Contents

1 Introduction

TxRegQuery addresses exploration of transcriptional regulatory networks by integrating data on eQTL, digital genomic footprinting (DGF), DnaseI hypersensitivity binding data (DHS), and transcription factor binding site (TFBS) data. Owing to the volume of emerging tissue-specific data, special data modalities are used.

2 Managing bed file content with mongodb

2.1 Querying the txregnet database

We have a long-running server that will respond to queries. We focus on mongolite as the interface.

2.1.1 The connection

suppressPackageStartupMessages({
library(TxRegInfra)
library(mongolite)
library(Gviz)
library(EnsDb.Hsapiens.v75)
library(BiocParallel)
register(SerialParam())
})
con1 = mongo(url=URL_txregInAWS(), db="txregnet")
con1
## <Mongo collection> 'test' 
##  $aggregate(pipeline = "{}", options = "{\"allowDiskUse\":true}", handler = NULL, pagesize = 1000, iterate = FALSE) 
##  $count(query = "{}") 
##  $disconnect(gc = TRUE) 
##  $distinct(key, query = "{}") 
##  $drop() 
##  $export(con = stdout(), bson = FALSE, query = "{}", fields = "{}", sort = "{\"_id\":1}") 
##  $find(query = "{}", fields = "{\"_id\":0}", sort = "{}", skip = 0, limit = 0, handler = NULL, pagesize = 1000) 
##  $import(con, bson = FALSE) 
##  $index(add = NULL, remove = NULL) 
##  $info() 
##  $insert(data, pagesize = 1000, stop_on_error = TRUE, ...) 
##  $iterate(query = "{}", fields = "{\"_id\":0}", sort = "{}", skip = 0, limit = 0) 
##  $mapreduce(map, reduce, query = "{}", sort = "{}", limit = 0, out = NULL, scope = NULL) 
##  $remove(query, just_one = FALSE) 
##  $rename(name, db = NULL) 
##  $replace(query, update = "{}", upsert = FALSE) 
##  $run(command = "{\"ping\": 1}", simplify = TRUE) 
##  $update(query, update = "{\"$set\":{}}", filters = NULL, upsert = FALSE, multiple = FALSE)

We will write methods that work with the ‘fields’ of this object.

There is not much explicit reflectance in the mongolite API. The following is improvised and may be fragile:

parent.env(con1)$orig
## $name
## [1] "test"
## 
## $db
## [1] "txregnet"
## 
## $url
## [1] "mongodb+srv://user:user123@txregnet-kui9i.mongodb.net/txregnet"
## 
## $options
## List of 6
##  $ pem_file              : NULL
##  $ ca_file               : NULL
##  $ ca_dir                : NULL
##  $ crl_file              : NULL
##  $ allow_invalid_hostname: logi FALSE
##  $ weak_cert_validation  : logi FALSE

2.1.2 Queries and aggregation

If the mongo utility is available as a system command, we can get a list of collections in the database as follows.

if (verifyHasMongoCmd()) {
  head(c1 <- listAllCollections(url=URL_txregInAWS(), db="txregnet"))
  }
## Error in system2(cmd, args = "--help", stdout = TRUE, stderr = TRUE) : 
##   error in running command
## install mongodb on your system to use this function

Otherwise, as long as mongolite is installed, as long as we know the collection names of interest, we can use them as noted throughout this vignette.

We can get a record from a given collection:

mongo(url=URL_txregInAWS(), db="txregnet", 
   collection="Adipose_Subcutaneous_allpairs_v7_eQTL")$find(limit=1)
##             gene_id       variant_id tss_distance ma_samples ma_count
## 1 ENSG00000238009.2 1_807994_C_T_b37       678771         17       18
##         maf pval_nominal     slope slope_se    qvalue chr snp_pos A1 A2
## 1 0.0233766   0.00126668 -0.759564 0.233531 0.0742794   1  807994  C  T
##   build
## 1   b37

Queries can be composed using JSON. We have a tool to generate queries that employ the mongodb aggregation method. Here we demonstrate this by computing, for each chromosome, the count and minimum values of the footprint statistic on CD14 cells.

m1 = mongo(url = URL_txregInAWS(), db = "txregnet",  collection="CD14_DS17215_hg19_FP")
newagg = makeAggregator( by="chr", vbl="stat", op="$min", opname="min")

The JSON layout of this aggregating query is

[
  {
    "$group": {
      "_id": ["$chr"],
      "count": {
        "$sum": [1]
      },
      "min": {
        "$min": ["$stat"]
      }
    }
  }
] 

Invocation returns a data frame:

head(m1$aggregate(newagg))
##     _id count        min
## 1  chrY   827 0.01907390
## 2 chr18 15868 0.06107950
## 3 chr10 40267 0.00601357
## 4  chr4 32947 0.02776440
## 5  chr6 54728 0.00565057
## 6 chr17 47987 0.01242310

3 An integrative container

We need to bind the metadata and information about the mongodb.

