LD score regression

Last updated: 2020-02-07

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/RNAbinding.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.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/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
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
html	6c6a1d2	brimittleman	2020-02-05	Build site.
Rmd	a1f5355	brimittleman	2020-02-05	add potential mechanisms and ld scripts
html	3d9f5c7	brimittleman	2020-01-31	Build site.
Rmd	fd5ccd7	brimittleman	2020-01-31	add LD regress notes and first var in apa

library(data.table)
library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

library(tidyverse)

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

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✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ 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)

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=12)

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 scroe regressino results for 1000 BP around each PAS", y="% of heritability explained")

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 scroe regressino results for 1000 BP around each PAS")

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_1.5.0   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

LD score regression

Briana Mittleman

1/30/2020

code to rerun those that needed manual fixing for trait: