Last updated: 2023-04-25

Checks: 6 1

Knit directory: ChromatinSplicingQTLs/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20191126)

The command set.seed(20191126) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: ddf600d

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version ddf600d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/figure/
    Ignored:    code/.DS_Store
    Ignored:    code/.RData
    Ignored:    code/._report.html
    Ignored:    code/.ipynb_checkpoints/
    Ignored:    code/.snakemake/
    Ignored:    code/APA_Processing/
    Ignored:    code/Alignments/
    Ignored:    code/ChromHMM/
    Ignored:    code/ENCODE/
    Ignored:    code/ExpressionAnalysis/
    Ignored:    code/ExtractPhenotypeBedByGenotype.py
    Ignored:    code/FastqFastp/
    Ignored:    code/FastqFastpSE/
    Ignored:    code/FastqSE/
    Ignored:    code/FineMapping/
    Ignored:    code/Genotypes/
    Ignored:    code/H3K36me3_CutAndTag.pdf
    Ignored:    code/IntronSlopes/
    Ignored:    code/LR.bed
    Ignored:    code/LR.seq.bed
    Ignored:    code/LongReads/
    Ignored:    code/Metaplots/
    Ignored:    code/Misc/
    Ignored:    code/MiscCountTables/
    Ignored:    code/Multiqc/
    Ignored:    code/Multiqc_chRNA/
    Ignored:    code/NonCodingRNA/
    Ignored:    code/NonCodingRNA_annotation/
    Ignored:    code/PairwisePi1Traits.P.all.txt.gz
    Ignored:    code/PeakCalling/
    Ignored:    code/Phenotypes/
    Ignored:    code/PlotGruberQTLs/
    Ignored:    code/PlotQTLs/
    Ignored:    code/ProCapAnalysis/
    Ignored:    code/QC/
    Ignored:    code/QTL_SNP_Enrichment/
    Ignored:    code/QTLs/
    Ignored:    code/RPKM_tables/
    Ignored:    code/ReadLengthMapExperiment/
    Ignored:    code/ReadLengthMapExperimentResults/
    Ignored:    code/ReadLengthMapExperimentSpliceCounts/
    Ignored:    code/ReferenceGenome/
    Ignored:    code/Rplots.pdf
    Ignored:    code/Session.vim
    Ignored:    code/SmallMolecule/
    Ignored:    code/SplicingAnalysis/
    Ignored:    code/TODO
    Ignored:    code/Tehranchi/
    Ignored:    code/bigwigs/
    Ignored:    code/bigwigs_FromNonWASPFilteredReads/
    Ignored:    code/config/.DS_Store
    Ignored:    code/config/._.DS_Store
    Ignored:    code/config/.ipynb_checkpoints/
    Ignored:    code/config/config.local.yaml
    Ignored:    code/dag.pdf
    Ignored:    code/dag.png
    Ignored:    code/dag.svg
    Ignored:    code/debug.ipynb
    Ignored:    code/debug_python.ipynb
    Ignored:    code/deepTools/
    Ignored:    code/featureCounts/
    Ignored:    code/featureCountsBasicGtf/
    Ignored:    code/gwas_summary_stats/
    Ignored:    code/hyprcoloc/
    Ignored:    code/igv_session.xml
    Ignored:    code/isoseqbams/
    Ignored:    code/log
    Ignored:    code/logs/
    Ignored:    code/notebooks/.ipynb_checkpoints/
    Ignored:    code/pi1/
    Ignored:    code/rules/.ProcessSmallMoleculeData.smk.swp
    Ignored:    code/rules/.ipynb_checkpoints/
    Ignored:    code/rules/OldRules/
    Ignored:    code/rules/notebooks/
    Ignored:    code/scratch/
    Ignored:    code/scripts/.ipynb_checkpoints/
    Ignored:    code/scripts/GTFtools_0.8.0/
    Ignored:    code/scripts/__pycache__/
    Ignored:    code/scripts/liftOverBedpe/liftOverBedpe.py
    Ignored:    code/snakemake.dryrun.log
    Ignored:    code/snakemake.log
    Ignored:    code/snakemake.sbatch.log
    Ignored:    code/snakemake_profiles/slurm/__pycache__/
    Ignored:    code/test.introns.bed
    Ignored:    code/test.introns2.bed
    Ignored:    code/test.log
    Ignored:    code/tracks.xml
    Ignored:    data/.DS_Store
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-10.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-11.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-2.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-3.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-4.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-5.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-6.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-7.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022-8.csv
    Ignored:    data/GWAS_catalog_summary_stats_sources/._list_gwas_summary_statistics_6_Apr_2022.csv
    Ignored:    data/Metaplots/.DS_Store

Untracked files:
    Untracked:  analysis/20230417_SusieFinemapEnrichment.Rmd
    Untracked:  analysis/20230420_FindeQTLsQTLGWAS_Colocs.Rmd
    Untracked:  data/Ben_ncQTL.groups.tsv
    Untracked:  output/ColocPlotData.MVP.tsv.gz
    Untracked:  output/ColocPlotData.NUDT15.tsv.gz

