Last updated: 2019-09-17

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Knit directory: ptb_workflowr/

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Locus Level Analysis

Below are the locus-level FDR for every region with an FDR of less than or equal to 0.1 in at least one of the models. There are 12 such regions in total

How many significant regions in each model?

For each model, the number of loci where FDR < 0.05 (out of a total of 1703)

# A tibble: 4 x 2
  model     n_sig
  <chr>     <int>
1 newfour      10
2 newnoeqtl    10
3 best          9
4 null          6

The newfour and newnoeqtl models both gave us 4 more loci than the null model. Neither of these models have negative multivariate effect estimates (besides the intercept). I’ll show results just for newnoeqtl from now on.

What are the new regions?

What are the four loci that have an FDR < 0.05 under the newnoeqtl, but have an FDR above 0.05 under the null model? What is the gene

Regions of interest

Xin was interested in the following SNPs because they had high p values but low pips with the null and high pips with the prior


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Manjaro Linux

Matrix products: default
BLAS/LAPACK: /usr/lib/libopenblas_haswellp-r0.3.6.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] tidyselect_0.2.5   RSSp_0.9.0.9000    ldmap_0.0.0.9000  
 [4] daprcpp_1.0.0.9000 ldshrink_1.0-1     furrr_0.1.0.9002  
 [7] future_1.13.0      bigsnpr_0.11.5     bigstatsr_0.9.9   
[10] vroom_1.0.2.9000   RSQLite_2.1.1      drake_7.6.2.9000  
[13] fs_1.3.1           susieR_0.8.1.0525  here_0.1          
[16] dbplyr_1.4.2       MonetDBLite_0.6.1  glue_1.3.1        
[19] DT_0.7.2           forcats_0.4.0      stringr_1.4.0     
[22] dplyr_0.8.3        purrr_0.3.2.9000   readr_1.3.1       
[25] tidyr_0.8.99.9000  tibble_2.1.3       ggplot2_3.2.1.9000
[28] tidyverse_1.2.1   

loaded via a namespace (and not attached):
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  [3] ellipsis_0.2.0.9000         rprojroot_1.3-2            
  [5] XVector_0.24.0              GenomicRanges_1.36.0       
  [7] rstudioapi_0.10             listenv_0.7.0              
  [9] bit64_0.9-7                 fansi_0.4.0                
 [11] lubridate_1.7.4             xml2_1.2.2                 
 [13] codetools_0.2-16            knitr_1.23                 
 [15] zeallot_0.1.0               jsonlite_1.6               
 [17] workflowr_1.4.0             Rsamtools_2.0.0            
 [19] broom_0.5.2                 shiny_1.3.2                
 [21] compiler_3.6.1              httr_1.4.1                 
 [23] backports_1.1.4             assertthat_0.2.1           
 [25] Matrix_1.2-17               cli_1.1.0                  
 [27] later_0.8.0                 htmltools_0.3.6            
 [29] tools_3.6.1                 igraph_1.2.4.1             
 [31] gtable_0.3.0                GenomeInfoDbData_1.2.1     
 [33] Rcpp_1.0.2                  Biobase_2.44.0             
 [35] cellranger_1.1.0            Biostrings_2.52.0          
 [37] vctrs_0.2.0.9002            nlme_3.1-140               
 [39] rtracklayer_1.44.0          crosstalk_1.0.0            
 [41] iterators_1.0.10            wavethresh_4.6.8           
 [43] xfun_0.8                    globals_0.12.4             
 [45] plyranges_1.4.3             rvest_0.3.4                
 [47] mime_0.7                    lifecycle_0.1.0            
 [49] XML_3.98-1.20               zlibbioc_1.30.0            
 [51] MASS_7.3-51.4               scales_1.0.0               
 [53] promises_1.0.1              hms_0.5.1                  
 [55] parallel_3.6.1              SummarizedExperiment_1.14.0
 [57] yaml_2.2.0                  memoise_1.1.0              
 [59] stringi_1.4.3               S4Vectors_0.22.0           
 [61] foreach_1.4.4               BiocGenerics_0.30.0        
 [63] filelock_1.0.2              BiocParallel_1.18.0        
 [65] storr_1.2.2                 GenomeInfoDb_1.20.0        
 [67] matrixStats_0.54.0          rlang_0.4.0.9002           
 [69] pkgconfig_2.0.2             bitops_1.0-6               
 [71] evaluate_0.14               lattice_0.20-38            
 [73] GenomicAlignments_1.20.0    htmlwidgets_1.3            
 [75] cowplot_1.0.0               bit_1.1-14                 
 [77] magrittr_1.5                R6_2.4.0                   
 [79] IRanges_2.18.1              generics_0.0.2             
 [81] base64url_1.4               DelayedArray_0.10.0        
 [83] txtq_0.1.6                  DBI_1.0.0                  
 [85] pillar_1.4.2                haven_2.1.0                
 [87] whisker_0.4                 withr_2.1.2                
 [89] RCurl_1.95-4.12             modelr_0.1.4               
 [91] crayon_1.3.4                utf8_1.1.4                 
 [93] rmarkdown_1.13              grid_3.6.1                 
 [95] readxl_1.3.1                data.table_1.12.2          
 [97] blob_1.1.1                  git2r_0.26.1               
 [99] digest_0.6.20               xtable_1.8-4               
[101] httpuv_1.5.1                RcppParallel_4.4.3         
[103] stats4_3.6.1                munsell_0.5.0