QTL Analysis - binary [ICI-Early vs PBS]

workflowr

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Last updated: 2022-04-15

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. 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(20220210)

The command set.seed(20220210) 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

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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
/Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow	.

Repository version: a8c2d1a

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 a8c2d1a. 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:

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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNC18311938_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028236_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028236_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028536_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028536_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC19970181_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC19970181_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC20090524_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-13_peak.marker-UNCHS036773_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-13_peak.marker-UNCHS036773_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-15_peak.marker-UNC26070435_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-15_peak.marker-UNC26070435_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCHS044241_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCHS044241_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCJPD006614_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCrs47191360_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNC4609527_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNC4609527_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00516812_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00517674_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00517674_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00518911r_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00518911r_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00522922_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00524205_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00529707r_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00529707r_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00535764_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4809586_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4809586_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4813186_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4813186_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4820379_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4820379_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4836550_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4836550_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4836550_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4875653_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4875653_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4901083_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4901083_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4909519_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4909519_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912039_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912039_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912288_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912288_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4915446_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4915446_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4923398_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4923398_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4938565_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4938565_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4945186_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4945186_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4947492_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4947492_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4956618_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4957168_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4957859_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4959266_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5008656_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5008656_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5008656_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5068215_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5076935_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5082757_lod.drop-1.5_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5213888_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5213888_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5257268_lod.drop-1.5_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5267813_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5270225_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5277061_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5278229_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5282259_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5287977_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5301460_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5309136_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5321559_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5322455_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5329171_lod.drop-1.5_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008193_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008219_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008219_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008229_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008242_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008260_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008260_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008271_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008281_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008286_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008338_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008338_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008363_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008363_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008379_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008379_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008409_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008432_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008511_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008511_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008609_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008613_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008613_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008614_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008627_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008659_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008725_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009066_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009067_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009068_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009112_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009224_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009224_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009383_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009383_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009693_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009821_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001170_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001170_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001198_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001198_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001276_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-sanger2496q_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-sanger2496q_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8250659_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8439633_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8439633_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNCHS012955_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-6_peak.marker-UNC11108920_lod.drop-1.5_snpsqc_5.batches.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-6_peak.marker-UNC11108920_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-1_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-9_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_blup_chr-X_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-JAX00153527_lod.drop-1.5_5.batches.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-JAX00153527_lod.drop-1.5_snpsqc_5.batches.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNCHS020486_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-JAX00153527_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020486_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_scanone_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_5.batches_mis.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_5.batches.Rdata
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    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches_mis.Rdata
    Untracked:  data/percent_missing_id_3.batches.RData
    Untracked:  data/percent_missing_id_4.batches.RData
    Untracked:  data/percent_missing_id_4.batches_bc.RData
    Untracked:  data/percent_missing_id_5.batches.RData
    Untracked:  data/percent_missing_marker_4.batches.RData
    Untracked:  data/percent_missing_marker_5.batches.RData
    Untracked:  data/pheno.csv
    Untracked:  data/pheno_BC312.csv
    Untracked:  data/physical_map.csv
    Untracked:  data/physical_map_BC312.csv
    Untracked:  data/qc_info_bad_sample_3.batches.RData
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    Untracked:  data/qc_info_bad_sample_5.batches.RData
    Untracked:  data/remaining.markers_geno.freq.xlsx
    Untracked:  data/sample_geno.csv
    Untracked:  data/sample_geno_AHB_BC312.csv
    Untracked:  data/sample_geno_bc.csv
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    Untracked:  data/serreze_probs.rds
    Untracked:  data/serreze_probs_BC312.rds
    Untracked:  data/serreze_probs_allqc.rds
    Untracked:  data/serreze_probs_allqc_5.batches.rds
    Untracked:  data/serreze_probs_allqc_5.batches_mis.rds
    Untracked:  data/summary.cg_3.batches.RData
    Untracked:  data/summary.cg_4.batches.RData
    Untracked:  data/summary.cg_4.batches_bc.RData
    Untracked:  data/summary.cg_5.batches.RData
    Untracked:  output/Percent_missing_genotype_data_5.batches.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_5.batches.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf
    Untracked:  output/genotype_error_marker_5.batches.pdf
    Untracked:  output/genotype_frequency_marker_5.batches.pdf