Unstaged changes:
    Modified:   code/scripts/GenometracksByGenotype

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development. 

knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)

library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(RColorBrewer)
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
The following object is masked from 'package:purrr':

    transpose
library(edgeR)
Loading required package: limma
# Set theme
theme_set(
  theme_classic() +
  theme(text=element_text(size=16,  family="Helvetica")))

# I use layer a lot, to rotate long x-axis labels
Rotate_x_labels <- theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

#test plot
ggplot(mtcars, aes(x=mpg, y=cyl)) +
  geom_point()

gwas.traits <- read_tsv("../code/config/gwas_table.tsv") %>%
  dplyr::rename(GWAS.accession=gwas, gwas.trait=trait)


hyprcoloc.results <- read_tsv("../code/hyprcoloc/Results/ForGWASColoc/GWASColoc_ChromatinAPAAndRNA/results.txt.gz") %>%
# hyprcoloc.results <- read_tsv("../code/hyprcoloc/Results/ForGWASColoc/GWASColoc_ChromatinAPAAndRNAYRI/results.txt.gz") %>%
  dplyr::rename(GWAS.Loci = GWASLeadSnpChrom_Pos_RefAllele_AltAllele_rsID_trait) %>%
  separate(GWAS.Loci, into=c("GWAS.LeadSNP.Chrom", "GWAS.LeadSNP.Pos", "GWAS.accession"), sep="_", remove=F) %>%
  separate_rows(ColocalizedTraits, sep = ",") %>%
  mutate(IsColocalizedWithSomething = !ColocalizedTraits == "None") %>%
  mutate(Trait = if_else(IsColocalizedWithSomething, ColocalizedTraits, DroppedTrait)) %>%
  dplyr::select(-DroppedTrait, -ColocalizedTraits) %>%
  mutate(Trait = str_replace_all(Trait, " ", "")) %>%
  mutate(GWAS.Loci = str_replace_all(GWAS.Loci, " ", "")) %>%
  mutate(Trait = if_else(Trait == GWAS.Loci, paste("GWAS",GWAS.Loci,sep = ";"),Trait)) %>%
  separate(Trait, into=c("PhenotypeClass", "Phenotype"), sep=";", remove=F) %>%
  group_by(GWAS.Loci, HyprcolocIteration) %>%
  mutate(ColocalizedClusterContainsGWASTrait = any(PhenotypeClass=="GWAS") & IsColocalizedWithSomething) %>%
  ungroup() %>%
  inner_join(gwas.traits %>%
               dplyr::select(1:2))

hyprcoloc.results$PhenotypeClass %>% unique()
 [1] "Expression.Splicing"       "H3K4ME1"                  
 [3] "H3K4ME3"                   "polyA.Splicing"           
 [5] "chRNA.Splicing"            "H3K27AC"                  
 [7] "GWAS"                      "H3K36ME3"                 
 [9] "chRNA.Expression.Splicing" "APA_Nuclear"              
[11] "APA_Total"                 NA                         
[13] "chr19_47135282_IMSGC2019"  "chr1_92687078_IMSGC2019"  
[15] "chr1_85264137_IMSGC2019"   "chr1_91756532_IMSGC2019"  
[17] "chr1_100824876_IMSGC2019"  "chr2_43128185_IMSGC2019"  
[19] "chr1_157716547_IMSGC2019"  "chr12_57713053_IMSGC2019" 
[21] "chr11_118872577_IMSGC2019" "chr12_123119506_IMSGC2019"
[23] "chr14_75547955_IMSGC2019"  "chr14_88057144_IMSGC2019" 
[25] "chr6_135512325_IMSGC2019"  "chr5_40396323_IMSGC2019"  
[27] "chr16_85987899_IMSGC2019"  "chr7_50288743_IMSGC2019"  
[29] "chr11_61026179_IMSGC2019"  "chr19_49333989_IMSGC2019" 
[31] "chr3_102030178_IMSGC2019"  "chr1_192571891_IMSGC2019" 
[33] "chr2_61015275_IMSGC2019"   "chr2_230257114_IMSGC2019" 
[35] "chr14_102799507_IMSGC2019" "chr20_54173204_IMSGC2019" 
[37] "chr3_28030595_IMSGC2019"   "chr22_50532837_IMSGC2019" 
[39] "chr6_27099878_IMSGC2019"   "chr6_159044945_IMSGC2019" 
[41] "chr6_137116920_IMSGC2019"  "chr6_137638318_IMSGC2019" 
[43] "chr5_159332892_IMSGC2019"  "chr15_90344352_IMSGC2019" 
[45] "chr6_27893892_IMSGC2019"   "chr16_79077400_IMSGC2019" 
[47] "chr8_127801845_IMSGC2019"  "chr7_149592373_IMSGC2019" 
[49] "chr18_58680812_IMSGC2019" 
hyprcoloc.results %>%
  filter(!GWAS.accession=="IMSGC2019") %>%
  pull(PhenotypeClass) %>% unique()
 [1] "Expression.Splicing"       "H3K4ME1"                  
 [3] "H3K4ME3"                   "polyA.Splicing"           
 [5] "chRNA.Splicing"            "H3K27AC"                  
 [7] "GWAS"                      "H3K36ME3"                 
 [9] "chRNA.Expression.Splicing" "APA_Nuclear"              
[11] "APA_Total"                 NA                         
PhenotypeRecodes = c("H3K36ME3"="hQTL", "H3K27AC"="hQTL", "H3K4ME3"="hQTL", "H3K4ME1"="hQTL",
                     "Expression.Splicing"="eQTL", "Expression.Splicing.Subset_YRI"="eQTL", "polyA.Splicing.Subset_YRI"="sQTL", "chRNA.Expression.Splicing"="chRNA eQTL",
                     "APA_Nuclear"="APA QTL", "APA_Total"="APA QTL", "polyA.Splicing"="sQTL", "GWAS"="GWAS")

