Last updated: 2023-01-11

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230109)

The command set.seed(20230109) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: absolute

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
/projects/serreze-lab/USERS/corneb/qtl2/workflowr/Serreze-T1D_Workflow .

Repository version: 850cb80

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 850cb80. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Untracked files:
    Untracked:  analysis/0.1.1_preparing.data_bqc_4.batches_myo.Rmd
    Untracked:  analysis/0.1_samples_batch_20220729.Rmd
    Untracked:  analysis/0.1_samples_batch_20220826.Rmd
    Untracked:  analysis/0.1_samples_batch_20221006.Rmd
    Untracked:  analysis/0.1_samples_batch_20221116.Rmd
    Untracked:  analysis/0.2_haplotype_comparison_bqc_4.batches_myo_minprob.Rmd
    Untracked:  analysis/2.1_sample_bqc_4.batches_myo.Rmd
    Untracked:  analysis/2.2.1_snp_qc_4.batches_myo.Rmd
    Untracked:  analysis/2.2.1_snp_qc_4.batches_myo_mis.Rmd
    Untracked:  analysis/2.4_preparing.data_aqc_4.batches_myo.Rmd
    Untracked:  analysis/2.4_preparing.data_aqc_4.batches_myo_mis.Rmd
    Untracked:  analysis/3.1_phenotype.qc_corrected_4.batches_myo.Rmd
    Untracked:  analysis/3.1_phenotype.qc_corrected_4.batches_myo_mis.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_het-ici-myo-yes.vs.het-ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_het-ici-myo-yes.vs.het-ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_het-ici.vs.het-pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_het-ici.vs.het-pbs_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici-myo-yes.vs.ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici-myo-yes.vs.ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici-sick.vs.ici-eoi_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici-sick.vs.ici-eoi_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_myo-yes.vs.myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_myo-yes.vs.myo-no_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_pbs-myo-yes.vs.pbs-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
    Untracked:  analysis/4.1.1_qtl.analysis_binary_pbs-myo-yes.vs.pbs-myo-no_snpsqc_dis_no-x_updated_4.batches_myo1.Rmd
    Untracked:  analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo_mis.Rmd
    Untracked:  analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.Rmd
    Untracked:  analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.Rmd
    Untracked:  analysis/index_4.batches_myo.Rmd
    Untracked:  code/cc_variants.sqlite
    Untracked:  code/mouse_genes.sqlite
    Untracked:  code/mouse_genes_mgi.sqlite
    Untracked:  data/GM_covar_4.batches_myo.csv
    Untracked:  data/bad_markers_all_4.batches_myo.RData
    Untracked:  data/covar_corrected.cleaned_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_het-ici.vs.het-pbs_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_het-ici.vs.het-pbs_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_ici-myo-yes.vs.ici-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_ici-sick.vs.ici-eoi_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_ici-sick.vs.ici-eoi_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_ici.vs.pbs_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_ici.vs.pbs_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_myo-yes.vs.myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_myo-yes.vs.myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected.cleaned_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected.cleaned_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_het-ici.vs.het-pbs_4.batches_myo.csv
    Untracked:  data/covar_corrected_het-ici.vs.het-pbs_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_ici-myo-yes.vs.ici-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_ici-sick.vs.ici-eoi_4.batches_myo.csv
    Untracked:  data/covar_corrected_ici-sick.vs.ici-eoi_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_ici.vs.pbs_4.batches_myo.csv
    Untracked:  data/covar_corrected_ici.vs.pbs_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_myo-yes.vs.myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected_myo-yes.vs.myo-no_4.batches_myo_mis.csv
    Untracked:  data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.csv
    Untracked:  data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.csv
    Untracked:  data/e_4.batches_myo.RData
    Untracked:  data/e_snpg_samqc_4.batches_myo.RData
    Untracked:  data/errors_ind_4.batches_myo.RData
    Untracked:  data/genetic_map_4.batches_myo.csv
    Untracked:  data/genetic_map_4.batches_myo_BC217.csv
    Untracked:  data/genotype_errors_marker_4.batches_myo.RData
    Untracked:  data/genotype_freq_marker_4.batches_myo.RData
    Untracked:  data/gm_allqc_4.batches_myo.RData
    Untracked:  data/gm_allqc_4.batches_myo_mis.RData
    Untracked:  data/gm_samqc_4.batches_myo.RData
    Untracked:  data/gm_serreze.BC312.RData
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNCHS031790_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-UNC20622785_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-UNCHS037125_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCHS043775_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNCHS006134_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031790_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC20622785_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS037125_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043775_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNCHS006134_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/percent_missing_id_4.batches_myo.RData
    Untracked:  data/percent_missing_marker_4.batches_myo.RData
    Untracked:  data/pheno_4.batches_myo.csv
    Untracked:  data/physical_map_4.batches_myo.csv
    Untracked:  data/physical_map_4.batches_myo_BC217.csv
    Untracked:  data/qc_info_bad_sample_4.batches_myo.RData
    Untracked:  data/sample_geno_AHB_4.batches_myo.csv
    Untracked:  data/sample_geno_AHB_4.batches_myo_BC217.csv
    Untracked:  data/sample_geno_bc_4.batches_myo.csv
    Untracked:  data/sample_geno_bc_4.batches_myo_BC217.csv
    Untracked:  data/sample_geno_raw_4.batches_myo_BC217.csv
    Untracked:  data/serreze_probs_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo_mis.rds
    Untracked:  data/summary.cg_4.batches_myo.RData

