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’

Knowles Comparison data:

Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes David A Knowles, Courtney K Burrows†, John D Blischak, Kristen M Patterson, Daniel J Serie, Nadine Norton, Carole Ober, Jonathan K Pritchard, Yoav Gilad

Knowles \(~~et~ al.~\) eLife 2018;7:e33480. DOI: https://doi.org/10.7554/eLife.33480 My first question was about transcription response at the 24 hour mark with my treatments. 3 hour RNA-seq had low levels of DEGs,so my focus is at 24 hours. This also happens to be when the Knowles paper collected their RNA-seq data

Supplementary 4 contains a list of 518 SNPs within 1 Mb of TSS, which had a detectable marginal effect on expression (5% FDR). When converted from ensembl gene id to entrez gene id, my list of unique Entrezgeneids = 521. I will call these meSNPs for marginal effect snps. In the meSNPs, 503 are within my DEG of 14084. Using an adj. P value of 0.05, There are 199/6864 in 24 hour daunorubicin, 184/6516 in 24 hour doxorubicin, 182/6202 in 24 hour epirubicin, 30/1327 in 24 hour mitoxantrone and 0 in Trastuzumb

Supplementary 5 contains a list of 376 response eQTLs (reQTLs). These are variants that were associated with modulation of transcriptomic response to doxorubicin treatment. After database name conversion, I have 377 unique Entregene ids. Of the reQTLs list, 374 are within my DEG of 14084. Using an adj. P value of 0.05, There are 187/6864 in 24 hour daunorubicin, 180/6516 in 24 hour doxorubicin, 176/6202 in 24 hour epirubicin, 40/1327 in 24 hour mitoxantrone and 0 in Trastuzumb.

Count of genes in each treatment by using adj. P value of <0.05
time id n K4 K5
24_hours Daunorubicin 6864 199 187
24_hours Doxorubicin 6516 184 180
24_hours Epirubicin 6202 172 176
24_hours Mitoxantrone 1327 30 40
Count of genes in each treatment by total expressed genes
time id n K4 K5
24_hours Daunorubicin 14084 503 374
24_hours Doxorubicin 14084 503 374
24_hours Epirubicin 14084 503 374
24_hours Mitoxantrone 14084 503 374
24_hours Trastuzumab 14084 503 374 
[1] "this is the meSNPs (mininmally expressed QTLS)"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time         pvalue
  <fct>        <chr>         <dbl>
1 Daunorubicin 24_hours 0.0000338 
2 Daunorubicin 3_hours  0.00188   
3 Doxorubicin  24_hours 0.0000113 
4 Doxorubicin  3_hours  0.923     
5 Epirubicin   24_hours 0.00000740
6 Epirubicin   3_hours  0.219     
7 Mitoxantrone 24_hours 0.00865   
8 Mitoxantrone 3_hours  0.685     
[1] "below is the reQTLS in knowles data ( reQTLS)"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours  0.658
2 Daunorubicin 3_hours   0.739
3 Doxorubicin  24_hours  0.497
4 Doxorubicin  3_hours   1.00 
5 Epirubicin   24_hours  0.254
6 Epirubicin   3_hours   0.571
7 Mitoxantrone 24_hours  0.445
8 Mitoxantrone 3_hours   0.395

Seaone 2019

Seone, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019

Significant (adj. P value of <0.05) and non-sig gene counts in Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 7065 155 6689 175
Doxorubicin 7407 161 6347 169
Epirubicin 7717 165 6037 165
Mitoxantrone 12483 274 1271 56
Trastuzumab 13754 330 NA NA 
# A tibble: 4 × 2
  id               pvalue
  <fct>             <dbl>
1 Daunorubicin 0.128     
2 Doxorubicin  0.0771    
3 Epirubicin   0.0314    
4 Mitoxantrone 0.00000326

3 hour data Seaone

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hours Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 13227 302 527 28
Doxorubicin 13738 330 16 NA
Epirubicin 13551 313 203 17
Mitoxantrone 13698 328 56 2
Trastuzumab 13754 330 NA NA

chi square test Seaone

##remove Trastuzumab in order to perform chi square tests by time and drug between  DE and non DE enrichment
chi_fun <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_fun
# A tibble: 8 × 3
# Groups:   id [4]
  id           time          pvalue
  <fct>        <chr>          <dbl>
1 Daunorubicin 24_hours 0.128      
2 Daunorubicin 3_hours  0.0000332  
3 Doxorubicin  24_hours 0.0771     
4 Doxorubicin  3_hours  1.00       
5 Epirubicin   24_hours 0.0314     
6 Epirubicin   3_hours  0.000000346
7 Mitoxantrone 24_hours 0.00000326 
8 Mitoxantrone 3_hours  0.902