PhenotypeRecodes.df <- data.frame(PhenotypeRecodes) %>%
  rownames_to_column("PhenotypeClass")

hyprcoloc.results %>%
  filter(GWAS.Loci == "chr9_79683075_GCST004627") %>%
  filter(is.na(PhenotypeClass))
# A tibble: 1 × 15
  GWAS.Loci    GWAS.LeadSNP.Ch… GWAS.LeadSNP.Pos GWAS.accession HyprcolocIterat…
  <chr>        <chr>            <chr>            <chr>                     <dbl>
1 chr9_796830… chr9             79683075         GCST004627                   NA
# … with 10 more variables: PosteriorColocalizationPr <dbl>,
#   RegionalAssociationPr <dbl>, TopCandidateSNP <chr>,
#   ProportionPosteriorPrExplainedByTopSNP <dbl>,
#   IsColocalizedWithSomething <lgl>, Trait <chr>, PhenotypeClass <chr>,
#   Phenotype <chr>, ColocalizedClusterContainsGWASTrait <lgl>,
#   gwas.trait <chr>
hyprcoloc.results.toplot <- hyprcoloc.results %>%
  filter(!GWAS.accession=="IMSGC2019") %>%
  left_join(PhenotypeRecodes.df) %>%
  mutate(PhenotypeRecodes = if_else(is.na(PhenotypeRecodes), PhenotypeClass, PhenotypeRecodes)) %>%
  filter(!PhenotypeRecodes == "APA QTL") %>%
  group_by(GWAS.Loci, HyprcolocIteration) %>%
  filter(any(ColocalizedClusterContainsGWASTrait) | PhenotypeClass=="GWAS") %>%
  mutate(Category = case_when(
    all(ColocalizedClusterContainsGWASTrait==FALSE) | all(is.na(HyprcolocIteration))  ~ "No molQTL colocs",
    all(PhenotypeRecodes %in% c("GWAS", "hQTL")) ~ "Only hQTL colocs",
    all(PhenotypeRecodes %in% c("GWAS", "eQTL")) ~ "Only eQTL colocs",
    all(PhenotypeRecodes %in% c("GWAS", "eQTL", "hQTL", "chRNA eQTL")) ~ "hQTL+eQTL colocs",
    all(PhenotypeRecodes %in% c("GWAS", "sQTL")) ~ "sQTL colocs",
    all(PhenotypeRecodes %in% c("GWAS", "sQTL", "eQTL", "eQTL")) ~ "sQTL+eQTL colocs",
    # all(PhenotypeRecodes %in% c("GWAS", "sQTL", "chRNA eQTL", "eQTL", "hQTL")) ~ "sQTL+eQTL+hQTL colocs",
    TRUE ~ "Other"
  )) %>%
  ungroup()
  # filter(Category=="Other")

Num.Gwas.loci <- hyprcoloc.results.toplot %>%
  distinct(GWAS.Loci, .keep_all=T) %>%
  count(GWAS.accession, gwas.trait)

hyprcoloc.results.toplot %>%
  distinct(GWAS.Loci, .keep_all=T) %>%
  count(Category,GWAS.accession, gwas.trait) %>%
  ggplot(aes(x=gwas.trait)) +
  geom_col(position="fill", aes( y=n, fill=Category)) +
  geom_text(data=Num.Gwas.loci, aes(x=gwas.trait, label=n), y=Inf, hjust=1.1, size=4, angle=90) +
  scale_fill_brewer(palette = "Dark2") +
  Rotate_x_labels +
  theme(axis.text.x = element_text(size=8)) +
  labs(fill="Exclusive\nCategory", caption="Categories are exclusive; sQTL means no hQTL or eQTL\nsQTL + eQTL means no hQTLs\nhQTL + sQTL + eQTL would be Other\nTotal number GWAS loci marked at top", y="Fraction of colocalizations") +
  scale_y_continuous(expand=c(0,0))