Unstaged changes:
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches_mis.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_5.batches.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_5.batches_mis.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches_mis.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
    Modified:   analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
    Modified:   analysis/index_5.batches.Rmd
    Modified:   analysis/index_5.batches_additional.Rmd

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.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches.Rmd) and HTML (docs/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	a8c2d1a	Belinda Cornes	2022-04-13	additonal analysis - peaks and continuous variables
html	a8c2d1a	Belinda Cornes	2022-04-13	additonal analysis - peaks and continuous variables

---

Data Information

• binary phenotype (therefore no kinship & no outliers)
• no Xcovar
• low frequency (< 5% in samples) removed
• disproportionate markers removed

Loading Data

We will load the data and subset indivials out that are in the groups of interest. We will create a binary phenotype from this (PBS ==0, ICI-Early == 1).

load("data/gm_allqc_5.batches.RData")

#gm_allqc
gm=gm_allqc
gm

Object of class cross2 (crosstype "bc")

Total individuals              308
No. genotyped individuals      308
No. phenotyped individuals     308
No. with both geno & pheno     308

No. phenotypes                   1
No. covariates                   6
No. phenotype covariates         0

No. chromosomes                 20
Total markers                34537

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2643 2629 1857 1890 1774 1941 1672 1627 1878 1176 1871 1300 1549 1578 1257  935 
  17   18   19    X 
 501  913 1014 4532 

#removing ici-late
table(gm$covar$group)

 (A2 Parental) (DQ8 Parental) B6.g7 Parental            EOI             F1 
             2              2              1            164              1 
           ICI            PBS 
           104             34 

table(gm$covar$"diabetic status")

  Both Projects   Diabetes Proj ICI-No Diabetes     Myocarditis      Spont. T1D 
              1               3             164               2              34 
        T1D <17          T1D<17          T1D>17 
             36              51              17 

ici.late_ids <- gm$covar[gm$covar$"diabetic status" == "T1D>17",]$id
length(ici.late_ids)

[1] 17

ici.late_ids

 [1] "NG00198-ICI-LATE" "NG00329-ICI-LATE" "NG00343-ICI-LATE" "NG00386-ICI-LATE"
 [5] "NG00442-ICI-LATE" "NG00443-ICI-LATE" "NG00526-ICI-LATE" "NG00522-ICI-LATE"
 [9] "NG00790-ICI-LATE" "NG00874-ICI-LATE" "NG00929-ICI-LATE" "NG01225-ICI-LATE"
[13] "NG01303-ICI-LATE" "NG01020-ICI-LATE" "NG01397-ICI-LATE" "NG01389-ICI-LATE"
[17] "NG01548-ICI-LATE"

gm <- gm[paste0("-",as.character(ici.late_ids)),]
table(gm$covar$"diabetic status")

  Both Projects   Diabetes Proj ICI-No Diabetes     Myocarditis      Spont. T1D 
              1               3             164               2              34 
        T1D <17          T1D<17 
             36              51 

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

ICI PBS 
 87  34 

mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm

Object of class cross2 (crosstype "bc")

Total individuals              121
No. genotyped individuals      121
No. phenotyped individuals     121
No. with both geno & pheno     121

No. phenotypes                   1
No. covariates                   6
No. phenotype covariates         0

No. chromosomes                 20
Total markers                34537

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2643 2629 1857 1890 1774 1941 1672 1627 1878 1176 1871 1300 1549 1578 1257  935 
  17   18   19    X 
 501  913 1014 4532 

table(gm$covar$group)