ArrGWAS to 24 hour DEG genes p < 0.05

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id sigcount ARR ARRcount
Daunorubicin notsig no 7169
Daunorubicin notsig y 51
Daunorubicin sig no 6795
Daunorubicin sig y 69
Doxorubicin notsig no 7512
Doxorubicin notsig y 56
Doxorubicin sig no 6452
Doxorubicin sig y 64
Epirubicin notsig no 7827
Epirubicin notsig y 55
Epirubicin sig no 6137
Epirubicin sig y 65
Mitoxantrone notsig no 12650
Mitoxantrone notsig y 107
Mitoxantrone sig no 1314
Mitoxantrone sig y 13
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id sigcount ARR ARRcount
Daunorubicin notsig no 7169
Daunorubicin notsig y 51
Daunorubicin sig no 6795
Daunorubicin sig y 69
Doxorubicin notsig no 7512
Doxorubicin notsig y 56
Doxorubicin sig no 6452
Doxorubicin sig y 64
Epirubicin notsig no 7827
Epirubicin notsig y 55
Epirubicin sig no 6137
Epirubicin sig y 65
Mitoxantrone notsig no 12650
Mitoxantrone notsig y 107
Mitoxantrone sig no 1314
Mitoxantrone sig y 13
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

chi square test ARR

chi_funarr <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(ARR, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funarr
# A tibble: 8 × 3
# Groups:   id [4]
  id           time      pvalue
  <fct>        <chr>      <dbl>
1 Daunorubicin 24_hours 0.0662 
2 Daunorubicin 3_hours  0.0246 
3 Doxorubicin  24_hours 0.142  
4 Doxorubicin  3_hours  1.00   
5 Epirubicin   24_hours 0.0313 
6 Epirubicin   3_hours  0.644  
7 Mitoxantrone 24_hours 0.708  
8 Mitoxantrone 3_hours  0.00409

HFGWAS

Significant (adj. P value of <0.05) and non-sig gene counts in HFhythmia GWAS
id sigcount HF HFcount
Daunorubicin notsig no 7209
Daunorubicin notsig y 11
Daunorubicin sig no 6842
Daunorubicin sig y 22
Doxorubicin notsig no 7556
Doxorubicin notsig y 12
Doxorubicin sig no 6495
Doxorubicin sig y 21
Epirubicin notsig no 7868
Epirubicin notsig y 14
Epirubicin sig no 6183
Epirubicin sig y 19
Mitoxantrone notsig no 12728
Mitoxantrone notsig y 29
Mitoxantrone sig no 1323
Mitoxantrone sig y 4
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

3 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS
id sigcount HF HFcount
Daunorubicin notsig no 13498
Daunorubicin notsig y 31
Daunorubicin sig no 553
Daunorubicin sig y 2
Doxorubicin notsig no 14035
Doxorubicin notsig y 33
Doxorubicin sig no 16
Epirubicin notsig no 13831
Epirubicin notsig y 33
Epirubicin sig no 220
Mitoxantrone notsig no 13993
Mitoxantrone notsig y 33
Mitoxantrone sig no 58
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

chi square test HF

chi_funhf <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(HF, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funhf
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.0589
2 Daunorubicin 3_hours  0.858 
3 Doxorubicin  24_hours 0.0674
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.164 
6 Epirubicin   3_hours  0.983 
7 Mitoxantrone 24_hours 0.816 
8 Mitoxantrone 3_hours  1.00

CAD GWAS

Significant (adj. P value of <0.05) and non-sig gene counts in CAD GWAS
id sigcount CAD CADcount
Daunorubicin notsig no 7107
Daunorubicin notsig y 113
Daunorubicin sig no 6748
Daunorubicin sig y 116
Doxorubicin notsig no 7447
Doxorubicin notsig y 121
Doxorubicin sig no 6408
Doxorubicin sig y 108
Epirubicin notsig no 7762
Epirubicin notsig y 120
Epirubicin sig no 6093
Epirubicin sig y 109
Mitoxantrone notsig no 12547
Mitoxantrone notsig y 210
Mitoxantrone sig no 1308
Mitoxantrone sig y 19
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS
id sigcount CAD CADcount
Daunorubicin notsig no 13317
Daunorubicin notsig y 212
Daunorubicin sig no 538
Daunorubicin sig y 17
Doxorubicin notsig no 13839
Doxorubicin notsig y 229
Doxorubicin sig no 16
Epirubicin notsig no 13643
Epirubicin notsig y 221
Epirubicin sig no 212
Epirubicin sig y 8
Mitoxantrone notsig no 13798
Mitoxantrone notsig y 228
Mitoxantrone sig no 57
Mitoxantrone sig y 1
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

chi square test CAD

chi_funCAD <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(CAD, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funCAD
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.604 
2 Daunorubicin 3_hours  0.0105
3 Doxorubicin  24_hours 0.836 
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.304 
6 Epirubicin   3_hours  0.0351
7 Mitoxantrone 24_hours 0.636 
8 Mitoxantrone 3_hours  1.00