3.1 Sample metadata

The following turns a very ad hoc filtering of the collection names into a DataFrame.

# cd = makeColData() # works when mongo does
cd = TxRegInfra::basicColData
head(cd,2)
## DataFrame with 2 rows and 3 columns
##                                                  base        type
##                                           <character> <character>
## Adipose_Subcutaneous_allpairs_v7_eQTL         Adipose        eQTL
## Adipose_Visceral_Omentum_allpairs_v7_eQTL     Adipose        eQTL
##                                                                    mid
##                                                            <character>
## Adipose_Subcutaneous_allpairs_v7_eQTL         Subcutaneous_allpairs_v7
## Adipose_Visceral_Omentum_allpairs_v7_eQTL Visceral_Omentum_allpairs_v7

3.2 Extended RaggedExperiment

rme0 = RaggedMongoExpt(con1, colData=cd)
rme1 = rme0[, which(cd$type=="FP")]

A key method in development is subsetting the archive by genomic coordinates.

si = GenomeInfoDb::Seqinfo(genome="hg19")["chr17"] # to fix query genome
myg = GRanges("chr17", IRanges(38.07e6,38.09e6), seqinfo=si)
s1 = sbov(rme1, myg, simplify=FALSE)
## ..........................................
s1
## class: RaggedExperiment 
## dim: 1676 42 
## assays(3): chr id stat
## rownames: NULL
## colnames(42): CD14_DS17215_hg19_FP CD19_DS17186_hg19_FP ...
##   iPS_19_11_DS15153_hg19_FP vHMEC_DS18406_hg19_FP
## colData names(6): base type ... type mid
dim(sa <- sparseAssay(s1, 3))  # compact gives segfault
## [1] 1676   42
sa[953:956,c("fLung_DS14724_hg19_FP", "fMuscle_arm_DS17765_hg19_FP")]
##                         fLung_DS14724_hg19_FP fMuscle_arm_DS17765_hg19_FP
## chr17:38084160-38084169              0.533333                          NA
## chr17:38084924-38084952              0.890476                          NA
## chr17:38080857-38080891                    NA                     0.54902
## chr17:38081914-38081926                    NA                     0.50000

4 Visualizing coincidence

ormm = txmodels("ORMDL3", plot=FALSE, name="ORMDL3")
sar = strsplit(rownames(sa), ":|-")
an = as.numeric
gr = GRanges(seqnames(ormm)[1], IRanges(an(sapply(sar,"[", 2)), an(sapply(sar,"[", 3))))
gr1 = gr
gr1$score = 1-sa[,1]
gr2 = gr
gr2$score = 1-sa[,2]
sc1 = DataTrack(gr1, name="Lung FP")
sc2 = DataTrack(gr2, name="Musc/Arm FP")
plotTracks(list(GenomeAxisTrack(), sc1, sc2, ormm), showId=TRUE)

5 Higher-level work with sbov

5.1 Building annotated GRanges for a selected target interval

We begin with three ‘single-concept’ assays with relevance to lung genomics. The v7 GTEx lung eQTL data, an encode DnaseI narrowPeak report on lung fibroblasts, and a digital genomic footprint report for fetal lung.

lname_eqtl = "Lung_allpairs_v7_eQTL"
lname_dhs = "ENCFF001SSA_hg19_HS" # see dnmeta, fibroblast of lung
lname_fp = "fLung_DS14724_hg19_FP"
si17 = GenomeInfoDb::Seqinfo(genome="hg19")["chr17"]
si17n = si17
GenomeInfoDb::seqlevelsStyle(si17n) = "NCBI"
s1 = sbov(rme0[,lname_eqtl], GRanges("17", IRanges(38.06e6, 38.15e6),
    seqinfo=si17n))
## .
s2 = sbov(rme0[,lname_dhs], GRanges("chr17", IRanges(38.06e6, 38.15e6),
   seqinfo=si17))
## .
s3 = sbov(rme0[,lname_fp], GRanges("chr17", IRanges(38.06e6, 38.15e6),
   seqinfo=si17))
## .

