Explaining pQTLS

Example CCDC51

Start with the exanple with the highest effect size:

CCDC51- ENSG00000164051 3:48476431

I need all of the results for this snp gene pair from the total, nuclear, and RNA nominal files. I can make a python script that will take the gene name and snp and create the relevent dataframe.

files: * /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out (need the ENSG ID)

APAandRNAfromProtQTLs.py

#use this by inserting a gene, gene_ensg (from prot), and snp for the protien QTLs
def main(gene, gene_ensg,snp):
    out_prot=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Protres.txt"%(gene, snp), "w")
    out_RNA=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_RNAres.txt"%(gene, snp), "w")
    out_Total=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Totalres.txt"%(gene, snp), "w")
    out_Nuclear=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Nuclearres.txt"%(gene, snp), "w")
    for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", "r"):
       s=ln.split()[1]
       g=ln.split()[0].split(":")[3].split("_")[0]
       if g==gene:
           out_Nuclear.write(ln)
    for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", "r"):
       s=ln.split()[1]
       g=ln.split()[0].split(":")[3].split("_")[0]
       if g==gene:
           out_Total.write(ln)
    for ln in open("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out", "r"):
        s=ln.split()[1]
        g=ln.split()[0]
        if gene_ensg in g:
          out_RNA.write(ln) 
    for ln in open("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out", "r"):
        s=ln.split()[1]
        g=ln.split()[0]
        if gene_ensg in g:
          out_prot.write(ln) 

    out_Total.close()
    out_Nuclear.close()
    out_RNA.close()
    out_prot.close()

if __name__ == "__main__":
    import sys
    gene=sys.argv[1]
    gene_ensg=sys.argv[2]
    snp=sys.argv[3]
    main(gene, gene_ensg, snp)
    

CCDC51_APAandRNAfromProtQTLs.sh

#!/bin/bash

#SBATCH --job-name=CCDC51_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=CCDC51_APAandRNAfromProtQTLs.out
#SBATCH --error=CCDC51_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python

python APAandRNAfromProtQTLs.py CCDC51 ENSG00000164051 3:48476431 

I first want to look at the effect sizes. I am interested in understanding the directions of the effect sizes.

3:48728204. This genotype is in the genotype file twice. I am gonig to remove this snp from the analysis.

nom_names=c("gene", "snp", "dist", "pval", "slope")
CCDC51_apaTot=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope)
CCDC51_apaTot=filter(CCDC51_apaTot, !grepl("3:48728204",snp))
CCDC51_apaNuc=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope)
CCDC51_apaNuc=filter(CCDC51_apaNuc, !grepl("3:48728204",snp))
CCDC51_RNA=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope)
CCDC51_RNA=filter(CCDC51_RNA, !grepl("3:48728204",snp))
CCDC51_Prot=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>%  select(snp, slope)
CCDC51_Prot=filter(CCDC51_Prot, !grepl("3:48728204",snp))

Start with RNA and protein because they are the most simple

CCDC51_ProtRNA=CCDC51_RNA %>% left_join(CCDC51_Prot, by="snp")
colnames(CCDC51_ProtRNA)=c("snp", "RNA", "Protein")

Plot this:

plot(CCDC51_ProtRNA$Protein ~ CCDC51_ProtRNA$RNA)
abline(lm(CCDC51_ProtRNA$Protein ~ CCDC51_ProtRNA$RNA))

For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.

CCDC51_apaTot_s=spread(CCDC51_apaTot, "peak", "slope",drop = F)

CCDC51_apaNuc_s=spread(CCDC51_apaNuc, "peak", "slope",drop = F)

I can look at the protein ~ apaTotal.

CCDC51_apaTotProt=CCDC51_apaTot_s %>% left_join(CCDC51_Prot, by="snp")
colnames(CCDC51_apaTotProt)=c("snp", "peak216857", "peak216858", "peak216859", "peak216860", "peak216867","Protein")

Try with all of it:

CCDC51_apaTotProtRNA=CCDC51_apaTot_s %>% left_join(CCDC51_ProtRNA, by="snp")
colnames(CCDC51_apaTotProtRNA)=c("snp", "peak216857", "peak216858", "peak216859", "peak216860", "peak216867","RNA","Protein")

I want to use the same data for each model:

CCDC51_lmProtRNA=lm(Protein ~ RNA, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtRNA_sum=glance(CCDC51_lmProtRNA)

CCDC51_lmProtAPA=lm(Protein ~peak216857 + peak216858 + peak216859 + peak216860 + peak216867, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtAPA_sum=glance(CCDC51_lmProtAPA)

