Last updated: 2024-05-31

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Knit directory: multigroup_ctwas_analysis/

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File	Version	Author	Date	Message
Rmd	163c010	XSun	2024-05-31	update
html	163c010	XSun	2024-05-31	update
Rmd	2b980bb	XSun	2024-05-30	update
html	2b980bb	XSun	2024-05-30	update

Overview

Traits

aFib, IBD, LDL, SBP, SCZ, WBC

details

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 = F,

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,

Memory requested

cpus-per-task=2
mem=200G

100G 2core got killed

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

Results from multi-group analysis

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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"

We then looked into Whole_Blood. Please not that the ealier single group eQTL analyses were performed under L=5 but the current analyses were under L=3.

So we re-ran the single group analysis using L=3 with for Whole_Blood.

[1] "The number of sig genes reported by single group analysis under L=3 is 10"

[1] "The number of overlapped genes is 7"

locus zoom plot for the regions containing these genes: https://uchicago.box.com/s/ziv0v006pxwnuprhxbagdfr0cnfk5e23

DT::datatable(df_summary,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','All genes with pip > 0.8 from single group analysis and all genes with combined pip >0.8 from multi group analysis'),options = list(pageLength = 5) )

ggplot(df_summary, aes(x = combined_pip_multi_group, y = combined_pip_nonzero_cs_multi_group)) +
  geom_point() +  # Add points
  geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +  
  labs(x = "Combined PIP Multi Group (adding up all pips)", 
       y = "Combined PIP Nonzero CS Multi Group (only pips with non-zero CS added)",
       title = "Comparison for combined pip") +
  theme_minimal()

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LDL

Results from multi-group analysis

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Comparing with single group eQTL results

[1] "the top tissues from single group analyses are Liver,Spleen,Adipose_Subcutaneous,Adrenal_Gland,Esophagus_Mucosa"

We then looked into Adipose_Subcutaneous. Please not that the ealier single group eQTL analyses were performed under L=5 but the current analyses were under L=3.

So we re-ran the single group analysis using L=3 with for Adipose_Subcutaneous.

[1] "The number of sig genes reported by single group analysis under L=3 is 16"

[1] "The number of overlapped genes is 4"

locus zoom plot for the regions containing these genes: https://uchicago.box.com/s/u93gpgil79ybf7akrl1obfkpgkxq5wkn

DT::datatable(df_summary,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','All genes with pip > 0.8 from single group analysis and all genes with combined pip >0.8 from multi group analysis'),options = list(pageLength = 5) )

ggplot(df_summary, aes(x = combined_pip_multi_group, y = combined_pip_nonzero_cs_multi_group)) +
  geom_point() +  # Add points
  geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
  labs(x = "Combined PIP Multi Group (adding up all pips)",
       y = "Combined PIP Nonzero CS Multi Group (only pips with non-zero CS added)",
       title = "Comparison for combined pip") +
  theme_minimal()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

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SBP

Results from multi-group analysis

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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

Results from multi-group analysis

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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

Results from multi-group analysis

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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] 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_0.2.1.9000  

