Proportion eQTLs explained

Last updated: 2020-02-18

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File	Version	Author	Date	Message
Rmd	8d8caba	brimittleman	2020-02-18	add lz
html	00cd66c	brimittleman	2019-09-10	Build site.
Rmd	5bcc43c	brimittleman	2019-09-10	test outlier removal
html	bec23da	brimittleman	2019-09-06	Build site.
Rmd	a2c35ac	brimittleman	2019-09-06	add correlation
html	22541b3	brimittleman	2019-09-06	Build site.
Rmd	5976ed7	brimittleman	2019-09-06	update post new filter
html	27af5c8	brimittleman	2019-08-01	Build site.
Rmd	e4d84f6	brimittleman	2019-08-01	chamge pdf sizes for figure 3
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Rmd	c53925a	brimittleman	2019-06-26	add graph labels
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Rmd	ec9c1d6	brimittleman	2019-06-26	add direction concordance plots
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Rmd	52e46bc	brimittleman	2019-06-26	add example plot code
html	0fae25e	brimittleman	2019-06-20	Build site.
Rmd	eb847c1	brimittleman	2019-06-20	add analysis by pval
html	ca379ce	brimittleman	2019-06-13	Build site.
Rmd	2fd2b27	brimittleman	2019-06-13	fix bug
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Rmd	178c5dc	brimittleman	2019-06-12	new geno
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Rmd	b39620d	brimittleman	2019-06-07	add bonfor results
html	458e494	brimittleman	2019-06-07	Build site.
Rmd	32091ee	brimittleman	2019-06-07	more prop explained to new analysis

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 1.6.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

I need to fix the explained_FDR10.sort.txt and unexplained_FDR10.sort.txt files because right now this file has multiple genes per snp.

python fixExandUnexeQTL.py ../data/Li_eQTLs/explained_FDR10.sort.txt ../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt
python fixExandUnexeQTL.py ../data/Li_eQTLs/unexplained_FDR10.sort.txt ../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt

There are 1195 explained and 814 unexplained eQTLs. I will next look at each of these in my apadata.

Convert nominal results to have snps rather than rsids:

python convertNominal2SNPLOC.py Total
python convertNominal2SNPLOC.py Nuclear

mkdir ../data/overlapeQTL_try2
sbatch run_getapafromeQTL.sh

total

I can group the unexplained by gene and snp then I can ask if there is at least 1 significat peak for each of these.

I will use the bonforoni correction here and multiply the pvalue by the number of peaks in the gene:snp association.

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames)
totalapaUnexplained=totalapaUnexplained %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>% mutate(nPeaks=n(), adjPval=pval* nPeaks)%>%  dplyr::slice(which.min(adjPval))

totalapaUnexplained_sig= totalapaUnexplained %>% filter(adjPval<.05)

Look at distribution of these pvals:

ggplot(totalapaUnexplained, aes(x=adjPval)) + geom_histogram(bins=50)

Version	Author	Date
22541b3	brimittleman	2019-09-06
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

Proportion explained:

nrow(totalapaUnexplained_sig)/nrow(totalapaUnexplained)

[1] 0.1678201

Compare to explained eQTLS:

totalapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

totalapaexplained_sig= totalapaexplained %>% filter(adjPval<.05)

nrow(totalapaexplained_sig)/nrow(totalapaexplained)