CCDC51_lmProtAPARNA=lm(Protein ~RNA +peak216857 + peak216858 + peak216859 + peak216860 + peak216867, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtAPARNA_sum=glance(CCDC51_lmProtAPARNA)
adjR2_CCDC51=cbind("CCDC51", CCDC51_lmProtRNA_sum[1,2], CCDC51_lmProtAPA_sum[1,2], CCDC51_lmProtAPARNA_sum[1,2])
colnames(adjR2_CCDC51)=c("Gene", "RNA", "APA", "RNAandAPA")

Example DTD1

DTD1- ENSG00000125821 20:18607243

DTD1_APAandRNAfromProtQTLs.sh

#!/bin/bash

#SBATCH --job-name=DTD1_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=DTD1_APAandRNAfromProtQTLs.out
#SBATCH --error=DTD1_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python

python APAandRNAfromProtQTLs.py DTD1 ENSG00000125821 20:18607243

nom_names=c("gene", "snp", "dist", "pval", "slope")

DTD1_RNA=read.table("../data/pQTL_otherphen/DTD1_20:18607243_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>%  filter(!duplicated(snp))

DTD1_Prot=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>%  select(snp, slope) %>%  filter(!duplicated(snp))



#FILTER snps in the list from Rna and prot
DTD1_apaTot=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 
DTD1_apaNuc=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 

Start with RNA and protein because they are the most simple

DTD1_ProtRNA=DTD1_RNA %>% left_join(DTD1_Prot, by="snp")
colnames(DTD1_ProtRNA)=c("snp", "RNA", "Protein")

DTD1_apaTot_s=spread(DTD1_apaTot, "peak", "slope",drop = F)

DTD1_apaNuc_s=spread(DTD1_apaNuc, "peak", "slope",drop = F)

DTD1_apaTotProt=DTD1_apaTot_s %>% inner_join(DTD1_Prot, by="snp")

colnames(DTD1_apaTotProt)=c("snp", "peak195416", "peak195418" ,"peak195419", "peak195420", "peak195423", "peak195424","peak195425" ,"peak195426", "peak195427", "peak195428", "peak195429", "peak195430", "peak195431","peak195432", "peak195433" ,"peak195434" ,"peak195436", "peak195438", "peak195443", "peak195444" ,"peak195445", "peak195446", "peak195447", "peak195449" ,"peak195450", "peak195451", "peak195452","peak195453", "peak195454", "peak195455" ,"protein")

`

Try with all:

DTD1_apaTotProtRNA=DTD1_apaTot_s %>% inner_join(DTD1_ProtRNA, by="snp")

colnames(DTD1_apaTotProtRNA)=c("snp", "peak195416", "peak195418" ,"peak195419", "peak195420", "peak195423", "peak195424","peak195425" ,"peak195426", "peak195427", "peak195428", "peak195429", "peak195430", "peak195431","peak195432", "peak195433" ,"peak195434" ,"peak195436", "peak195438", "peak195443", "peak195444" ,"peak195445", "peak195446", "peak195447", "peak195449" ,"peak195450", "peak195451", "peak195452","peak195453", "peak195454", "peak195455" ,"RNA", "protein")

Model:

DTD1_lmProtRNA=lm(protein ~ RNA, data=DTD1_apaTotProtRNA)
DTD1_lmProtRNA_sum=glance(DTD1_lmProtRNA)

DTD1_lmProtAPA=lm(protein ~ peak195416+ peak195418 +peak195419+peak195420+ peak195423+ peak195424+peak195425 +peak195426+ peak195427+ peak195428+ peak195429+ peak195430 + peak195431+peak195432 + peak195433+peak195434+peak195436+peak195438+peak195443+peak195444+peak195445+peak195446+peak195447+peak195449+peak195450+peak195451+peak195452+peak195453+ peak195454 +peak195455, data=DTD1_apaTotProtRNA)
DTD1_lmProtAPA_sum=glance(DTD1_lmProtAPA)

DTD1_lmProtAPARNA=lm(protein ~RNA+ peak195416+ peak195418 +peak195419+peak195420+ peak195423+ peak195424+peak195425 +peak195426+ peak195427+ peak195428+ peak195429+ peak195430 + peak195431+peak195432 + peak195433+peak195434+peak195436+peak195438+peak195443+peak195444+peak195445+peak195446+peak195447+peak195449+peak195450+peak195451+peak195452+peak195453+ peak195454 +peak195455, data=DTD1_apaTotProtRNA)
DTD1_lmProtAPARNA_sum=glance(DTD1_lmProtAPARNA)

adjR2_DTD1=cbind("DTD1",DTD1_lmProtRNA_sum[1,2], DTD1_lmProtAPA_sum[1,2], DTD1_lmProtAPARNA_sum[1,2])
colnames(adjR2_DTD1)=c("Gene", "RNA", "APA", "RNAandAPA")

