Last updated: 2024-06-13

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Overview

Traits

aFib, IBD, LDL, SBP, SCZ, WBC

details

Tissues

The independent tissues are selected by single tissue analysis

Omics

eQTL, sQTL, apaQTL weights are from Munro et al.

Settings

  1. 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,
  1. 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,
  1. Memory requested
  • cpus-per-task=2
  • mem=200G

over 30h running time

Results

Results from multi-group analysis

The results are summarized by

  1. 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.

  2. Combined PIP by omics: we aggregate the Susie PIPs by omics

  3. Combined PIP by contexts: we aggregate the Susie PIPs by tissues, making it easier to understand the distribution of PIP across different genetic contexts.

  4. 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

Results from multi-group analysis

[1] "Esophagus_Mucosa"           "Adipose_Subcutaneous"      
[3] "Whole_Blood"                "Heart_Left_Ventricle"      
[5] "Cells_Cultured_fibroblasts"

LDL

Results from multi-group analysis

[1] "Esophagus_Mucosa"     "Adipose_Subcutaneous" "Liver"               
[4] "Adrenal_Gland"        "Spleen"

SBP

Results from multi-group analysis

[1] "Artery_Tibial"        "Heart_Left_Ventricle" "Spleen"              
[4] "Adipose_Subcutaneous" "Brain_Cortex"

plot for this locus https://uchicago.box.com/s/uca02ksxb4hz67lohjpko35nkjhp5pti

We ran regular SuSiE (uniform prior) in a region, and obtain the number of CS from the results. Then we ran fine-mapping step for this region.

By doing these, we have

estimated_L <- readRDS("/project/xinhe/xsun/multi_group_ctwas/5.multi_group_testing/results_preL/SBP-ukb-a-360/SBP-ukb-a-360.estimated_L.RDS")

sprintf("the pre-estimated L is %s", estimated_L["16:2714828-3951195"])
[1] "the pre-estimated L is 1"
load("/project/xinhe/xsun/multi_group_ctwas/5.multi_group_testing/analy_results/NAA60.preL.rdata")
DT::datatable(finemap_res_region_multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Updated PIP (run with pre-estimated L)'),options = list(pageLength = 5) )

SCZ

Results from multi-group analysis

[1] "Heart_Left_Ventricle" "Adrenal_Gland"        "Brain_Cerebellum"    
[4] "Stomach"              "Artery_Coronary"

WBC

Results from multi-group analysis

[1] "Whole_Blood"                "Skin_Sun_Exposed_Lower_leg"
[3] "Adipose_Subcutaneous"       "Artery_Aorta"              
[5] "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] gridExtra_2.3      RColorBrewer_1.1-3 forcats_0.5.1      stringr_1.5.1     
 [5] dplyr_1.1.4        purrr_1.0.2        readr_2.1.2        tidyr_1.3.0       
 [9] tibble_3.2.1       ggplot2_3.5.1      tidyverse_1.3.1    data.table_1.14.2 
[13] ctwas_0.2.30      

loaded via a namespace (and not attached):
  [1] readxl_1.4.0                backports_1.4.1            
  [3] workflowr_1.7.0             BiocFileCache_2.4.0        
  [5] plyr_1.8.7                  lazyeval_0.2.2             
  [7] crosstalk_1.2.0             BiocParallel_1.30.3        
  [9] GenomeInfoDb_1.39.9         LDlinkR_1.2.3              
 [11] digest_0.6.29               foreach_1.5.2              
 [13] ensembldb_2.20.2            htmltools_0.5.2            
 [15] fansi_1.0.3                 magrittr_2.0.3             
 [17] memoise_2.0.1               doParallel_1.0.17          
 [19] tzdb_0.4.0                  Biostrings_2.64.0          
 [21] modelr_0.1.8                matrixStats_0.62.0         
 [23] locuszoomr_0.2.1            prettyunits_1.1.1          
 [25] colorspace_2.0-3            blob_1.2.3                 
 [27] rvest_1.0.2                 rappdirs_0.3.3             
 [29] ggrepel_0.9.1               haven_2.5.0                
 [31] xfun_0.41                   crayon_1.5.1               
 [33] RCurl_1.98-1.7              jsonlite_1.8.0             
 [35] zoo_1.8-10                  iterators_1.0.14           
 [37] glue_1.6.2                  gtable_0.3.0               
 [39] zlibbioc_1.42.0             XVector_0.36.0             
 [41] DelayedArray_0.22.0         BiocGenerics_0.42.0        
 [43] scales_1.3.0                DBI_1.2.2                  
 [45] Rcpp_1.0.8.3                viridisLite_0.4.0          
 [47] progress_1.2.2              bit_4.0.4                  
 [49] stats4_4.2.0                DT_0.22                    
 [51] htmlwidgets_1.5.4           httr_1.4.3                 
 [53] ellipsis_0.3.2              pkgconfig_2.0.3            
 [55] XML_3.99-0.14               farver_2.1.0               
 [57] sass_0.4.1                  dbplyr_2.1.1               
 [59] utf8_1.2.2                  tidyselect_1.2.0           
 [61] labeling_0.4.2              rlang_1.1.2                
 [63] later_1.3.0                 AnnotationDbi_1.58.0       
 [65] munsell_0.5.0               pgenlibr_0.3.3             
 [67] cellranger_1.1.0            tools_4.2.0                
 [69] cachem_1.0.6                cli_3.6.1                  
 [71] generics_0.1.2              RSQLite_2.3.1              
 [73] broom_0.8.0                 evaluate_0.15              
 [75] fastmap_1.1.0               yaml_2.3.5                 
 [77] knitr_1.39                  bit64_4.0.5                
 [79] fs_1.5.2                    KEGGREST_1.36.3            
 [81] AnnotationFilter_1.20.0     xml2_1.3.3                 
 [83] biomaRt_2.54.1              compiler_4.2.0             
 [85] rstudioapi_0.13             plotly_4.10.0              
 [87] filelock_1.0.2              curl_4.3.2                 
 [89] png_0.1-7                   reprex_2.0.1               
 [91] bslib_0.3.1                 stringi_1.7.6              
 [93] highr_0.9                   GenomicFeatures_1.48.3     
 [95] lattice_0.20-45             ProtGenerics_1.28.0        
 [97] Matrix_1.5-3                vctrs_0.6.5                
 [99] pillar_1.9.0                lifecycle_1.0.4            
[101] jquerylib_0.1.4             cowplot_1.1.1              
[103] bitops_1.0-7                irlba_2.3.5                
[105] httpuv_1.6.5                rtracklayer_1.56.0         
[107] GenomicRanges_1.48.0        R6_2.5.1                   
[109] BiocIO_1.6.0                promises_1.2.0.1           
[111] IRanges_2.30.0              codetools_0.2-18           
[113] assertthat_0.2.1            SummarizedExperiment_1.26.1
[115] rprojroot_2.0.3             rjson_0.2.21               
[117] withr_2.5.0                 GenomicAlignments_1.32.0   
[119] Rsamtools_2.12.0            S4Vectors_0.34.0           
[121] GenomeInfoDbData_1.2.8      parallel_4.2.0             
[123] hms_1.1.1                   grid_4.2.0                 
[125] gggrid_0.2-0                rmarkdown_2.25             
[127] MatrixGenerics_1.8.0        logging_0.10-108           
[129] git2r_0.30.1                mixsqp_0.3-43              
[131] Biobase_2.56.0              lubridate_1.8.0            
[133] restfulr_0.0.14