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] BiocParallel_1.30.3         crosstalk_1.2.0            
  [9] GenomeInfoDb_1.39.9         LDlinkR_1.2.3              
 [11] digest_0.6.29               ensembldb_2.20.2           
 [13] htmltools_0.5.2             fansi_1.0.3                
 [15] magrittr_2.0.3              memoise_2.0.1              
 [17] tzdb_0.4.0                  Biostrings_2.64.0          
 [19] modelr_0.1.8                matrixStats_0.62.0         
 [21] locuszoomr_0.2.1            prettyunits_1.1.1          
 [23] colorspace_2.0-3            blob_1.2.3                 
 [25] rvest_1.0.2                 rappdirs_0.3.3             
 [27] ggrepel_0.9.1               haven_2.5.0                
 [29] xfun_0.41                   crayon_1.5.1               
 [31] RCurl_1.98-1.7              jsonlite_1.8.0             
 [33] zoo_1.8-10                  glue_1.6.2                 
 [35] gtable_0.3.0                zlibbioc_1.42.0            
 [37] XVector_0.36.0              DelayedArray_0.22.0        
 [39] BiocGenerics_0.42.0         scales_1.3.0               
 [41] DBI_1.2.2                   Rcpp_1.0.8.3               
 [43] viridisLite_0.4.0           progress_1.2.2             
 [45] bit_4.0.4                   stats4_4.2.0               
 [47] DT_0.22                     htmlwidgets_1.5.4          
 [49] httr_1.4.3                  ellipsis_0.3.2             
 [51] pkgconfig_2.0.3             XML_3.99-0.14              
 [53] farver_2.1.0                sass_0.4.1                 
 [55] dbplyr_2.1.1                utf8_1.2.2                 
 [57] tidyselect_1.2.0            labeling_0.4.2             
 [59] rlang_1.1.2                 later_1.3.0                
 [61] AnnotationDbi_1.58.0        munsell_0.5.0              
 [63] pgenlibr_0.3.3              cellranger_1.1.0           
 [65] tools_4.2.0                 cachem_1.0.6               
 [67] cli_3.6.1                   generics_0.1.2             
 [69] RSQLite_2.3.1               broom_0.8.0                
 [71] evaluate_0.15               fastmap_1.1.0              
 [73] yaml_2.3.5                  knitr_1.39                 
 [75] bit64_4.0.5                 fs_1.5.2                   
 [77] KEGGREST_1.36.3             AnnotationFilter_1.20.0    
 [79] whisker_0.4                 xml2_1.3.3                 
 [81] biomaRt_2.54.1              compiler_4.2.0             
 [83] rstudioapi_0.13             plotly_4.10.0              
 [85] filelock_1.0.2              curl_4.3.2                 
 [87] png_0.1-7                   reprex_2.0.1               
 [89] bslib_0.3.1                 stringi_1.7.6              
 [91] highr_0.9                   GenomicFeatures_1.48.3     
 [93] lattice_0.20-45             ProtGenerics_1.28.0        
 [95] Matrix_1.5-3                vctrs_0.6.5                
 [97] pillar_1.9.0                lifecycle_1.0.4            
 [99] jquerylib_0.1.4             cowplot_1.1.1              
[101] bitops_1.0-7                irlba_2.3.5                
[103] httpuv_1.6.5                rtracklayer_1.56.0         
[105] GenomicRanges_1.48.0        R6_2.5.1                   
[107] BiocIO_1.6.0                promises_1.2.0.1           
[109] IRanges_2.30.0              codetools_0.2-18           
[111] assertthat_0.2.1            SummarizedExperiment_1.26.1
[113] rprojroot_2.0.3             rjson_0.2.21               
[115] withr_2.5.0                 GenomicAlignments_1.32.0   
[117] Rsamtools_2.12.0            S4Vectors_0.34.0           
[119] GenomeInfoDbData_1.2.8      parallel_4.2.0             
[121] hms_1.1.1                   grid_4.2.0                 
[123] gggrid_0.2-0                rmarkdown_2.25             
[125] MatrixGenerics_1.8.0        logging_0.10-108           
[127] git2r_0.30.1                mixsqp_0.3-43              
[129] Biobase_2.56.0              lubridate_1.8.0            
[131] restfulr_0.0.14

Multi group analysis: 6 traits, 5 tissues, eQTL + sQTL + apaQTL – Compute UKBB LD

XSun

2024-05-27

Overview

Traits

Tissues

Omics

Settings

Results

aFib

IBD

Results from multi-group analysis

Comparing with single group eQTL results

LDL

Results from multi-group analysis

Comparing with single group eQTL results

SBP

Results from multi-group analysis

Comparing with single group eQTL results

SCZ

Results from multi-group analysis

Comparing with single group eQTL results

WBC

Results from multi-group analysis

Comparing with single group eQTL results