[1] 0.1248455

difference of proportions:

prop.test(x=c(nrow(totalapaUnexplained_sig),nrow(totalapaexplained_sig)), n=c(nrow(totalapaUnexplained),nrow(totalapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(totalapaUnexplained_sig), nrow(totalapaexplained_sig)) out of c(nrow(totalapaUnexplained), nrow(totalapaexplained))
X-squared = 4.7427, df = 1, p-value = 0.02942
alternative hypothesis: two.sided
95 percent confidence interval:
 0.003452285 0.082496876
sample estimates:
   prop 1    prop 2 
0.1678201 0.1248455

ggplot(totalapaUnexplained_sig,aes(x=loc)) + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(y="Proportion", title = "Total apaQTLs explaining eQTLs")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
22541b3	brimittleman	2019-09-06
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

totalapaUnexplained_sig_loc= totalapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocTotalUn=n()) %>% mutate(propTotalUn=nLocTotalUn/nrow(totalapaUnexplained_sig))
totalapaexplained_sig_loc= totalapaexplained_sig %>% group_by(loc) %>% summarise(nLocTotalEx=n()) %>% mutate(propTotalEx=nLocTotalEx/nrow(totalapaexplained_sig))

BothTotalLoc=totalapaUnexplained_sig_loc %>% full_join(totalapaexplained_sig_loc,by="loc") %>%  replace_na(list(propTotalUn = 0, nLocTotalUn = 0,propTotalEx=0,nLocTotalEx=0  ))

BothTotalLoc

# A tibble: 5 x 5
  loc    nLocTotalUn propTotalUn nLocTotalEx propTotalEx
  <chr>        <dbl>       <dbl>       <dbl>       <dbl>
1 cds              6      0.0619           7      0.0693
2 end              7      0.0722           9      0.0891
3 intron          16      0.165           15      0.149 
4 utr3            65      0.670           68      0.673 
5 utr5             3      0.0309           2      0.0198

nuclear

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

nuclearapaUnexplained_sig= nuclearapaUnexplained %>% filter(adjPval<.05)

nrow(nuclearapaUnexplained_sig)/nrow(nuclearapaUnexplained)

[1] 0.1726496

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

nuclearapaexplained_sig= nuclearapaexplained %>% filter(adjPval<.05)

nrow(nuclearapaexplained_sig)/nrow(nuclearapaexplained)

[1] 0.1239264

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(nuclearapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(nuclearapaexplained))
X-squared = 6.1593, df = 1, p-value = 0.01307
alternative hypothesis: two.sided
95 percent confidence interval:
 0.009179856 0.088266529
sample estimates:
   prop 1    prop 2 
0.1726496 0.1239264

ggplot(nuclearapaUnexplained_sig,aes(x=loc))  + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(title = "Nuclear apaQTLs explaining eQTLs", y="Proportion")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
22541b3	brimittleman	2019-09-06
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

nuclearapaUnexplained_sig_loc= nuclearapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearUn=n()) %>% mutate(propnuclearUn=nLocnuclearUn/nrow(nuclearapaUnexplained_sig))
nuclearapaexplained_sig_loc= nuclearapaexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearEx=n()) %>% mutate(propnuclearEx=nLocnuclearEx/nrow(nuclearapaexplained_sig))

BothnuclearLoc=nuclearapaUnexplained_sig_loc %>% full_join(nuclearapaexplained_sig_loc,by="loc") %>%  replace_na(list(propnuclearUn = 0, nLocnuclearUn = 0,propnuclearEx=0,nLocnuclearEx=0  ))

BothnuclearLoc

# A tibble: 5 x 5
  loc    nLocnuclearUn propnuclearUn nLocnuclearEx propnuclearEx
  <chr>          <dbl>         <dbl>         <dbl>         <dbl>
1 cds                3        0.0297             3       0.0297 
2 end               11        0.109             12       0.119  
3 intron            23        0.228             32       0.317  
4 utr3              64        0.634             53       0.525  
5 utr5               0        0                  1       0.00990

total v nuclear

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(totalapaUnexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(totalapaUnexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(totalapaUnexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(totalapaUnexplained))
X-squared = 0.019903, df = 1, p-value = 0.8878
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.04008930  0.04974831
sample estimates:
   prop 1    prop 2 
0.1726496 0.1678201

