QTL Analysis - binary [ICI vs PBS] (conditioning on eoi vs. ici peaks)

workflowr

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Last updated: 2022-07-02

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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File paths: absolute

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

Repository version: a8c2d1a

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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
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    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
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    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
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    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
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    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
    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_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-9_peak.marker-UNC17203597_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-9_peak.marker-UNC17203597_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-X_peak.marker-UNCHS048314_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-X_peak.marker-UNCHS048314_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-JAX00020646_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-JAX00292499_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-JAX00292927_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-JAX00294019_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18216614_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18240977_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18311938_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18311938_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18311938_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18311938_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18343181_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18363544_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18363544_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18363544_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_chr-10_peak.marker-UNC18376338_lod.drop-1.5_snpsqc_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_chr-11_peak.marker-UNC20090524_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_chr-2_peak.marker-UNCHS008007_lod.drop-1.5_5.batches_mis.csv
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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
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    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
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    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
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    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/genotype.frequencies_ici.vs.eoi_5.batches.Rmd
    Modified:   analysis/genotype.frequencies_ici.vs.eoi_5.batches_mis.Rmd
    Modified:   analysis/genotype.frequencies_ici.vs.pbs_5.batches.Rmd
    Modified:   analysis/genotype.frequencies_ici.vs.pbs_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.

---

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---

Data Information

• binary phenotype (therefore no kinship & no outliers)
• no Xcovar
• low frequency (< 5% in samples) removed
• disproportionate markers removed
• adding ici vs. eoi peak markers as covariates

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 == 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 

#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 

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

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

[1] 138

table(covars$group)

ICI PBS 
104  34 

#keeping only informative mice
gm <- gm[covars$Mouse.ID]
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                   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 
104  34 

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

##adding peaks as covariates: UNCHS008487, UNC8250659, UNC18240977

mar.covar <- pull_markers(gm, c("UNCHS008487", "UNC8250659", "UNC18240977"))
mar.covar.g <- do.call("cbind", mar.covar$geno)

covars.2 <- merge(covars, mar.covar.g, by='row.names', sort=F)
table(covars.2$group)

ICI PBS 
104  34 

rownames(covars.2) <- covars.2$Row.names
covars.2 <- covars.2[-1]
covars = covars.2

##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] 7989

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	3814
gfmar_mar_1	4068
gfmar_mar_5	7989
gfmar_mar_10	8527
gfmar_mar_15	8598
gfmar_mar_25	9313
gfmar_mar_50	33753
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              138
No. genotyped individuals      138
No. phenotyped individuals     138
No. with both geno & pheno     138

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

No. chromosomes                 20
Total markers                26548

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2374 2374 1623 1691 1568 1703 1464 1468 1685  974 1671 1134 1377 1411  876  751 
  17   18   19    X 
 330  770  920  384 

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

[1] 26548

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

[1] 19992

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

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

No. chromosomes                18
Total markers                6556

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   16   18 
 165  819    3  254  432  185  768 1294  572   94  415  442   55  262   28  505 
  19    X 
 153  110 

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)

Conditionaing on eoi vs. ici chr 3 peak (UNCHS008487) & chr 4 peak (UNC8250659) & chr 10 peak (UNC18240977)

Genome-wide scan

addcovar = model.matrix(~UNCHS008487+UNC8250659+UNC18240977, data = covars)[,-1]

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

operm <- scan1perm(pr.qc, gm$covar["ICI.vs.PBS"], model="binary", n_perm=1000, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap), addcovar = addcovar)

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	3.773210	3.870279
0.05	2.850223	3.010717
0.1	2.596105	2.648603

The figures below show QTL maps for each phenotype

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

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


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

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

plot_lod(out,gm$gmap)

LOD peaks

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

Centimorgan (cM)

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

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

rownames(peaks) <- NULL

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

#peaks[] %>%
#  kable(escape = F,align = c("cccccccc")) %>%
#  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 2.85022338728557 [autosomes]/3.01071732100715 [x-chromosome]”

Megabase (MB)

print("peaks in MB positions")

[1] “peaks in MB positions”

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

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


rownames(peaks_mba) <- NULL

print(kable(peaks_mba, "html") 
  %>% kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)
  )


#peaks_mba[] %>%
#  kable(escape = F,align = c("cccccccc")) %>%
#  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 2.85022338728557 [autosomes]/3.01071732100715 [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$phenotype[i]],  addcovar = addcovar)
  blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]],  addcovar = addcovar)

  write.csv(as.data.frame(blup_sub), paste0("data/ici.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_conditional_3-peaks-chr3-4-10.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.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_conditional_3-peaks-chr3-4-10.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 2.85022338728557 [autosomes]/3.01071732100715 [x-chromosome]”

