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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    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_additive_snpsqc_snpsqc_5.batches_peaks.txt
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    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_interacting_snpsqc_snpsqc_5.batches_peaks.txt
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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
    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.csv
    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_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
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    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_5.batches_mis.csv
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    Untracked:  data/ici.vs.pbs_scanone_5.batches.Rdata
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    Untracked:  data/ici.vs.pbs_scanone_snpsqc_5.batches.Rdata
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    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
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    Untracked:  data/pheno.csv
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    Untracked:  data/sample_geno.csv
    Untracked:  data/sample_geno_AHB_BC312.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
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    Untracked:  data/summary.cg_3.batches.RData
    Untracked:  data/summary.cg_4.batches.RData
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    Untracked:  data/summary.cg_5.batches.RData
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    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/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.

---

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

---

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_mis.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                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

#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                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

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

#bin_pheno <- NULL
#bin_pheno$PBS <- ifelse(gm$covar$group == "PBS", 1, 0)
#bin_pheno$ICI <- ifelse(gm$covar$group == "ICI", 1, 0)
#bin_pheno <- as.data.frame(bin_pheno)
#rownames(bin_pheno) <- rownames(gm$covar)

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


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

ICI PBS 
104  34 

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


##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] 98665

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	88924
gfmar_mar_1	92460
gfmar_mar_5	98665
gfmar_mar_10	99264
gfmar_mar_15	99368
gfmar_mar_25	100303
gfmar_mar_50	130381
total_snps	131355

gm_qc <- drop_markers(gm, low_freq_bad$marker)
gm_qc <- drop_nullmarkers(gm_qc)
 
gm = gm_qc
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                32690

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064  940 
  17   18   19    X 
 401 1095 1067  575 

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)

Genome-wide scan

#gm

Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]

addcovar = model.matrix(~UNCHS008487+UNC8250659, 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$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$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	2.820068	3.943180
0.05	2.814595	3.254822
0.1	2.807736	2.354649

The figures below show QTL maps for each phenotype

out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, 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, "html") 
#  %>% kable_styling("striped", full_width = T) %>%
#  column_spec(1, bold=TRUE)
#  )

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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	2.934	3.654792	1.972	4.621	UNCHS045343

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

print(kable(peaks_mba, escape = F, align = c("cccccccc"), "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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	4.861199	3.654792	3.01212	7.97679	UNCHS045343

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_5.batches_mis_conditional_2-peaks-chr3-4.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_5.batches_mis_conditional_2-peaks-chr3-4.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]"))
}