ICI PBS 
 87  34 

gm$covar$ICI_Early.vs.PBS <- ifelse(gm$covar$group == "PBS", 0, 1)
gm.full <- gm

covars <- read_csv("data/covar_corrected_ici-early.vs.pbs_5.batches.csv")
#removing any missing info
covars <- subset(covars, covars$ICI_Early.vs.PBS!='')
nrow(covars)

[1] 121

table(covars$group)

ICI PBS 
 87  34 

#keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm

Object of class cross2 (crosstype "bc")

Total individuals              121
No. genotyped individuals      121
No. phenotyped individuals     121
No. with both geno & pheno     121

No. phenotypes                   1
No. covariates                   7
No. phenotype covariates         0

No. chromosomes                 20
Total markers                34537

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2643 2629 1857 1890 1774 1941 1672 1627 1878 1176 1871 1300 1549 1578 1257  935 
  17   18   19    X 
 501  913 1014 4532 

table(gm$covar$group)

ICI PBS 
 87  34 

#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] 7737

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	3817
gfmar_mar_1	4077
gfmar_mar_5	7472
gfmar_mar_10	8528
gfmar_mar_15	8601
gfmar_mar_25	9370
gfmar_mar_50	34335
total_snps	34537

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              121
No. genotyped individuals      121
No. phenotyped individuals     121
No. with both geno & pheno     121

No. phenotypes                   1
No. covariates                   7
No. phenotype covariates         0

No. chromosomes                 20
Total markers                26800

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2386 2398 1639 1702 1582 1722 1479 1482 1698  984 1684 1146 1386 1427  878  768 
  17   18   19    X 
 339  781  931  388 

## dropping disproportionate markers
dismark <- read.csv("data/ici-early.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_5.batches.csv")
nrow(dismark)

[1] 26800

names(dismark)[1] <- c("marker")
dismark <- dismark[!dismark$Include,]
nrow(dismark)

[1] 19572

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             121
No. genotyped individuals     121
No. phenotyped individuals    121
No. with both geno & pheno    121

No. phenotypes                  1
No. covariates                  7
No. phenotype covariates        0

No. chromosomes                18
Total markers                7228

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   16   18 
 166  632    3  337  488 1190  872 1091  535  158  320  526    5  258   19  378 
  19    X 
 108  142 

markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.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)

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, data = covars)[,-1]

#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)

#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI_Early.vs.PBS"], model="binary", n_perm=1000, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$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	3.505219	3.457428
0.05	2.768200	2.737783
0.1	2.557422	2.557284

The figures below show QTL maps for each phenotype

#out <- scan1(pr.qc, gm$covar["ICI_Early.vs.PBS"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, gm$covar["ICI_Early.vs.PBS"], model="binary")

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 2.76819954743779 [autosomes]/2.73778354645465 [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]

#peaks_mbl <- list()
##corresponding info in Mb
#for(i in 1:nrow(peaks)){
#  #lodindex <- peaks$lodindex[i]
#  phenotype <- peaks$phenotype[i]
#  chr <- as.character(peaks$chr[i])
#  lod <- peaks$lod[i]
#  mark <- peaks$marker[i]
#  pos <- mapdf[mapdf$marker==mark,]$pmapdf
#  ci_lo <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_lo[i] & mapdfnd$chr == peaks$chr[i])]
#  ci_hi <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_hi[i] & mapdfnd$chr == peaks$chr[i])]
#  peaks_mb=as.data.frame(cbind(phenotype, chr, pos, lod, ci_lo, ci_hi, mark))
#  names(peaks_mb)[7] <- c("marker")
#  peaks_mbl[[i]] <- peaks_mb
#}
#peaks_mba2 <- do.call(rbind, peaks_mbl)
#peaks_mba2 <- as.data.frame(peaks_mba)
#peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")] <- sapply(peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")], as.numeric)

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 2.76819954743779 [autosomes]/2.73778354645465 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")

