PEER generates multiple output data frames after:

PEER_update(model)

I'm assuming the matrix you're generating comes from:

PEER_getX(model) 

If you want to combine PEER with MatrixEQTL, I would suggest using the "residual matrix" from:

PEER_getResiduals(model)

As your "gene" variable within:

Matrix_eQTL_main()

I've attach some code just to show you the basic work-flow:

model = PEER()
    PEER_setNk(model,k)
    PEER_setPhenoMean(model,as.matrix(expr_cov)) # Give PEER your expression data
    PEER_setAdd_mean(model, TRUE)
    PEER_setCovariates(model, as.matrix(covar4))  # PEER ask NxC matrix, where N=samples and C=covariates
    PEER_setNmax_iterations(model, 100000)
    PEER_update(model)
    residuals = PEER_getResiduals(model)  # convert to GxN
    colnames(residuals) = colnames(expr_cov)
    rownames(residuals) = rownames(expr_cov)
    # add mean
    residuals = residuals + apply(expr_cov, 2, mean)
    # convert to SliceData format
    genes = SlicedData$new();
    genes$CreateFromMatrix(residuals); # using your residuals as expression input
    rm(residuals, model)
    # run eQTL with residuals
    me = Matrix_eQTL_main(
        snps = snps,
        gene = genes,
        cvrt = SlicedData$new(),  # or cvrt_snp
        output_file_name = "",
        pvOutputThreshold = 0,  # no tran
        useModel = modelLINEAR,
        errorCovariance = numeric(),
        verbose = FALSE,
        output_file_name.cis = paste0("step1.cis.eQTL.K",k,".xls"),
        pvOutputThreshold.cis = 1e-5,
        snpspos = snp_loc,
        genepos = exons_8_25_2015,
        cisDist = 1e6,
        pvalue.hist = FALSE,
        min.pv.by.genesnp = FALSE,
        noFDRsaveMemory = TRUE);