Fine Mapping using Torus and Susie
CreRecombinase
2019-07-18
Last updated: 2019-07-31
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Introduction
In this document I will summarize my current progress analyzing the latest the GWAS results. I’ll explain briefly how torus does enrichment analysis (and what I mean by “enrichment analysis”). Then I’ll go over the results of that analysis. After that I’ll give a brief overview of susie, and then I’ll show some results. Before any of that, I’m going to talk a little bit about the dataset(s) I’m working with.
The GWAS
manhattan
A few numbers about the GWAS summary statistics:
14991824variants- top hit has \(p\)=3.03610^{-26}
p<1e-8on chromosomes 1,3,5
#Enrichment analysis and fine mapping pipeline overview
- Estimate the relationship between a particular genomic annotation (e.g ATAC-seq peaks) and GWAS significance genome-wide (Torus)
- Use the enrichment estimate to specify a per-variant prior.
- Using the prior and a reference LD panel, identify putative causal variants (
susie)
For both the enrichment analysis and the fine mapping, the genome is broken in to chunks according to approximately independent blocks as determined by ldetect. These blocks are then broken into blocks no greater than 50000 and no less than data_config$min_snp. For torus (enrichment) there is an assumption that there is at most 1 causal variant per chunk, and for susie (fine-mapping) the assumption is that there are at most \(L\), where \(L\) is a tuneable parameter. I ran susie with 3 values of \(L\): 1, 3 and 10. These assumptions make chunk size an important parameter when performing either fine mapping or enrichment analysis.
Epigenomic Data
Noboru has provided me with bed file annotations of the genome generated from the various experiments performed on the cell lines.
- ATAC seq
- H3K4me1 chip
- H3K4me3 chip
- H3K27ac chip
Within these categories there are three subcategories:
- Per-cell-line. Each cell line was either a control (
ctr) or underwent decidualizationdec. - Annotations consistent across the three control or decidualized cell line samples.
- Differental peaks. Peaks that have increased read counts in decidualized over control
In addition I have:
- Endometrial eQTL (Ober Lab)
- Hi-C from one cell line
hic_all_interacting_DT1_dTL4_D_48h - Annotation predicting repressive regions (from Hoffman et al.)
All the univariate results
Below are the univariate enrichment results. Using a single epigenomic dataset, I ran torus, and got an effect size and standard error of the enrichment of each dataset for GWAS hits. These are plotted below
Multivariate effect size estimates
Below are the multivariate effect size estimates for 5 features that came out of a forward selection.
It should be noted that not all of these features are significant when fit as 5 univariate models.
Fine mapping results
Each SNP that underwent fine mapping has a \(p\)-value, a prior, and a posterior inclusion probability, or pip, which is the predicted probability that the SNP is a causal variant (i.e that the effect size estimate is not a sample from the null distribution).
Below is an example of what this looks like for a region
Susie plots of top regions
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What is the effect of the prior?
How does the pip change as a consequence of the prior? I ran susie using a prior derived from the enrichment model and compared it to a result from running with a uniform prior.
Joining, by = "region_id"
Joining, by = "region_id"
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Joining, by = "region_id"
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Joining, by = "region_id"
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Joining, by = "region_id"
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And here’s another perspective on the same data
Joining, by = "region_id"
Joining, by = "region_id"
Joining, by = "region_id"
Joining, by = "region_id"
Joining, by = "region_id"
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 bigsnpr_0.11.5
[7] bigstatsr_0.9.9 vroom_1.0.2.9000 RSQLite_2.1.1
[10] glue_1.3.1 drake_7.4.0.9000 fs_1.3.1
[13] susieR_0.8.1.0525 here_0.1 forcats_0.4.0
[16] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2
[19] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
[22] tidyverse_1.2.1 dbplyr_1.4.2 MonetDBLite_0.6.1
[25] plotly_4.9.0 ggplot2_3.2.0 gtable_0.3.0
[28] gridExtra_2.3 scales_1.0.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 RcppEigen_0.3.3.5.0 rprojroot_1.3-2
[4] XVector_0.24.0 GenomicRanges_1.36.0 rstudioapi_0.10
[7] DT_0.7 bit64_0.9-7 lubridate_1.7.4
[10] xml2_1.2.0 codetools_0.2-16 knitr_1.23
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[16] shiny_1.3.2 compiler_3.6.1 httr_1.4.0
[19] backports_1.1.4 assertthat_0.2.1 Matrix_1.2-17
[22] lazyeval_0.2.2 cli_1.1.0 later_0.8.0
[25] htmltools_0.3.6 tools_3.6.1 igraph_1.2.4.1
[28] GenomeInfoDbData_1.2.1 reshape2_1.4.3 Rcpp_1.0.2
[31] cellranger_1.1.0 nlme_3.1-140 iterators_1.0.10
[34] crosstalk_1.0.0 wavethresh_4.6.8 xfun_0.7
[37] rvest_0.3.4 mime_0.7 zlibbioc_1.30.0
[40] MASS_7.3-51.4 hms_0.4.2 promises_1.0.1
[43] parallel_3.6.1 yaml_2.2.0 memoise_1.1.0
[46] stringi_1.4.3 highr_0.8 S4Vectors_0.22.0
[49] foreach_1.4.4 BiocGenerics_0.30.0 filelock_1.0.2
[52] storr_1.2.2 GenomeInfoDb_1.20.0 rlang_0.4.0.9000
[55] pkgconfig_2.0.2 bitops_1.0-6 evaluate_0.14
[58] lattice_0.20-38 htmlwidgets_1.3 labeling_0.3
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[67] generics_0.0.2 base64url_1.4 txtq_0.1.3
[70] DBI_1.0.0 pillar_1.4.2 haven_2.1.0
[73] whisker_0.3-2 withr_2.1.2 RCurl_1.95-4.12
[76] modelr_0.1.4 crayon_1.3.4 rmarkdown_1.13
[79] grid_3.6.1 readxl_1.3.1 data.table_1.12.2
[82] blob_1.1.1 git2r_0.26.1 digest_0.6.20
[85] xtable_1.8-4 httpuv_1.5.1 RcppParallel_4.4.3
[88] stats4_3.6.1 munsell_0.5.0 viridisLite_0.3.0