R/qtl

scanone

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                  7
No. phenotype covariates        0

No. chromosomes                18
Total markers                6556

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   16   18 
 165  819    3  254  432  185  768 1294  572   94  415  442   55  262   28  505 
  19    X 
 153  110 

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

cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_5.batches_conditional_3-peaks-chr3-4-10.csv",alleles=c("A","B"))

 --Read the following data:
     138  individuals
     6556  markers
     6  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    138 

    No. phenotypes:     6 
    Percent phenotyped: 100 100 100 100 100 100 

    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:      6556 
    No. markers:        165 819 3 254 432 185 768 1294 572 94 415 442 55 262 28 
                        505 153 110 
    Percent genotyped:  99.6 
    Genotypes (%):    
          Autosomes:    AA:50.2  AB:49.8 
       X chromosome:    AA:48.9  AB:51.1 

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

print("method == hk")

[1] "method == hk"

add.covars = qtl::pull.pheno(cross.probs, c("UNCHS008487","UNC8250659","UNC18240977"))

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

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

Autosome LOD thresholds (1000 permutations)
     lod
1%  3.58
5%  2.84
10% 2.50

X chromosome LOD thresholds (24556 permutations)
     lod
1%  3.66
5%  2.96
10% 2.62

#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
UNCHS001366    1 40.855000 0.18100952 1.0000000
UNCHS004864    2 27.709003 0.97088344 0.9766933
UNC6588127     3 79.274001 0.05976942 1.0000000
UNCHS011492    4 33.279197 0.07084374 1.0000000
UNC9338984     5 32.874299 0.81888960 0.9968055
UNC12231526    6 69.779140 0.28251060 1.0000000
JAX00651930r   7 60.825563 0.71236942 0.9984472
UNCHS023353    8 35.513686 1.77681206 0.4378136
UNCHS026419    9 44.013080 0.55597102 1.0000000
JAX00301069   10 71.015092 0.89388335 0.9891073
UNC20465557   11 80.903338 0.59574773 1.0000000
UNC21998787   12 63.153416 0.23136093 1.0000000
UNC22273332   13 10.282019 0.91294929 0.9882340
UNC24911002   14 65.221251 0.36732197 1.0000000
UNC26264492   16  2.534000 0.40975944 1.0000000
UNC29319194   18 30.552272 0.53977823 1.0000000
UNC29831860   19  3.956008 1.44933506 0.7153456
UNC31316617    X 53.064039 0.13279851 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.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.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 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15.7

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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_1.4.3    ggplot2_3.3.6    
 [5] tibble_3.1.7      psych_2.2.5       readxl_1.4.0      cluster_2.0.7-1  
 [9] dplyr_1.0.9       optparse_1.6.6    rhdf5_2.26.2      mclust_5.4.5     
[13] tidyr_1.2.0       data.table_1.14.2 knitr_1.29        kableExtra_1.3.4 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.3        bit64_0.9-7       viridisLite_0.3.0 assertthat_0.2.1 
 [5] highr_0.8         blob_1.2.1        cellranger_1.1.0  yaml_2.2.1       
 [9] gdtools_0.2.1     pillar_1.7.0      RSQLite_2.2.0     backports_1.1.5  
[13] lattice_0.20-35   glue_1.6.2        digest_0.6.25     promises_1.1.0   
[17] rvest_1.0.2       colorspace_1.4-1  htmltools_0.5.2   httpuv_1.5.2     
[21] plyr_1.8.6        pkgconfig_2.0.3   purrr_0.3.4       scales_1.1.1     
[25] webshot_0.5.3     svglite_1.2.3     qtl_1.46-2        getopt_1.20.3    
[29] later_1.0.0       git2r_0.26.1      generics_0.0.2    ellipsis_0.3.2   
[33] withr_2.5.0       cli_3.3.0         mnormt_1.5-6      magrittr_2.0.3   
[37] crayon_1.5.1      memoise_1.1.0     evaluate_0.14     fs_1.3.2         
[41] fansi_0.4.1       nlme_3.1-137      xml2_1.3.2        tools_3.5.1      
[45] lifecycle_1.0.1   stringr_1.4.0     Rhdf5lib_1.4.3    munsell_0.5.0    
[49] compiler_3.5.1    systemfonts_0.1.1 rlang_1.0.3       grid_3.5.1       
[53] rstudioapi_0.13   rmarkdown_2.3     gtable_0.3.0      DBI_1.1.0        
[57] R6_2.4.1          fastmap_1.1.0     bit_1.1-15.2      utf8_1.1.4       
[61] rprojroot_1.3-2   stringi_1.4.6     parallel_3.5.1    Rcpp_1.0.4.6     
[65] vctrs_0.4.1       tidyselect_1.1.2  xfun_0.15