Example DHRS7B

DHRS7B ENSG00000109016 17:21036822 DHRS7B_APAandRNAfromProtQTLs.sh

#!/bin/bash

#SBATCH --job-name=DHRS7B_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=DHRS7B_APAandRNAfromProtQTLs.out
#SBATCH --error=DHRS7B_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python

python APAandRNAfromProtQTLs.py DHRS7B ENSG00000109016 17:21036822

nom_names=c("gene", "snp", "dist", "pval", "slope")

DHRS7B_RNA=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>%  filter(!duplicated(snp))

DHRS7B_Prot=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>%  select(snp, slope) %>%  filter(!duplicated(snp))



#FILTER snps in the list from Rna and prot
DHRS7B_apaTot=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 
DHRS7B_apaNuc=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 

Start with RNA and protein because they are the most simple

DHRS7B_ProtRNA=DHRS7B_RNA %>% left_join(DHRS7B_Prot, by="snp")
colnames(DHRS7B_ProtRNA)=c("snp", "RNA", "Protein")

For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.

DHRS7B_apaTot_s=spread(DHRS7B_apaTot, "peak", "slope",drop = F)

DHRS7B_apaNuc_s=spread(DHRS7B_apaNuc, "peak", "slope",drop = F)

I can look at the protein ~ apaTotal.

DHRS7B_apaTotProt=DHRS7B_apaTot_s %>% left_join(DHRS7B_Prot, by="snp")
colnames(DHRS7B_apaTotProt)=c(names(DHRS7B_apaTot_s),"Protein")

Try with all:

DHRS7B_apaTotProtRNA=DHRS7B_apaTot_s %>% inner_join(DHRS7B_ProtRNA, by="snp")

colnames(DHRS7B_apaTotProtRNA)=c(names(DHRS7B_apaTot_s),"RNA", "protein")

DHRS7B_lmProtRNA=lm(protein ~ RNA, data=DHRS7B_apaTotProtRNA)

DHRS7B_lmProtRNA_sum=glance(DHRS7B_lmProtRNA)

DHRS7B_lmProtAPA=lm(protein ~ peak132690+peak132692+peak132693+peak132694+peak132720+peak132721+peak132722+peak132723+peak132724+peak132725+peak132728+peak132729+peak132730+peak132732+peak132733+peak132734+peak132735+peak132738+peak132739+peak132740+peak132741+peak132742+peak132744+peak132745 +peak132746+peak132747+peak132748+peak132749+peak132750+peak132751+peak132753+peak132754+ peak132755+peak132756+peak132757+peak132760+peak132761+peak132762+peak132763+peak132764+peak132766+peak132774+peak132775+peak132777+peak132778+peak132780, data=DHRS7B_apaTotProtRNA)
DHRS7B_lmProtAPA_sum=glance(DHRS7B_lmProtAPA)


DHRS7B_lmProtAPARNA=lm(protein ~ peak132690+peak132692+peak132693+peak132694+peak132720+peak132721+peak132722+peak132723+peak132724+peak132725+peak132728+peak132729+peak132730+peak132732+peak132733+peak132734+peak132735+peak132738+peak132739+peak132740+peak132741+peak132742+peak132744+peak132745 +peak132746+peak132747+peak132748+peak132749+peak132750+peak132751+peak132753+peak132754+ peak132755+peak132756+peak132757+peak132760+peak132761+peak132762+peak132763+peak132764+peak132766+peak132774+peak132775+peak132777+peak132778+peak132780, data=DHRS7B_apaTotProtRNA)

DHRS7B_lmProtAPARNA_sum=glance(DHRS7B_lmProtAPARNA)

adjR2_DHRS7B=cbind("DHRS7B",DHRS7B_lmProtRNA_sum[1,2], DHRS7B_lmProtAPA_sum[1,2], DHRS7B_lmProtAPARNA_sum[1,2])
colnames(adjR2_DHRS7B)=c("Gene",  "RNA", "APA", "RNAandAPA")

Example MMAB

ENSG00000139428 MMAB 12:109997847

MMAB_APAandRNAfromProtQTLs.sh

#!/bin/bash

#SBATCH --job-name=MMAB_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MMAB_APAandRNAfromProtQTLs.out
#SBATCH --error=MMAB_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python

python APAandRNAfromProtQTLs.py MMAB ENSG00000139428 12:109997847 

nom_names=c("gene", "snp", "dist", "pval", "slope")

MMAB_RNA=read.table("../data/pQTL_otherphen/MMAB_12:109997847_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>%  filter(!duplicated(snp))

MMAB_Prot=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>%  select(snp, slope) %>%  filter(!duplicated(snp))



#FILTER snps in the list from Rna and prot
MMAB_apaTot=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 
MMAB_apaNuc=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 

Start with RNA and protein because they are the most simple

MMAB_ProtRNA=MMAB_RNA %>% left_join(MMAB_Prot, by="snp")
colnames(MMAB_ProtRNA)=c("snp", "RNA", "Protein")

For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.