Now we have annotated GRanges for each assay. The eQTL data in part are:

names(mcols(s1))
##  [1] "gene_id"      "variant_id"   "tss_distance" "ma_samples"  
##  [5] "ma_count"     "maf"          "pval_nominal" "slope"       
##  [9] "slope_se"     "qvalue"       "chr"          "snp_pos"     
## [13] "A1"           "A2"           "build"        "origin"
head(s1[, c("gene_id", "variant_id", "maf", "pval_nominal")])
## GRanges object with 6 ranges and 4 metadata columns:
##       seqnames    ranges strand |            gene_id          variant_id
##          <Rle> <IRanges>  <Rle> |           <factor>            <factor>
##   [1]       17  38061054      * |  ENSG00000266469.1 17_38061054_G_A_b37
##   [2]       17  38061439      * |  ENSG00000161395.8 17_38061439_T_C_b37
##   [3]       17  38061439      * | ENSG00000073605.14 17_38061439_T_C_b37
##   [4]       17  38061439      * |  ENSG00000172057.5 17_38061439_T_C_b37
##   [5]       17  38061439      * |  ENSG00000167914.6 17_38061439_T_C_b37
##   [6]       17  38062196      * |  ENSG00000161395.8 17_38062196_G_A_b37
##             maf pval_nominal
##       <numeric>    <numeric>
##   [1] 0.0195822  0.000772192
##   [2]  0.420366  0.000399212
##   [3]  0.420366  6.87714e-10
##   [4]  0.420366  1.08337e-10
##   [5]  0.420366  2.15704e-10
##   [6]  0.418848  0.000309568
##   -------
##   seqinfo: 1 sequence from hg19 genome

The names of genes and variants used here are cumbersome – symbols and rsids are preferable.

addsyms = function(x, EnsDb=EnsDb.Hsapiens.v75::EnsDb.Hsapiens.v75) {
  ensids = gsub("\\..*", "", x$gene_id) # remove post period
  gns = genes(EnsDb)
  x$symbol = gns[ensids]$symbol
  x
}
s1 = addsyms(s1)

Note that it is possible to retrieve rsids for the SNPs by address. But this is a slow operation involving a huge SNPlocs package that we do not want to work with directly for this vignette.

> snpsByOverlaps(SNPlocs.Hsapiens.dbSNP144.GRCh37, s1b)
UnstitchedGPos object with 265 positions and 2 metadata columns:
        seqnames       pos strand |   RefSNP_id alleles_as_ambig
           <Rle> <integer>  <Rle> | <character>      <character>
    [1]       17  38061054      * |  rs36049276                R
    [2]       17  38061439      * |   rs4795399                Y
    [3]       17  38062196      * |   rs2305480                R
    [4]       17  38062217      * |   rs2305479                Y
    [5]       17  38062503      * |  rs35104165                Y
    ...      ...       ...    ... .         ...              ...
  [261]       17  38149258      * |  rs58212353                K
  [262]       17  38149350      * |   rs8073254                V
  [263]       17  38149411      * |  rs34648856                R
  [264]       17  38149724      * |   rs3785549                Y
  [265]       17  38149727      * |   rs3785550                H
  -------
  seqinfo: 25 sequences (1 circular) from GRCh37.p13 genome

5.2 A bipartite graph for eQTL-gene relationships

The object s1 computed above is available as demo_eQTL_granges. We convert it to a graph via

library(graph)
## 
## Attaching package: 'graph'
## The following object is masked from 'package:Biostrings':
## 
##     complement
g1 = sbov_to_graphNEL(demo_eQTL_granges)
g1
## A graphNEL graph with directed edges
## Number of Nodes = 312 
## Number of Edges = 693

Nodes are SNPs and genes, edges are present when the resource (in this case the GTEx lung study) declares an association (in this case, an FDR for SNP-gene association not exceeding 0.10.) The graph library includes functions for creation of incidence matrices from graphs, and vice versa.

5.3 Connecting eQTL-SNPs via DHS and DGF

Given the GRanges representations for sbov results, we can use overlap computations to conveniently identify relationships between eQTL SNPs, genes, and hypersensitivity or footprint regions.

We use sbov_output_HS as a persistent instance of s2 computed above.

seqlevelsStyle(demo_eQTL_granges) = "UCSC"
fo1 = findOverlaps(demo_eQTL_granges, sbov_output_HS)
fo1 
## Hits object with 11 hits and 0 metadata columns:
##        queryHits subjectHits
##        <integer>   <integer>
##    [1]       205           2
##    [2]       206           2
##    [3]       207           2
##    [4]       458           9
##    [5]       459           9
##    [6]       460           9
##    [7]       461           9
##    [8]       462           9
##    [9]       463           9
##   [10]       464           9
##   [11]       465           9
##   -------
##   queryLength: 693 / subjectLength: 11
eq_by_hs = split(demo_eQTL_granges[queryHits(fo1)],
   subjectHits(fo1))
eq_by_hs
## GRangesList object of length 2:
## $`2`
## GRanges object with 3 ranges and 17 metadata columns:
##       seqnames    ranges strand |            gene_id          variant_id
##          <Rle> <IRanges>  <Rle> |           <factor>         <character>
##   [1]    chr17  38085385      * | ENSG00000073605.14 17_38085385_A_C_b37
##   [2]    chr17  38085385      * |  ENSG00000172057.5 17_38085385_A_C_b37
##   [3]    chr17  38085385      * |  ENSG00000264968.1 17_38085385_A_C_b37
##       tss_distance ma_samples  ma_count       maf pval_nominal     slope
##          <integer>  <integer> <integer> <numeric>    <numeric> <numeric>
##   [1]        10482        172       207  0.270235   3.7188e-08  0.161276
##   [2]         1531        172       207  0.270235  3.07153e-09  0.193448
##   [3]         1390        172       207  0.270235    0.0004948 -0.230682
##        slope_se               qvalue       chr   snp_pos       A1       A2
##       <numeric>            <numeric> <integer> <integer> <factor> <factor>
##   [1] 0.0285803 9.35454706201541e-06        17  38085385        A        C
##   [2] 0.0317052 9.35523646756825e-07        17  38085385        A        C
##   [3]  0.065533   0.0400268770769804        17  38085385        A        C
##          build                origin        symbol
##       <factor>           <character>   <character>
##   [1]      b37 Lung_allpairs_v7_eQTL         GSDMB
##   [2]      b37 Lung_allpairs_v7_eQTL        ORMDL3
##   [3]      b37 Lung_allpairs_v7_eQTL RP11-387H17.4
##   -------
##   seqinfo: 1 sequence from hg19 genome
## 
## $`9`
## GRanges object with 8 ranges and 17 metadata columns:
##       seqnames    ranges strand |            gene_id            variant_id
##          <Rle> <IRanges>  <Rle> |           <factor>           <character>
##   [1]    chr17  38115299      * |  ENSG00000167914.6   17_38115299_C_T_b37
##   [2]    chr17  38115299      * |  ENSG00000188895.7   17_38115299_C_T_b37
##   [3]    chr17  38115429      * | ENSG00000073605.14 17_38115429_C_CTG_b37
##   [4]    chr17  38115429      * |  ENSG00000172057.5 17_38115429_C_CTG_b37
##   [5]    chr17  38115429      * |  ENSG00000167914.6 17_38115429_C_CTG_b37
##   [6]    chr17  38115430      * | ENSG00000073605.14   17_38115430_A_C_b37
##   [7]    chr17  38115430      * |  ENSG00000172057.5   17_38115430_A_C_b37
##   [8]    chr17  38115430      * |  ENSG00000167914.6   17_38115430_A_C_b37
##       tss_distance ma_samples  ma_count       maf pval_nominal     slope
##          <integer>  <integer> <integer> <numeric>    <numeric> <numeric>
##   [1]        -3927         59        62 0.0809399  1.73014e-06 -0.530695
##   [2]      -163252         59        62 0.0809399  0.000512527 -0.197247
##   [3]        40526        267       350  0.456919  3.30451e-06  0.126418
##   [4]        31575        267       350  0.456919  6.33228e-06  0.137325
##   [5]        -3797        267       350  0.456919  1.04416e-17  -0.49968
##   [6]        40527        267       350  0.456919  3.30451e-06  0.126418
##   [7]        31576        267       350  0.456919  6.33228e-06  0.137325
##   [8]        -3796        267       350  0.456919  1.04416e-17  -0.49968
##        slope_se               qvalue       chr   snp_pos       A1       A2
##       <numeric>            <numeric> <integer> <integer> <factor> <factor>
##   [1]  0.108872  0.00030774973449386        17  38115299        C        T
##   [2] 0.0561895   0.0411569937076073        17  38115299        C        T
##   [3] 0.0266958 0.000547360697034689        17  38115429        C      CTG
##   [4]  0.029902 0.000976300253765482        17  38115429        C      CTG
##   [5] 0.0549122  8.9303103388208e-15        17  38115429        C      CTG
##   [6] 0.0266958 0.000547360697034689        17  38115430        A        C
##   [7]  0.029902 0.000976300253765482        17  38115430        A        C
##   [8] 0.0549122  8.9303103388208e-15        17  38115430        A        C
##          build                origin      symbol
##       <factor>           <character> <character>
##   [1]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   [2]      b37 Lung_allpairs_v7_eQTL        MSL1
##   [3]      b37 Lung_allpairs_v7_eQTL       GSDMB
##   [4]      b37 Lung_allpairs_v7_eQTL      ORMDL3
##   [5]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   [6]      b37 Lung_allpairs_v7_eQTL       GSDMB
##   [7]      b37 Lung_allpairs_v7_eQTL      ORMDL3
##   [8]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   -------
##   seqinfo: 1 sequence from hg19 genome