Differences in proportion by location

allLocProp=BothnuclearLoc %>% full_join(BothTotalLoc, by="loc") %>% select(loc,propnuclearUn,propnuclearEx,propTotalUn,propTotalEx )

allLocPropmelt= melt(allLocProp, id.vars = "loc") %>% mutate(Fraction=ifelse(grepl("Total", variable), "Total", "Nuclear"),eQTL=ifelse(grepl("Un", variable), "Unexplained", "Explained"))


ggplot(allLocPropmelt,aes(x=loc, fill=eQTL, y=value)) + geom_histogram(stat="identity", position = "dodge") + facet_grid(~Fraction)+ labs(y="Proportion of PAS", title="apaQTLs overlaping eQTLs by PAS location")  + scale_fill_manual(values=c("orange", "blue"))

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
22541b3	brimittleman	2019-09-06
b907ac1	brimittleman	2019-06-12
6b164c8	brimittleman	2019-06-07

This is a very stringent test. A less stringent way to get an upper bound would be to make an informed decision about which peak to use. This will make it so I am only testing one PAS per gene.

Vary the pvalue cuttoff

To test if .05 is a good cuttoff for this analysis I will create a function that computes the overlap at different cutoffs. I will go from .01 to .5 by .05

totalapaUnexplained totalapaexplained

nuclearapaUnexplained nuclearapaexplained

prop_overlap=function(status, fraction, cutoff){
  if (fraction=="Total"){
    if (status=="Explained"){
      file=totalapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
    }else {
      file=totalapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
    }
  } else{
    if (status=="Explained"){
      file=nuclearapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
     }else {
      file=nuclearapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
     }
  }
  return(proportion)
}

cutoffs=c(0.001,0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.3,0.4,0.5)

TotalExplained_Proportions=c()
for(i in cutoffs){
  TotalExplained_Proportions=c( TotalExplained_Proportions, prop_overlap("Explained", "Total", i))
}
TotalExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Total", 11)))

TotalUnexplained_Proportions=c()
for(i in cutoffs){
  TotalUnexplained_Proportions=c(TotalUnexplained_Proportions, prop_overlap("Unexplained", "Total", i))
}
TotalUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Total", 11)))

NuclearExplained_Proportions=c()
for(i in cutoffs){
  NuclearExplained_Proportions=c( NuclearExplained_Proportions, prop_overlap("Explained", "Nuclear", i))
}
NuclearExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Nuclear", 11)))


NuclearUnexplained_Proportions=c()
for(i in cutoffs){
  NuclearUnexplained_Proportions=c( NuclearUnexplained_Proportions, prop_overlap("Unexplained", "Nuclear", i))
}
NuclearUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Nuclear", 11)))



AllPropDF=bind_rows(TotalExplained_ProportionsDF,TotalUnexplained_ProportionsDF,NuclearExplained_ProportionsDF,NuclearUnexplained_ProportionsDF)

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

AllPropDF$Prop=as.numeric(AllPropDF$Prop)

Plot this:

ggplot(AllPropDF, aes(x=cutoffs, y=Prop, fill=Status)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction) + labs(title="Proportion of eQTLs explained by apaQTLs", y="Proportion", "P-Value cut off") + scale_fill_manual(values=c("orange", "blue"))

Version	Author	Date
22541b3	brimittleman	2019-09-06

Concordance of directions for intronic pas usage and eQTL

I want to look at the intronic pas and the eQTLs. To do this I want to look at a correaltion of effect sizes for the eQTLs and and intronic PAS.