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

#for (i in 1:1){
  #Plot 1

  #marker = find_marker(gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i])
  #gp <- g[,marker]
  #gp[gp==1] <- "AA"
  #gp[gp==2] <- "AB"
  #gp[gp==0] <- NA
  #plot_pxg(gp, gm$covar[,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()

  g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
  #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
  #par(mar=c(4.1, 4.1, 1.5, 0.6))
  plot_pxg(g, gm$covar[,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$ci_hi[i]))
  #coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
  #coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
  #coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
  #coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
  blup <- scan1blup(pr.qc[,chr], gm$covar[peaks$phenotype[i]])
  blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]])

  write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv"), quote=F)

  #plot_coef(coeff, 
  #     gm$gmap, columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM])")
  #     )

  #plot_coef(coeff_sub, 
  #     gm$gmap, columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
  #     )


  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])")
       )


 # Plot 3
  #c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
  #c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
  ##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
  ##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
  #plot(c2effB, gm$gmap[chr], columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
  #     )
  #plot(c2effBb, gm$gmap[chr], columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
  #     )
  ##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
  ##for(t in seq(along=last_coef))
  ##  axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)


  #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/ici-early.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv"), quote=F)

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

  #genesgss <- 
  #table <- 
  #genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
  #kable(escape = F,align = c("ccccccccccc")) %>%
  #kable_styling("striped", full_width = T) #%>% 
  #cat #%>%
  #column_spec(1, bold=TRUE)
#
  #print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))

  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 2.76819954743779 [autosomes]/2.73778354645465 [x-chromosome]”

R/qtl

scanone

gm

Object of class cross2 (crosstype "bc")

Total individuals             121
No. genotyped individuals     121
No. phenotyped individuals    121
No. with both geno & pheno    121

No. phenotypes                  1
No. covariates                  7
No. phenotype covariates        0

No. chromosomes                18
Total markers                7228

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   16   18 
 166  632    3  337  488 1190  872 1091  535  158  320  526    5  258   19  378 
  19    X 
 108  142 

#detach("package:qtl2", unload=TRUE)
#library(qtl)

cross <- qtl::read.cross("csv", file = "data/ici-early.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_5.batches.csv",alleles=c("A","B"))

 --Read the following data:
     121  individuals
     7228  markers
     3  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    121 

    No. phenotypes:     3 
    Percent phenotyped: 100 100 100 

    No. chromosomes:    18 
        Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 18 19 
        X chr:          X 

    Total markers:      7228 
    No. markers:        166 632 3 337 488 1190 872 1091 535 158 320 526 5 258 19 
                        378 108 142 
    Percent genotyped:  99.6 
    Genotypes (%):    
          Autosomes:    AA:50.3  AB:49.7 
       X chromosome:    AA:48.9  AB:51.1 

cross.probs <- qtl::calc.genoprob(cross)

print("method == hk")

[1] "method == hk"

scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI_Early.vs.PBS" , model="binary", method="hk")
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI_Early.vs.PBS", n.perm = 1000, perm.Xsp = TRUE, model="binary", verbose=FALSE)
plot(operm.hk)

print(summary(operm.hk, alpha=c(0.01,  0.05, 0.1)))

Autosome LOD thresholds (1000 permutations)
     lod
1%  3.47
5%  2.86
10% 2.56

X chromosome LOD thresholds (22747 permutations)
     lod
1%  3.44
5%  2.77
10% 2.57

#plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")  
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

ymx <- maxlod(out) # overall maximum LOD score
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]"))  
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

ymx <- 11
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]\n(using same scale as eoi vs ici for easier comparison)"))
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

print(as.data.frame(summary(scanone.hk, perms=operm.hk, pvalues=TRUE, format="allpeaks")))

