Power Analysis - ICI vs PBS

workflowr

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Last updated: 2022-05-17

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

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Environment: empty

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Seed: set.seed(20220210)

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Session information: recorded

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Cache: none

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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

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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.

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    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_blup_sub_chr-1_peak.marker-UNCHS001938_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_blup_sub_chr-6_peak.marker-UNC11008761_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_blup_sub_chr-6_peak.marker-UNCHS016990_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-1_peak.marker-ICR134_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-1_peak.marker-ICR4119_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-1_peak.marker-JAX00262446_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-1_peak.marker-UNCHS001938_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-6_peak.marker-UNC11008761_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-interactive.covariate_genes_chr-6_peak.marker-UNCHS016990_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCJPD006614_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-18_peak.marker-UNC28655293_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-5_peak.marker-UNC10044126_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-5_peak.marker-UNC8675939_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-JAX00667121_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_blup_sub_chr-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-14_peak.marker-UNC24056202_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-17_peak.marker-UNCJPD006614_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-18_peak.marker-UNC28655293_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-5_peak.marker-UNC10044126_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-5_peak.marker-UNC8675939_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-8_peak.marker-JAX00667121_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_age.of.onset-no.covariates_genes_chr-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs.ici-late_blup.full_chr-10_5.batches.csv
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    Untracked:  data/ici-early.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNC13239362_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNC13239362_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv
    Untracked:  data/ici-early.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNC13239362_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
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    Untracked:  data/ici-early.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNC13239362_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv
    Untracked:  data/ici-early.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNC13239362_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici-early.vs.pbs_scanone_5.batches.Rdata
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    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
    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_52.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_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
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNCHS008007_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNCHS008007_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-ICR1338_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-ICR1338_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-ICR1338_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-ICR5263_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-ICR5263_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-JAX00105649_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00105649_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-JAX00106210_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-JAX00106957_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00106957_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00106957_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-JAX00107193_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-JAX00220631_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00220631_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-JAX00514662_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00514662_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00516812_lod.drop-1.5_5.batches_mis.csv
    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
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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_0.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
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    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
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    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
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    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
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    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
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    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
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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
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    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-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_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008286_lod.drop-1.5_5.batches_52.csv
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    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
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    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
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    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
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    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
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    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
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    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008511_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_rz.age-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_rz.age-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_rz.age-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-9_peak.marker-UNC17203597_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_rz.age-no.covariates_blup_sub_chr-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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Unstaged changes:
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches.Rmd
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---

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Loading Data

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 

#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)

ICI PBS 
104  34 

mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm

Object of class cross2 (crosstype "bc")

Total individuals              138
No. genotyped individuals      138
No. phenotyped individuals     138
No. with both geno & pheno     138

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 
104  34 

##loading genotypes

genos.1 <- read.csv("data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_5.batches.csv")
dim(genos.1)

[1] 26548   149

genos.1 <- genos.1[genos.1$Include,]
dim(genos.1)

[1] 6556  149

#dis.genos.1 <- read.csv("data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_5.batches_mis.csv")
#dim(dis.genos.1)
rownames(genos.1) <- genos.1$marker
genos.1 <- t(genos.1[3:(ncol(genos.1)-9)])
rownames(genos.1) <- gsub("\\.","-",rownames(genos.1))

genos.2<- read.csv("data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_5.batches.csv")
dim(genos.2)

[1] 26548   149

genos.2 <- genos.2[genos.2$Include,]
dim(genos.2)

[1] 6556  149

#dis.genos.2 <- read.csv("data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv")
rownames(genos.2) <- genos.2$marker
genos.2 <- t(genos.2[3:(ncol(genos.2)-9)])
rownames(genos.2) <- gsub("\\.","-",rownames(genos.2))