The eQTL information is in ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName.txt. I need to converte the RSID into snp loc.

python eQTL_switch2snploc.py

prepare eQTL:

eQTLeffect=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName_snploc.txt", stringsAsFactors = F, col.names = c("gene","snp","dist", "pval", "eQTL_es")) %>% select(gene, snp, eQTL_es)

total:

#totalunex_all=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_")

#totalex_all=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>%  separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_")


alleQTLS_total=bind_rows(totalapaUnexplained, totalapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))


ggplot(alleQTLS_total,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") +geom_text(y=-1, x=-1.5, label="slope: -0.22 p-value: 0.00002, r2=0.08") + labs(title="Total apa effect sizes vs eQTL eqtl effect sizes", y="Total apaQTL effect size",x="eQTL effect size")

Version	Author	Date
22541b3	brimittleman	2019-09-06

summary(lm(alleQTLS_total$slope ~alleQTLS_total$eQTL_es))


Call:
lm(formula = alleQTLS_total$slope ~ alleQTLS_total$eQTL_es)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.15866 -0.31339 -0.00043  0.26661  1.46869 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             0.03214    0.03132   1.026    0.306    
alleQTLS_total$eQTL_es -0.21510    0.04901  -4.389 1.83e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4474 on 202 degrees of freedom
Multiple R-squared:  0.08707,   Adjusted R-squared:  0.08255 
F-statistic: 19.27 on 1 and 202 DF,  p-value: 1.833e-05

cor.test(alleQTLS_total$slope ,alleQTLS_total$eQTL_es, alternative="less")


    Pearson's product-moment correlation

data:  alleQTLS_total$slope and alleQTLS_total$eQTL_es
t = -4.3892, df = 202, p-value = 9.163e-06
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
 -1.000000 -0.185907
sample estimates:
       cor 
-0.2950724

Nuclear:

alleQTLS_nuclear=bind_rows(nuclearapaUnexplained,nuclearapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))


ggplot(alleQTLS_nuclear,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") +geom_text(y=1.5, x=-1, label="slope: -0.20 p-value: 9.0 * 10 ^ -9, r2=0.08") + labs(title="", y="apaQTL effect size",x="eQTL effect size")

Version	Author	Date
22541b3	brimittleman	2019-09-06
d73d818	brimittleman	2019-06-26
06de9df	brimittleman	2019-06-26

summary(lm(alleQTLS_nuclear$slope ~alleQTLS_nuclear$eQTL_es))


Call:
lm(formula = alleQTLS_nuclear$slope ~ alleQTLS_nuclear$eQTL_es)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.19658 -0.29003 -0.00934  0.26184  1.54707 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)              -0.008894   0.022167  -0.401    0.688    
alleQTLS_nuclear$eQTL_es -0.205079   0.034819  -5.890 8.97e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.418 on 355 degrees of freedom
Multiple R-squared:  0.08902,   Adjusted R-squared:  0.08646 
F-statistic: 34.69 on 1 and 355 DF,  p-value: 8.97e-09

cor.test(alleQTLS_nuclear$slope, alleQTLS_nuclear$eQTL_es, alternative="less")


    Pearson's product-moment correlation

data:  alleQTLS_nuclear$slope and alleQTLS_nuclear$eQTL_es
t = -5.8899, df = 355, p-value = 4.485e-09
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
 -1.0000000 -0.2168049
sample estimates:
       cor 
-0.2983651

remove outlier and see if it holds:

alleQTLS_nuclear_noOut=alleQTLS_nuclear %>% filter(eQTL_es > -2)
ggplot(alleQTLS_nuclear_noOut,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") + labs(title="", y="apaQTL effect size",x="eQTL effect size")

Version	Author	Date
00cd66c	brimittleman	2019-09-10
22541b3	brimittleman	2019-09-06
ab8482d	brimittleman	2019-08-01
d73d818	brimittleman	2019-06-26
06de9df	brimittleman	2019-06-26

summary(lm(alleQTLS_nuclear_noOut$slope ~alleQTLS_nuclear_noOut$eQTL_es))