PC2.filter <- c("Expression.Splicing", "Expression.Splicing.Subset_YRI")
PC2.SignificanceFilter <- c("H3K27AC", "H3K4ME3", "H3K36ME3")
PC1.filter <- c("polyA.Splicing", "H3K27AC", "H3K4ME1", "H3K4ME3")
PC1.filter.Splicing <- PC1.filter[str_detect(PC1.filter, "Splicing")]
PC1.filter.NonSplicing <- PC1.filter[!str_detect(PC1.filter, "Splicing")]

dat.foreQTLQQ <- fread("../code/pi1/PairwisePi1Traits.P.all.txt.gz") %>%
  filter((PC1 %in% PC1.filter))

IntronAnnotatins <- read_tsv("../data/IntronAnnotationsFromYang.tsv.gz") %>%
  mutate(chrom = str_remove_all(chrom, "chr")) %>%
  mutate(Intron = paste(chrom, start, end, sep=":")) %>%
  filter(!str_detect(SuperAnnotation, "NoncodingGene"))

dat.foreQTLQQ.sQTLs <- dat.foreQTLQQ %>%
    filter(PC1 %in% PC1.filter.Splicing) %>%
    group_by(PC1, P1) %>%
    filter(!any((PC2 %in% PC2.SignificanceFilter) & (trait.x.p.in.y < 0.01))) %>%
    ungroup() %>%
    filter(PC2 %in% PC2.filter) %>%
    separate(P1, into=c("Intron", "Cluster"), sep=":clu", remove=F) %>%
    inner_join(
      IntronAnnotatins %>%
        dplyr::select(Intron, SuperAnnotation),
      by="Intron") %>%
    group_by(PC1, PC2, Cluster) %>%
    mutate(SNP_group = case_when(
      all(str_detect(SuperAnnotation, "Productive")) ~ "Productive sQTL cluster",
      any(str_detect(SuperAnnotation, "Unproductive")) ~ "Unproductive sQTL cluster",
      TRUE ~ "sQTL Other"
    )) 


# bind_rows(
#   hyprcoloc.results.toplot %>%
#   filter(Category == "sQTL+eQTL colocs") %>%
#   filter(PhenotypeClass=="Expression.Splicing") %>%
#   dplyr::select(GWAS.Loci, TopCandidateSNP),
# )

hyprcoloc.results.toplot %>%
  # filter(Category == "sQTL+eQTL colocs") %>%
  group_by(Category, GWAS.Loci) %>%
  count(PhenotypeClass) %>%
  ungroup() %>%
  ggplot(aes(x=n)) +
  geom_histogram(aes(fill=PhenotypeClass)) +
  facet_wrap(~Category, scales="free") +
  labs(x="Num molQTLs in colocalized cluster")

CategoriesWith_sQTLs <- c("sQTL+eQTL colocs", "Other", "sQTL")


inner_join(
  hyprcoloc.results.toplot %>%
    filter(Category %in% CategoriesWith_sQTLs) %>%
    filter(PhenotypeClass %in% c("polyA.Splicing")) %>%
    dplyr::select(GWAS.Loci, sQTL=Trait),
  hyprcoloc.results.toplot %>%
    filter(Category %in% CategoriesWith_sQTLs) %>%
    filter(PhenotypeClass %in% c("Expression.Splicing")) %>%
    dplyr::select(GWAS.Loci, eQTL=Trait, everything())
) %>%
  inner_join(
    dat.foreQTLQQ.sQTLs %>%
      mutate(sQTL=paste(PC1, P1, sep=";"))
  ) %>%
  mutate(IntronGroup = if_else(str_detect(SuperAnnotation, "Unproductive"), "Unproductive", "Productive")) %>%
  group_by(GWAS.Loci, PC2) %>%
  filter(p_permutation.x == min(p_permutation.x)) %>%
  ungroup() %>%
  mutate(Category = recode(Category, "Other"="...+hQTL")) %>%
  mutate(PC2 = recode(PC2, "Expression.Splicing"="eQTL beta from all GEU","Expression.Splicing.Subset_YRI"="eQTL beta from YRI" )) %>%
  ggplot(aes(x=beta.x, y=x.beta.in.y, color=SNP_group)) +
  geom_point() +
  facet_grid(IntronGroup ~ Category ~ PC2) +
  theme_bw() +
  # geom_smooth(method='lm') +
  labs(x="sQTL beta", y="eQTL beta", title="sQTL and eQTL effects for eQTL/sQTL/GWAS colocs", caption="Only top sQTL intron per GWAS colocalization plotted\nEffects relative to top sQTL SNP\nFacets are intron level category category, gwas coloc category (Other includes sQTL+eQTL+hQTL) and whether eQTL effect measured in YRI or all")

Let’s also include the sQTL/GWAS colocs, because i’m curious how many of them have direction effects also consistent with eQTL regulation…

CandidateGenesToHighlight <- inner_join(
  hyprcoloc.results.toplot %>%
    filter(Category == "sQTL+eQTL colocs") %>%
    filter(PhenotypeClass %in% c("polyA.Splicing")) %>%
    dplyr::select(GWAS.Loci, sQTL=Trait),
  hyprcoloc.results.toplot %>%
    filter(Category == "sQTL+eQTL colocs") %>%
    filter(PhenotypeClass %in% c("Expression.Splicing")) %>%
    dplyr::select(GWAS.Loci, eQTL=Trait, everything())
) %>%
  inner_join(
    dat.foreQTLQQ.sQTLs %>%
      mutate(sQTL=paste(PC1, P1, sep=";")) %>%
      filter(PC2 == "Expression.Splicing")
  ) %>%
  mutate(IntronGroup = if_else(str_detect(SuperAnnotation, "Unproductive"), "Unproductive", "Productive"))

CandidateGenesToHighlight %>%
  filter(SNP_group == "Unproductive sQTL cluster") %>%
  group_by(eQTL, GWAS.Loci) %>%
  filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
  ungroup() %>%
  distinct(GWAS.Loci)
# A tibble: 50 × 1
   GWAS.Loci                
   <chr>                    
 1 chr16_30091839_GCST004606
 2 chr17_46676279_GCST004606
 3 chr12_6181375_GCST004616 
 4 chr17_46534416_GCST004626
 5 chr16_30049923_GCST004617
 6 chr17_46676279_GCST004617
 7 chr22_41459919_GCST007800
 8 chr16_29855474_GCST004599
 9 chr6_10535358_GCST004609 
10 chr12_8698552_GCST004619 
# … with 40 more rows
CandidateGenesToHighlight %>%
  filter(SNP_group == "Unproductive sQTL cluster") %>%
  group_by(eQTL, GWAS.Loci) %>%
  filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
  ungroup() %>%
  distinct(P2, .keep_all=T)
# A tibble: 16 × 45
   GWAS.Loci        sQTL  eQTL  GWAS.LeadSNP.Ch… GWAS.LeadSNP.Pos GWAS.accession
   <chr>            <chr> <chr> <chr>            <chr>            <chr>         
 1 chr16_30091839_… poly… Expr… chr16            30091839         GCST004606    
 2 chr17_46676279_… poly… Expr… chr17            46676279         GCST004606    
 3 chr17_46676279_… poly… Expr… chr17            46676279         GCST004606    
 4 chr12_6181375_G… poly… Expr… chr12            6181375          GCST004616    
 5 chr22_41459919_… poly… Expr… chr22            41459919         GCST007800    
 6 chr16_29855474_… poly… Expr… chr16            29855474         GCST004599    
 7 chr6_10535358_G… poly… Expr… chr6             10535358         GCST004609    
 8 chr12_8698552_G… poly… Expr… chr12            8698552          GCST004619    
 9 chr15_41404734_… poly… Expr… chr15            41404734         GCST004629    
10 chr17_1879658_G… poly… Expr… chr17            1879658          GCST004630    
11 chr14_105178084… poly… Expr… chr14            105178084        GCST004611    
12 chr19_48871431_… poly… Expr… chr19            48871431         GCST004611    
13 chr16_11373920_… poly… Expr… chr16            11373920         GCST004622    
14 chr13_42452525_… poly… Expr… chr13            42452525         GCST004632    
15 chr19_58440326_… poly… Expr… chr19            58440326         GCST004603    
16 chr1_155208991_… poly… Expr… chr1             155208991        GCST004604    
# … with 39 more variables: HyprcolocIteration <dbl>,
#   PosteriorColocalizationPr <dbl>, RegionalAssociationPr <dbl>,
#   TopCandidateSNP <chr>, ProportionPosteriorPrExplainedByTopSNP <dbl>,
#   IsColocalizedWithSomething <lgl>, PhenotypeClass <chr>, Phenotype <chr>,
#   ColocalizedClusterContainsGWASTrait <lgl>, gwas.trait <chr>,
#   PhenotypeRecodes <chr>, Category <chr>, PC1 <chr>, P1 <chr>, Intron <chr>,
#   Cluster <chr>, GeneLocus <chr>, p_permutation.x <dbl>, beta.x <dbl>, …
CandidateGenesToHighlight.ToPlot <- CandidateGenesToHighlight %>%
  separate(Intron, into=c("IntronChrom", "IntronStart", "IntronEnd"), sep=":", convert=T, remove=F ) %>%
  filter(SNP_group == "Unproductive sQTL cluster") %>%
  group_by(eQTL, GWAS.Loci) %>%
  filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
  mutate(MinIntron = min(IntronStart), MaxIntron=max(IntronEnd)) %>%
  ungroup()

So far we have a list of 16 genes, many of which are represented as hits in multiple gwas for a total of 50ish sQTL/eQTL/GWAS colocs with large sQTL/eQTL effects that I want to plot…

dir.create("../code/scratch/Plot_sQTLeQTLGWASColocs/")

