QTLs

Author: Brian M. Schilder

Updated: Mar-16-2026

library(echolocatoR)

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## ── 🦇  🦇  🦇 e c h o l o c a t o R 🦇  🦇  🦇 ─────────────────────────────────

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## ── v2.0.5 ──────────────────────────────────────────────────────────────────────

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## ⠊⠉⠡⣀⣀⠊⠉⠡⣀⣀⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠                                    
## ⓞ If you use echolocatoR or any of the echoverse subpackages, please cite:      
##      ▶ Brian M Schilder, Jack Humphrey, Towfique                                
##      Raj (2021) echolocatoR: an automated                                       
##      end-to-end statistical and functional                                      
##      genomic fine-mapping pipeline,                                             
##      Bioinformatics; btab658,                                                   
##      https://doi.org/10.1093/bioinformatics/btab658                             
## ⓞ Please report any bugs/feature requests on GitHub:
##      ▶
##      https://github.com/RajLabMSSM/echolocatoR/issues
## ⓞ Contributions are welcome!:
##      ▶
##      https://github.com/RajLabMSSM/echolocatoR/pulls

has_internet <- tryCatch(
    !is.null(curl::nslookup("github.com", error = FALSE)),
    error = function(e) FALSE
)

QTL pipeline

• Here, we will use GWAS-eQTL colocalization results provided via the echolocatoR Fine-mapping Portal, from the publication:

Lopes, K.d.P., Snijders, G.J.L., Humphrey, J. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet 54, 4–17 (2022). https://doi.org/10.1038/s41588-021-00976-y

Import QTL data

This data is actually merged GWAS-QTL colocalization results, but it contains all of the necessary columns from the original eQTL summary stats that we need to perform eQTL fine-mapping.

coloc_res <- echodata::get_Kunkle2019_coloc(return_path = TRUE)

Prepare colmap

Prepare a column mapping object for the summary statistics. We’ll reuse this for both the import_topSNPs and finemap_loci steps.

colmap <- echodata::construct_colmap(
      CHR = "chr",
      POS = "pos",
      N = "qtl.N",
      SNP = "snp",
      P = "qtl.pvalues",
      Effect = "qtl.beta",
      StdErr = "qtl.varbeta",
      MAF = "qtl.MAF",
      Locus = "Locus",
      Gene = "gene")

Prepare top_SNPs data.frame

• In this case, we don’t have a top SNPs file ready. So we’re just going to make one directly from the full summary stats file itself (NOTE: You can only use this approach if you can fit the entire file in memory).
• In this case, you’ll want to make sure to set grouping_vars=c("Locus","Gene") so that you get top SNPs for each eGene-locus pair (not just one SNP per locus).

topSNPs <- echodata::import_topSNPs(
  topSS = coloc_res$path,
  colmap = colmap,
  ## Important for QTLs: group by both Locus and Gene
  grouping_vars = c("Locus","Gene"))

## Loading required namespace: MungeSumstats

## Renaming column: chr ==> CHR

## Renaming column: pos ==> POS

## Renaming column: snp ==> SNP

## Renaming column: qtl.pvalues ==> P

## Renaming column: qtl.beta ==> Effect

## Renaming column: qtl.varbeta ==> StdErr

## Renaming column: qtl.MAF ==> MAF

## Renaming column: gene ==> Gene

## Renaming column: qtl.N ==> N

## [1] "+ Assigning Gene and Locus independently."

## Standardising column headers.

## First line of summary statistics file:

## Locus    SNP V.df1   z.df1   r.df1   lABF.df1    V.df2   z.df2   r.df2   lABF.df2    internal.sum.lABF   SNP.PP.H4   gwas.pvalues    gwas.beta   gwas.varbeta    gwas.MAF    CHR POS A1  A2  gwas.N  gwas.type   s   Gene    P   Effect  StdErr  MAF QTL_chr QTL_pos N   qtl.type    N_cases N_controls  

## Returning unmapped column names without making them uppercase.

## + Mapping colnames from MungeSumstats ==> echolocatoR

head(topSNPs)

