Assessing heritability enrichment for lung OCRs
2025-05-22
Last updated: 2025-05-22
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Knit directory: Lung_scMultiomics_paper/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2575a8a | Jing Gu | 2025-05-22 | figure for h2g enrichment |
| html | 9b45ce0 | Jing Gu | 2025-05-22 | Build site. |
| Rmd | 25b622a | Jing Gu | 2025-05-22 | figure for h2g enrichment |
Compare and contrast between lung and blood
Peak overlapping
Venn Diagram to compare against a union set of peaks
Lung and Blood peaks were called by different procedures. Lung peaks have fixed size (500bp), while blood peaks have sizes ranging from 200 to 5K bp. To make comparison, I first made a union set of peaks and then comparing peaks from each tissue against the union set.
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
heritability enrichment with S-LDSC
One focal cell-type vs. lung and blood union set
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
Bubble plot
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
Individual test across lung and blood major lymphocytes
For individual test, the quantity of heritability enrichment is used to demonstrate overall contribution of each annotation. We can use p-values for cell-type specific coefficients to compare their contributions across cell types.
Legends:
- x-axis: heritability enrichment
- p-values on top of the data points: p-values for tau*
All lung and spleen lymphocytes show significant enrichment for genetic risks of asthma and related diseases, but not for BMI and height. B cells are less significant than T and NK cells.
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
Joint test of lung and blood separately for each major lymphocyte
Compared with blood, open chromatin regions of lung T cells are significantly enriched for genetic risks of allergy, atopic dermatitis, and asthma, but not for control traits as well as IPF and COPD. Lung NK cells also show moderate enrichment.
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
Joint test of lung and blood T subsets
- Lung regulatory T cells show significant enrichment across all asthma and related diseases
- Lung CD4+ T, CD8+T and Th17 cells show enrichment for genetic risks
of specific traits.
- Activating CD4+ T cells in blood consistently show enrichment for risks of atopic dermatitis.
$RegTWarning: Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
$CD4T
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
$CD8T
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
$Th17
| Version | Author | Date |
|---|---|---|
| 9b45ce0 | Jing Gu | 2025-05-22 |
Overlapping cell-type specific peaks with COA GWAS SNPs
Differential CA peaks from MAST
We check differential CA peaks aggregated from different cell types at each fdr threshold and plot quantile-quantile plots for GWAS SNPs within those peaks. The LD blocks were labeled on SNPs at z-score greater than 2.
`
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 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cowplot_1.1.3
[2] liftOver_1.22.0
[3] Homo.sapiens_1.3.1
[4] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[5] org.Hs.eg.db_3.16.0
[6] GO.db_3.16.0
[7] OrganismDbi_1.40.0
[8] GenomicFeatures_1.50.4
[9] AnnotationDbi_1.60.2
[10] rtracklayer_1.58.0
[11] gwascat_2.30.0
[12] scales_1.3.0
[13] colorRamp2_0.1.0
[14] eulerr_7.0.2
[15] rhdf5_2.42.1
[16] SummarizedExperiment_1.28.0
[17] Biobase_2.58.0
[18] MatrixGenerics_1.10.0
[19] Rcpp_1.0.14
[20] Matrix_1.6-5
[21] GenomicRanges_1.50.2
[22] GenomeInfoDb_1.34.9
[23] IRanges_2.32.0
[24] S4Vectors_0.36.2
[25] BiocGenerics_0.44.0
[26] matrixStats_1.5.0
[27] data.table_1.17.0
[28] stringr_1.5.1
[29] plyr_1.8.9
[30] magrittr_2.0.3
[31] ggplot2_3.5.2
[32] gtable_0.3.6
[33] gtools_3.9.5
[34] gridExtra_2.3
[35] ArchR_1.0.2
[36] tidyr_1.3.1
[37] dplyr_1.1.4
loaded via a namespace (and not attached):
[1] colorspace_2.1-1 rjson_0.2.23 rprojroot_2.0.4
[4] XVector_0.38.0 fs_1.6.5 rstudioapi_0.17.1
[7] farver_2.1.2 bit64_4.0.5 xml2_1.3.8
[10] codetools_0.2-20 splines_4.2.0 snpStats_1.48.0
[13] cachem_1.1.0 knitr_1.50 polyclip_1.10-7
[16] jsonlite_2.0.0 workflowr_1.7.1 Rsamtools_2.14.0
[19] dbplyr_2.5.0 png_0.1-8 graph_1.76.0
[22] BiocManager_1.30.25 readr_2.1.5 compiler_4.2.0
[25] httr_1.4.7 fastmap_1.2.0 cli_3.6.4
[28] later_1.4.2 htmltools_0.5.8.1 prettyunits_1.2.0
[31] tools_4.2.0 glue_1.8.0 GenomeInfoDbData_1.2.9
[34] rappdirs_0.3.3 jquerylib_0.1.4 vctrs_0.6.5
[37] Biostrings_2.66.0 rhdf5filters_1.10.1 polylabelr_0.3.0
[40] xfun_0.52 lifecycle_1.0.4 restfulr_0.0.15
[43] XML_3.99-0.18 zlibbioc_1.44.0 BSgenome_1.66.3
[46] VariantAnnotation_1.44.1 hms_1.1.3 promises_1.3.2
[49] RBGL_1.74.0 parallel_4.2.0 yaml_2.3.10
[52] curl_6.2.2 memoise_2.0.1 sass_0.4.9
[55] biomaRt_2.54.1 stringi_1.8.4 RSQLite_2.3.9
[58] BiocIO_1.8.0 filelock_1.0.3 BiocParallel_1.32.6
[61] rlang_1.1.5 pkgconfig_2.0.3 bitops_1.0-9
[64] evaluate_1.0.3 lattice_0.22-7 purrr_1.0.4
[67] Rhdf5lib_1.20.0 labeling_0.4.3 GenomicAlignments_1.34.1
[70] bit_4.6.0 tidyselect_1.2.1 R6_2.6.1
[73] generics_0.1.3 DelayedArray_0.24.0 DBI_1.2.3
[76] pillar_1.10.2 whisker_0.4.1 withr_3.0.2
[79] survival_3.8-3 KEGGREST_1.38.0 RCurl_1.98-1.17
[82] tibble_3.2.1 crayon_1.5.3 BiocFileCache_2.6.1
[85] tzdb_0.5.0 rmarkdown_2.29 progress_1.2.3
[88] blob_1.2.4 git2r_0.33.0 digest_0.6.37
[91] httpuv_1.6.15 munsell_0.5.1 viridisLite_0.4.2
[94] bslib_0.9.0