AD Enrichment Analysis
Jing Gu
2024-10-01
Last updated: 2024-10-01
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Knit directory: m6A_in_disease_genetics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 01e2114 | Jing Gu | 2024-10-01 | m6A enrichment for AD risk variants |
Inputs preparation
GWAS: Bellenguez et al.(2022) harmonized summary statistics.
LDSC parameters:
- GWAS variants filtered by hapmap3 SNPs
- LD based on Europeans from 1000 genome project
workdir=/project/xinhe/shared/GWAS_sumstat/AD
ldsc_base=/home/jinggu/cluster/data/gwas/gwas_ldsc/sldsc
zcat 35379992-GCST90027158-MONDO_0004975.h.tsv.gz | cut -f 2,6,5,8,11,14,25,26 - > $workdir/Bellenguez2022/GCST90027158_AD_sumstats.txt
python /home/jinggu/cluster/github/ldsc/munge_sumstats.py \
--sumstats $workdir/Bellenguez2022/GCST90027158_AD_sumstats.txt \
--snp hm_rsid \
--a1 hm_effect_allele \
--a2 hm_other_allele \
--frq hm_effect_allele_frequency \
--p p_value \
--N-cas-col n_cas \
--N-con-col n_con \
--signed-sumstats hm_odds_ratio,1 \
--chunksize 500000 \
--merge-alleles $ldsc_base/w_hm3.snplist \
--out /home/jinggu/cluster/data/gwas/gwas_ldsc/ADEnrichment results in barplots
- Labels on top of the bars: enrichment p-values
- Legend for y-axis: m6A fetal tissue (percent of genome wide variants that fall in the corresponding peaks)
Compared with other traits, m6A peaks across all fetal tissues do not
show enrichment with AD risk variants.
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggplot2_3.4.0 rtracklayer_1.58.0 GenomicRanges_1.50.2
[4] GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.2
[7] BiocGenerics_0.44.0 dplyr_1.1.4 data.table_1.15.4
[10] workflowr_1.7.1
loaded via a namespace (and not attached):
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[5] jsonlite_1.8.8 bslib_0.7.0
[7] getPass_0.2-2 highr_0.10
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[11] yaml_2.3.8 pillar_1.9.0
[13] lattice_0.22-5 glue_1.7.0
[15] digest_0.6.35 promises_1.3.0
[17] XVector_0.38.0 colorspace_2.1-0
[19] htmltools_0.5.8.1 httpuv_1.6.14
[21] Matrix_1.6-5 XML_3.99-0.16.1
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[25] scales_1.3.0 processx_3.8.3
[27] whisker_0.4.1 later_1.3.2
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[31] tibble_3.2.1 generics_0.1.3
[33] farver_2.1.1 DT_0.33
[35] cachem_1.0.8 withr_3.0.0
[37] SummarizedExperiment_1.28.0 cli_3.6.2
[39] magrittr_2.0.3 crayon_1.5.2
[41] evaluate_0.23 ps_1.7.6
[43] fs_1.6.4 fansi_1.0.6
[45] tools_4.2.0 BiocIO_1.8.0
[47] lifecycle_1.0.4 matrixStats_1.2.0
[49] stringr_1.5.1 munsell_0.5.1
[51] DelayedArray_0.24.0 callr_3.7.3
[53] Biostrings_2.66.0 compiler_4.2.0
[55] jquerylib_0.1.4 rlang_1.1.3
[57] grid_4.2.0 RCurl_1.98-1.14
[59] rstudioapi_0.15.0 rjson_0.2.21
[61] htmlwidgets_1.6.4 crosstalk_1.2.1
[63] bitops_1.0-7 labeling_0.4.3
[65] rmarkdown_2.26 restfulr_0.0.15
[67] gtable_0.3.5 codetools_0.2-19
[69] R6_2.5.1 GenomicAlignments_1.34.1
[71] knitr_1.46 fastmap_1.1.1
[73] utf8_1.2.4 rprojroot_2.0.4
[75] stringi_1.7.6 parallel_4.2.0
[77] Rcpp_1.0.12 vctrs_0.6.5
[79] tidyselect_1.2.1 xfun_0.43