Multi group analysis: 6 traits, 5 tissues, eQTL + sQTL + apaQTL
XSun
2024-05-27
Last updated: 2024-05-28
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Knit directory: multigroup_ctwas_analysis/
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Overview
Tissues
The independent tissues are selected by single tissue analysis
Omics
eQTL, sQTL weights are from GTEx PredictDB
apaQTL wetights are from https://www.nature.com/articles/s41467-024-46064-7#Sec2. Top 10 SNPs with largest abs(weights) were selected after harmonization
Settings
- Weight processing:
PredictDB:
- drop_strand_ambig = TRUE,
- scale_by_ld_variance = TRUE,
- load_predictdb_LD = T,
FUSION:
- method_FUSION = “enet”,
- fusion_top_n_snps = 10,
- drop_strand_ambig = TRUE,
- scale_by_ld_variance = F,
- load_predictdb_LD = F,
- Parameter estimation and fine-mapping
- niter_prefit = 5,
- niter = 60,
- L = 3,
- group_prior_var_structure = “shared_type”,
- maxSNP = 20000,
- min_nonSNP_PIP = 0.5,
Results
Results from multi-group analysis
The results are summarized by
Heritability contribution by contexts: we aggregate the PVE values by omics and tissues, making it easier to understand the distribution of PVE across different genetic contexts.
Combined PIP by omics: we aggregate the Susie PIPs by omics
Combined PIP by contexts: we aggregate the Susie PIPs by tissues, making it easier to understand the distribution of PIP across different genetic contexts.
Specific molecular traits of top genes: we creates a pie chart to visualize the proportion of genes classified into different categories based on their PIPs contributed by each genetics contexts. The categories are based on the proportion of each QTL type relative to the combined PIP value:
- by eQTL: Number of genes where the ratio of eQTL to combined PIP is greater than 0.8.
- by sQTL: Number of genes where the ratio of sQTL to combined PIP is greater than 0.8.
- by apaQTL: Number of genes where the ratio of apaQTL to combined PIP is greater than 0.8.
- by sQTL+apaQTL: Number of genes where the combined ratio of apaQTL and sQTL to combined PIP is greater than 0.8, but neither apaQTL nor sQTL individually exceed 0.8.
- unspecified: Number of genes not classified into any of the above categories.
Comparing with single group eQTL results
Please not that the ealier single group eQTL analyses were performed under L=5 but the current analyses were under L=3
We compared number of significant genes, overlapping genes and the changes in PVE for eQTLs across five tissues reported by single eQTL analysi
aFib
TO DO
IBD
Comparing with single group eQTL results
[1] "the top tissues from single group analyses are Cells_Cultured_fibroblasts,Whole_Blood,Adipose_Subcutaneous,Esophagus_Mucosa,Heart_Left_Ventricle"LDL
Results from multi-group analysis
Comparing with single group eQTL results
[1] "the top tissues from single group analyses are Liver,Spleen,Adipose_Subcutaneous,Adrenal_Gland,Esophagus_Mucosa"SBP
Results from multi-group analysis