Call:
lm(formula = alleQTLS_nuclear_noOut$slope ~ alleQTLS_nuclear_noOut$eQTL_es)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.19565 -0.29112 -0.00921  0.25549  1.54399 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    -0.01106    0.02205  -0.502    0.616    
alleQTLS_nuclear_noOut$eQTL_es -0.19013    0.03520  -5.402 1.21e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4155 on 354 degrees of freedom
Multiple R-squared:  0.07615,   Adjusted R-squared:  0.07354 
F-statistic: 29.18 on 1 and 354 DF,  p-value: 1.213e-07

Examples for overlap:

unexplained_snps=read.table("../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt", col.names = c("chr", "loc", "gene"),stringsAsFactors = F)
totQTL=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.bed", header = T, stringsAsFactors = F, col.names = c("chr", "bedstart","loc","ID", "score", "strand"))
nucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed", stringsAsFactors = F, header = T, col.names = c("chr", "bedstart","loc","ID", "score", "strand"))

Overlap:

totQTL_unex=totQTL %>% inner_join(unexplained_snps, by=c("chr", "loc"))
nucQTL_unex=nucQTL %>% inner_join(unexplained_snps, by=c("chr", "loc"))

totQTL_unex

  chr  bedstart       loc                        ID     score strand
1  10 124693586 124693587 C10orf88:peak19682:intron  0.829354      .
2  19  57706377  57706378   ZNF264:peak67214:intron -0.765818      .
3  20   1350708   1350709     FKBP1A:peak79304:utr3 -0.569411      .
4   2 197855151 197855152    ANKRD44:peak76705:utr3  0.464009      .
5   2 197855151 197855152  ANKRD44:peak76708:intron -1.022620      .
6   6  44275010  44275011     AARS2:peak113590:utr3  0.968958      .
7   7   6497500   6497501    KDELR2:peak118586:utr3  1.003000      .
8   7   6497500   6497501    KDELR2:peak118588:utr3 -1.032740      .
      gene
1 C10orf88
2   ZNF264
3   FKBP1A
4  ANKRD44
5  ANKRD44
6    AARS2
7   KDELR2
8   KDELR2

nucQTL_unex

  chr  bedstart       loc                        ID     score strand
1  10 124693586 124693587 C10orf88:peak19682:intron  1.255120      .
2  19  57706377  57706378   ZNF264:peak67214:intron -0.496966      .
3   4  44702719  44702720       GUF1:peak97168:utr3  0.882583      .
4   4  44702719  44702720       GUF1:peak97169:utr3 -1.377620      .
      gene
1 C10orf88
2   ZNF264
3   GNPDA2
4   GNPDA2

Make a plot for KDELR2 7:6497501

genohead=as.data.frame(read.table("../data/ExampleQTLPlots/genotypeHeader.txt", stringsAsFactors = F, header = F)[,10:128] %>% t())
colnames(genohead)=c("header")
genotype=as.data.frame(read.table("../data/ExampleQTLPlots/KDELR2_TotalPeaksGenotype.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())

full_geno=bind_cols(Ind=genohead$header, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "TT", ifelse(numdose==1, "TG", "GG")))

RNAhead=as.data.frame(read.table("../data/molPhenos/RNAhead.txt", stringsAsFactors = F, header = F)[,5:73] %>% t())

RNApheno=as.data.frame(read.table("../data/molPhenos/RNA_KDELr2.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())

full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)

allRNA=full_geno %>% inner_join(full_pheno, by="Ind")

allRNA$genotype=as.factor(allRNA$genotype)

Ref,T Alt= G

ggplot(allRNA, aes(x=genotype, y=Expression,group=genotype, fill=genotype)) + geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "YlOrRd") + labs(title="Unexplained eQTL: KDELR2 - rs6962012")

Make locus zoom

mkdir ../data/locusZoom

peak119699 KDELR2 ENSG00000136240.5


grep peak119699  ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt > ../data/locusZoom/TotalAPA.peak119699.KDELR2.nomNuc.txt

grep ENSG00000136240.5 ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.txt > ../data/locusZoom/RNA.KDELR2.txt

