Comparing predictdb & Munro: predictdb eQTL + sQTL VS Munro 6 modalities – updated functions – thin method V1
XSun
2024-08-08
Last updated: 2024-09-24
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Knit directory: multigroup_ctwas_analysis/
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We compare the results from Munro weights & predictdb weights here. We are figuring out how the number of high PIP genes compare with PredictDB results with the same tissues?
Settings
6 modalities from Munro
- Weight processing:
PredictDB:
all the PredictDB are converted from FUSION weights
- drop_strand_ambig = TRUE,
- scale_by_ld_variance = F (FUSION converted weights)
- load_predictdb_LD = F,
- Parameter estimation and fine-mapping
- niter_prefit = 5,
- niter = 30(default),
- L: determined by uniform susie,
- group_prior_var_structure = “shared_type”,
- maxSNP = 20000,
- min_nonSNP_PIP = 0.5,
weights from predictdb
- Weight processing:
PredictDB (eqtl, sqtl)
- drop_strand_ambig = TRUE,
- scale_by_ld_variance = T
- load_predictdb_LD = F,
- Parameter estimation and fine-mapping
- niter_prefit = 5,
- niter = 30(default),
- L: determined by uniform susie,
- group_prior_var_structure = “shared_type”,
- maxSNP = 20000,
- min_nonSNP_PIP = 0.5,
mem: 150g 5cores
Results
IBD – Colon_Transverse
Predictdb: eqtl and sqtl
2024-09-24 13:02:18 INFO::Annotating fine-mapping result ...
2024-09-24 13:02:18 INFO::Map molecular traits to genes
2024-09-24 13:02:18 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-24 13:02:25 INFO::Add gene positions
2024-09-24 13:02:26 INFO::Add SNP positions2024-09-24 13:02:36 INFO::Limit gene results to credible setsMunro et al : 6 modalities
2024-09-24 13:02:55 INFO::Annotating fine-mapping result ...
2024-09-24 13:02:55 INFO::Map molecular traits to genes
2024-09-24 13:02:57 INFO::Add gene positions
2024-09-24 13:02:57 INFO::Add SNP positions2024-09-24 13:03:06 INFO::Limit gene results to credible setsCompare the results from Predictdb & Munro weights
If we filter by combined pip >0.8 in both settings, we have
There’s 1 overlapped genes at combined_pip > 0.8.
We noticed that, when using Munro’s weights, we have GNA12 as the top1 IBD risk gene, which has been reported by literature. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323775/
But when using predictdb weights, we missed this gene.
[1] "Locus plot -- Predictdb"2024-09-24 13:03:07 INFO::Limit to protein coding genes
2024-09-24 13:03:07 INFO::focal id: ENSG00000146535.13|Colon_Transverse_eQTL
2024-09-24 13:03:07 INFO::focal molecular trait: GNA12 Colon_Transverse eQTL
2024-09-24 13:03:07 INFO::Range of locus: chr7:2732556-4533638
2024-09-24 13:03:07 INFO::focal molecular trait QTL positions: 2760492,2820213
2024-09-24 13:03:07 INFO::Limit PIPs to credible sets
[1] "Locus plot -- Munro"2024-09-24 13:03:09 INFO::Limit to protein coding genes
2024-09-24 13:03:09 INFO::focal id: ENSG00000146535|Colon_Transverse_eQTL
2024-09-24 13:03:09 INFO::focal molecular trait: GNA12 Colon_Transverse eQTL
2024-09-24 13:03:09 INFO::Range of locus: chr7:2732556-4533638
2024-09-24 13:03:09 INFO::focal molecular trait QTL positions: 2750246,2760492,3092158
2024-09-24 13:03:09 INFO::Limit PIPs to credible sets
We are trying to figure out why GNA12 was missed by predictdb weights.
In predictdb eQTL model, there are 2 SNPs,
weight
rs208345 0.0925621
rs2533879 -0.1090777In Munro eQTL model, there are 5 SNPs,
weight
rs755179 0.01220240
rs798544 -0.04916490
rs798502 -0.06499180
rs208345 0.03188710
rs12540595 -0.00183916We extracted the EUR LD R2 from 1000G using https://ldlink.nih.gov/?tab=ldmatrix
We noticed that, the 2 SNPs in predictdb weights are either in Munro weights (rs208345) or in LD with the Munro eQTLs (rs2533879).