MMAB_apaTot_s=spread(MMAB_apaTot, "peak", "slope",drop = F)

MMAB_apaNuc_s=spread(MMAB_apaNuc, "peak", "slope",drop = F)

I can look at the protein ~ apaTotal.

MMAB_apaTotProt=MMAB_apaTot_s %>% left_join(MMAB_Prot, by="snp")
colnames(MMAB_apaTotProt)=c(names(MMAB_apaTot_s),"Protein")

Try with all:

MMAB_apaTotProtRNA=MMAB_apaTot_s %>% inner_join(MMAB_ProtRNA, by="snp")

colnames(MMAB_apaTotProtRNA)=c(names(MMAB_apaTot_s),"RNA", "protein")

MMAB_lmProtRNA=lm(protein ~ RNA, data=MMAB_apaTotProtRNA)
MMAB_lmProtRNA_sum=glance(MMAB_lmProtRNA)


MMAB_lmProtAPA=lm(protein ~ peak78754+peak78755+peak78756+peak78757+peak78758+peak78759+peak78760+peak78761+peak78762+peak78763+peak78764+peak78765+peak78766, data=MMAB_apaTotProtRNA)
MMAB_lmProtAPA_sum=glance(MMAB_lmProtAPA)


MMAB_lmProtAPARNA=lm(protein ~RNA+ peak78754+peak78755+peak78756+peak78757+peak78758+peak78759+peak78760+peak78761+peak78762+peak78763+peak78764+peak78765+peak78766, data=MMAB_apaTotProtRNA)
MMAB_lmProtAPARNA_sum=glance(MMAB_lmProtAPARNA)

adjR2_MMAB=cbind("MMAB",MMAB_lmProtRNA_sum[1,2], MMAB_lmProtAPA_sum[1,2], MMAB_lmProtAPARNA_sum[1,2])
colnames(adjR2_MMAB)=c("Gene",  "RNA", "APA", "RNAandAPA")

Example FYTTD1

FYTTD1 ENSG00000122068 3:197035232

FYTTD1_APAandRNAfromProtQTLs.sh

#!/bin/bash

#SBATCH --job-name=FYTTD1_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=FYTTD1_APAandRNAfromProtQTLs.out
#SBATCH --error=FYTTD1_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python

python APAandRNAfromProtQTLs.py FYTTD1 ENSG00000122068 3:197035232  

nom_names=c("gene", "snp", "dist", "pval", "slope")

FYTTD1_RNA=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>%  filter(!duplicated(snp))

FYTTD1_Prot=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>%  select(snp, slope) %>%  filter(!duplicated(snp))



#FILTER snps in the list from Rna and prot
FYTTD1_apaTot=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 
FYTTD1_apaNuc=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>%  filter(!duplicated(snp)) 

Start with RNA and protein because they are the most simple

FYTTD1_ProtRNA=FYTTD1_RNA %>% left_join(FYTTD1_Prot, by="snp")
colnames(FYTTD1_ProtRNA)=c("snp", "RNA", "Protein")

For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.

FYTTD1_apaTot_s=spread(FYTTD1_apaTot, "peak", "slope",drop = F)

FYTTD1_apaNuc_s=spread(FYTTD1_apaNuc, "peak", "slope",drop = F)

I can look at the protein ~ apaTotal.

FYTTD1_apaTotProt=FYTTD1_apaTot_s %>% left_join(FYTTD1_Prot, by="snp")
colnames(FYTTD1_apaTotProt)=c(names(FYTTD1_apaTot_s),"Protein")

FYTTD1_apaTotProtRNA=FYTTD1_apaTot_s %>% inner_join(FYTTD1_ProtRNA, by="snp")

colnames(FYTTD1_apaTotProtRNA)=c(names(FYTTD1_apaTot_s),"RNA", "protein")

Model:

FYTTD1_lmProtRNA=lm(protein ~ RNA, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtRNA_sum=glance(FYTTD1_lmProtRNA)

FYTTD1_lmProtAPA=lm(protein ~ peak234016 +peak234082 +peak234088+peak234089+peak234090+peak234092+peak234093+peak234094+peak234095+peak234099+peak234101+peak234103+peak234104+peak234105+peak234106+peak234107+peak234108+peak234111+peak234113+peak234114+peak234117+peak234119+peak234120+peak234122+peak234123+peak234124+peak234125+peak234131+peak234135, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtAPA_sum=glance(FYTTD1_lmProtAPA)


