I like to run QTL analysis using Matrixeqtl on the normalized beta values from my epic array to the genotypes of the same samples.

The issue is epic array data run ( Minfi & DMRcate )is based on IlluminaHumanMethylationEPICanno.ilm10b4.hg19 and the Genotype is based on Grch38.

How could I liftover the Position of CpgIDs from hg19 to grch38 to run QTL analysis(matrixQTL needs gene(cpg) cordinates)

Do you think ensembl lifover "https://asia.ensembl.org/Homo_sapiens/Tools/AssemblyConverter?db=core" and using the bed file formate a way?

`sessionInfo()`

> R version 3.5.1 (2018-07-02)

Platform: x86_64-conda_cos6-linux-gnu (64-bit)

Running under: CentOS Linux 7 (Core)



Matrix products: default

BLAS/LAPACK: /scratch/mv83/Software/DMRcate/lib/R/lib/libRblas.so



locale:

 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              

 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    

 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   

 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 

 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       



attached base packages:

 [1] splines   stats4    parallel  stats     graphics  grDevices utils    

 [8] datasets  methods   base     



other attached packages:

 [1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0

 [2] DMRcate_1.18.0                                     

 [3] DMRcatedata_1.16.0                                 

 [4] DSS_2.30.0                                         

 [5] bsseq_1.18.0                                       

 [6] minfi_1.28.0                                       

 [7] bumphunter_1.24.5                                  

 [8] locfit_1.5-9.1                                     

 [9] iterators_1.0.10                                   

[10] foreach_1.4.4                                      

[11] Biostrings_2.50.2                                  

[12] XVector_0.22.0                                     

[13] SummarizedExperiment_1.12.0                        

[14] DelayedArray_0.8.0                                 

[15] BiocParallel_1.16.2                                

[16] matrixStats_0.54.0                                 

[17] Biobase_2.42.0                                     

[18] GenomicRanges_1.34.0                               

[19] GenomeInfoDb_1.18.1                                

[20] IRanges_2.16.0                                     

[21] S4Vectors_0.20.1                                   

[22] BiocGenerics_0.28.0                                



loaded via a namespace (and not attached):