##merge with phenotypes (age.of.onset & binary)

names(gm$covar)[3] <- c("age.of.onset")
gm$covar[gm$covar$id=="NG00453",]$age.of.onset = 13.7
gm$covar[gm$covar$age.of.onset=='',] <- 0
gm$covar$age.of.onset <- as.numeric(gm$covar$age.of.onset)

geno.pheno.1 <- merge(data.frame(genos.1), gm$covar,  by="row.names", all.x=T, sort=F)
aov.1 <- stats::aov(age.of.onset ~ group, geno.pheno.1)
mse.1 <- mean(aov.1$residuals^2) 
geno.pheno.2 <- merge(data.frame(genos.2), gm$covar,  by="row.names", all.x=T, sort=F)
aov.2 <- stats::aov(age.of.onset ~ group, geno.pheno.2)
mse.2 <- mean(aov.2$residuals^2) 


## calculating effect (age.of.onset)

age.marker.sum.1 <- list()
geno.pheno.1a <- geno.pheno.1[-1]
for(i in 1:(ncol(geno.pheno.1a)-7)){
#geno.pheno.1$snp=geno.pheno.1[i]
age.marker.1 <- geno.pheno.1a %>% 
  group_by(geno.pheno.1a[i])  %>%
  summarize(mean_age.of.onset = mean(as.numeric(age.of.onset), na.rm =T))


  names(age.marker.1) <- c("genotypes", paste0(names(geno.pheno.1a[i]),"_age.of.onset"))
  genotypes <- as.data.frame(c("-","AA","AB"))
  names(genotypes) <- c("genotypes")
  age.marker.1 <- merge(genotypes, age.marker.1, by=c("genotypes"), all=T, sort=T)
  #print(age.marker.1)

  age.marker.sum.1[[i]] <- as.data.frame(age.marker.1)
}

age.marker.sum.1.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.1)
rownames(age.marker.sum.1.all) <- age.marker.sum.1.all$genotypes
age.marker.sum.1.all.t <- t(age.marker.sum.1.all[-1])
rownames(age.marker.sum.1.all.t) <- gsub("_age.of.onset","",rownames(age.marker.sum.1.all.t))
age.marker.sum.1.all.t <- as.data.frame(age.marker.sum.1.all.t)
age.marker.sum.1.all.t$diffhethom <- age.marker.sum.1.all.t$AB - age.marker.sum.1.all.t$AA
max.1 <- max(age.marker.sum.1.all.t$diffhethom)
min.1 <- min(age.marker.sum.1.all.t$diffhethom)
write.csv(age.marker.sum.1.all.t,"data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_5.batches.csv")

age.marker.sum.2 <- list()
geno.pheno.2a <- geno.pheno.2[-1]
for(i in 1:(ncol(geno.pheno.2a)-7)){
#geno.pheno.2$snp=geno.pheno.2[i]
age.marker.2 <- geno.pheno.2a %>% 
  group_by(geno.pheno.2a[i])  %>%
  summarize(mean_age.of.onset = mean(as.numeric(age.of.onset), na.rm =T))


  names(age.marker.2) <- c("genotypes", paste0(names(geno.pheno.2a[i]),"_age.of.onset"))
  genotypes <- as.data.frame(c("-","AA","AB"))
  names(genotypes) <- c("genotypes")
  age.marker.2 <- merge(genotypes, age.marker.2, by=c("genotypes"), all=T, sort=T)
  #print(age.marker.2)

  age.marker.sum.2[[i]] <- as.data.frame(age.marker.2)
}

age.marker.sum.2.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.2)
rownames(age.marker.sum.2.all) <- age.marker.sum.2.all$genotypes
age.marker.sum.2.all.t <- t(age.marker.sum.2.all[-1])
rownames(age.marker.sum.2.all.t) <- gsub("_age.of.onset","",rownames(age.marker.sum.2.all.t))
age.marker.sum.2.all.t <- as.data.frame(age.marker.sum.2.all.t)
age.marker.sum.2.all.t$diffhethom <- age.marker.sum.2.all.t$AB - age.marker.sum.2.all.t$AA
max.2 <- max(age.marker.sum.2.all.t$diffhethom)
min.2 <- min(age.marker.sum.2.all.t$diffhethom)
write.csv(age.marker.sum.2.all.t,"data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_5.batches.csv")


max.a <- max(max.1, max.2)
print(max.a)