chr	type	start	stop	strand	ID	Name	Dbxref	gene_id	mgi_type	description
18	pseudogene	3.015908	3.016159	positive	MGI_C57BL6J_6275399	Gm50072	ENSEMBL:ENSMUSG00000117547	MGI:6275399	pseudogene	predicted gene, 50072
18	pseudogene	3.026901	3.027882	negative	MGI_C57BL6J_3852490	Vmn1r-ps151	NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774	MGI:3852490	pseudogene	vomeronasal 1 receptor, pseudogene 151
18	pseudogene	3.080778	3.081476	negative	MGI_C57BL6J_3852491	Vmn1r-ps152	NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444	MGI:3852491	pseudogene	vomeronasal 1 receptor, pseudogene 152
18	pseudogene	3.105167	3.105396	negative	MGI_C57BL6J_6275400	Gm50073	ENSEMBL:ENSMUSG00000117531	MGI:6275400	pseudogene	predicted gene, 50073
18	gene	3.122492	3.130012	negative	MGI_C57BL6J_3852494	Vmn1r238	NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539	MGI:3852494	protein coding gene	vomeronasal 1 receptor, 238
18	pseudogene	3.139032	3.139305	negative	MGI_C57BL6J_6275401	Gm50074	ENSEMBL:ENSMUSG00000117353	MGI:6275401	pseudogene	predicted gene, 50074
18	pseudogene	3.171447	3.171932	negative	MGI_C57BL6J_6275402	Gm50075	ENSEMBL:ENSMUSG00000117464	MGI:6275402	pseudogene	predicted gene, 50075
18	gene	3.266048	3.338176	negative	MGI_C57BL6J_88495	Crem	NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889	MGI:88495	protein coding gene	cAMP responsive element modulator
18	gene	3.336416	3.366863	positive	MGI_C57BL6J_3643252	Gm6225	NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746	MGI:3643252	lncRNA gene	predicted gene 6225
18	gene	3.382970	3.436700	positive	MGI_C57BL6J_1918995	Cul2	NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231	MGI:1918995	protein coding gene	cullin 2
18	pseudogene	3.446654	3.447503	positive	MGI_C57BL6J_3648491	Gm6235	NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370	MGI:3648491	pseudogene	predicted gene 6235
18	pseudogene	3.466371	3.466502	negative	MGI_C57BL6J_6275423	Gm50088	ENSEMBL:ENSMUSG00000117389	MGI:6275423	pseudogene	predicted gene, 50088
18	gene	3.471630	3.474315	positive	MGI_C57BL6J_3645649	G430049J08Rik	ENSEMBL:ENSMUSG00000096528	MGI:3645649	unclassified gene	RIKEN cDNA G430049J08 gene
18	pseudogene	3.484218	3.485193	positive	MGI_C57BL6J_6275424	Gm50089	ENSEMBL:ENSMUSG00000117469	MGI:6275424	pseudogene	predicted gene, 50089
18	gene	3.507923	3.516404	positive	MGI_C57BL6J_1915260	Bambi	NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232	MGI:1915260	protein coding gene	BMP and activin membrane-bound inhibitor
18	pseudogene	3.558675	3.560037	positive	MGI_C57BL6J_6275425	Gm50090	ENSEMBL:ENSMUSG00000117454	MGI:6275425	pseudogene	predicted gene, 50090
18	gene	3.616281	3.618242	positive	MGI_C57BL6J_6275426	Gm50091	ENSEMBL:ENSMUSG00000117506	MGI:6275426	unclassified gene	predicted gene, 50091
18	gene	3.770741	3.770804	negative	MGI_C57BL6J_5453420	Gm23643	ENSEMBL:ENSMUSG00000088480	MGI:5453420	snRNA gene	predicted gene, 23643
18	pseudogene	3.857730	3.858585	negative	MGI_C57BL6J_3647465	Rpl7a-ps6	NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463	MGI:3647465	pseudogene	ribosomal protein L7A, pseudogene 6
18	gene	3.860108	3.860430	positive	MGI_C57BL6J_5454409	Gm24632	ENSEMBL:ENSMUSG00000084719	MGI:5454409	unclassified non-coding RNA gene	predicted gene, 24632
18	pseudogene	4.010143	4.011335	positive	MGI_C57BL6J_3646622	Gm7378	NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395	MGI:3646622	pseudogene	predicted gene 7378
18	pseudogene	4.052351	4.052771	negative	MGI_C57BL6J_3644899	Gm6248	NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565	MGI:3644899	pseudogene	predicted gene 6248
18	gene	4.165832	4.182236	positive	MGI_C57BL6J_1914578	Lyzl1	NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233	MGI:1914578	protein coding gene	lysozyme-like 1