APATotal_KDELR2_LZ=read.table("../data/locusZoom/TotalAPA.peak119699.KDELR2.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(APATotal_KDELR2_LZ,"../data/locusZoom/apaTotalKDELR2_LZ.txt", col.names = T, row.names = F, quote = F)

RNA_KDELR2_LZ=read.table("../data/locusZoom/RNA.KDELR2.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(RNA_KDELR2_LZ,"../data/locusZoom/RNAKDELR2_LZ.txt", col.names = T, row.names = F, quote = F)

Use locuszoom.org

locus zoom plot for C10ofr88 variant in nuclear:

peak19682


grep peak19682  ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt > ../data/locusZoom/NuclearAPA.peak19882.C10ofr88.nomNuc.txt

grep ENSG00000119965 ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.txt > ../data/locusZoom/RNA.C10ofr88.txt

APATNuclear_orf_LZ=read.table("../data/locusZoom/NuclearAPA.peak19882.C10ofr88.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)


write.table(APATNuclear_orf_LZ,"../data/locusZoom/apaNuclearC10orf88_LZ.txt", col.names = T, row.names = F, quote = F)

RNA_orf_LZ=read.table("../data/locusZoom/RNA.C10ofr88.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(RNA_orf_LZ,"../data/locusZoom/RNAC10orf88_LZ.txt", col.names = T, row.names = F, quote = F)

Locus zoom for all of the examples

module load R
module load plink
module load htslib

mkdir ../data/eQTL_LZ
mkdir ../data/eQTL_LZ/NuclearAssoc/
mkdir ../data/eQTL_LZ/RNAAssoc/

I need to extract the PAS and genes from the nominal files. Do this for nuclear. The below dataframes come from looking at the original eQTL snp in the apa data. I choose the more sig PAS per gene. I will use these for this as well.

I need to get the RSIDs for these

RSID=read.table("/project2/gilad/briana/li_genotypes/RSID2snploc.txt",header = T, stringsAsFactors = F)

AllNuclear_sig= nuclearapaexplained_sig %>% bind_rows(nuclearapaUnexplained_sig) %>% inner_join(RSID, by="snp") %>% ungroup() %>% select(gene, PASnum, RSID)

write.table(AllNuclear_sig,"../data/eQTL_LZ/PasGENEsnpstoUse.txt", col.names = F, row.names = F, quote = F)

sbatch ExtractPAS4eQTLsLZ.sh 
cd ../data/eQTL_LZ/NuclearAssoc
sbatch CreateAPALZeQTLs.sh


sbatch extractGeneLZfileseQTLs.sh
cd ../data/eQTL_LZ/RNAAssoc
sbatch CreateRNALZforeQTLs.sh

Looks like a lot of these do. I can use the snps for explained vs unexplained to copy them to seperate files.

explainedRS=nuclearapaexplained_sig  %>% inner_join(RSID, by="snp") %>% ungroup() %>% select(RSID)
write.table(explainedRS, "../data/eQTL_LZ/explainedRS.txt", col.names = F, row.names = F, quote = F)

UnexplainedRS=nuclearapaUnexplained_sig  %>% inner_join(RSID, by="snp") %>% ungroup() %>% select(RSID)
write.table(UnexplainedRS, "../data/eQTL_LZ/UnexplainedRS.txt", col.names = F, row.names = F, quote = F)

I need a way to use these lists to move the correct plots to seperate places
SWITCH DIR

mkdir UnexplainedeQTLs
mkdir ExplainedeQTLs

I can do this in bash.

cat UnexplainedRS.txt | while read line
do
read -a strarr <<< $line
cp NuclearAssoc/200217_${line}/*.pdf UnexplainedeQTLs
done


cat explainedRS.txt | while read line
do
read -a strarr <<< $line
cp NuclearAssoc/200217_${line}/*.pdf ExplainedeQTLs
done

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] reshape2_1.4.3  workflowr_1.6.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1