Input data

Brian has Fine-mapped all the disease GWAS and provided the following:

  1. microgliaQTL_multiGWAS.finemapping_merged.csv.gz For all loci that colocalise with MiGA eQTLs: The fine-mapped SNPs and which tools they were proposed by

I have performed COLOC using the same GWAS

The lead GWAS SNP should be defined by the summary stats (within all_coloc), not by Brian.

TODO - fix lead GWAS SNP issue - so that lead GWAS SNP from COLOC has hg19 coordinates. For this either

take the original summary stat files as they have the hg19 coordinates lift back over the COLOC hg38 coordinates to hg19 (more feasible).

Prepare fine-mapping results

Prepare COLOC

This requires getting the hg19 coordinates for all lead SNPs

## 
## FALSE  TRUE 
##     6   214

FUNCTIONS

binding the lead QTL SNP to the fine-mapped GWAS SNPs for that locus getting the LD between them (using LDlinkR) overlapping Nott promoters and enhancers

## [1] " * calculating LD with LDlinkR"

## [1] " * calculating LD with LDlinkR"

Sanity Checks

Verify that all COLOC loci have been fine-mapped

Verify that all lead QTL SNPs in COLOC are also lead QTL SNPs according to Brian’s table

## 
## FALSE  TRUE 
##     5   425
## # A tibble: 5 x 3
##   Dataset           Locus                fine_mapped
##   <chr>             <chr>                <lgl>      
## 1 IMSGC_2019        locus_chr7_rs4728142 FALSE      
## 2 Jansen_2018       ALPK2                FALSE      
## 3 Lambert_2013      CD33                 FALSE      
## 4 Lambert_2013      ABCA7                FALSE      
## 5 Nalls23andMe_2019 CASC16               FALSE
## # A tibble: 8 x 2
##   Dataset           `sum(fine_mapped)/n()`
##   <chr>                              <dbl>
## 1 IMSGC_2019                         0.993
## 2 Jansen_2018                        0.964
## 3 Kunkle_2019                        1    
## 4 Lambert_2013                       0.895
## 5 Marioni_2018                       1    
## 6 Nalls23andMe_2019                  0.986
## 7 Ripke_2014                         1    
## 8 Stahl_2019                         1

