apaQTL and PTTqtls

Last updated: 2019-07-25

Checks: 6 1

Knit directory: apaQTL/analysis/

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File	Version	Author	Date	Message
Rmd	f3e3e16	brimittleman	2019-07-25	small edits from paper writing and add gwas analysis
html	272b0b4	brimittleman	2019-07-08	Build site.
Rmd	b1e6dd1	brimittleman	2019-07-08	update ptt analysis

I am interesting in understanding the PTTqtls a bit more. I want to ask if pttQTLs are apaQTLs. I will ask if the PTTqtl genes are also apaQTL genes.

library(tidyverse)

── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(workflowr)

This is workflowr version 1.4.0
Run ?workflowr for help getting started

PTT

totptt=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()

totpttQTL=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1)
write.table(totpttQTL, file="../data/pttQTL/TotalPttQTL.txt", col.names = T, row.names = F, quote = F, sep="\t")

nucptt=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()


nucpttQTL=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1)
write.table(nucpttQTL, file="../data/pttQTL/NuclearPttQTL.txt", col.names = T, row.names = F, quote = F, sep="\t")

APA

nucAPA=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F)
totAPA=read.table("../data/apaQTLs/TotalapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F)

totptt %>% inner_join(totAPA,by="gene") %>% nrow()

[1] 32

totptt %>% anti_join(totAPA,by="gene") %>% nrow()

[1] 8

nucptt %>% inner_join(nucAPA,by="gene") %>% nrow()

[1] 89

nucptt %>% anti_join(nucAPA,by="gene") %>% nrow()

[1] 14

There are 8 total ptt genes that are not apaQTL genes and there are 15 nuclear ptt genes that are not apaQTL genes.

Look at the distribution. To do this I will pull out the ptt associations from the apa data.

I will right a script that takes a the fraction, pulls the ptt associations into 2 files, one for the associations and one for the non associations.

python pttQTLsinapaQTL.py Total
python pttQTLsinapaQTL.py Nuclear

names=c('pid' ,'nvar' ,'shape1' ,'shape2', 'dummy' ,'sid', 'dist', 'npval', 'slope', 'ppval' ,'bpval', 'bh')
totapaPTT=read.table("../data/pttQTL/Totalapa_TotalPttQTL.txt", col.names = names, stringsAsFactors = F)
totapaNotPTT=read.table("../data/pttQTL/Totalapa_NOT_TotalPttQTL.txt", col.names = names, stringsAsFactors = F)

qqplot(-log10(runif(nrow(totapaNotPTT))), -log10(totapaNotPTT$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="Total PTT genes in Total apaQTL")
points(sort(-log10(runif(nrow(totapaPTT)))), sort(-log10(totapaPTT$bpval)),col= alpha("Red"))
abline(0,1)
legend("topleft", legend=c("ptt gene", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version	Author	Date
272b0b4	brimittleman	2019-07-08

nucapaPTT=read.table("../data/pttQTL/Nuclearapa_NuclearPttQTL.txt", col.names = names, stringsAsFactors = F)
nucapaNotPTT=read.table("../data/pttQTL/Nuclearapa_NOT_NuclearPttQTL.txt", col.names = names, stringsAsFactors = F)

qqplot(-log10(runif(nrow(nucapaNotPTT))), -log10(nucapaNotPTT$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="Nuclear PTT genes in Nuclear apaQTL")
points(sort(-log10(runif(nrow(nucapaPTT)))), sort(-log10(nucapaPTT$bpval)),col= alpha("Red"))
abline(0,1)

legend("topleft", legend=c("ptt gene", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version	Author	Date
272b0b4	brimittleman	2019-07-08

These show the sharing is super high and we are getting at similar phenotypes in ptt and apa.

wilcox.test(nucapaPTT$bpval,nucapaNotPTT$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  nucapaPTT$bpval and nucapaNotPTT$bpval
W = 3440100, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0

Is there something interesting about the non overlap genes:

pttOnlyTot=totptt %>% anti_join(totAPA,by="gene")


totapaPTT_pttstatus=totapaPTT %>% separate(pid, into=c("chr", "start", "end", "geneID"),sep=":")  %>% separate(geneID, into=c("gene", "pos", "strand", "pas"),sep="_") %>% mutate(APAQTL=ifelse(gene %in% pttOnlyTot$gene, "No", "Yes"))

ggplot(totapaPTT_pttstatus,aes(x=APAQTL, y=bpval)) + geom_boxplot()

pttOnlyTot$gene

[1] "C10orf88" "UROS"     "LRRC57"   "ELMOD3"   "USP37"    "PLSCR1"  
[7] "ZNF718"   "ZSCAN12"

pttOnlyNuc=nucptt %>% anti_join(nucAPA,by="gene")


nucapaPTT_pttstatus=nucapaPTT %>% separate(pid, into=c("chr", "start", "end", "geneID"),sep=":")  %>% separate(geneID, into=c("gene", "pos", "strand", "pas"),sep="_") %>% mutate(APAQTL=ifelse(gene %in% pttOnlyTot$gene, "No", "Yes"))


ggplot(nucapaPTT_pttstatus,aes(x=APAQTL, y=bpval)) + geom_boxplot()

pttOnlyNuc$gene

 [1] "PPFIA1"    "ZDHHC17"   "ZNF844"    "PRKCZ"     "POU2F1"   
 [6] "MAP3K7CL"  "BCL2L13"   "LINC00342" "DZIP3"     "SUB1"     
[11] "BLVRA"     "NAPEPLD"   "WNT2"      "HACD4"

Plot these:

nucpttQTL %>% separate(pid, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene","loc"),sep="_") %>%  anti_join(nucAPA,by="gene") %>% select(gene, chr,sid)

        gene chr         sid
1     PPFIA1  11 rs188630869
2    ZDHHC17  12  rs74792673
3    ZDHHC17  12  rs74792673
4     ZNF844  19 rs116146966
5      PRKCZ   1  rs13302944
6      PRKCZ   1  rs13302944
7     POU2F1   1   rs6668193
8     POU2F1   1   rs6668193
9   MAP3K7CL  21   rs9808644
10   BCL2L13  22   rs8141618
11   BCL2L13  22   rs8141618
12 LINC00342   2 rs193252777
13     DZIP3   3 rs112409302
14      SUB1   5  rs76941541
15      SUB1   5  rs76941541
16     BLVRA   7   rs1181594
17     BLVRA   7   rs1181594
18   NAPEPLD   7  rs56047178
19   NAPEPLD   7  rs56047178
20      WNT2   7  rs76105832
21     HACD4   9  rs57721805
22     HACD4   9  rs57721805

sbatch  run_qtlFacetBoxplots.sh Nuclear PPFIA1 11 rs188630869 none
sbatch  run_qtlFacetBoxplots.sh Nuclear ZDHHC17 12  rs74792673 none
sbatch  run_qtlFacetBoxplots.sh Nuclear ZNF844  19  rs116146966 none
sbatch  run_qtlFacetBoxplots.sh Nuclear PRKCZ   1   rs13302944 none


sbatch  run_qtlFacetBoxplots.sh Nuclear POU2F1  1   rs6668193 none
sbatch  run_qtlFacetBoxplots.sh Nuclear MAP3K7CL    21  rs9808644 none
sbatch  run_qtlFacetBoxplots.sh Nuclear BCL2L13 22  rs8141618 none
sbatch  run_qtlFacetBoxplots.sh Nuclear LINC00342   2   rs193252777 none
sbatch  run_qtlFacetBoxplots.sh Nuclear DZIP3   3   rs112409302 none

sbatch  run_qtlFacetBoxplots.sh Nuclear SUB1    5   rs76941541 none
sbatch  run_qtlFacetBoxplots.sh Nuclear BLVRA   7   rs1181594 none
sbatch  run_qtlFacetBoxplots.sh Nuclear NAPEPLD 7   rs56047178 none
sbatch  run_qtlFacetBoxplots.sh Nuclear WNT2    7   rs76105832 none
sbatch  run_qtlFacetBoxplots.sh Nuclear HACD4   9   rs57721805 none

totpttQTL %>% separate(pid, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene","loc"),sep="_") %>%  anti_join(totAPA,by="gene") %>% select(gene, chr,sid)

       gene chr        sid
1  C10orf88  10  rs7091776
2  C10orf88  10  rs7904973
3      UROS  10 rs11244646
4    LRRC57  15 rs61489160
5    LRRC57  15 rs61489160
6    ELMOD3   2   rs908302
7    ELMOD3   2   rs908302
8     USP37   2 rs79468589
9    PLSCR1   3 rs59690244
10   PLSCR1   3 rs59690244
11   ZNF718   4  rs6814287
12   ZNF718   4  rs3747693
13  ZSCAN12   6  rs3799500
14  ZSCAN12   6  rs3799500

sbatch  run_qtlFacetBoxplots.sh Total C10orf88  10  rs7091776 none
sbatch  run_qtlFacetBoxplots.sh Total UROS  10  rs11244646 none
sbatch  run_qtlFacetBoxplots.sh Total LRRC57    15  rs61489160 none
sbatch  run_qtlFacetBoxplots.sh Total ELMOD3    2   rs908302 none
sbatch  run_qtlFacetBoxplots.sh Total USP37 2   rs79468589 none
sbatch  run_qtlFacetBoxplots.sh Total PLSCR1    3   rs59690244 none
sbatch  run_qtlFacetBoxplots.sh Total ZNF718    4   rs6814287 none
sbatch  run_qtlFacetBoxplots.sh Total ZSCAN12   6   rs3799500 none

Moved these reslts to a ptt dir in the example qtl dir.

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] workflowr_1.4.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

apaQTL and PTTqtls

Briana Mittleman

7/8/2019