  [1] backports_1.1.3                                   

  [2] Hmisc_4.1-1                                       

  [3] plyr_1.8.4                                        

  [4] lazyeval_0.2.2                                    

  [5] ggplot2_3.1.1                                     

  [6] digest_0.6.18                                     

  [7] ensembldb_2.6.3                                   

  [8] htmltools_0.3.6                                   

  [9] GO.db_3.7.0                                       

 [10] magrittr_1.5                                      

 [11] checkmate_1.9.1                                   

 [12] memoise_1.1.0                                     

 [13] BSgenome_1.50.0                                   

 [14] cluster_2.0.8                                     

 [15] limma_3.36.5                                      

 [16] readr_1.3.1                                       

 [17] annotate_1.60.0                                   

 [18] R.utils_2.8.0                                     

 [19] askpass_1.1                                       

 [20] siggenes_1.56.0                                   

 [21] prettyunits_1.0.2                                 

 [22] colorspace_1.4-1                                  

 [23] blob_1.1.1                                        

 [24] BiasedUrn_1.07                                    

 [25] xfun_0.6                                          

 [26] dplyr_0.8.0.1                                     

 [27] crayon_1.3.4                                      

 [28] RCurl_1.95-4.12                                   

 [29] genefilter_1.64.0                                 

 [30] GEOquery_2.50.0                                   

 [31] IlluminaHumanMethylationEPICmanifest_0.3.0        

 [32] survival_2.44-1.1                                 

 [33] VariantAnnotation_1.28.3                          

 [34] glue_1.3.1                                        

 [35] ruv_0.9.6                                         

 [36] registry_0.5                                      

 [37] gtable_0.3.0                                      

 [38] zlibbioc_1.28.0                                   

 [39] Rhdf5lib_1.4.3                                    

 [40] HDF5Array_1.10.1                                  

 [41] scales_1.0.0                                      

 [42] DBI_1.0.0                                         

 [43] rngtools_1.3.1                                    

 [44] bibtex_0.4.2                                      

 [45] Rcpp_1.0.1                                        

 [46] xtable_1.8-3                                      

 [47] progress_1.2.0                                    

 [48] htmlTable_1.13.1                                  

 [49] foreign_0.8-71                                    

 [50] bit_1.1-14                                        

 [51] mclust_5.4.3                                      

 [52] preprocessCore_1.44.0                             

 [53] Formula_1.2-3                                     

 [54] missMethyl_1.16.0                                 

 [55] htmlwidgets_1.3                                   

 [56] httr_1.4.0                                        

 [57] RColorBrewer_1.1-2                                

 [58] acepack_1.4.1                                     

 [59] pkgconfig_2.0.2                                   

 [60] reshape_0.8.8                                     

 [61] XML_3.98-1.16                                     

 [62] R.methodsS3_1.7.1                                 

 [63] Gviz_1.26.4                                       

 [64] nnet_7.3-12                                       

 [65] tidyselect_0.2.5                                  

 [66] rlang_0.3.3                                       

 [67] AnnotationDbi_1.44.0                              

 [68] munsell_0.5.0                                     

 [69] tools_3.5.1                                       

 [70] RSQLite_2.1.1                                     

 [71] stringr_1.4.0                                     

 [72] org.Hs.eg.db_3.7.0                                

 [73] knitr_1.22                                        

 [74] bit64_0.9-7                                       

 [75] beanplot_1.2                                      

 [76] methylumi_2.28.0                                  

 [77] purrr_0.3.2                                       

 [78] AnnotationFilter_1.6.0                            

 [79] nlme_3.1-137                                      

 [80] doRNG_1.7.1                                       

 [81] nor1mix_1.2-3                                     

 [82] R.oo_1.22.0                                       

 [83] xml2_1.2.0                                        

 [84] biomaRt_2.38.0                                    

 [85] compiler_3.5.1                                    

 [86] rstudioapi_0.10                                   

 [87] curl_3.3                                          

 [88] statmod_1.4.30                                    

 [89] tibble_2.1.1                                      

 [90] stringi_1.4.3                                     

 [91] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0

 [92] GenomicFeatures_1.34.1                            

 [93] lattice_0.20-38                                   

 [94] ProtGenerics_1.14.0                               

 [95] Matrix_1.2-17                                     

 [96] IlluminaHumanMethylation450kmanifest_0.4.0        

 [97] permute_0.9-5                                     

 [98] multtest_2.38.0                                   

 [99] pillar_1.3.1                                      

[100] data.table_1.12.2                                 

[101] bitops_1.0-6                                      

[102] rtracklayer_1.42.1                                

[103] R6_2.4.0                                          

[104] latticeExtra_0.6-28                               

[105] gridExtra_2.3                                     

[106] codetools_0.2-16                                  

[107] dichromat_2.0-0                                   

[108] MASS_7.3-51.3                                     

[109] gtools_3.8.1                                      

[110] assertthat_0.2.1                                  

[111] rhdf5_2.26.2                                      

[112] openssl_1.3                                       

[113] pkgmaker_0.27                                     

[114] withr_2.1.2                                       

[115] GenomicAlignments_1.18.1                          

[116] Rsamtools_1.34.0                                  

[117] GenomeInfoDbData_1.1.0                            

[118] hms_0.4.2                                         

[119] quadprog_1.5-5                                    

[120] grid_3.5.1                                        

[121] rpart_4.1-13                                      

[122] tidyr_0.8.3                                       

[123] base64_2.0                                        

[124] DelayedMatrixStats_1.4.0                          

[125] illuminaio_0.24.0                                 

[126] biovizBase_1.30.1                                 

[127] base64enc_0.1-3`

Given that MatrixEQTL is a CRAN package, your question is halfway in the wrong place. But if all you want to know is how to lift your hg19 CpGs to hg38, that's easy. Say you have (as I do) a GenomicMethylSet, called eset:

> eset
class: GenomicMethylSet 
dim: 856644 189 
metadata(0):
assays(2): Meth Unmeth
rownames(856644): cg26928153 cg16269199 ... cg07587934 cg16855331
rowData names(0):
colnames(189): 203219730010_R01C01 203219730010_R02C01 ...
  203219720217_R07C01 203219720217_R08C01
colData names(12): Sample_Name Sample_Well ... yMed predictedSex
Annotation
  array: IlluminaHumanMethylationEPIC
  annotation: ilm10b4.hg19
Preprocessing
  Method: NA
  minfi version: NA
  Manifest version: NA

> library(rtracklayer)
> library(AnnotationHub)
> hub <- AnnotationHub()

> query(hub, c("chain","hg38","hg19")) 
AnnotationHub with 2 records
# snapshotDate(): 2019-05-02 
# $dataprovider: UCSC
# $species: Homo sapiens
# $rdataclass: ChainFile
# additional mcols(): taxonomyid, genome, description,
#   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#   rdatapath, sourceurl, sourcetype 
# retrieve records with, e.g., 'object[["AH14108"]]' 

            title                   
  AH14108 | hg38ToHg19.over.chain.gz
  AH14150 | hg19ToHg38.over.chain.gz
> liftOver(rowRanges(eset), hub[["AH14150"]])
downloading 1 resources
retrieving 1 resource
  |======================================================================| 100%

loading from cache 
     AH14150 : 18245 
GRangesList object of length 856644:
$cg26928153 
GRanges object with 1 range and 0 metadata columns:
      seqnames    ranges strand
         <Rle> <IRanges>  <Rle>
  [1]     chr1     10848      *

$cg16269199 
GRanges object with 1 range and 0 metadata columns:
      seqnames ranges strand
  [1]     chr1  10850      *

$cg13869341 
GRanges object with 1 range and 0 metadata columns:
      seqnames ranges strand
  [1]     chr1  15865      *

...
<856641 more elements>
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengths

How you would then use that information with MatrixEQTL is up to you, possibly with help from that package's help pages or maintainer.