Create overlaps - save tables and plots

## [1] "locus_chr16_rs3809627"
## [1] "locus_chr22_rs140522"
## [1] "locus_chr16_rs3809627"
## [1] "locus_chr12_rs701006"
## [1] "locus_chr16_rs3809627"
## [1] "locus_chr8_rs28703878"
## [1] "locus_chr22_rs4820955"
## [1] "locus_chr1_chr1-32738415"
## [1] "locus_chr12_rs701006"
## [1] "locus_chr1_rs67934705"
## [1] "locus_chr11_rs6589939"
## [1] "locus_chr16_chr16-11213951"
## [1] "locus_chr17_rs9909593"
## [1] "locus_chr19_rs1465697"
## [1] "locus_chr16_rs3809627"
## [1] "locus_chr19_rs28834106"
## [1] "locus_chr1_chr1-32738415"
## [1] "BIN1"
## [1] "ECHDC3"
## [1] "ECHDC3"
## [1] "PICALM"
## [1] "KAT8"
## [1] "CASS4"
## [1] "CR1"
## [1] "BZRAP1-AS1"
## [1] "ADAM10"
## [1] "KAT8"
## [1] "CD33"
## [1] "PICALM"
## [1] "BIN1"
## [1] "CASS4"
## [1] "CR1"
## [1] "IQCK"
## [1] "ECHDC3"
## [1] "SPI1"
## [1] "PICALM"
## [1] "BIN1"
## [1] "NME8"
## [1] "EPHA1"
## [1] "CASS4"
## [1] "CR1"
## [1] "NME8"
## [1] "EPHA1"
## [1] "PICALM"
## [1] "ECHDC3"
## [1] "ECHDC3"
## [1] "BIN1"
## [1] "CASS4"
## [1] "CR1"
## [1] "ADAM10"
## [1] "SPI1"
## [1] "TMEM163"
## [1] "BIN3"
## [1] "CD19"
## [1] "IP6K2"
## [1] "MED12L"
## [1] "GPNMB"
## [1] "ITPKB"
## [1] "GPNMB"
## [1] "CTSB"
## [1] "CD19"
## [1] "GCH1"
## [1] "FGF20"
## [1] "CHRNB1"
## [1] "SETD1A"
## [1] "MED12L"
## [1] "CD19"
## [1] "CLCN3"
## [1] "SETD1A"
## [1] "16"
## [1] "42"
## [1] "51"
## [1] "16"
## [1] "17"
## [1] "21"
## [1] "15"
## [1] "40"
## [1] "33"
## # A tibble: 79 x 39
##    Dataset  Locus  Gene  overlap_df overlap_plot max_R2 microglia_enhan… plot_file  disease GWAS_SNP   GWAS_P GWAS_chr GWAS_pos QTL   type  QTL_SNP    QTL_P QTL_Beta QTL_MAF QTL_chr QTL_pos QTL_junction QTL_Ensembl nsnps PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf cell_type SNP_distance     LD
##    <chr>    <chr>  <chr> <list>     <list>        <dbl> <lgl>            <chr>      <chr>   <chr>       <dbl> <chr>       <dbl> <chr> <chr> <chr>      <dbl>    <dbl>   <dbl> <chr>     <dbl> <chr>        <chr>       <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> <chr>            <dbl>  <dbl>
##  1 IMSGC_2… locus… QPRT  <data.tab… <patchwrk>    0.72  FALSE            plots/eQT… MS      rs38096… 3.25e- 8 chr16    30091839 MiGA… eQTL  rs1483… 5.98e- 9   -0.332  0.401  chr16    2.97e7 .            ENSG000001…  1386         0    0.0420         0    0.0257     0.932 Microglia       408771  0.004
##  2 IMSGC_2… locus… PLXN… <data.tab… <patchwrk>    1     FALSE            plots/eQT… MS      rs140522 2.85e-10 chr22    50532837 MiGA… eQTL  rs9306… 1.43e- 7    0.206  0.314  chr22    5.04e7 .            ENSG000001…  2603         0    0.0463         0    0.0976     0.856 Microglia        95189  0.01 
##  3 IMSGC_2… locus… ITGAL <data.tab… <patchwrk>    0.067 FALSE            plots/eQT… MS      rs38096… 3.25e- 8 chr16    30091839 MiGA… eQTL  rs2549… 1.09e- 7    0.562  0.0895 chr16    2.96e7 .            ENSG000000…   988         0    0.133          0    0.0477     0.819 Microglia       498741  0.022
##  4 IMSGC_2… locus… STAT2 <data.tab… <patchwrk>    0.09  FALSE            plots/eQT… MS      rs701006 1.35e-11 chr12    57713053 MiGA… eQTL  rs1117… 3.28e- 6    0.338  0.489  chr12    5.73e7 .            ENSG000001…   745         0    0.139          0    0.0501     0.811 Microglia       450131  0.038
##  5 IMSGC_2… locus… PAGR1 <data.tab… <patchwrk>    0.006 FALSE            plots/eQT… MS      rs38096… 3.25e- 8 chr16    30091839 MiGA… eQTL  rs1176… 2.29e- 6    0.532  0.112  chr16    2.98e7 .            ENSG000002…  1434         0    0.167          0    0.0307     0.802 Microglia       253597  0.001
##  6 IMSGC_2… locus… ZC2H… <data.tab… <patchwrk>    0.94  FALSE            plots/eQT… MS      rs28703… 4.51e-10 chr8     78504987 MiGA… eQTL  rs1095… 3.03e-16    0.528  0.304  chr8     7.87e7 .            ENSG000001…  3191         0    0.132          0    0.0797     0.788 Microglia       190671  0.069
##  7 IMSGC_2… locus… SLC5… <data.tab… <patchwrk>    0.032 FALSE            plots/eQT… MS      rs48209… 1.82e- 5 chr22    31226553 MiGA… eQTL  rs7288… 1.50e- 5   -0.271  0.367  chr22    3.15e7 .            ENSG000002…  1207         0    0.241          0    0.0383     0.721 Microglia       229868 NA    
##  8 IMSGC_2… locus… TMEM… <data.tab… <patchwrk>    0.005 FALSE            plots/eQT… MS      chr1:32… 5.04e- 7 chr1     32272814 MiGA… eQTL  rs3737… 3.27e- 5   -0.586  0.0952 chr1     3.26e7 .            ENSG000001…   889         0    0.234          0    0.0553     0.710 Microglia       348394 NA    
##  9 IMSGC_2… locus… TIME… <data.tab… <patchwrk>    0.09  FALSE            plots/eQT… MS      rs701006 1.35e-11 chr12    57713053 MiGA… eQTL  rs1117… 1.52e- 5    0.327  0.489  chr12    5.73e7 .            ENSG000001…   830         0    0.231          0    0.0807     0.688 Microglia       450131  0.038
## 10 IMSGC_2… locus… E2F2  <data.tab… <patchwrk>    0.026 FALSE            plots/eQT… MS      rs67934… 1.01e- 5 chr1     23881014 MiGA… eQTL  rs7989… 1.62e- 4   -0.404  0.0974 chr1     2.41e7 .            ENSG000000…  2467         0    0.232          0    0.0799     0.688 Microglia       247398 NA    
## # … with 69 more rows, and 7 more variables: gene_snp <chr>, mashr_lsfr <lgl>, distance_filter <chr>, QTL_pos_hg19 <dbl>, QTL_chr_hg19 <dbl>, GWAS_pos_hg19 <dbl>, GWAS_chr_hg19 <dbl>

MiGA eQTLs

Fine-mapping and Epigenomic overlap plots

Restricted to loci with R^2 between QTL and GWAS > 0.1 and a COLOC PP H4 > 0.5

Alzheimer’s Disease

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Parkinson’s Disease

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Multiple Sclerosis

Schizophrenia

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Bipolar Disorder

CRISPR candidates

USP6NL

P2RY12

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MiGA splicing QTLs

CD33

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MS4A6A