## Key: <Locus>
##                     Locus        SNP      V.df1      z.df1     r.df1   lABF.df1
##                    <char>     <char>      <num>      <num>     <num>      <num>
## 1:  ABCA7_ENSG00000160953 rs76951864 0.00227529 -1.2138365 0.9461792 -0.7639979
## 2:   BIN1_ENSG00000136731  rs4663110 0.00021904  2.0405405 0.9945538 -0.5358563
## 3:    CR1_ENSG00000266094  rs1518110 0.00030276 -1.0977011 0.9924879 -1.8476694
## 4: INPP5D_ENSG00000168918  rs1881492 0.00036100  0.1631579 0.9910557 -2.3451794
## 5: MS4A6A_ENSG00000149476  rs6591611 0.00028900 -1.4176471 0.9928268 -1.4710500
## 6:  PILRA_ENSG00000106366  rs2227631 0.00020736 -0.1041667 0.9948427 -2.6282771
##          V.df2     z.df2     r.df2   lABF.df2 internal.sum.lABF    SNP.PP.H4
##          <num>     <num>     <num>      <num>             <num>        <num>
## 1: 0.007802613 -5.826752 0.2374864  3.8958871         3.1318892 6.231637e-08
## 2: 0.004149176  2.778507 0.4321435  1.3851521         0.8492958 1.013056e-25
## 3: 0.002913938 -3.976592 0.4592553  3.3237628         1.4760934 4.495411e-11
## 4: 0.012164546 -2.654756 0.1470877  0.4387679        -1.9064116 3.587352e-08
## 5: 0.015193274  2.675112 0.2112492  0.6372210        -0.8338290 8.201862e-13
## 6: 0.006858275  8.459513 0.5879401 20.5941901        17.9659130 9.907667e-01
##    gwas.pvalues gwas.beta gwas.varbeta gwas.MAF   CHR       POS     A1     A2
##           <num>     <num>        <num>    <num> <num>     <num> <char> <char>
## 1:      0.22500   -0.0579   0.00227529   0.1054    19    374604      A      G
## 2:      0.04153    0.0302   0.00021904   0.6262     2 127922463      T      C
## 3:      0.27270   -0.0191   0.00030276   0.7803     1 206944861      A      C
## 4:      0.87100    0.0031   0.00036100   0.7763     2 233406998      T      G
## 5:      0.15760   -0.0241   0.00028900   0.2207    11  60405172      A      C
## 6:      0.91860   -0.0015   0.00020736   0.4056     7 100769538      A      G
##    gwas.N gwas.type     s            Gene           P    Effect      StdErr
##     <int>    <char> <num>          <char>       <num>     <num>       <num>
## 1:  94437        cc  0.37 ENSG00000160953 8.31563e-09 -0.514691 0.007802613
## 2:  94437        cc  0.37 ENSG00000136731 7.93573e-05  0.178975 0.004149176
## 3:  94437        cc  0.37 ENSG00000266094 1.00516e-04 -0.214660 0.002913938
## 4:  94437        cc  0.37 ENSG00000168918 6.42575e-06 -0.292801 0.012164546
## 5:  94437        cc  0.37 ENSG00000149476 2.20256e-04  0.329737 0.015193274
## 6:  94437        cc  0.37 ENSG00000106366 5.45534e-17  0.700572 0.006858275
##       Freq QTL_chr   QTL_pos     N qtl.type N_cases N_controls
##      <num>  <char>     <int> <int>   <char>   <int>      <int>
## 1: 0.08770   chr19    374604    90    quant   34941      59496
## 2: 0.34385    chr2 127164887    90    quant   34941      59496
## 3: 0.25363    chr1 206771516    90    quant   34941      59496
## 4: 0.14447    chr2 232542288    90    quant   34941      59496
## 5: 0.18503   chr11  60637699    90    quant   34941      59496
## 6: 0.42796    chr7 101126257    90    quant   34941      59496

Run fine-mapping pipeline

The following call runs the full fine-mapping pipeline on QTL data. This is computationally expensive and may take 10–30 minutes per locus.

res <- echolocatoR::finemap_loci(fullSS_path = coloc_res$path,
                                 topSNPs = topSNPs,
                                 ## Let's just fine-map 1 locus for demo purposes
                                 loci = topSNPs$Locus[1],
                                 dataset_name = "Kunkle_2019.microgliaQTL",
                                 dataset_type = "QTL",
                                 bp_distance = 1000,
                                 colmap = colmap,
                                 show_plot = TRUE,
                                 finemap_methods = c("ABF","FINEMAP","SUSIE") )

Next steps

• Explore and interpret results: vignette("explore_results")
• Visualize loci: vignette("plotting")
• Summarise across loci: vignette("summarise")
• GWAS fine-mapping (non-QTL): vignette("echolocatoR")

Session info

utils::sessionInfo()

## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.3.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] echolocatoR_2.0.5 BiocStyle_2.38.0 
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