| rs755179 | rs798544 | rs798502 | rs208345 | rs12540595 | |
|---|---|---|---|---|---|
| rs208345 | 0.004 | 0.049 | 0.051 | 1.0 | 0.001 |
| rs2533879 | 0.002 | 0.8 | 0.929 | 0.051 | 0.004 |
We also noticed that, 2 (rs798502,rs798544) of the 5 Munro eQTLs are in LD with each other
| rs755179 | rs798544 | rs798502 | rs208345 | rs12540595 | |
|---|---|---|---|---|---|
| rs755179 | 1.0 | 0.003 | 0.003 | 0.004 | 0.0 |
| rs798544 | 0.003 | 1.0 | 0.838 | 0.049 | 0.005 |
| rs798502 | 0.003 | 0.838 | 1.0 | 0.051 | 0.005 |
| rs208345 | 0.004 | 0.049 | 0.051 | 1.0 | 0.001 |
| rs12540595 | 0.0 | 0.005 | 0.005 | 0.001 | 1.0 |
We checked the z scores for these SNPs,
id A1 A2 z
4216443 rs755179 T C 0.4172662
4217471 rs798544 C T 4.7883212
4217631 rs798502 A C 5.2463768
4217679 rs208345 A G -2.0923913
4218004 rs2533879 G A 4.7518248
4219465 rs12540595 G A -1.7364341The z-scores for GNA12 are:
predictdb: -4.461474
Munro: -6.736242
Checking why Predicdb results missed many Munro genes
[1] "# of Unique munro genes = 26"[1] "# of Unique munro genes included in predictdb data = 19"There are some genes have large abs(z) but low PIPs in predictdb setting (RTEL1, USP4), we make locus plots for these genes to understand why
RTEL1: this gene was assigned to region 20_63558827_64333810 in predictdb setting but 20_62670503_63558827 in Munro setting
[1] "Locus plot -- Predictdb"2024-09-24 13:03:19 INFO::Limit to protein coding genes
2024-09-24 13:03:19 INFO::focal id: intron_20_63736712_63737533|Colon_Transverse_sQTL
2024-09-24 13:03:19 INFO::focal molecular trait: LIME1 Colon_Transverse sQTL
2024-09-24 13:03:19 INFO::Range of locus: chr20:63558727-64328976
2024-09-24 13:03:19 INFO::focal molecular trait QTL positions: 63737253
2024-09-24 13:03:19 INFO::Limit PIPs to credible sets
We noticed in the top panel, there is a gene with outstanding pvalue, but 2 SNPs have the largest PIPs (second panel). We try to understand this case, we run ctwas in this region with L=1
[1] "the pre-estimated L for this region is 2"2024-09-24 13:03:22 INFO::Fine-mapping 1 regions ...2024-09-24 13:03:24 INFO::Annotating fine-mapping result ...
2024-09-24 13:03:24 INFO::Map molecular traits to genes
2024-09-24 13:03:25 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-24 13:03:25 INFO::Add gene positions
2024-09-24 13:03:25 INFO::Add SNP positions2024-09-24 13:03:28 INFO::Limit to protein coding genes
2024-09-24 13:03:28 INFO::focal id: intron_20_63695854_63696760|Colon_Transverse_sQTL
2024-09-24 13:03:28 INFO::focal molecular trait: TNFRSF6B Colon_Transverse sQTL
2024-09-24 13:03:28 INFO::Range of locus: chr20:63558727-64328976
2024-09-24 13:03:28 INFO::focal molecular trait QTL positions: 63697746
2024-09-24 13:03:28 INFO::Limit PIPs to credible sets
[1] "Locus plot -- Munro"2024-09-24 13:03:31 INFO::Limit to protein coding genes
2024-09-24 13:03:31 INFO::focal id: ENSG00000258366:ENST00000370003|Colon_Transverse_isoQTL
2024-09-24 13:03:31 INFO::focal molecular trait: RTEL1 Colon_Transverse isoQTL
2024-09-24 13:03:31 INFO::Range of locus: chr20:62664015-63677132
2024-09-24 13:03:31 INFO::focal molecular trait QTL positions: 63403750
2024-09-24 13:03:31 INFO::Limit PIPs to credible sets
If we merge the RTEL1 region
2024-09-24 13:03:35 INFO::Limit to protein coding genes
2024-09-24 13:03:35 INFO::focal id: ENSG00000197114:ENST00000478385|Colon_Transverse_isoQTL
2024-09-24 13:03:35 INFO::focal molecular trait: ZGPAT Colon_Transverse isoQTL