Comparing with single group eQTL results
[1] "the top tissues from single group analyses are Artery_Tibial,Adipose_Subcutaneous,Brain_Cortex,Heart_Left_Ventricle,Spleen"SCZ
Comparing with single group eQTL results
[1] "the top tissues from single group analyses are Heart_Left_Ventricle,Adrenal_Gland,Artery_Coronary,Brain_Cerebellum,Stomach"WBC
Comparing with single group eQTL results
[1] "the top tissues from single group analyses are Whole_Blood,Adipose_Subcutaneous,Artery_Aorta,Skin_Sun_Exposed_Lower_leg,Spleen"
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-3 forcats_0.5.1 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.2 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.5.1 tidyverse_1.3.1 data.table_1.14.2 ctwas_0.2.1.9000
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] GenomicRanges_1.48.0 fs_1.5.2
[9] rstudioapi_0.13 farver_2.1.0
[11] DT_0.22 ggrepel_0.9.1
[13] bit64_4.0.5 lubridate_1.8.0
[15] AnnotationDbi_1.58.0 fansi_1.0.3
[17] xml2_1.3.3 codetools_0.2-18
[19] logging_0.10-108 cachem_1.0.6
[21] knitr_1.39 jsonlite_1.8.0
[23] workflowr_1.7.0 Rsamtools_2.12.0
[25] broom_0.8.0 dbplyr_2.1.1
[27] png_0.1-7 compiler_4.2.0
[29] httr_1.4.3 backports_1.4.1
[31] assertthat_0.2.1 Matrix_1.5-3
[33] fastmap_1.1.0 lazyeval_0.2.2
[35] cli_3.6.1 later_1.3.0
[37] htmltools_0.5.2 prettyunits_1.1.1
[39] tools_4.2.0 gtable_0.3.0
[41] glue_1.6.2 GenomeInfoDbData_1.2.8
[43] rappdirs_0.3.3 Rcpp_1.0.8.3
[45] Biobase_2.56.0 cellranger_1.1.0
[47] jquerylib_0.1.4 vctrs_0.6.5
[49] Biostrings_2.64.0 rtracklayer_1.56.0
[51] crosstalk_1.2.0 xfun_0.41
[53] rvest_1.0.2 lifecycle_1.0.4
[55] irlba_2.3.5 restfulr_0.0.14
[57] ensembldb_2.20.2 XML_3.99-0.14
[59] zlibbioc_1.42.0 zoo_1.8-10
[61] scales_1.3.0 gggrid_0.2-0
[63] hms_1.1.1 promises_1.2.0.1
[65] MatrixGenerics_1.8.0 ProtGenerics_1.28.0
[67] parallel_4.2.0 SummarizedExperiment_1.26.1
[69] AnnotationFilter_1.20.0 LDlinkR_1.2.3
[71] yaml_2.3.5 curl_4.3.2
[73] memoise_2.0.1 sass_0.4.1
[75] biomaRt_2.54.1 stringi_1.7.6
[77] RSQLite_2.3.1 highr_0.9
[79] S4Vectors_0.34.0 BiocIO_1.6.0
[81] GenomicFeatures_1.48.3 BiocGenerics_0.42.0
[83] filelock_1.0.2 BiocParallel_1.30.3
[85] GenomeInfoDb_1.39.9 rlang_1.1.2
[87] pkgconfig_2.0.3 matrixStats_0.62.0
[89] bitops_1.0-7 evaluate_0.15
[91] lattice_0.20-45 labeling_0.4.2
[93] GenomicAlignments_1.32.0 htmlwidgets_1.5.4
[95] cowplot_1.1.1 bit_4.0.4
[97] tidyselect_1.2.0 plyr_1.8.7
[99] magrittr_2.0.3 R6_2.5.1
[101] IRanges_2.30.0 generics_0.1.2
[103] DelayedArray_0.22.0 DBI_1.2.2
[105] withr_2.5.0 haven_2.5.0
[107] pgenlibr_0.3.3 pillar_1.9.0
[109] whisker_0.4 KEGGREST_1.36.3
[111] RCurl_1.98-1.7 mixsqp_0.3-43
[113] modelr_0.1.8 crayon_1.5.1
[115] utf8_1.2.2 BiocFileCache_2.4.0
[117] plotly_4.10.0 tzdb_0.4.0
[119] rmarkdown_2.25 progress_1.2.2
[121] readxl_1.4.0 grid_4.2.0
[123] blob_1.2.3 git2r_0.30.1
[125] reprex_2.0.1 digest_0.6.29
[127] httpuv_1.6.5 stats4_4.2.0
[129] munsell_0.5.0 viridisLite_0.4.0
[131] bslib_0.3.1