Last updated: 2020-02-13

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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/propeQTLs_explained.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 295b72e brimittleman 2020-02-13 add error bars
html 3455a9e brimittleman 2020-02-07 Build site.
Rmd f3da578 brimittleman 2020-02-07 add LD res
html 7477dd7 brimittleman 2020-02-06 Build site.
Rmd 8c10277 brimittleman 2020-02-06 add code for LD and fix double y axis
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Rmd a1f5355 brimittleman 2020-02-05 add potential mechanisms and ld scripts
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Rmd fd5ccd7 brimittleman 2020-01-30 add LD regress notes and first var in apa 
library(data.table)
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()   masks data.table::between()
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✖ dplyr::lag()       masks stats::lag()
✖ dplyr::last()      masks data.table::last()
✖ purrr::transpose() masks data.table::transpose()

LD score regression with apaQTLs and multiple myeloma?

https://www.nature.com/articles/s41467-018-04989-w

QTLs:

Region around permuted snps. 500bp

try a few different

PAS:

maybe 1000 base pairs around each PAS.

bed file chrom start and end. - use CHR

-b cells: lymphocite

GWAS atlas

http://www.computationalmedicine.fi/data#Cytokine_GWAS

GWAS from Phenix= /project2/yangili1/zpmu/GWAS_loci/27863252

sle_Vyse_chr1-22.txt.gz

RA_GWASmeta_TransEthnic_v2.txt.gz

http://www.nealelab.is/uk-biobank - myeloma and luekemias

HG 19 LD scores

  • change to numpy 1.16

https://github.com/bulik/ldsc

03,05,06,07

/project2/yangili1/zpmu/ldsc/scripts

First I will prepare the PAS and apaQTL filese:

mkdir ../data/LDSR_annotations

Nuclear 5% PAS

PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",col.names = c("Chr","start","end","name","score","stand"),stringsAsFactors = F)

PAS_500=PAS%>% mutate(chrom=paste("chr", Chr, sep=""),newStart=end-250, newEnd=end+250 ) %>% select(chrom,newStart,newEnd)

PAS_1000=PAS%>% mutate(chrom=paste("chr", Chr, sep=""),newStart=end-1000, newEnd=end+1000 ) %>% select(chrom, newStart,newEnd)
write.table(PAS_500,"../data/LDSR_annotations/PAS_Nuclear500.bed", col.names = F, row.names = F, quote = F, sep="\t")

write.table(PAS_1000,"../data/LDSR_annotations/PAS_Nuclear1000.bed", col.names = F, row.names = F, quote = F, sep="\t")

Sort:

sort -k1,1 -k2,2n ../data/LDSR_annotations/PAS_Nuclear500.bed > ../data/LDSR_annotations/PAS_Nuclear500.sort.bed

sort -k1,1 -k2,2n ../data/LDSR_annotations/PAS_Nuclear1000.bed > ../data/LDSR_annotations/PAS_Nuclear1000.sort.bed

QTLS:

RSID=read.table("../../../briana/li_genotypes/RSID2snploc.txt", header = T, stringsAsFactors = F, col.names = c("snp","sid", "ref","alt"))
NucQTL=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T ,stringsAsFactors = F) %>% inner_join(RSID, by="sid") %>% separate(snp, into=c("chr","loc"),sep=":")

NucQTL500=NucQTL%>% mutate(start=as.integer(loc)-250,end=as.integer(loc)+250, chrom=paste("chr",chr,by="")) %>% select(chrom,start,end)

NucQTL1000=NucQTL%>% mutate(start=as.integer(loc)-500,end=as.integer(loc)+500, chrom=paste("chr",chr,by="")) %>% select(chrom,start,end)
write.table(NucQTL500,"../data/LDSR_annotations/QTL_Nuclear500.bed", col.names = F, row.names = F, quote = F, sep="\t")

write.table(NucQTL1000,"../data/LDSR_annotations/QTL_Nuclear1000.bed", col.names = F, row.names = F, quote = F, sep="\t")

Sort:

sort -k1,1 -k2,2n ../data/LDSR_annotations/QTL_Nuclear500.bed > ../data/LDSR_annotations/QTL_Nuclear500.sort.bed

sort -k1,1 -k2,2n ../data/LDSR_annotations/QTL_Nuclear1000.bed > ../data/LDSR_annotations/QTL_Nuclear1000.sort.bed

Choose test gwas:

Start with RA because it works with Phenoix’s code.

