Comparing results from Munro weights & predictdb weights
XSun
2024-08-08
Last updated: 2024-08-09
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Knit directory: multigroup_ctwas_analysis/
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We compare the results from Munro weights & predictdb weights here. We are figuring out how the number of high PIP genes compare with PredictDB results with the same tissues?
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 = 60,
- 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
- niter_prefit = 5,
- niter = 60,
- L: determined by uniform susie,
- group_prior_var_structure = “shared_type”,
- maxSNP = 20000,
- min_nonSNP_PIP = 0.5,
mem: 150g 5cores
Results
LDL - Liver
Predictdb: eqtl and sqtl
2024-08-09 11:44:45 INFO::Annotating ctwas finemapping result ...
2024-08-09 11:44:55 INFO::add gene_name and gene_type
2024-08-09 11:44:55 INFO::split PIPs for traits mapped to multiple genes
2024-08-09 11:44:56 INFO::use gene mid positions
2024-08-09 11:44:56 INFO::add SNP positionsMunro et al : 6 modalities
2024-08-09 11:45:17 INFO::Annotating ctwas finemapping result ...
2024-08-09 11:45:22 INFO::add gene_name and gene_type
2024-08-09 11:45:22 INFO::use gene mid positions
2024-08-09 11:45:22 INFO::add SNP positionsCompare the results from Predictdb & Munro weights
If we filter by combined pip >0.8 in both settings, we have
Checking why Predicdb results missed many Munro genes
[1] "# of Unique munro genes = 25"[1] "# of Unique munro genes included in predictdb data = 18"IBD – Colon_Transverse
Predictdb: eqtl and sqtl
2024-08-09 11:45:43 INFO::Annotating ctwas finemapping result ...
2024-08-09 11:45:48 INFO::add gene_name and gene_type
2024-08-09 11:45:48 INFO::split PIPs for traits mapped to multiple genes
2024-08-09 11:45:48 INFO::use gene mid positions
2024-08-09 11:45:48 INFO::add SNP positionsMunro et al : 6 modalities
2024-08-09 11:46:04 INFO::Annotating ctwas finemapping result ...
2024-08-09 11:46:09 INFO::add gene_name and gene_type
2024-08-09 11:46:10 INFO::use gene mid positions
2024-08-09 11:46:10 INFO::add SNP positionsCompare the results from Predictdb & Munro weights
If we filter by combined pip >0.8 in both settings, we have
There’s no overlapped genes at combined_pip > 0.8.
We noticed that, when using Munro’s weights, we have GNA12 as the top1 IBD risk gene, which has been reported by literature. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323775/
But when using predictdb weights, we missed this gene.
Checking why Predicdb results missed many Munro genes
[1] "# of Unique munro genes = 22"[1] "# of Unique munro genes included in predictdb data = 16"
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] cowplot_1.1.1 ggrepel_0.9.1
[3] locuszoomr_0.2.1 logging_0.10-108
[5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2
[7] AnnotationFilter_1.20.0 GenomicFeatures_1.48.3
[9] AnnotationDbi_1.58.0 Biobase_2.56.0
[11] GenomicRanges_1.48.0 GenomeInfoDb_1.39.9
[13] IRanges_2.30.0 S4Vectors_0.34.0
[15] BiocGenerics_0.42.0 gridExtra_2.3
[17] forcats_0.5.1 stringr_1.5.1
[19] dplyr_1.1.4 purrr_1.0.2
[21] readr_2.1.2 tidyr_1.3.0
[23] tibble_3.2.1 ggplot2_3.5.1
[25] tidyverse_1.3.1 data.table_1.14.2
[27] ctwas_0.4.5
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 fs_1.5.2
[7] rstudioapi_0.13 farver_2.1.0
[9] DT_0.22 bit64_4.0.5
[11] lubridate_1.8.0 fansi_1.0.3
[13] xml2_1.3.3 codetools_0.2-18
[15] cachem_1.0.6 knitr_1.39
[17] jsonlite_1.8.0 workflowr_1.7.0
[19] Rsamtools_2.12.0 broom_0.8.0
[21] dbplyr_2.1.1 png_0.1-7
[23] compiler_4.2.0 httr_1.4.3
[25] backports_1.4.1 assertthat_0.2.1
[27] Matrix_1.5-3 fastmap_1.1.0
[29] lazyeval_0.2.2 cli_3.6.1
[31] later_1.3.0 htmltools_0.5.2
[33] prettyunits_1.1.1 tools_4.2.0
[35] gtable_0.3.0 glue_1.6.2
[37] GenomeInfoDbData_1.2.8 rappdirs_0.3.3
[39] Rcpp_1.0.12 cellranger_1.1.0
[41] jquerylib_0.1.4 vctrs_0.6.5
[43] Biostrings_2.64.0 rtracklayer_1.56.0
[45] crosstalk_1.2.0 xfun_0.41
[47] rvest_1.0.2 lifecycle_1.0.4
[49] irlba_2.3.5 restfulr_0.0.14
[51] XML_3.99-0.14 zlibbioc_1.42.0
[53] zoo_1.8-10 scales_1.3.0
[55] gggrid_0.2-0 hms_1.1.1
[57] promises_1.2.0.1 MatrixGenerics_1.8.0
[59] ProtGenerics_1.28.0 parallel_4.2.0
[61] SummarizedExperiment_1.26.1 LDlinkR_1.2.3
[63] yaml_2.3.5 curl_4.3.2
[65] memoise_2.0.1 sass_0.4.1
[67] biomaRt_2.54.1 stringi_1.7.6
[69] RSQLite_2.3.1 highr_0.9
[71] BiocIO_1.6.0 filelock_1.0.2
[73] BiocParallel_1.30.3 rlang_1.1.2
[75] pkgconfig_2.0.3 matrixStats_0.62.0
[77] bitops_1.0-7 evaluate_0.15
[79] lattice_0.20-45 labeling_0.4.2
[81] GenomicAlignments_1.32.0 htmlwidgets_1.5.4
[83] bit_4.0.4 tidyselect_1.2.0
[85] magrittr_2.0.3 R6_2.5.1
[87] generics_0.1.2 DelayedArray_0.22.0
[89] DBI_1.2.2 withr_2.5.0
[91] haven_2.5.0 pgenlibr_0.3.3
[93] pillar_1.9.0 KEGGREST_1.36.3
[95] RCurl_1.98-1.7 mixsqp_0.3-43
[97] modelr_0.1.8 crayon_1.5.1
[99] utf8_1.2.2 BiocFileCache_2.4.0
[101] plotly_4.10.0 tzdb_0.4.0
[103] rmarkdown_2.25 progress_1.2.2
[105] readxl_1.4.0 grid_4.2.0
[107] blob_1.2.3 git2r_0.30.1
[109] reprex_2.0.1 digest_0.6.29
[111] httpuv_1.6.5 munsell_0.5.0
[113] viridisLite_0.4.0 bslib_0.3.1