Last updated: 2019-09-17
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Knit directory: ptb_workflowr/
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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
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 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
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):
[1] colorspace_1.4-1 RcppEigen_0.3.3.5.0
[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