Unstaged changes:
    Modified:   analysis/_site.yml

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

Data Information

Loading Data

We will load the data and subset indivials out that are in the groups of interest.

load("data/gm_allqc_4.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm
Object of class cross2 (crosstype "bc")

Total individuals              208
No. genotyped individuals      208
No. phenotyped individuals     208
No. with both geno & pheno     208

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                32610

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109  835 
  17   18   19    X 
 674  813  940 4501 
#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")

##extracting animals with ici and pbs group status
#miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
#table(miceinfo$group)
#mice.ids <- rownames(miceinfo)

#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)

#gm$covar$het.ici.myo.yes_vs_het.ici.myo.no <- ifelse(gm$covar$group == "PBS", 0, 1)
#gm.full <- gm

covars <- read_csv("data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv")
#removing any missing info
#covars <- subset(covars, covars$het.ici.myo.yes_vs_het.ici.myo.no!='')
nrow(covars)
[1] 27
table(covars$"Myocarditis Status")

 NO YES 
 20   7 
table(covars$"Murine MHC KO Status")

HET 
 27 
table(covars$"Drug Treatment")

ICI 
 27 
table(covars$"clinical pheno")

 EOI SICK 
  13   14 
#keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm
Object of class cross2 (crosstype "bc")

Total individuals               27
No. genotyped individuals       27
No. phenotyped individuals      27
No. with both geno & pheno      27

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                32610

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109  835 
  17   18   19    X 
 674  813  940 4501 
table(gm$covar$"Myocarditis Status")

 NO YES 
 20   7 
table(gm$covar$"Murine MHC KO Status")

HET 
 27 
table(gm$covar$"Drug Treatment")

ICI 
 27 
table(gm$covar$"clinical pheno")

 EOI SICK 
  13   14 
#pr.qc.ids <- pr
#for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

##removing problmetic marker

#gm <- drop_markers(gm, "UNCHS013106")

##dropping monomorphic markers within the dataset

g <- do.call("cbind", gm$geno)

gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
#gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))))

gf_mar <- gf_mar[gf_mar[,2] != "NaN",]

count <- rowSums(gf_mar <=0.05)
low_freq_df <- merge(as.data.frame(gf_mar),as.data.frame(count), by="row.names",all=T)
low_freq_df[is.na(low_freq_df)] <- ''
low_freq_df <- low_freq_df[low_freq_df$count == 1,]
rownames(low_freq_df) <- low_freq_df$Row.names

low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)

nrow(low_freq)
[1] 6683
low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
names(low_freq_bad)[1] <- c("marker")

gf_mar <- gf_mar[gf_mar[,2] != "NaN",]
MAF <- apply(gf_mar, 1, function(x) min(x))
MAF <- as.data.frame(MAF)
MAF$index <- 1:nrow(gf_mar)
gf_mar_maf <- merge(gf_mar,as.data.frame(MAF), by="row.names")
gf_mar_maf <- gf_mar_maf[order(gf_mar_maf$index),]

gfmar <- NULL
gfmar$gfmar_mar_0 <- sum(gf_mar_maf$MAF==0)
gfmar$gfmar_mar_1 <- sum(gf_mar_maf$MAF< 0.01)
gfmar$gfmar_mar_5 <- sum(gf_mar_maf$MAF< 0.05)
gfmar$gfmar_mar_10 <- sum(gf_mar_maf$MAF< 0.10)
gfmar$gfmar_mar_15 <- sum(gf_mar_maf$MAF< 0.15)
gfmar$gfmar_mar_25 <- sum(gf_mar_maf$MAF< 0.25)
gfmar$gfmar_mar_50 <- sum(gf_mar_maf$MAF< 0.50)
gfmar$total_snps <- nrow(as.data.frame(gf_mar_maf))

gfmar <- t(as.data.frame(gfmar))
gfmar <- as.data.frame(gfmar)
gfmar$count <- gfmar$V1

gfmar[c(2)] %>%
  kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
  kable_styling(full_width = F) %>%
  kable_styling("striped", full_width = F)  %>%
  row_spec(8 ,bold=T,color= "white",background = "black")
count
gfmar_mar_0 5157
gfmar_mar_1 5157
gfmar_mar_5 6681
gfmar_mar_10 6815
gfmar_mar_15 6837
gfmar_mar_25 7003
gfmar_mar_50 32133
total_snps 32610 
gm_qc <- drop_markers(gm, low_freq_bad$marker)
gm_qc <- drop_nullmarkers(gm_qc)

gm_qc
Object of class cross2 (crosstype "bc")

Total individuals               27
No. genotyped individuals       27
No. phenotyped individuals      27
No. with both geno & pheno      27

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                25927

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2281 2225 1572 1628 1497 1662 1425 1407 1622  940 1613 1091 1343 1374 1002  715 
  17   18   19    X 
 549  729  883  369 
## dropping disproportionate markers
dismark <- read.csv("data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv")
nrow(dismark)
[1] 25927
names(dismark)[1] <- c("marker")
dismark <- dismark[!dismark$Include,]
nrow(dismark)
[1] 18820
gm_qc_dis <- drop_markers(gm_qc, dismark$marker)
gm_qc_dis <- drop_nullmarkers(gm_qc_dis)

gm = gm_qc_dis
gm
Object of class cross2 (crosstype "bc")

Total individuals              27
No. genotyped individuals      27
No. phenotyped individuals     27
No. with both geno & pheno     27

No. phenotypes                  1
No. covariates                 11
No. phenotype covariates        0

No. chromosomes                18
Total markers                7107

No. markers by chr:
   1    3    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
 785  686  436  853  159    9 1196  369  269  221  549  181  486  310    5  379 
  19    X 
   1  213 
markers <- marker_names(gm)
gmapdf <- read.csv("data/genetic_map_4.batches_myo_BC217.csv")
pmapdf <- read.csv("data/physical_map_4.batches_myo_BC217.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]

pr.qc <- calc_genoprob(gm)

colnames(covars) <- gsub(" ", ".", colnames(covars))

Genome-wide scan

For each of the phenotype analyzed, permutations were used for each model to obtain genome-wide LOD significance threshold for p < 0.01, p < 0.05, p < 0.10, respectively, separately for X and automsomes (A).

The table shows the estimated significance thresholds from permutation test.

We also looked at the kinship to see how correlated each sample is. Kinship values between pairs of samples range between 0 (no relationship) and 1.0 (completely identical). The darker the colour the more indentical the pairs are.

#Xcovar <- get_x_covar(gm)
addcovar = model.matrix(~sex+Histology.Score, data = covars)[,-1]
covars$het.ici.myo.yes_vs_het.ici.myo.no= as.numeric(covars$het.ici.myo.yes_vs_het.ici.myo.no)

kinship <- calc_kinship(pr.qc)
heatmap(kinship)

operm <- scan1perm(pr.qc, covars["het.ici.myo.yes_vs_het.ici.myo.no"], model="binary", addcovar=addcovar, n_perm=1000, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)

rownames(summary_table) <- NULL

summary_table[c(3,1:2)] %>%
  kable(escape = F,align = c("ccc")) %>%
  kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)
significance.level autosomes X
0.01 0 0
0.05 0 0
0.1 0 0

The figures below show QTL maps for each phenotype

#out <- scan1(pr.qc, covars["het.ici.myo.yes_vs_het.ici.myo.no"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, covars["het.ici.myo.yes_vs_het.ici.myo.no"], model="binary",addcovar=addcovar)

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))


plot_lod<-function(out,map){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

    ##par(mar=c(5.1, 6.1, 1.1, 1.1))
    #ymx <- 11 # overall maximum LOD score
    #plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
    #add_threshold(map,  summary(operm, alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    ##for (j in 1: dim(summary_table)[1]){
    ##  abline(h=summary_table[j, i],col="red")
    ##  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    ##}
    ##dev.off()
  }
}

plot_lod(out,gm$gmap)

LOD peaks

The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.