GTEx V8 heart-left ventrical egenes

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id time sigcount no y
Daunorubicin 24_hours notsig 866 6354
Daunorubicin 24_hours sig 797 6067
Daunorubicin 3_hours notsig 1628 11901
Daunorubicin 3_hours sig 35 520
Doxorubicin 24_hours notsig 920 6648
Doxorubicin 24_hours sig 743 5773
Doxorubicin 3_hours notsig 1662 12406
Doxorubicin 3_hours sig 1 15
Epirubicin 24_hours notsig 953 6929
Epirubicin 24_hours sig 710 5492
Epirubicin 3_hours notsig 1651 12213
Epirubicin 3_hours sig 12 208
Mitoxantrone 24_hours notsig 1503 11254
Mitoxantrone 24_hours sig 160 1167
Mitoxantrone 3_hours notsig 1660 12366
Mitoxantrone 3_hours sig 3 55
Trastuzumab 24_hours notsig 1663 12421
Trastuzumab 3_hours notsig 1663 12421 
[1] "after performing chi square test between DEgenes, and non DE genes"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time        pvalue
  <fct>        <chr>        <dbl>
1 Daunorubicin 24_hours 0.498    
2 Daunorubicin 3_hours  0.0000556
3 Doxorubicin  24_hours 0.175    
4 Doxorubicin  3_hours  0.763    
5 Epirubicin   24_hours 0.251    
6 Epirubicin   3_hours  0.00454  
7 Mitoxantrone 24_hours 0.802    
8 Mitoxantrone 3_hours  0.172    

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.4.2       broom_1.0.3        kableExtra_1.3.4   sjmisc_2.8.9      
 [5] scales_1.2.1       ggpubr_0.6.0       cowplot_1.1.1      RColorBrewer_1.1-3
 [9] biomaRt_2.52.0     ggsignif_0.6.4     lubridate_1.9.2    forcats_1.0.0     
[13] stringr_1.5.0      dplyr_1.1.0        purrr_1.0.1        readr_2.1.4       
[17] tidyr_1.3.0        tibble_3.1.8       ggplot2_3.4.1      tidyverse_2.0.0   
[21] limma_3.52.4       workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] colorspace_2.1-0       ellipsis_0.3.2         sjlabelled_1.2.0      
  [4] rprojroot_2.0.3        XVector_0.36.0         fs_1.6.1              
  [7] rstudioapi_0.14        farver_2.1.1           bit64_4.0.5           
 [10] AnnotationDbi_1.58.0   fansi_1.0.4            xml2_1.3.3            
 [13] cachem_1.0.7           knitr_1.42             jsonlite_1.8.4        
 [16] dbplyr_2.3.1           png_0.1-8              compiler_4.2.2        
 [19] httr_1.4.5             backports_1.4.1        fastmap_1.1.1         
 [22] cli_3.6.0              later_1.3.0            htmltools_0.5.4       
 [25] prettyunits_1.1.1      tools_4.2.2            gtable_0.3.1          
 [28] glue_1.6.2             GenomeInfoDbData_1.2.8 rappdirs_0.3.3        
 [31] Rcpp_1.0.10            carData_3.0-5          Biobase_2.56.0        
 [34] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.5.2           
 [37] Biostrings_2.64.1      svglite_2.1.1          insight_0.19.0        
 [40] xfun_0.37              ps_1.7.2               rvest_1.0.3           
 [43] timechange_0.2.0       lifecycle_1.0.3        rstatix_0.7.2         
 [46] XML_3.99-0.13          getPass_0.2-2          zlibbioc_1.42.0       
 [49] vroom_1.6.1            hms_1.1.2              promises_1.2.0.1      
 [52] parallel_4.2.2         yaml_2.3.7             curl_5.0.0            
 [55] memoise_2.0.1          sass_0.4.5             stringi_1.7.12        
 [58] RSQLite_2.3.0          highr_0.10             S4Vectors_0.34.0      
 [61] BiocGenerics_0.42.0    filelock_1.0.2         GenomeInfoDb_1.32.4   
 [64] rlang_1.0.6            pkgconfig_2.0.3        systemfonts_1.0.4     
 [67] bitops_1.0-7           evaluate_0.20          labeling_0.4.2        
 [70] bit_4.0.5              processx_3.8.0         tidyselect_1.2.0      
 [73] magrittr_2.0.3         R6_2.5.1               IRanges_2.30.1        
 [76] generics_0.1.3         DBI_1.1.3              pillar_1.8.1          
 [79] whisker_0.4.1          withr_2.5.0            KEGGREST_1.36.3       
 [82] abind_1.4-5            RCurl_1.98-1.10        crayon_1.5.2          
 [85] car_3.1-1              utf8_1.2.3             BiocFileCache_2.4.0   
 [88] tzdb_0.3.0             rmarkdown_2.20         progress_1.2.2        
 [91] grid_4.2.2             blob_1.2.3             callr_3.7.3           
 [94] git2r_0.31.0           digest_0.6.31          webshot_0.5.4         
 [97] httpuv_1.6.9           stats4_4.2.2           munsell_0.5.0         
[100] viridisLite_0.4.1      bslib_0.4.2