This shows that there are two DHS sites that overlap with SNPs showing eQTL associations with various genes.

For the footprint data, we have:

fo2 = findOverlaps(demo_eQTL_granges, sbov_output_FP)
fo2 
## Hits object with 4 hits and 0 metadata columns:
##       queryHits subjectHits
##       <integer>   <integer>
##   [1]       348          44
##   [2]       349          44
##   [3]       613         101
##   [4]       614         101
##   -------
##   queryLength: 693 / subjectLength: 107
eq_by_fp = split(demo_eQTL_granges[queryHits(fo2)],
   subjectHits(fo2))
eq_by_fp
## GRangesList object of length 2:
## $`44`
## GRanges object with 2 ranges and 17 metadata columns:
##       seqnames    ranges strand |           gene_id          variant_id
##          <Rle> <IRanges>  <Rle> |          <factor>         <character>
##   [1]    chr17  38109075      * | ENSG00000172057.5 17_38109075_T_C_b37
##   [2]    chr17  38109075      * | ENSG00000167914.6 17_38109075_T_C_b37
##       tss_distance ma_samples  ma_count       maf pval_nominal     slope
##          <integer>  <integer> <integer> <numeric>    <numeric> <numeric>
##   [1]        25221        182       203  0.285915  0.000335618  0.135266
##   [2]       -10151        182       203  0.285915  2.23245e-14 -0.555363
##        slope_se               qvalue       chr   snp_pos       A1       A2
##       <numeric>            <numeric> <integer> <integer> <factor> <factor>
##   [1] 0.0373061    0.029325773492785        17  38109075        T        C
##   [2] 0.0693376 1.36746052792526e-11        17  38109075        T        C
##          build                origin      symbol
##       <factor>           <character> <character>
##   [1]      b37 Lung_allpairs_v7_eQTL      ORMDL3
##   [2]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   -------
##   seqinfo: 1 sequence from hg19 genome
## 
## $`101`
## GRanges object with 2 ranges and 17 metadata columns:
##       seqnames    ranges strand |            gene_id          variant_id
##          <Rle> <IRanges>  <Rle> |           <factor>         <character>
##   [1]    chr17  38137033      * | ENSG00000008838.13 17_38137033_A_G_b37
##   [2]    chr17  38137033      * |  ENSG00000167914.6 17_38137033_A_G_b37
##       tss_distance ma_samples  ma_count       maf pval_nominal     slope
##          <integer>  <integer> <integer> <numeric>    <numeric> <numeric>
##   [1]       -80435        274       359  0.468668  0.000771652   0.12397
##   [2]        17807        274       359  0.468668  5.75457e-33  0.679408
##        slope_se               qvalue       chr   snp_pos       A1       A2
##       <numeric>            <numeric> <integer> <integer> <factor> <factor>
##   [1] 0.0365067   0.0565349739572534        17  38137033        A        G
##   [2] 0.0504649 1.32845831960793e-29        17  38137033        A        G
##          build                origin      symbol
##       <factor>           <character> <character>
##   [1]      b37 Lung_allpairs_v7_eQTL       MED24
##   [2]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   -------
##   seqinfo: 1 sequence from hg19 genome

5.4 Relationships to FIMO-based TFBS

We have a small number of cloud-resident FIMO search results through the TFutils package.