FYTTD1_lmProtAPARNA=lm(protein ~RNA+ peak234016 +peak234082 +peak234088+peak234089+peak234090+peak234092+peak234093+peak234094+peak234095+peak234099+peak234101+peak234103+peak234104+peak234105+peak234106+peak234107+peak234108+peak234111+peak234113+peak234114+peak234117+peak234119+peak234120+peak234122+peak234123+peak234124+peak234125+peak234131+peak234135, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtAPARNA_sum=glance(FYTTD1_lmProtAPARNA)

adjR2_FYTTD1=cbind("FYTTD1",FYTTD1_lmProtRNA_sum[1,2], FYTTD1_lmProtAPA_sum[1,2], FYTTD1_lmProtAPARNA_sum[1,2])
colnames(adjR2_FYTTD1)=c("Gene",  "RNA", "APA", "RNAandAPA")

Analysis

In each of these examples the total APA explains more of the protein variability. I am going to plot the adjR2 for each gene and model

adjR2=rbind(adjR2_CCDC51,adjR2_DTD1,adjR2_DHRS7B, adjR2_MMAB, adjR2_FYTTD1)

adjR2_melt=melt(adjR2, id.vars="Gene")
colnames(adjR2_melt)=c("Gene", "Model", "AdjR2")

Plot this

ggplot(adjR2_melt, aes(x=Gene, y=AdjR2, by=Model, fill=Model)) + geom_bar(stat="identity", position = "dodge") + labs(title="Adjusted R2 for variation\n in protein effect sizes explained in each model",x="pQTL Gene") + scale_fill_brewer(palette="Paired")

Residuals method:

I want to use the resid(lm) method to get the residuals for the RNA model and see if I can explain some of these with the APA information:

CCDC51_RNA_Resid=resid(CCDC51_lmProtRNA)
DTD1_RNA_Resid=resid(DTD1_lmProtRNA)
DHRS7B_RNA_Resid=resid(DHRS7B_lmProtRNA)
MMAB_RNA_Resid=resid(MMAB_lmProtRNA)
FYTTD1_RNA_Resid=resid(FYTTD1_lmProtRNA)

CCDC51_lmResidAPA=lm(CCDC51_RNA_Resid ~ CCDC51_apaTotProt$peak216857 +CCDC51_apaTotProt$peak216858 + CCDC51_apaTotProt$peak216859 + CCDC51_apaTotProt$peak216860 + CCDC51_apaTotProt$peak216867)
CCDC51_lmResidRNA_sum=glance(CCDC51_lmResidAPA)

DTD1_lmResidAPA=lm(DTD1_RNA_Resid ~ DTD1_apaTotProtRNA$peak195416+ DTD1_apaTotProtRNA$peak195418 +DTD1_apaTotProtRNA$peak195419+DTD1_apaTotProtRNA$peak195420+ DTD1_apaTotProtRNA$peak195423+ DTD1_apaTotProtRNA$peak195424+DTD1_apaTotProtRNA$peak195425 +DTD1_apaTotProtRNA$peak195426+ DTD1_apaTotProtRNA$peak195427+ DTD1_apaTotProtRNA$peak195428+ DTD1_apaTotProtRNA$peak195429+ DTD1_apaTotProtRNA$peak195430 + DTD1_apaTotProtRNA$peak195431+DTD1_apaTotProtRNA$peak195432 + DTD1_apaTotProtRNA$peak195433+DTD1_apaTotProtRNA$peak195434+DTD1_apaTotProtRNA$peak195436+DTD1_apaTotProtRNA$peak195438+DTD1_apaTotProtRNA$peak195443+DTD1_apaTotProtRNA$peak195444+DTD1_apaTotProtRNA$peak195445+DTD1_apaTotProtRNA$peak195446+DTD1_apaTotProtRNA$peak195447+DTD1_apaTotProtRNA$peak195449+DTD1_apaTotProtRNA$peak195450+DTD1_apaTotProtRNA$peak195451+DTD1_apaTotProtRNA$peak195452+DTD1_apaTotProtRNA$peak195453+ DTD1_apaTotProtRNA$peak195454 +DTD1_apaTotProtRNA$peak195455)
DTD1_lmResidRNA_sum=glance(DTD1_lmResidAPA)