2024-09-24 13:03:35 INFO::Range of locus: chr20:62664015-64328976
2024-09-24 13:03:35 INFO::focal molecular trait QTL positions: 63730385
2024-09-24 13:03:35 INFO::Limit PIPs to credible sets
USP4: this gene was assigned to region 3_49279539_51797999 in predictdb setting but 3_47685722_49279539 in Munro setting
[1] "Locus plot -- Predictdb"2024-09-24 13:03:38 INFO::Limit to protein coding genes
2024-09-24 13:03:38 INFO::focal id: intron_3_49125169_49125269|Colon_Transverse_sQTL
2024-09-24 13:03:38 INFO::focal molecular trait: LAMB2 Colon_Transverse sQTL
2024-09-24 13:03:38 INFO::Range of locus: chr3:47688075-49277627
2024-09-24 13:03:38 INFO::focal molecular trait QTL positions: 49124851
2024-09-24 13:03:38 INFO::Limit PIPs to credible sets
2024-09-24 13:03:40 INFO::Limit to protein coding genes
2024-09-24 13:03:40 INFO::focal id: intron_3_49676515_49676609|Colon_Transverse_sQTL
2024-09-24 13:03:40 INFO::focal molecular trait: APEH Colon_Transverse sQTL
2024-09-24 13:03:40 INFO::Range of locus: chr3:49279498-51794719
2024-09-24 13:03:40 INFO::focal molecular trait QTL positions: 49676792
2024-09-24 13:03:41 INFO::Limit PIPs to credible sets
[1] "Locus plot -- Munro"2024-09-24 13:03:45 INFO::Limit to protein coding genes
2024-09-24 13:03:45 INFO::focal id: ENSG00000178467.grp_2.downstream.ENST00000609406|Colon_Transverse_apaQTL
2024-09-24 13:03:45 INFO::focal molecular trait: P4HTM Colon_Transverse apaQTL
2024-09-24 13:03:45 INFO::Range of locus: chr3:47255638-49426236
2024-09-24 13:03:45 INFO::focal molecular trait QTL positions: 49015786,49017259,49070995
2024-09-24 13:03:45 INFO::Limit PIPs to credible sets
2024-09-24 13:03:47 INFO::Limit to protein coding genes
2024-09-24 13:03:47 INFO::focal id: ENSG00000173531:chr3:49684189:49684314:clu_47947_-|Colon_Transverse_sQTL
2024-09-24 13:03:47 INFO::focal molecular trait: MST1 Colon_Transverse sQTL
2024-09-24 13:03:47 INFO::Range of locus: chr3:48998420-51794719
2024-09-24 13:03:47 INFO::focal molecular trait QTL positions: 49298312,49322510,49345492,49603616,49641218,49644878,49665051,49674864,49682296,49694428
2024-09-24 13:03:47 INFO::Limit PIPs to credible sets
If we merge the 2 regions above
2024-09-24 13:03:52 INFO::Limit to protein coding genes
2024-09-24 13:03:52 INFO::focal id: ENSG00000173531:chr3:49684189:49684314:clu_47947_-|splicing_Colon_Transverse
2024-09-24 13:03:52 INFO::focal molecular trait: MST1 Colon_Transverse sQTL
2024-09-24 13:03:52 INFO::Range of locus: chr3:47255638-51794719
2024-09-24 13:03:52 INFO::focal molecular trait QTL positions:
2024-09-24 13:03:52 INFO::Limit PIPs to credible sets
Comparing z-scores
library(dplyr)
gene_predictdb <- finemap_res_predictdb[finemap_res_predictdb$type !="SNP",]
gene_predictdb <- gene_predictdb[,c("gene_name","z","susie_pip")]
gene_predictdb_clean <- gene_predictdb %>%
group_by(gene_name) %>%
dplyr::filter(abs(z) == max(abs(z))) %>%
ungroup()
gene_munro <- finemap_res_munro[finemap_res_munro$type !="SNP",]
gene_munro <- gene_munro[,c("gene_name","z","susie_pip")]
gene_munro_clean <- gene_munro %>%
group_by(gene_name) %>%
dplyr::filter(abs(z) == max(abs(z))) %>%
ungroup()
merge_eqtl <- merge(gene_predictdb_clean, gene_munro_clean, by="gene_name")
colnames(merge_eqtl) <- c("gene_name","z_predictdb", "pip_predictdb","z_munro", "pip_munro")
ggplot(merge_eqtl, aes(x = z_predictdb, y = z_munro)) +
geom_point() +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y=x line