## Make bwList

bwList <- read_tsv("../code/PlotQTLs/bwList.tsv") %>%
  filter(Group_label %in% c("polyA.RNA", "chRNA", "MetabolicLabelled.30min"))

FullGeuvadisBigwigs <- data.frame(BigwigFilepath = list.files("../code/bigwigs/Expression.Splicing")) %>%
  mutate(BigwigFilepath = paste0("bigwigs/Expression.Splicing/",BigwigFilepath)) %>%
  mutate(SampleID = str_replace(BigwigFilepath, "bigwigs/Expression.Splicing/(.+?).1.bw", "\\1")) %>%
  mutate(Group_label = "polyA.RNA", Strand=".")


bwList %>% count(Group_label)

full_join(FullGeuvadisBigwigs, bwList) %>%
  dplyr::select(SampleID, BigwigFilepath, Group_label, Strand) %>%
  write_tsv("../code/PlotQTLs/bwList.AllPolyAAndchRNA.tsv")

read_tsv("../code/PlotQTLs/bwList.Groups.4col.tsv") %>%
  filter(Group_label %in% c("polyA.RNA", "MetabolicLabelled.30min", "chRNA")) %>%
  write_tsv("../code/PlotQTLs/bwList.Groups.4col.RNA.tsv")

#Write a bash script for plotting. Just pick one gwas row randomly for each of the distinct genes
  
CandidateGenesToHighlight.ToPlot %>%
  mutate(score = ".", strand = str_extract(P1, "[+-]")) %>%
  dplyr::select(IntronChrom, IntronStart, IntronEnd, P1, score, strand) %>%
  mutate(IntronChrom=paste0("chr", IntronChrom)) %>%
  arrange(IntronChrom, IntronStart, IntronEnd) %>%
  distinct(.keep_all=T) %>%
  write_tsv("/scratch/midway2/bjf79/Juncs.bed", col_names = F)


CandidateGenesToHighlight.ToPlot %>%
  distinct(eQTL, .keep_all=T) %>%
  mutate(n = row_number()) %>%
  mutate(npad = str_pad(n, side="left", pad="0", width=2)) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/AggregateBigwigsForPlotting.py --BigwigList PlotQTLs/bwList.AllPolyAAndchRNA.tsv --BigwigListType KeyFile --OutputPrefix /scratch/midway2/bjf79/PlotDat --FilterJuncsByBed /scratch/midway2/bjf79/Juncs.bed --VCF Genotypes/1KG_GRCh38/{IntronChrom}.vcf.gz --SnpPos {GWAS.LeadSNP.Chrom}:{SNP_Pos} --SnpName {TopCandidateSNP} --Region {GWAS.LeadSNP.Chrom}:{MinIntron-300}-{MaxIntron+300} --GroupSettingsFile PlotQTLs/bwList.Groups.4col.RNA.tsv -vv --Bed12GenesToIni scripts/GenometracksByGenotype/PremadeTracks/gencode.v26.FromGTEx.genes.bed12.gz"),
         PyGenometracksCall = str_glue("pyGenomeTracks --region {GWAS.LeadSNP.Chrom}:{MinIntron-300}-{MaxIntron+300} -out scratch/Plot_sQTLeQTLGWASColocs/{npad}_{GWAS.Loci}_{P2}.png --tracks /scratch/midway2/bjf79/PlotDattracks.ini")) %>%
  dplyr::select(CustomScriptCall, PyGenometracksCall, n) %>%
  gather("CommandType", "Command", -n) %>%
  arrange(n) %>%
  dplyr::select(Command) %>%
  write_tsv("../code/scratch/Plot_sQTLeQTLGWASColocs/MakePlots.sh",col_names = F)

After plotting those, some of the nicest examples that i think are worth highlighting in the manuscript are here:

Example1

Example2

I think I will also plot genotype/phenotype boxplots for these, in polyA splicing, chRNA splicing, polyA Expression, and chRNA Expression.

# Some scratch code to quickly get data or making these specific boxplots

##NUDT14 phenotypes
CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
  filter(sQTL == "polyA.Splicing;14:105175987:105176534:clu_34934_-") %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/polyA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.sQTL.polyA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
  filter(sQTL == "polyA.Splicing;14:105175987:105176534:clu_34934_-") %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.sQTL.chRNA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c("chr14_105178084_GCST004611")) %>%
  filter(eQTL == "Expression.Splicing;ENSG00000183828.15") %>%
  distinct(eQTL, .keep_all=T) %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.eQTL.chRNA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
  filter(eQTL == "Expression.Splicing;ENSG00000183828.15") %>%
  distinct(eQTL, .keep_all=T) %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.eQTL.polyA.tsv")) %>%
  pull(CustomScriptCall)


##MVP phenotypes
CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
  filter(sQTL == "polyA.Splicing;16:29834437:29835704:clu_38570_+") %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/polyA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.sQTL.polyA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
  filter(sQTL == "polyA.Splicing;16:29834437:29835704:clu_38570_+") %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.sQTL.chRNA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
  filter(eQTL == "Expression.Splicing;ENSG00000013364.19") %>%
  distinct(eQTL, .keep_all=T) %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.eQTL.chRNA.tsv")) %>%
  pull(CustomScriptCall)

CandidateGenesToHighlight.ToPlot %>%
  filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
  filter(eQTL == "Expression.Splicing;ENSG00000013364.19") %>%
  distinct(eQTL, .keep_all=T) %>%
  mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
  mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
  separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
  mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid}  --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.eQTL.polyA.tsv")) %>%
  pull(CustomScriptCall)
Sys.glob("../code/scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.*.tsv") %>%
  setNames(str_replace(., "../code/scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.(.+?).tsv", "\\1")) %>%
  lapply(fread, col.names=c("SampleID", "NormalizedExpression", "genotype", "Ref", "Alt", "ID")) %>%
  bind_rows(.id="Phenotype") %>%
  filter(!genotype==3) %>%
  separate(Phenotype, into=c("Locus", "xQTL", "Dataset"), sep="\\.", remove=F) %>%
  mutate(genotype = factor(genotype)) %>%
  ggplot(aes(x=genotype, y=NormalizedExpression)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter(width=0.25, alpha=0.25) +
  geom_smooth(method='lm') +
  facet_grid(xQTL~Dataset~Locus) +
  theme_bw()

Ok, I think the last thing I might consider plotting is the sort of coloc plot with -log10P vs -log10P scatter for pairs of colocalized traits…

library(GGally)
library(RColorBrewer)

lower_point <- function(data, mapping, ...) { 
  ggplot(data = data, mapping = mapping, ...) + 
    geom_point(..., size=0.5, alpha=0.2, color="gray") + 
    geom_point(data = (
                       data %>% filter(!is.na(TopCandidateSNP))),
                       shape = 21, color="black", alpha=1, size=1.5
                       ) +
    scale_fill_identity() +
    # scale_fill_brewer(palette="Dark2", type="qualitative", na.value="gray") +
    theme_classic()
}

my_fn <- function(data, mapping, method="p", use="pairwise", ...){
              # grab data
              x <- eval_data_col(data, mapping$x)
              y <- eval_data_col(data, mapping$y)
              # calculate correlation
              corr <- cor(x, y, method=method, use=use)
              # calculate colour based on correlation value
              # Here I have set a correlation of minus one to blue, 
              # zero to white, and one to red 
              # Change this to suit: possibly extend to add as an argument of `my_fn`
              colFn <- colorRampPalette(c("blue", "white", "red"), interpolate ='spline')
              fill <- colFn(100)[findInterval(corr, seq(-1, 1, length=100))]
              ggally_cor(data = data, mapping = mapping, ...) + 
                theme_void() +
                theme(panel.background = element_rect(fill=fill))
}

Plot the coloc for NUDT14

MolPhenotypes <- c("14:105175987:105176534:clu_34934_-", "ENSG00000183828.15")
GwasLocus <- "chr14_105178084_GCST004611"
TopSNP <- "14:105172594:T:C"
GwasLocus.trait <- str_replace(GwasLocus, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")
GWAS <- str_replace(GwasLocus.trait, ".+?_N_N_(.+?)", "\\1")
TopSNP_Pos <- as.numeric(str_replace(TopSNP, "^.+?:(.+?):.+?:.+?$", "\\1"))


PlotColoc <- function(){
  molQTL.SummaryStats <- fread(paste0("../code/hyprcoloc/LociWiseSummaryStatsInput/ForGWASColoc/", GWAS, ".txt.gz")) %>%
  filter(gwas_locus == GwasLocus.trait) %>%
  filter(phenotype %in% MolPhenotypes) %>%
  mutate(xQTL = str_replace(source_file, "QTLs/QTLTools/(.+?)/.+", "\\1"))


GWAS.SummaryStats <- fread(paste0("../code/gwas_summary_stats/StatsForColoc/", GWAS, ".standardized.txt.gz")) %>%
  filter(loci == GwasLocus.trait)

dat.toplot <- molQTL.SummaryStats %>%
  separate(snp, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F, convert=T) %>%
  mutate(SNP_Chrom = paste0("chr", SNP_Chrom)) %>%
  mutate(logP = -log10(p)) %>%
  dplyr::select(SNP_Chrom, SNP_Pos, logP, phenotype, xQTL ) %>%
  unite(phenotype_xQTL, xQTL, phenotype) %>%
  pivot_wider(names_from = phenotype_xQTL, values_from=logP) %>%
  inner_join(
    GWAS.SummaryStats %>%
      mutate(logP.gwas=-log10(2*pnorm(abs(beta/SE), lower.tail=F))) %>%
      dplyr::select(SNP_Chrom=chrom, SNP_Pos=start, logP.gwas)
  ) %>%
  mutate(TopCandidateSNP = if_else(SNP_Pos == TopSNP_Pos, TopSNP, NA_character_)) %>%
  dplyr::select(SNP_Chrom, SNP_Pos, TopCandidateSNP, everything())