DHRS7B_lmResidAPA=lm(DHRS7B_RNA_Resid ~ DHRS7B_apaTotProtRNA$peak132690+DHRS7B_apaTotProtRNA$peak132692+DHRS7B_apaTotProtRNA$peak132693+DHRS7B_apaTotProtRNA$peak132694+DHRS7B_apaTotProtRNA$peak132720+DHRS7B_apaTotProtRNA$peak132721+DHRS7B_apaTotProtRNA$peak132722+DHRS7B_apaTotProtRNA$peak132723+DHRS7B_apaTotProtRNA$peak132724+DHRS7B_apaTotProtRNA$peak132725+DHRS7B_apaTotProtRNA$peak132728+DHRS7B_apaTotProtRNA$peak132729+DHRS7B_apaTotProtRNA$peak132730+DHRS7B_apaTotProtRNA$peak132732+DHRS7B_apaTotProtRNA$peak132733+DHRS7B_apaTotProtRNA$peak132734+DHRS7B_apaTotProtRNA$peak132735+DHRS7B_apaTotProtRNA$peak132738+DHRS7B_apaTotProtRNA$peak132739+DHRS7B_apaTotProtRNA$peak132740+DHRS7B_apaTotProtRNA$peak132741+DHRS7B_apaTotProtRNA$peak132742+DHRS7B_apaTotProtRNA$peak132744+DHRS7B_apaTotProtRNA$peak132745 +DHRS7B_apaTotProtRNA$peak132746+DHRS7B_apaTotProtRNA$peak132747+DHRS7B_apaTotProtRNA$peak132748+DHRS7B_apaTotProtRNA$peak132749+DHRS7B_apaTotProtRNA$peak132750+DHRS7B_apaTotProtRNA$peak132751+DHRS7B_apaTotProtRNA$peak132753+DHRS7B_apaTotProtRNA$peak132754+ DHRS7B_apaTotProtRNA$peak132755+DHRS7B_apaTotProtRNA$peak132756+DHRS7B_apaTotProtRNA$peak132757+DHRS7B_apaTotProtRNA$peak132760+DHRS7B_apaTotProtRNA$peak132761+DHRS7B_apaTotProtRNA$peak132762+DHRS7B_apaTotProtRNA$peak132763+DHRS7B_apaTotProtRNA$peak132764+DHRS7B_apaTotProtRNA$peak132766+DHRS7B_apaTotProtRNA$peak132774+DHRS7B_apaTotProtRNA$peak132775+DHRS7B_apaTotProtRNA$peak132777+DHRS7B_apaTotProtRNA$peak132778+DHRS7B_apaTotProtRNA$peak132780)
DHRS7B_lmResidRNA_sum=glance(DHRS7B_lmResidAPA)

MMAB_lmResidAPA=lm(MMAB_RNA_Resid ~ MMAB_apaTotProtRNA$peak78754+MMAB_apaTotProtRNA$peak78755+MMAB_apaTotProtRNA$peak78756+MMAB_apaTotProtRNA$peak78757+MMAB_apaTotProtRNA$peak78758+MMAB_apaTotProtRNA$peak78759+MMAB_apaTotProtRNA$peak78760+MMAB_apaTotProtRNA$peak78761+MMAB_apaTotProtRNA$peak78762+MMAB_apaTotProtRNA$peak78763+MMAB_apaTotProtRNA$peak78764+MMAB_apaTotProtRNA$peak78765+MMAB_apaTotProtRNA$peak78766)
MMAB_lmResidRNA_sum=glance(MMAB_lmResidAPA)

FYTTD1_lmResidAPA=lm(FYTTD1_RNA_Resid ~ FYTTD1_apaTotProtRNA$peak234016 +FYTTD1_apaTotProtRNA$peak234082 +FYTTD1_apaTotProtRNA$peak234088+FYTTD1_apaTotProtRNA$peak234089+FYTTD1_apaTotProtRNA$peak234090+FYTTD1_apaTotProtRNA$peak234092+FYTTD1_apaTotProtRNA$peak234093+FYTTD1_apaTotProtRNA$peak234094+FYTTD1_apaTotProtRNA$peak234095+FYTTD1_apaTotProtRNA$peak234099+FYTTD1_apaTotProtRNA$peak234101+FYTTD1_apaTotProtRNA$peak234103+FYTTD1_apaTotProtRNA$peak234104+FYTTD1_apaTotProtRNA$peak234105+FYTTD1_apaTotProtRNA$peak234106+FYTTD1_apaTotProtRNA$peak234107+FYTTD1_apaTotProtRNA$peak234108+FYTTD1_apaTotProtRNA$peak234111+FYTTD1_apaTotProtRNA$peak234113+FYTTD1_apaTotProtRNA$peak234114+FYTTD1_apaTotProtRNA$peak234117+FYTTD1_apaTotProtRNA$peak234119+FYTTD1_apaTotProtRNA$peak234120+FYTTD1_apaTotProtRNA$peak234122+FYTTD1_apaTotProtRNA$peak234123+FYTTD1_apaTotProtRNA$peak234124+FYTTD1_apaTotProtRNA$peak234125+FYTTD1_apaTotProtRNA$peak234131+FYTTD1_apaTotProtRNA$peak234135)
FYTTD1_lmResidRNA_sum=glance(FYTTD1_lmResidAPA)

RNA_Residuals=rbind(CCDC51_lmResidRNA_sum[1,2],DTD1_lmResidRNA_sum[1,2],DHRS7B_lmResidRNA_sum[1,2],MMAB_lmResidRNA_sum[1,2],FYTTD1_lmResidRNA_sum[1,2])
adjR2_resid=cbind(adjR2,RNA_Residuals)
colnames(adjR2_resid)=c("Gene", "RNA", "APA", "RNAandAPA", "APAinRNAResiduals")

adjR2_resid_melt=melt(adjR2_resid, id.vars="Gene")
colnames(adjR2_resid_melt)=c("Gene", "Model", "AdjR2")
adjR2_resid_melt_sub=adjR2_resid_melt %>% filter(Model=="APAinRNAResiduals" | Model== "RNA")

ggplot(adjR2_resid_melt_sub, aes(x=Gene, y=AdjR2, by=Model, fill=Model)) + geom_bar(stat="identity", position = "dodge") + labs(title="Adjusted R2 for variation\n in protein effect sizes explained in each model",x="pQTL Gene") + scale_fill_brewer(palette="Paired") 

Inference

RSS= residuial sum of squares

#subset model 
RSS_sub=anova(CCDC51_lmProtRNA)["Residuals", "Sum Sq"]
df_sub=1466

#full model
RSS_full=anova(CCDC51_lmProtAPARNA)["Residuals", "Sum Sq"]
df_full=14661
anova(CCDC51_lmProtAPARNA)

Analysis of Variance Table

Response: Protein
             Df  Sum Sq Mean Sq F value    Pr(>F)    
RNA           1  19.427  19.427 175.950 < 2.2e-16 ***
peak216857    1 100.409 100.409 909.411 < 2.2e-16 ***
peak216858    1  26.726  26.726 242.057 < 2.2e-16 ***
peak216859    1   3.528   3.528  31.951 1.898e-08 ***
peak216860    1  13.273  13.273 120.211 < 2.2e-16 ***
peak216867    1   6.939   6.939  62.846 4.402e-15 ***
Residuals  1461 161.310   0.110                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

F_CCDC51=((RSS_sub-RSS_full)/(df_sub-df_full))/(RSS_full/df_full)
F_CCDC51

[1] -1.039217

#i can do this with the annova function 
anova(CCDC51_lmProtRNA,CCDC51_lmProtAPARNA)

Analysis of Variance Table

Model 1: Protein ~ RNA
Model 2: Protein ~ RNA + peak216857 + peak216858 + peak216859 + peak216860 + 
    peak216867
  Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
1   1466 312.18                                 
2   1461 161.31  5    150.87 273.3 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(MMAB_lmProtRNA,MMAB_lmProtAPARNA)

Analysis of Variance Table

Model 1: protein ~ RNA
Model 2: protein ~ RNA + peak78754 + peak78755 + peak78756 + peak78757 + 
    peak78758 + peak78759 + peak78760 + peak78761 + peak78762 + 
    peak78763 + peak78764 + peak78765 + peak78766
  Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
1   2942 529.17                                  
2   2929 245.38 13    283.79 260.57 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(DHRS7B_lmProtRNA,DHRS7B_lmProtAPARNA)

Analysis of Variance Table

Model 1: protein ~ RNA
Model 2: protein ~ peak132690 + peak132692 + peak132693 + peak132694 + 
    peak132720 + peak132721 + peak132722 + peak132723 + peak132724 + 
    peak132725 + peak132728 + peak132729 + peak132730 + peak132732 + 
    peak132733 + peak132734 + peak132735 + peak132738 + peak132739 + 
    peak132740 + peak132741 + peak132742 + peak132744 + peak132745 + 
    peak132746 + peak132747 + peak132748 + peak132749 + peak132750 + 
    peak132751 + peak132753 + peak132754 + peak132755 + peak132756 + 
    peak132757 + peak132760 + peak132761 + peak132762 + peak132763 + 
    peak132764 + peak132766 + peak132774 + peak132775 + peak132777 + 
    peak132778 + peak132780
  Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
1   3071 479602                                 
2   3026   2144 45    477458 14978 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(DHRS7B_lmProtRNA,DHRS7B_lmProtAPARNA)