library(TFutils)
data(demo_fimo_granges)
seqlevelsStyle(demo_eQTL_granges) = "UCSC"
lapply(demo_fimo_granges, lapply, function(x) 
   subsetByOverlaps(demo_eQTL_granges, x))
## $VDR
## $VDR$`chr17:38070000-38090000`
## GRanges object with 0 ranges and 17 metadata columns:
##    seqnames    ranges strand |  gene_id  variant_id tss_distance ma_samples
##       <Rle> <IRanges>  <Rle> | <factor> <character>    <integer>  <integer>
##     ma_count       maf pval_nominal     slope  slope_se    qvalue       chr
##    <integer> <numeric>    <numeric> <numeric> <numeric> <numeric> <integer>
##      snp_pos       A1       A2    build      origin      symbol
##    <integer> <factor> <factor> <factor> <character> <character>
##   -------
##   seqinfo: 1 sequence from hg19 genome
## 
## 
## $POU2F1
## $POU2F1$`chr17:38070000-38090000`
## GRanges object with 8 ranges and 17 metadata columns:
##       seqnames    ranges strand |            gene_id             variant_id
##          <Rle> <IRanges>  <Rle> |           <factor>            <character>
##   [1]    chr17  38073968      * |  ENSG00000161395.8    17_38073968_G_C_b37
##   [2]    chr17  38073968      * | ENSG00000073605.14    17_38073968_G_C_b37
##   [3]    chr17  38073968      * |  ENSG00000172057.5    17_38073968_G_C_b37
##   [4]    chr17  38073968      * |  ENSG00000167914.6    17_38073968_G_C_b37
##   [5]    chr17  38076198      * |  ENSG00000161395.8 17_38076198_TATA_T_b37
##   [6]    chr17  38076198      * | ENSG00000073605.14 17_38076198_TATA_T_b37
##   [7]    chr17  38076198      * |  ENSG00000172057.5 17_38076198_TATA_T_b37
##   [8]    chr17  38076198      * |  ENSG00000167914.6 17_38076198_TATA_T_b37
##       tss_distance ma_samples  ma_count       maf pval_nominal     slope
##          <integer>  <integer> <integer> <numeric>    <numeric> <numeric>
##   [1]       220918        251       321   0.41906  0.000246542 0.0992119
##   [2]         -935        251       321   0.41906  6.84037e-10  0.172741
##   [3]        -9886        251       321   0.41906  4.19548e-11  0.205608
##   [4]       -45258        251       321   0.41906  4.86746e-10 -0.388867
##   [5]       223148        285       378  0.497368   4.6723e-06  0.120394
##   [6]         1295        285       378  0.497368  3.28042e-15  0.211642
##   [7]        -7656        285       378  0.497368  1.62754e-14  0.231067
##   [8]       -43028        285       378  0.497368  1.64858e-08 -0.346576
##        slope_se               qvalue       chr   snp_pos       A1       A2
##       <numeric>            <numeric> <integer> <integer> <factor> <factor>
##   [1] 0.0267552   0.0228108733681083        17  38073968        G        C
##   [2] 0.0271367 2.32091930777634e-07        17  38073968        G        C
##   [3] 0.0300749 1.71833992847155e-08        17  38073968        G        C
##   [4] 0.0605305 1.69012834889834e-07        17  38073968        G        C
##   [5] 0.0258368 0.000745086232487142        17  38076198     TATA        T
##   [6] 0.0255291 2.19298356188838e-12        17  38076198     TATA        T
##   [7] 0.0286818 1.01078884855481e-11        17  38076198     TATA        T
##   [8]  0.059801 4.42486787325189e-06        17  38076198     TATA        T
##          build                origin      symbol
##       <factor>           <character> <character>
##   [1]      b37 Lung_allpairs_v7_eQTL       PGAP3
##   [2]      b37 Lung_allpairs_v7_eQTL       GSDMB
##   [3]      b37 Lung_allpairs_v7_eQTL      ORMDL3
##   [4]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   [5]      b37 Lung_allpairs_v7_eQTL       PGAP3
##   [6]      b37 Lung_allpairs_v7_eQTL       GSDMB
##   [7]      b37 Lung_allpairs_v7_eQTL      ORMDL3
##   [8]      b37 Lung_allpairs_v7_eQTL       GSDMA
##   -------
##   seqinfo: 1 sequence from hg19 genome