18	pseudogene	4.198320	4.199135	negative	MGI_C57BL6J_3708638	Gm10557	NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647	MGI:3708638	pseudogene	predicted gene 10557
18	gene	4.244121	4.250779	negative	MGI_C57BL6J_5624542	Gm41657	NCBI_Gene:105246358	MGI:5624542	lncRNA gene	predicted gene, 41657
18	pseudogene	4.293097	4.294538	negative	MGI_C57BL6J_3647488	Gm7400	NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482	MGI:3647488	pseudogene	predicted gene 7400
18	gene	4.331325	4.353412	negative	MGI_C57BL6J_1346878	Map3k8	NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235	MGI:1346878	protein coding gene	mitogen-activated protein kinase kinase kinase 8
18	gene	4.353547	4.368945	positive	MGI_C57BL6J_1921165	4833419F23Rik	NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401	MGI:1921165	lncRNA gene	RIKEN cDNA 4833419F23 gene
18	gene	4.353644	4.380723	positive	MGI_C57BL6J_6275434	Gm50096	ENSEMBL:ENSMUSG00000117594	MGI:6275434	lncRNA gene	predicted gene, 50096
18	gene	4.373669	4.379985	negative	MGI_C57BL6J_5477359	Gm26865	ENSEMBL:ENSMUSG00000097641	MGI:5477359	lncRNA gene	predicted gene, 26865
18	gene	4.375592	4.398798	positive	MGI_C57BL6J_1914690	Mtpap	NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234	MGI:1914690	protein coding gene	mitochondrial poly(A) polymerase
18	gene	4.399328	4.400910	positive	MGI_C57BL6J_6275321	Gm50023	ENSEMBL:ENSMUSG00000117579	MGI:6275321	lncRNA gene	predicted gene, 50023
18	pseudogene	4.484469	4.485501	positive	MGI_C57BL6J_3644066	Gm7411	NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640	MGI:3644066	pseudogene	predicted gene 7411
18	gene	4.541612	4.562599	negative	MGI_C57BL6J_5624543	Gm41658	NCBI_Gene:105246359	MGI:5624543	lncRNA gene	predicted gene, 41658
18	gene	4.590763	4.616064	positive	MGI_C57BL6J_6275323	Gm50024	ENSEMBL:ENSMUSG00000117521	MGI:6275323	lncRNA gene	predicted gene, 50024
18	gene	4.590792	4.682869	positive	MGI_C57BL6J_2685174	Jcad	NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960	MGI:2685174	protein coding gene	junctional cadherin 5 associated
18	pseudogene	4.743061	4.743876	positive	MGI_C57BL6J_3645969	Gm5047	NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585	MGI:3645969	pseudogene	predicted gene 5047
18	gene	4.812486	4.850959	positive	MGI_C57BL6J_3642809	Gm10556	NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043	MGI:3642809	lncRNA gene	predicted gene 10556
18	gene	4.820303	4.826659	negative	MGI_C57BL6J_5593428	Gm34269	NCBI_Gene:102637466	MGI:5593428	lncRNA gene	predicted gene, 34269
18	gene	4.869777	4.912263	positive	MGI_C57BL6J_5624544	Gm41659	NCBI_Gene:105246360	MGI:5624544	lncRNA gene	predicted gene, 41659
18	gene	4.920245	5.119299	positive	MGI_C57BL6J_2147319	Svil	NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236	MGI:2147319	protein coding gene	supervillin
18	gene	4.959636	4.960300	positive	MGI_C57BL6J_3642287	BC025933	NA	NA	protein coding gene	cDNA sequence BC025933
18	gene	5.042818	5.048867	negative	MGI_C57BL6J_5593485	Gm34326	NCBI_Gene:102637544	MGI:5593485	lncRNA gene	predicted gene, 34326
18	gene	5.139349	5.142068	positive	MGI_C57BL6J_5593700	Gm34541	NCBI_Gene:102637831	MGI:5593700	lncRNA gene	predicted gene, 34541
18	gene	5.141762	5.165731	negative	MGI_C57BL6J_5477176	Gm26682	NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888	MGI:5477176	lncRNA gene	predicted gene, 26682
18	gene	5.210027	5.334963	negative	MGI_C57BL6J_2444919	Zfp438	NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945	MGI:2444919	protein coding gene	zinc finger protein 438
18	gene	5.348258	5.361861	negative	MGI_C57BL6J_6275385	Gm50064	ENSEMBL:ENSMUSG00000117520	MGI:6275385	lncRNA gene	predicted gene, 50064
18	gene	5.368541	5.370482	negative	MGI_C57BL6J_6275387	Gm50065	ENSEMBL:ENSMUSG00000117617	MGI:6275387	lncRNA gene	predicted gene, 50065
18	gene	5.389802	5.402870	negative	MGI_C57BL6J_5593789	Gm34630	NCBI_Gene:102637944	MGI:5593789	lncRNA gene	predicted gene, 34630