Analysis of Variance Table

Model 1: protein ~ RNA
Model 2: protein ~ peak132690 + peak132692 + peak132693 + peak132694 + 
    peak132720 + peak132721 + peak132722 + peak132723 + peak132724 + 
    peak132725 + peak132728 + peak132729 + peak132730 + peak132732 + 
    peak132733 + peak132734 + peak132735 + peak132738 + peak132739 + 
    peak132740 + peak132741 + peak132742 + peak132744 + peak132745 + 
    peak132746 + peak132747 + peak132748 + peak132749 + peak132750 + 
    peak132751 + peak132753 + peak132754 + peak132755 + peak132756 + 
    peak132757 + peak132760 + peak132761 + peak132762 + peak132763 + 
    peak132764 + peak132766 + peak132774 + peak132775 + peak132777 + 
    peak132778 + peak132780
  Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
1   3071 479602                                 
2   3026   2144 45    477458 14978 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(FYTTD1_lmProtRNA,FYTTD1_lmProtAPARNA)

Analysis of Variance Table

Model 1: protein ~ RNA
Model 2: protein ~ RNA + peak234016 + peak234082 + peak234088 + peak234089 + 
    peak234090 + peak234092 + peak234093 + peak234094 + peak234095 + 
    peak234099 + peak234101 + peak234103 + peak234104 + peak234105 + 
    peak234106 + peak234107 + peak234108 + peak234111 + peak234113 + 
    peak234114 + peak234117 + peak234119 + peak234120 + peak234122 + 
    peak234123 + peak234124 + peak234125 + peak234131 + peak234135
  Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
1   3409 143.379                                  
2   3380  77.295 29    66.083 99.645 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The models are significantly different

CCDC51_apaTotProtRNA_rand=transform(CCDC51_apaTotProtRNA,peak216857=sample(peak216857), peak216858=sample(peak216858),peak216859=sample(peak216859), peak216860=sample(peak216860) ,peak216867=sample(peak216867))


CCDC51_lmProtAPA_rand=lm(CCDC51_RNA_Resid ~ CCDC51_apaTotProtRNA_rand$peak216857 + CCDC51_apaTotProtRNA_rand$peak216858 + CCDC51_apaTotProtRNA_rand$peak216859 + CCDC51_apaTotProtRNA_rand$peak216860 + CCDC51_apaTotProtRNA_rand$peak216867)


summary(CCDC51_lmProtAPA_rand)

Call:
lm(formula = CCDC51_RNA_Resid ~ CCDC51_apaTotProtRNA_rand$peak216857 + 
    CCDC51_apaTotProtRNA_rand$peak216858 + CCDC51_apaTotProtRNA_rand$peak216859 + 
    CCDC51_apaTotProtRNA_rand$peak216860 + CCDC51_apaTotProtRNA_rand$peak216867)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.28924 -0.25246 -0.06547  0.25200  2.14234 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)
(Intercept)                           0.008342   0.012878   0.648   0.5172
CCDC51_apaTotProtRNA_rand$peak216857  0.080512   0.042575   1.891   0.0588
CCDC51_apaTotProtRNA_rand$peak216858  0.020707   0.030660   0.675   0.4995
CCDC51_apaTotProtRNA_rand$peak216859 -0.046768   0.046426  -1.007   0.3139
CCDC51_apaTotProtRNA_rand$peak216860 -0.027177   0.042205  -0.644   0.5197
CCDC51_apaTotProtRNA_rand$peak216867 -0.031990   0.040653  -0.787   0.4315
                                      
(Intercept)                           
CCDC51_apaTotProtRNA_rand$peak216857 .
CCDC51_apaTotProtRNA_rand$peak216858  
CCDC51_apaTotProtRNA_rand$peak216859  
CCDC51_apaTotProtRNA_rand$peak216860  
CCDC51_apaTotProtRNA_rand$peak216867  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4611 on 1462 degrees of freedom
Multiple R-squared:  0.004125,  Adjusted R-squared:  0.0007196 
F-statistic: 1.211 on 5 and 1462 DF,  p-value: 0.3014

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  cowplot_0.9.3   broom_0.5.0     reshape2_1.4.3 
 [5] forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5    
 [9] readr_1.1.1     tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0  
[13] tidyverse_1.2.1 workflowr_1.1.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4   haven_1.1.2        lattice_0.20-35   
 [4] colorspace_1.3-2   htmltools_0.3.6    yaml_2.2.0        
 [7] rlang_0.2.2        R.oo_1.22.0        pillar_1.3.0      
[10] glue_1.3.0         withr_2.1.2        R.utils_2.7.0     
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] bindr_0.1.1        plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] R.methodsS3_1.7.1  evaluate_0.11      labeling_0.3      
[25] knitr_1.20         Rcpp_0.12.19       scales_1.0.0      
[28] backports_1.1.2    jsonlite_1.5       hms_0.4.2         
[31] digest_0.6.17      stringi_1.2.4      grid_3.5.1        
[34] rprojroot_1.3-2    cli_1.0.1          tools_3.5.1       
[37] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[40] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[43] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[46] httr_1.3.1         rstudioapi_0.8     R6_2.3.0          
[49] nlme_3.1-137       git2r_0.23.0       compiler_3.5.1