Proportion eQTLs explained

Last updated: 2019-08-01

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Knit directory: apaQTL/analysis/

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File	Version	Author	Date	Message
Rmd	99c751e	brimittleman	2019-08-01	pdf for figure 3
html	d73d818	brimittleman	2019-06-26	Build site.
Rmd	c53925a	brimittleman	2019-06-26	add graph labels
html	06de9df	brimittleman	2019-06-26	Build site.
Rmd	ec9c1d6	brimittleman	2019-06-26	add direction concordance plots
html	ec8d7dc	brimittleman	2019-06-26	Build site.
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
html	b907ac1	brimittleman	2019-06-12	Build site.
Rmd	178c5dc	brimittleman	2019-06-12	new geno
html	6b164c8	brimittleman	2019-06-07	Build site.
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 ──

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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(workflowr)

This is workflowr version 1.4.0
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
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

Proportion explained:

nrow(totalapaUnexplained_sig)/nrow(totalapaUnexplained)

[1] 0.1632653

I tested 588 unexplained eQTLs in the total fraction and 96 have a bonforoni corrected significant peak.

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] 0.1304878

I am testing 820 explained eQTLs and of those 107 have a bonforoni corrected significant peak.

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 = 2.722, df = 1, p-value = 0.09898
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.00641871  0.07197371
sample estimates:
   prop 1    prop 2 
0.1632653 0.1304878

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
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              7      0.0729           8      0.0748
2 end              9      0.0938           7      0.0654
3 intron          17      0.177           20      0.187 
4 utr3            59      0.615           70      0.654 
5 utr5             4      0.0417           2      0.0187

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] 0.1649832

I tested 594 unexplained eQTLs in the nuclear fraction and 98 have a bonforoni corrected significant peak.

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] 0.13269

I tested 829 explained eQTLs in the nuclear fraction and 110 have a nominally significant peak. difference of proportions:

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 = 2.6386, df = 1, p-value = 0.1043
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.006890426  0.071476780
sample estimates:
   prop 1    prop 2 
0.1649832 0.1326900

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
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                4        0.0408             3        0.0273
2 end               10        0.102              9        0.0818
3 intron            18        0.184             33        0.3   
4 utr3              66        0.673             63        0.573 
5 utr5               0        0                  2        0.0182

prop.test(x=c(18,33), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(18, 33) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 3.1869, df = 1, p-value = 0.07423
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.240913267  0.008260206
sample estimates:
   prop 1    prop 2 
0.1836735 0.3000000

prop.test(x=c(66,63), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(66, 63) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 1.8258, df = 1, p-value = 0.1766
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.03992433  0.24140856
sample estimates:
   prop 1    prop 2 
0.6734694 0.5727273

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 = 1.4301e-06, df = 1, p-value = 0.999
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.04220475  0.04564046
sample estimates:
   prop 1    prop 2 
0.1649832 0.1632653

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
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

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
ec8d7dc	brimittleman	2019-06-26
0fae25e	brimittleman	2019-06-20

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: -0.21 p-value: .01, r2=0.06") + labs(title="Total apa effect sizes vs eQTL eqtl effect sizes", y="Total apaQTL effect size",x="eQTL effect size")

Version	Author	Date
d73d818	brimittleman	2019-06-26
06de9df	brimittleman	2019-06-26

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 
-1.12882 -0.29223 -0.00704  0.29545  1.11825 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)             0.04771    0.04766   1.001    0.320  
alleQTLS_total$eQTL_es -0.20567    0.07919  -2.597    0.011 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.447 on 87 degrees of freedom
Multiple R-squared:  0.07196,   Adjusted R-squared:  0.0613 
F-statistic: 6.746 on 1 and 87 DF,  p-value: 0.01103

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: -0.18 p-value: .005, r2=0.05") + labs(title="", y="apaQTL effect size",x="eQTL effect size")

Version	Author	Date
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 
-1.18775 -0.26653 -0.03384  0.29319  1.16355 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.009364   0.036972  -0.253  0.80041   
alleQTLS_nuclear$eQTL_es -0.178352   0.062232  -2.866  0.00478 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4455 on 144 degrees of freedom
Multiple R-squared:  0.05396,   Adjusted R-squared:  0.04739 
F-statistic: 8.213 on 1 and 144 DF,  p-value: 0.004782

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  19  57706377  57706378  ZNF264:peak67836:intron -0.732823      .
2  20   1350708   1350709    FKBP1A:peak80048:utr3 -0.565488      .
3   2 197855151 197855152 ANKRD44:peak77452:intron -0.976492      .
4   7   6497500   6497501   KDELR2:peak119697:utr3  0.962082      .
5   7   6497500   6497501   KDELR2:peak119699:utr3 -1.013800      .
     gene
1  ZNF264
2  FKBP1A
3 ANKRD44
4  KDELR2
5  KDELR2

nucQTL_unex

  chr  bedstart       loc                        ID     score strand
1  10 124693586 124693587 C10orf88:peak19881:intron  1.314860      .
2  19  57706377  57706378   ZNF264:peak67836:intron -0.541008      .
3   4  44702719  44702720       GUF1:peak98095:utr3  0.837665      .
4   4  44702719  44702720       GUF1:peak98096:utr3 -1.361180      .
5   7 156760698 156760699      NOM1:peak127488:utr3  0.972826      .
      gene
1 C10orf88
2   ZNF264
3   GNPDA2
4   GNPDA2
5     NOM1

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")

Warning: Column `Ind` joining factors with different levels, coercing to
character vector

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")

Version	Author	Date
06de9df	brimittleman	2019-06-26

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:

peak19881


grep peak19881  ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt > ../data/locusZoom/NuclearAPA.peak119699.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.peak119699.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)

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_1.4.0 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   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         fansi_0.4.0       
[25] highr_0.7          broom_0.5.1        Rcpp_1.0.0        
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.25.2       compiler_3.5.1