[1] 3.343679

min.a <- min(min.1, min.2)
print(min.a)

[1] -3.122579

mse.a <- (mse.1 + mse.2)/2
print(mse.a)

[1] 27.12483

## calculating effect (group)

age.marker.sum.1 <- list()
geno.pheno.1a <- geno.pheno.1[-1]
for(i in 1:(ncol(geno.pheno.1a)-7)){
#geno.pheno.1$snp=geno.pheno.1[i]
age.marker.1 <- geno.pheno.1a %>% 
  group_by(geno.pheno.1a[i])  %>%
  summarize(mean_group = mean(as.numeric(group), na.rm =T))


  names(age.marker.1) <- c("genotypes", paste0(names(geno.pheno.1a[i]),"_group"))
  genotypes <- as.data.frame(c("-","AA","AB"))
  names(genotypes) <- c("genotypes")
  age.marker.1 <- merge(genotypes, age.marker.1, by=c("genotypes"), all=T, sort=T)
  #print(age.marker.1)

  age.marker.sum.1[[i]] <- as.data.frame(age.marker.1)
}

age.marker.sum.1.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.1)
rownames(age.marker.sum.1.all) <- age.marker.sum.1.all$genotypes
age.marker.sum.1.all.t <- t(age.marker.sum.1.all[-1])
rownames(age.marker.sum.1.all.t) <- gsub("_group","",rownames(age.marker.sum.1.all.t))
age.marker.sum.1.all.t <- as.data.frame(age.marker.sum.1.all.t)
age.marker.sum.1.all.t$diffhethom <- age.marker.sum.1.all.t$AB - age.marker.sum.1.all.t$AA
write.csv(age.marker.sum.1.all.t,"data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_5.batches.csv")

age.marker.sum.2 <- list()
geno.pheno.2a <- geno.pheno.2[-1]
for(i in 1:(ncol(geno.pheno.2a)-7)){
#geno.pheno.2$snp=geno.pheno.2[i]
age.marker.2 <- geno.pheno.2a %>% 
  group_by(geno.pheno.2a[i])  %>%
  summarize(mean_group = mean(as.numeric(group), na.rm =T))


  names(age.marker.2) <- c("genotypes", paste0(names(geno.pheno.2a[i]),"_group"))
  genotypes <- as.data.frame(c("-","AA","AB"))
  names(genotypes) <- c("genotypes")
  age.marker.2 <- merge(genotypes, age.marker.2, by=c("genotypes"), all=T, sort=T)
  #print(age.marker.2)

  age.marker.sum.2[[i]] <- as.data.frame(age.marker.2)
}

age.marker.sum.2.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.2)
rownames(age.marker.sum.2.all) <- age.marker.sum.2.all$genotypes
age.marker.sum.2.all.t <- t(age.marker.sum.2.all[-1])
rownames(age.marker.sum.2.all.t) <- gsub("_group","",rownames(age.marker.sum.2.all.t))
age.marker.sum.2.all.t <- as.data.frame(age.marker.sum.2.all.t)
age.marker.sum.2.all.t$diffhethom <- age.marker.sum.2.all.t$AB - age.marker.sum.2.all.t$AA
write.csv(age.marker.sum.2.all.t,"data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_5.batches.csv")


max.g <- max(max.1, max.2)
print(max.g)

[1] 3.343679

min.g <- min(min.1, min.2)
print(min.g)

[1] -3.122579

mse.g <- (mse.1 + mse.2)/2
print(mse.g)

[1] 27.12483

Power

For powercalc the power is returned, along with the proportion of variance explained. LOD threshold set to 3 (which is roughly what is seen for suggestive significance)