I moved the scripts to my code directory. I will make a copy of it for PAS_500_Lymph

cp -R scripts scripts_PAS_500_Lymph

../data/LDSR_annotations/

Change the places in 03,05,06,07

#03 will annotate all 4 of these  

sbatch scripts_PAS_500_Lymph/03_immuneAtlas_annot.sh 

sbatch scripts_PAS_500_Lymph/05_immuneAtlas_ldscore.sh

sbatch scripts_PAS_500_Lymph/06_munge_gwas_lymphPAS500.sh

mkdir /project2/gilad/briana/apaQTL/data/LDSR_annotations/results/

sbatch scripts_PAS_500_Lymph/07_partition_h2g.sh

stuck on 7. need baseline LD files

Try to munge another GWAS :

I need to understand the column names for the GWAS to put in the script.

VARIANT ID_dbSNP49 CHR BP REF ALT ALT_MINOR DIRECTION EFFECT SE P MLOG10P ALT_FREQ MA_FREQ 1:10177_A_AC rs367896724 1 10177 A AC TRUE + 5.330523e-03 4.971989e-03 0.2837 5.471861e-01 3.926e-01 3.926e-01

SNP: ID_dbSNP49 P: P A1: REF A2: ALT MAF: MA_FREQ EFFECT SE

Missing N baso_neut_sum, eo_baso_sum, and neut_eo_sum

metaDt <- fread("/project2/yangili1/zpmu/GWAS_loci/gwasATLAS_threePMID.txt") %>% 
  filter(PMID == 27863252) %>% select(N, uniqTrait)

traits=fread("/project2/yangili1/zpmu/GWAS_loci/27863252/TRAIT_MAP.tsv")


metandTrait=traits %>% dplyr::rename("uniqTrait"= long_name) %>% full_join(metaDt,by="uniqTrait")


write.table(metandTrait, "/project2/yangili1/zpmu/GWAS_loci/MetawTrait.txt", col.names = F,row.names = F,quote = F,sep="\t")

I need

SNP – SNP identifier (e.g., rs number) N – sample size (which may vary from SNP to SNP). Z – z-score. Sign with respect to A1 (warning, possible gotcha) A1 – first allele (effect allele) A2– second allele (other allele)

SNP is EFFECT/SE

split up to _N for the name of the file.

sbatch runFixGWAS4Munge.sh 
mkdir ../data/LDSR_annotations/Munge/
sbatch scripts_PAS_500_Lymph/mungeGWAS.sh
sbatch scripts_PAS_500_Lymph/par_h2g_allGWAS_PAS1000.sh
sbatch scripts_PAS_500_Lymph/par_h2g_allGWAS_PAS500.sh
sbatch scripts_PAS_500_Lymph/par_h2g_allGWAS_qtl1000.sh
sbatch scripts_PAS_500_Lymph/par_h2g_allGWAS_qtl500.sh

Process the results:

Create a python script that prints out the names of the gwas, the region, and the results.

sbatch parseLDresBothPAS.sh

Results:

reshead=c("GWAS",colnames(read.table("../data/LDSR_annotations/results/PAS_Nuclear1000_baso_N171846_narrow_form.baseline.results",header=T)))
PAS500res=read.table("../data/LDSR_annotations/results/allPAS_Nuclear500.txt", col.names = reshead) %>% separate(GWAS, into=c("PAS", "Type", "extra"),sep="_N") %>%separate(Type, into=c("nuc", "pheno"),sep="0_")
Warning: Expected 3 pieces. Missing pieces filled with `NA` in 1 rows [30].
PAS500_snpsincluded=round(PAS500res$Prop._SNPs[1],3) * 100


PAS1000res=read.table("../data/LDSR_annotations/results/allPAS_Nuclear1000.txt", col.names = reshead) %>% separate(GWAS, into=c("PAS", "Type", "extra"),sep="_N") %>%separate(Type, into=c("nuc", "pheno"),sep="0_") 
Warning: Expected 3 pieces. Missing pieces filled with `NA` in 1 rows [30].
PAS1000_snpsincluded=round(PAS1000res$Prop._SNPs[1],3) *100

Plot the H2 explained:

ggplot(PAS500res,aes(x=pheno, y=Prop._h2,fill=pheno)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(legend.position = "none") + annotate("text", label=paste("% of SNPs included", PAS500_snpsincluded), x=10, y=.08) + geom_errorbar(aes(ymin=Prop._h2-Prop._h2_std_error, ymax=Prop._h2+Prop._h2_std_error), width=.2,position=position_dodge(.9))

Version Author Date
3455a9e brimittleman 2020-02-07 
ggplot(PAS500res,aes(x=pheno, y=Enrichment, fill=pheno)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(legend.position = "none") + annotate("text", label=paste("% of SNPs included", PAS500_snpsincluded), x=10, y=15) + geom_errorbar(aes(ymin=Enrichment-Enrichment_std_error, ymax=Enrichment+Enrichment_std_error), width=.2,position=position_dodge(.9)) + geom_hline(yintercept = 1, linetype="dotted")

Version Author Date
3455a9e brimittleman 2020-02-07

Plot the H2 explained:

ggplot(PAS1000res,aes(x=pheno, y=Prop._h2,fill=pheno)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(legend.position = "none") + annotate("text", label=paste("% of SNPs included", PAS1000_snpsincluded), x=10, y=.15)+labs(x="", title="LD score regression results for 1000 BP around each PAS", y="% of heritability explained")+ geom_errorbar(aes(ymin=Prop._h2-Prop._h2_std_error, ymax=Prop._h2+Prop._h2_std_error), width=.2,position=position_dodge(.9))

Version Author Date
3455a9e brimittleman 2020-02-07 
ggplot(PAS1000res,aes(x=pheno, y=Enrichment, fill=pheno)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(legend.position = "none") + annotate("text", label=paste("% of SNPs included", PAS1000_snpsincluded), x=10, y=12) +labs(x="", title="LD score regression results for 1000 BP around each PAS") + geom_errorbar(aes(ymin=Enrichment-Enrichment_std_error, ymax=Enrichment+Enrichment_std_error), width=.2,position=position_dodge(.9)) + geom_hline(yintercept = 1, linetype="dotted")

Version Author Date
3455a9e brimittleman 2020-02-07

code to rerun those that needed manual fixing for trait:

gunzip /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.tsv.gz

python fixGWAS4Munge.py /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.tsv /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.fixed4Munge.tsv



gzip /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.tsv
gzip /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.fixed4Munge.tsv
 
/project2/gilad/briana/ldsc/munge_sumstats.py --sumstats /project2/yangili1/zpmu/GWAS_loci/27863252/baso_neut_sum_N170143_narrow_form.fixed4Munge.tsv.gz --out /project2/gilad/briana/apaQTL/data/LDSR_annotations/Munge/baso_neut_sum_N170143_narrow_form.munge --merge-alleles /project2/yangili1/zpmu/ldsc/data/w_hm3.snplist --chunksize 500000
    
#2  
gunzip /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.tsv.gz


python fixGWAS4Munge.py /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.tsv /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.fixed4Munge.tsv   

gzip /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.fixed4Munge.tsv   

gzip /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.tsv

 /project2/gilad/briana/ldsc/munge_sumstats.py \
    --sumstats /project2/yangili1/zpmu/GWAS_loci/27863252/eo_baso_sum_N171771_narrow_form.fixed4Munge.tsv.gz \
    --out /project2/gilad/briana/apaQTL/data/LDSR_annotations/Munge/eo_baso_sum_N171771_narrow_form.munge \
    --merge-alleles /project2/yangili1/zpmu/ldsc/data/w_hm3.snplist \
    --chunksize 500000

#3  

gunzip /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.tsv.gz


python fixGWAS4Munge.py /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.tsv /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.fixed4Munge.tsv 

gzip /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.fixed4Munge.tsv 
gzip /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.tsv


 /project2/gilad/briana/ldsc/munge_sumstats.py \
    --sumstats /project2/yangili1/zpmu/GWAS_loci/27863252/neut_eo_sum_N170384_narrow_form.fixed4Munge.tsv.gz \
    --out /project2/gilad/briana/apaQTL/data/LDSR_annotations/Munge/neut_eo_sum_N170384_narrow_form.munge \
    --merge-alleles /project2/yangili1/zpmu/ldsc/data/w_hm3.snplist \
    --chunksize 500000

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] forcats_0.3.0     stringr_1.3.1     dplyr_0.8.0.1    
 [4] purrr_0.3.2       readr_1.3.1       tidyr_0.8.3      
 [7] tibble_2.1.1      ggplot2_3.1.1     tidyverse_1.2.1  
[10] workflowr_1.6.0   data.table_1.12.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     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] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4