             chr       pos        lod      pval
ICR177         1 52.115135 0.18874739 1.0000000
UNC2987704     2 27.689042 1.24082105 0.8427402
UNC6588127     3 79.274001 0.07188556 1.0000000
UNCHS012131    4 45.626336 0.06106389 1.0000000
UNC9338984     5 32.874350 0.84894969 0.9909783
UNCHS016657    6 10.674011 0.97784625 0.9706965
JAX00651930r   7 60.825760 0.69920846 0.9992619
UNCHS023364    8 35.540563 1.24082105 0.8514214
UNCHS026235    9 39.965000 0.53208381 1.0000000
ICR4262       10  0.868000 0.64621326 1.0000000
UNC20465557   11 80.903243 0.76411516 0.9960389
UNC21998787   12 63.153500 0.18874718 1.0000000
UNCHS034948   13  3.373000 0.61029394 1.0000000
UNC24913816   14 65.312249 0.45888867 1.0000000
UNC26264492   16  2.534000 0.18310148 1.0000000
UNC29118691   18 22.057213 0.48907041 1.0000000
UNCHS047059   19  3.956009 1.71141050 0.4754477
UNC31386211    X 61.374077 0.10311720 1.0000000

print("all peaks with a p-value less or equal to 0.05 (suggestive)")

[1] "all peaks with a p-value less or equal to 0.05 (suggestive)"

print(as.data.frame(summary(scanone.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE, format="allpeaks")))

[1] chr pos lod
<0 rows> (or 0-length row.names)

#print("method == ehk")

#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI_Early.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI_Early.vs.PBS", n.perm = 1000, perm.Xsp = TRUE, model="binary", verbose=FALSE)
#plot(operm.ehk)
#print(summary(operm.ehk, alpha=c(0.01,  0.05, 0.1)))

#plot(scanone.ehk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")  
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.1, col = 'purple')

#print(as.data.frame(summary(scanone.ehk)))
#print(as.data.frame(summary(scanone.ehk, perms=operm.ehk, alpha=0.05, pvalues=TRUE, format="allpeaks")))

Session information

R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

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_3.3.5    
 [5] tibble_3.1.2      psych_2.0.7       readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.8       optparse_1.6.6    rhdf5_2.28.1      mclust_5.4.6     
[13] tidyr_1.0.2       data.table_1.14.0 knitr_1.33        kableExtra_1.1.0 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.1        bit64_4.0.5       viridisLite_0.4.0 assertthat_0.2.1 
 [5] highr_0.9         blob_1.2.1        cellranger_1.1.0  yaml_2.2.1       
 [9] pillar_1.6.1      RSQLite_2.2.7     backports_1.2.1   lattice_0.20-38  
[13] glue_1.4.2        digest_0.6.27     promises_1.1.0    rvest_0.3.5      
[17] colorspace_2.0-2  htmltools_0.5.1.1 httpuv_1.5.2      plyr_1.8.6       
[21] pkgconfig_2.0.3   purrr_0.3.4       scales_1.1.1      webshot_0.5.2    
[25] qtl_1.46-2        whisker_0.4       getopt_1.20.3     later_1.0.0      
[29] git2r_0.26.1      generics_0.0.2    ellipsis_0.3.2    cachem_1.0.5     
[33] withr_2.4.2       cli_3.0.0         mnormt_1.5-7      magrittr_2.0.1   
[37] crayon_1.4.1      memoise_2.0.0     evaluate_0.14     fs_1.4.1         
[41] fansi_0.5.0       nlme_3.1-142      xml2_1.3.1        tools_3.6.2      
[45] hms_0.5.3         lifecycle_1.0.1   stringr_1.4.0     Rhdf5lib_1.6.3   
[49] munsell_0.5.0     compiler_3.6.2    rlang_1.0.2       grid_3.6.2       
[53] rstudioapi_0.13   rmarkdown_2.1     gtable_0.3.0      DBI_1.1.1        
[57] R6_2.5.0          fastmap_1.1.0     bit_4.0.4         utf8_1.2.1       
[61] rprojroot_1.3-2   readr_1.3.1       stringi_1.7.2     parallel_3.6.2   
[65] Rcpp_1.0.7        vctrs_0.3.8       tidyselect_1.1.2  xfun_0.24