theme_minimal() +
labs(
x = "Z PredictDB",
y = "Z Munro",
title = "Scatter Plot of Z PredictDB vs Z Munro"
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.position = "none" # Remove the legend
)
print("we take the abs(z)")[1] "we take the abs(z)"merge_eqtl$z_predictdb <- abs(merge_eqtl$z_predictdb)
merge_eqtl$z_munro <- abs(merge_eqtl$z_munro)
ggplot(merge_eqtl, aes(x = z_predictdb, y = z_munro)) +
geom_point() +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y=x line
theme_minimal() +
labs(
x = "Z PredictDB",
y = "Z Munro",
title = "Scatter Plot of Z PredictDB vs Z Munro (abs z scores)"
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.position = "none" # Remove the legend
)
gene_predictdb <- finemap_res_predictdb[finemap_res_predictdb$type =="eQTL",]
gene_predictdb <- gene_predictdb[,c("gene_name","z","susie_pip")]
gene_munro <- finemap_res_munro[finemap_res_munro$type =="eQTL",]
gene_munro <- gene_munro[,c("gene_name","z","susie_pip")]
merge_eqtl <- merge(gene_predictdb, gene_munro, by="gene_name")
colnames(merge_eqtl) <- c("gene_name","z_predictdb", "pip_predictdb","z_munro", "pip_munro")
merge_eqtl <- data.frame(lapply(merge_eqtl, function(x) {
if(is.numeric(x)) format(round(x, 4), nsmall = 4)
else x
}))
merge_eqtl$label <- ifelse(merge_eqtl$gene_name %in% overlapped_gene_all$genename, merge_eqtl$gene_name, NA)
merge_eqtl$z_predictdb <- as.numeric(merge_eqtl$z_predictdb)
merge_eqtl$z_munro<- as.numeric(merge_eqtl$z_munro)
merge_eqtl$pip_predictdb <- as.numeric(merge_eqtl$pip_predictdb)
merge_eqtl$pip_munro<- as.numeric(merge_eqtl$pip_munro)
# Create the scatter plot with labels for specific genes
ggplot(merge_eqtl, aes(x = z_predictdb, y = z_munro)) +
geom_point(aes(color = !is.na(label))) + # Color based on whether label is NA
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y=x line
theme_minimal() +
labs(
x = "Z PredictDB",
y = "Z Munro",
title = "Scatter Plot of Z PredictDB vs Z Munro"
) +
geom_text(aes(label = label), vjust = 1.5, hjust = 1.5, size = 3, color = "red") + # Label the points with red color
scale_color_manual(values = c("black", "red")) + # Set colors: black for non-labeled, red for labeled
theme(
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.position = "none" # Remove the legend
)Warning: Removed 553 rows containing missing values or values outside the scale range
(`geom_text()`).
ggplot(merge_eqtl, aes(x = pip_predictdb, y = pip_munro)) +
geom_point(aes(color = !is.na(label))) + # Color based on whether label is NA
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y=x line
theme_minimal() +
labs(
x = "PIP PredictDB",
y = "PIP Munro",
title = "Scatter Plot of PIP PredictDB vs PIP Munro"
) +
geom_text(aes(label = label), vjust = 1.5, hjust = 1.5, size = 3, color = "red") + # Label the points with red color
scale_color_manual(values = c("black", "red")) + # Set colors: black for non-labeled, red for labeled
theme(
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
legend.position = "none" # Remove the legend
)Warning: Removed 553 rows containing missing values or values outside the scale range
(`geom_text()`).
DT::datatable(merge_eqtl,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Z-scores and PIPs computed from eQTL, for the overlapped genes'),options = list(pageLength = 5) )If we don’t consider CS, I noticed there is a gene NXPE1. The z-predictdb = 4.5985 and z-munro = 4.4947. But pip_predictdb = 0.4679 and pip_munro = 0.0138.