Centimorgan (cM)

peaks <- find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]

rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

#plot only peak chromosomes

plot_lod_chr<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    
    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

  }
}


for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
  #plot_lod_chr(out,gm$gmap, peaks$chr[i])
  plot_lod_chr(out,gm$gmap, i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

Megabase (MB)

print("peaks in MB positions")

[1] “peaks in MB positions”

peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]


rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

plot_lod_chr_mb<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')


  }
}

for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
  #plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
  plot_lod_chr_mb(out,gm$pmap,i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("code/cc_variants.sqlite")
query_genes <- create_gene_query_func("code/mouse_genes_mgi.sqlite")

if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){


  g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
  #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
  #par(mar=c(4.1, 4.1, 1.5, 0.6))
  plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
  title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
  ##dev.off()

  chr = peaks$chr[i]

# Plot 2
  pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
  blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]],addcovar = addcovar)
  blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]], addcovar = addcovar)

  write.csv(as.data.frame(blup_sub), paste0("data/het-ici-myo-yes.vs.het-ici-myo-no_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv"), quote=F)

  plot_coef(blup, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
       )

  plot_coef(blup_sub, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
       )

  #Table 1
  chr = peaks_mba$chr[i]
  start=as.numeric(peaks_mba$ci_lo[i])
  end=as.numeric(peaks_mba$ci_hi[i])

  genesgss = query_genes(chr, start, end)

  write.csv(genesgss, file=paste0("data/het-ici-myo-yes.vs.het-ici-myo-no_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv"), quote=F)

  rownames(genesgss) <- NULL
  genesgss$strand_old = genesgss$strand
  genesgss$strand[genesgss$strand=="+"] <- "positive"
  genesgss$strand[genesgss$strand=="-"] <- "negative"

  print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))


}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

R/qtl

scanone


R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] abind_1.4-5       qtl2_0.22         reshape2_1.4.4    ggplot2_3.4.0    
 [5] tibble_3.1.8      psych_2.2.9       readxl_1.4.1      cluster_2.1.4    
 [9] dplyr_1.0.10      optparse_1.7.3    rhdf5_2.34.0      mclust_6.0.0     
[13] tidyr_1.2.1       data.table_1.14.6 knitr_1.41        kableExtra_1.3.4 
[17] workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] httr_1.4.4         sass_0.4.4         bit64_4.0.5        jsonlite_1.7.2    
 [5] viridisLite_0.4.1  bslib_0.4.2        assertthat_0.2.1   getPass_0.2-2     
 [9] highr_0.8          blob_1.2.1         cellranger_1.1.0   yaml_2.2.1        
[13] pillar_1.8.1       RSQLite_2.2.3      lattice_0.20-41    glue_1.6.2        
[17] digest_0.6.31      promises_1.1.1     rvest_1.0.3        colorspace_2.0-0  
[21] htmltools_0.5.4    httpuv_1.5.5       plyr_1.8.8         pkgconfig_2.0.3   
[25] purrr_0.3.4        scales_1.2.1       webshot_0.5.4      processx_3.8.0    
[29] svglite_2.1.0      whisker_0.4        getopt_1.20.3      later_1.1.0.1     
[33] git2r_0.28.0       generics_0.1.3     cachem_1.0.3       withr_2.5.0       
[37] cli_3.6.0          mnormt_2.1.1       magrittr_2.0.1     memoise_2.0.1     
[41] evaluate_0.19      ps_1.5.0           fs_1.5.2           fansi_0.4.2       
[45] nlme_3.1-149       xml2_1.3.3         tools_4.0.3        lifecycle_1.0.3   
[49] stringr_1.4.0      Rhdf5lib_1.12.1    munsell_0.5.0      callr_3.7.3       
[53] compiler_4.0.3     jquerylib_0.1.3    systemfonts_1.0.1  rlang_1.0.6       
[57] grid_4.0.3         rhdf5filters_1.2.1 rstudioapi_0.13    rmarkdown_2.19    
[61] gtable_0.3.0       DBI_1.1.1          R6_2.5.0           bit_4.0.4         
[65] fastmap_1.1.0      utf8_1.1.4         rprojroot_2.0.2    stringi_1.5.3     
[69] parallel_4.0.3     Rcpp_1.0.6         vctrs_0.5.1        tidyselect_1.2.0  
[73] xfun_0.36