0.3
[89] R6_2.5.1 generics_0.1.2
[91] DelayedArray_0.22.0 DBI_1.2.2
[93] withr_2.5.0 haven_2.5.0
[95] pgenlibr_0.3.3 pillar_1.9.0
[97] whisker_0.4 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