## age.of onset


powercalc(cross = "bc",n=length(mice.ids),effect=min.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1)

         power percent.var.explained
[1,] 0.4225683              8.245675

powercalc(cross = "bc",n=length(mice.ids),effect=max.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1)

         power percent.var.explained
[1,] 0.5215428              9.341773

#powercalc("bc",100,31,sigma2=1,sel.frac=1,theta=0)

## group (binary)

powercalc(cross = "bc",n=length(mice.ids),effect=min.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1)

         power percent.var.explained
[1,] 0.4225683              8.245675

powercalc(cross = "bc",n=length(mice.ids),effect=max.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1)

         power percent.var.explained
[1,] 0.5215428              9.341773

## graph

Sample Size (with 80% power)

For samplesize the sample size (rounded up to the nearest integer) is returned along with the proportion of variance explained. LOD threshold set to 3 (which is roughly what is seen for suggestive significance) and 80% power

## age.of onset
samplesize(cross = "bc",effect=min.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1)

     sample.size percent.var.explained
[1,]         232              8.245675

samplesize(cross = "bc",effect=max.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1)

     sample.size percent.var.explained
[1,]         202              9.341773

## group (binary)
samplesize(cross = "bc",effect=min.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1)

     sample.size percent.var.explained
[1,]         232              8.245675

samplesize(cross = "bc",effect=max.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1)

     sample.size percent.var.explained
[1,]         202              9.341773

## graph

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] qtlDesign_0.941   abind_1.4-5       qtl2_0.28         reshape2_1.4.4   
 [5] ggplot2_3.3.6     tibble_3.1.7      psych_2.0.7       readxl_1.4.0     
 [9] cluster_2.1.0     dplyr_1.0.9       optparse_1.6.6    rhdf5_2.28.1     
[13] mclust_5.4.6      tidyr_1.2.0       data.table_1.14.2 knitr_1.39       
[17] kableExtra_1.1.0  workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.3        sass_0.4.1        bit64_4.0.5       jsonlite_1.8.0   
 [5] viridisLite_0.4.0 bslib_0.3.1       assertthat_0.2.1  highr_0.9        
 [9] blob_1.2.3        cellranger_1.1.0  yaml_2.3.5        pillar_1.7.0     
[13] RSQLite_2.2.14    lattice_0.20-38   glue_1.6.2        digest_0.6.29    
[17] promises_1.1.0    rvest_1.0.2       colorspace_2.0-3  htmltools_0.5.2  
[21] httpuv_1.5.2      plyr_1.8.7        pkgconfig_2.0.3   purrr_0.3.4      
[25] scales_1.2.0      webshot_0.5.2     getopt_1.20.3     later_1.0.0      
[29] tzdb_0.3.0        git2r_0.26.1      generics_0.1.2    ellipsis_0.3.2   
[33] cachem_1.0.6      withr_2.5.0       cli_3.3.0         mnormt_1.5-7     
[37] magrittr_2.0.3    crayon_1.5.1      memoise_2.0.1     evaluate_0.15    
[41] fs_1.5.2          fansi_1.0.3       nlme_3.1-142      xml2_1.3.3       
[45] tools_3.6.2       hms_1.1.1         lifecycle_1.0.1   stringr_1.4.0    
[49] Rhdf5lib_1.6.3    munsell_0.5.0     compiler_3.6.2    jquerylib_0.1.4  
[53] rlang_1.0.2       grid_3.6.2        rstudioapi_0.13   rmarkdown_2.14   
[57] gtable_0.3.0      DBI_1.1.2         R6_2.5.1          fastmap_1.1.0    
[61] bit_4.0.4         utf8_1.2.2        rprojroot_2.0.3   readr_2.1.2      
[65] stringi_1.7.6     parallel_3.6.2    Rcpp_1.0.8.3      vctrs_0.4.1      
[69] tidyselect_1.1.2  xfun_0.31