P <- ggpairs(dat.toplot,
            columnLabels = gsub('[;.]', ' ', colnames(dat.toplot)[-c(1:3)], perl=T),
            labeller = label_wrap_gen(10),
            columns = 4:ncol(dat.toplot),
            diag=list(continuous = "blankDiag"),
            lower=list(continuous = lower_point),
            upper=list(continuous = my_fn)
            )
return(list(dat=dat.toplot, P=P))
}

NUDT14.ColocPlot <- PlotColoc()

NUDT14.ColocPlot$P + labs(title="NUDT14 sQTL/eQTL/GWAS coloc")

Same for MVP

MolPhenotypes <- c("16:29834437:29835704:clu_38570_+", "ENSG00000013364.19")
GwasLocus <- "chr16_29855474_GCST004599"
TopSNP <- "16:29839384:C:T"
GwasLocus.trait <- str_replace(GwasLocus, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")
GWAS <- str_replace(GwasLocus.trait, ".+?_N_N_(.+?)", "\\1")
TopSNP_Pos <- as.numeric(str_replace(TopSNP, "^.+?:(.+?):.+?:.+?$", "\\1"))


MVP.ColocPlot <- PlotColoc()

MVP.ColocPlot$P + labs(title="MVP sQTL/eQTL/GWAS coloc")

MVP.ColocPlot$dat %>%
  dplyr::select(1:3, 4, 7, 9) %>%
  ggpairs(
            columnLabels = gsub('[;.]', ' ', colnames(.)[-c(1:3)], perl=T),
            labeller = label_wrap_gen(10),
            columns = 4:ncol(.),
            diag=list(continuous = "blankDiag"),
            lower=list(continuous = lower_point),
            upper=list(continuous = my_fn)
            ) +
   labs(title="MVP sQTL/eQTL/GWAS coloc")

Ok, let’s write out the data for these plots in case I want to easily plot them prettier later…

MVP.ColocPlot$dat %>%
  write_tsv("../output/ColocPlotData.MVP.tsv.gz")

NUDT14.ColocPlot$dat %>%
  write_tsv("../output/ColocPlotData.NUDT15.tsv.gz")

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] 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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] GGally_2.1.2       edgeR_3.38.4       limma_3.52.4       data.table_1.14.2 
 [5] RColorBrewer_1.1-3 forcats_0.5.1      stringr_1.4.0      dplyr_1.0.9       
 [9] purrr_0.3.4        readr_2.1.2        tidyr_1.2.0        tibble_3.1.7      
[13] ggplot2_3.3.6      tidyverse_1.3.1   

loaded via a namespace (and not attached):
 [1] httr_1.4.3        sass_0.4.1        bit64_4.0.5       vroom_1.5.7      
 [5] jsonlite_1.8.0    R.utils_2.11.0    modelr_0.1.8      bslib_0.3.1      
 [9] assertthat_0.2.1  highr_0.9         cellranger_1.1.0  yaml_2.3.5       
[13] pillar_1.7.0      backports_1.4.1   lattice_0.20-45   glue_1.6.2       
[17] digest_0.6.29     promises_1.2.0.1  rvest_1.0.2       colorspace_2.0-3 
[21] plyr_1.8.7        R.oo_1.24.0       htmltools_0.5.2   httpuv_1.6.5     
[25] pkgconfig_2.0.3   broom_0.8.0       haven_2.5.0       scales_1.2.0     
[29] later_1.3.0       tzdb_0.3.0        git2r_0.30.1      generics_0.1.2   
[33] farver_2.1.0      ellipsis_0.3.2    withr_2.5.0       cli_3.3.0        
[37] magrittr_2.0.3    crayon_1.5.1      readxl_1.4.0      evaluate_0.15    
[41] R.methodsS3_1.8.1 fs_1.5.2          fansi_1.0.3       xml2_1.3.3       
[45] tools_4.2.0       hms_1.1.1         lifecycle_1.0.1   munsell_0.5.0    
[49] reprex_2.0.1      locfit_1.5-9.7    compiler_4.2.0    jquerylib_0.1.4  
[53] rlang_1.0.2       grid_4.2.0        rstudioapi_0.13   labeling_0.4.2   
[57] rmarkdown_2.14    gtable_0.3.0      reshape_0.8.9     DBI_1.1.2        
[61] R6_2.5.1          lubridate_1.8.0   knitr_1.39        fastmap_1.1.0    
[65] bit_4.0.4         utf8_1.2.2        workflowr_1.7.0   rprojroot_2.0.3  
[69] stringi_1.7.6     parallel_4.2.0    Rcpp_1.0.8.3      vctrs_0.4.1      
[73] dbplyr_2.1.1      tidyselect_1.1.2  xfun_0.30