Last updated: 2019-07-10
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Knit directory: ptb_workflowr/
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The genome was broken in to 1703 regions (using Pickrell’s ldetect
). Regions were broken into subregions if they contained greater than 5000 SNPs, unless this would result in a subregion with fewer than 50
SNPs. This means that region size varied from 50
SNPs to 5049
SNPs. The functional enrichment program torus
was run individually on each of the following features:
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Fine mapping with susie
was then run on the top 5 loci, after generating priors using torus
. susie
was run assuming 1 causal variant per locus.
A variable selection procedure (forward selection) was used to come up with a set of four features to use to generate a per-variant prior for susie.
The top hit is on chromosome 1.
Version | Author | Date |
---|---|---|
791ea4a | Nicholas Knoblauch | 2019-07-01 |
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 ldshrink_1.0-1 archive_1.0.0
[7] vroom_1.0.2.9000 RSQLite_2.1.1 glue_1.3.1
[10] drake_7.4.0.9000 fs_1.3.1 susieR_0.8.1.0525
[13] here_0.1 forcats_0.4.0 stringr_1.4.0
[16] dplyr_0.8.3 purrr_0.3.2 readr_1.3.1
[19] tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0
[22] tidyverse_1.2.1 dbplyr_1.4.2 MonetDBLite_0.6.1
[25] DT_0.7
loaded via a namespace (and not attached):
[1] nlme_3.1-140 bitops_1.0-6 lubridate_1.7.4
[4] bit64_0.9-7 filelock_1.0.2 httr_1.4.0
[7] rprojroot_1.3-2 GenomeInfoDb_1.20.0 tools_3.6.1
[10] backports_1.1.4 R6_2.4.0 DBI_1.0.0
[13] lazyeval_0.2.2 BiocGenerics_0.30.0 colorspace_1.4-1
[16] wavethresh_4.6.8 withr_2.1.2 bit_1.1-14
[19] compiler_3.6.1 git2r_0.26.1 cli_1.1.0
[22] rvest_0.3.4 xml2_1.2.0 labeling_0.3
[25] scales_1.0.0 digest_0.6.20 txtq_0.1.3
[28] rmarkdown_1.13 XVector_0.24.0 pkgconfig_2.0.2
[31] htmltools_0.3.6 htmlwidgets_1.3 rlang_0.4.0.9000
[34] readxl_1.3.1 rstudioapi_0.10 shiny_1.3.2
[37] generics_0.0.2 jsonlite_1.6 crosstalk_1.0.0
[40] RCurl_1.95-4.12 magrittr_1.5 GenomeInfoDbData_1.2.1
[43] Matrix_1.2-17 Rcpp_1.0.1 munsell_0.5.0
[46] S4Vectors_0.22.0 stringi_1.4.3 whisker_0.3-2
[49] yaml_2.2.0 MASS_7.3-51.4 storr_1.2.2
[52] zlibbioc_1.30.0 grid_3.6.1 blob_1.1.1
[55] promises_1.0.1 parallel_3.6.1 crayon_1.3.4
[58] lattice_0.20-38 haven_2.1.0 hms_0.4.2
[61] knitr_1.23 pillar_1.4.2 igraph_1.2.4.1
[64] GenomicRanges_1.36.0 base64url_1.4 codetools_0.2-16
[67] stats4_3.6.1 evaluate_0.14 RcppParallel_4.4.3
[70] modelr_0.1.4 httpuv_1.5.1 cellranger_1.1.0
[73] gtable_0.3.0 assertthat_0.2.1 xfun_0.7
[76] mime_0.7 xtable_1.8-4 RcppEigen_0.3.3.5.0
[79] broom_0.5.2 later_0.8.0 memoise_1.1.0
[82] IRanges_2.18.1 workflowr_1.4.0