In predictdb eQTL model, there are 1 SNPs,
weight
rs661946 0.07486575In Munro eQTL model, there are 3 SNPs,
weight
rs238925 -0.00283751
rs561722 0.27416500
rs1850521 -0.02065090We checked the z scores for these SNPs,
id A1 A2 z
6612785 rs238925 G T 1.8692308
6614255 rs561722 C T 4.6439394
6614384 rs661946 C T 4.5984848
6614420 rs1850521 G T 0.3854749The both predictdb eQTL and munro eQTLs have large GWAS z-score. We checked more about the finemapping results
The Munro PIPs are from sQTLs. So, as shown in the earlier scatter plot, the eQTL PIP is very low.
sessionInfo()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] C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_1.1.1 ggrepel_0.9.1
[3] locuszoomr_0.2.1 logging_0.10-108
[5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2
[7] AnnotationFilter_1.20.0 GenomicFeatures_1.48.3
[9] AnnotationDbi_1.58.0 Biobase_2.56.0
[11] GenomicRanges_1.48.0 GenomeInfoDb_1.39.9
[13] IRanges_2.30.0 S4Vectors_0.34.0
[15] BiocGenerics_0.42.0 gridExtra_2.3
[17] forcats_0.5.1 stringr_1.5.1
[19] dplyr_1.1.4 purrr_1.0.2
[21] readr_2.1.2 tidyr_1.3.0
[23] tibble_3.2.1 ggplot2_3.5.1
[25] tidyverse_1.3.1 data.table_1.14.2
[27] ctwas_0.4.14
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1
[3] workflowr_1.7.0 BiocFileCache_2.4.0
[5] lazyeval_0.2.2 BiocParallel_1.30.3
[7] crosstalk_1.2.0 LDlinkR_1.2.3
[9] digest_0.6.29 htmltools_0.5.2
[11] fansi_1.0.3 magrittr_2.0.3
[13] memoise_2.0.1 tzdb_0.4.0
[15] Biostrings_2.64.0 modelr_0.1.8
[17] matrixStats_0.62.0 prettyunits_1.1.1
[19] colorspace_2.0-3 blob_1.2.3
[21] rvest_1.0.2 rappdirs_0.3.3
[23] haven_2.5.0 xfun_0.41
[25] crayon_1.5.1 RCurl_1.98-1.7
[27] jsonlite_1.8.0 zoo_1.8-10
[29] glue_1.6.2 gtable_0.3.0
[31] zlibbioc_1.42.0 XVector_0.36.0
[33] DelayedArray_0.22.0 RcppZiggurat_0.1.6
[35] scales_1.3.0 DBI_1.2.2
[37] Rcpp_1.0.12 viridisLite_0.4.0
[39] progress_1.2.2 bit_4.0.4
[41] DT_0.22 htmlwidgets_1.5.4
[43] httr_1.4.3 ellipsis_0.3.2
[45] pkgconfig_2.0.3 XML_3.99-0.14
[47] farver_2.1.0 sass_0.4.1
[49] dbplyr_2.1.1 utf8_1.2.2
[51] tidyselect_1.2.0 labeling_0.4.2
[53] rlang_1.1.2 later_1.3.0
[55] munsell_0.5.0 pgenlibr_0.3.3
[57] cellranger_1.1.0 tools_4.2.0
[59] cachem_1.0.6 cli_3.6.1
[61] generics_0.1.2 RSQLite_2.3.1
[63] broom_0.8.0 evaluate_0.15
[65] fastmap_1.1.0 yaml_2.3.5
[67] knitr_1.39 bit64_4.0.5
[69] fs_1.5.2 KEGGREST_1.36.3
[71] xml2_1.3.3 biomaRt_2.54.1
[73] compiler_4.2.0 rstudioapi_0.13
[75] plotly_4.10.0 filelock_1.0.2
[77] curl_4.3.2 png_0.1-7
[79] reprex_2.0.1 bslib_0.3.1
[81] stringi_1.7.6 highr_0.9
[83] lattice_0.20-45 ProtGenerics_1.28.0
[85] Matrix_1.5-3 vctrs_0.6.5
[87] pillar_1.9.0 lifecycle_1.0.4
[89] jquerylib_0.1.4 bitops_1.0-7
[91] irlba_2.3.5 httpuv_1.6.5
[93] rtracklayer_1.56.0 R6_2.5.1
[95] BiocIO_1.6.0 promises_1.2.0.1
[97] codetools_0.2-18 assertthat_0.2.1
[99] SummarizedExperiment_1.26.1 rprojroot_2.0.3
[101] rjson_0.2.21 withr_2.5.0
[103] GenomicAlignments_1.32.0 Rsamtools_2.12.0
[105] GenomeInfoDbData_1.2.8 parallel_4.2.0
[107] hms_1.1.1 grid_4.2.0
[109] gggrid_0.2-0 rmarkdown_2.25
[111] Rfast_2.0.7 MatrixGenerics_1.8.0
[113] git2r_0.30.1 mixsqp_0.3-43
[115] lubridate_1.8.0 restfulr_0.0.14