18	gene	5.491501	5.593505	negative	MGI_C57BL6J_3642044	Gm10125	NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087	MGI:3642044	lncRNA gene	predicted gene 10125
18	gene	5.572824	5.575055	negative	MGI_C57BL6J_5477069	Gm26575	ENSEMBL:ENSMUSG00000097926	MGI:5477069	lncRNA gene	predicted gene, 26575
18	gene	5.588043	5.591711	negative	MGI_C57BL6J_3780408	Gm2238	ENSEMBL:ENSMUSG00000103138	MGI:3780408	unclassified gene	predicted gene 2238
18	gene	5.591860	5.775468	positive	MGI_C57BL6J_1344313	Zeb1	NCBI_Gene:21417,ENSEMBL:ENSMUSG00000024238	MGI:1344313	protein coding gene	zinc finger E-box binding homeobox 1
18	pseudogene	5.611558	5.611930	negative	MGI_C57BL6J_3801802	Gm16147	ENSEMBL:ENSMUSG00000090078	MGI:3801802	pseudogene	predicted gene 16147
18	gene	5.614473	5.614583	positive	MGI_C57BL6J_5452486	Gm22709	ENSEMBL:ENSMUSG00000080543	MGI:5452486	miRNA gene	predicted gene, 22709
18	gene	5.885791	5.889876	negative	MGI_C57BL6J_5593899	Gm34740	NCBI_Gene:102638095	MGI:5593899	lncRNA gene	predicted gene, 34740
18	gene	5.945183	5.966908	positive	MGI_C57BL6J_5593963	Gm34804	NCBI_Gene:102638178,ENSEMBL:ENSMUSG00000117508	MGI:5593963	lncRNA gene	predicted gene, 34804
18	gene	6.004578	6.015278	positive	MGI_C57BL6J_5624548	Gm41663	NCBI_Gene:105246364	MGI:5624548	lncRNA gene	predicted gene, 41663
18	gene	6.014253	6.024013	negative	MGI_C57BL6J_5624547	Gm41662	NCBI_Gene:105246363,ENSEMBL:ENSMUSG00000117548	MGI:5624547	lncRNA gene	predicted gene, 41662
18	gene	6.024427	6.136098	negative	MGI_C57BL6J_1922665	Arhgap12	NCBI_Gene:75415,ENSEMBL:ENSMUSG00000041225	MGI:1922665	protein coding gene	Rho GTPase activating protein 12
18	pseudogene	6.029179	6.030456	negative	MGI_C57BL6J_5011425	Gm19240	NCBI_Gene:100418482	MGI:5011425	pseudogene	predicted gene, 19240
18	gene	6.201002	6.242174	negative	MGI_C57BL6J_1098268	Kif5b	NCBI_Gene:16573,ENSEMBL:ENSMUSG00000006740	MGI:1098268	protein coding gene	kinesin family member 5B
18	gene	6.213913	6.227324	positive	MGI_C57BL6J_4937863	Gm17036	ENSEMBL:ENSMUSG00000091165	MGI:4937863	lncRNA gene	predicted gene 17036
18	gene	6.234853	6.326693	positive	MGI_C57BL6J_5624549	Gm41664	NCBI_Gene:105246365,ENSEMBL:ENSMUSG00000117519	MGI:5624549	lncRNA gene	predicted gene, 41664
18	pseudogene	6.332591	6.333082	positive	MGI_C57BL6J_3646174	Rpl27-ps3	NCBI_Gene:621100,ENSEMBL:ENSMUSG00000073640	MGI:3646174	pseudogene	ribosomal protein L27, pseudogene 3
18	pseudogene	6.368360	6.368797	positive	MGI_C57BL6J_6275267	Gm49986	ENSEMBL:ENSMUSG00000117387	MGI:6275267	pseudogene	predicted gene, 49986
18	pseudogene	6.371352	6.371660	negative	MGI_C57BL6J_3648803	Gm6291	NCBI_Gene:102637200,ENSEMBL:ENSMUSG00000095614	MGI:3648803	pseudogene	predicted gene 6291
18	gene	6.435951	6.516108	negative	MGI_C57BL6J_1278322	Epc1	NCBI_Gene:13831,ENSEMBL:ENSMUSG00000024240	MGI:1278322	protein coding gene	enhancer of polycomb homolog 1
18	gene	6.490143	6.496962	positive	MGI_C57BL6J_5579235	Gm28529	ENSEMBL:ENSMUSG00000101320	MGI:5579235	lncRNA gene	predicted gene 28529
18	gene	6.490564	6.490646	negative	MGI_C57BL6J_3811424	Mir1893	miRBase:MI0008327,NCBI_Gene:100316773,ENSEMBL:ENSMUSG00000084514	MGI:3811424	miRNA gene	microRNA 1893
18	pseudogene	6.557130	6.557796	positive	MGI_C57BL6J_3645335	Gm7464	NCBI_Gene:665047,ENSEMBL:ENSMUSG00000117425	MGI:3645335	pseudogene	predicted gene 7464
18	gene	6.603629	6.640435	positive	MGI_C57BL6J_1918151	4921524L21Rik	NCBI_Gene:70901,ENSEMBL:ENSMUSG00000039540	MGI:1918151	protein coding gene	RIKEN cDNA 4921524L21 gene
18	pseudogene	6.687843	6.691317	negative	MGI_C57BL6J_6275405	Gm50077	ENSEMBL:ENSMUSG00000117620	MGI:6275405	pseudogene	predicted gene, 50077
18	gene	6.733905	6.794429	positive	MGI_C57BL6J_102790	Rab18	NCBI_Gene:19330,ENSEMBL:ENSMUSG00000073639	MGI:102790	protein coding gene	RAB18, member RAS oncogene family
18	pseudogene	6.760073	6.760959	negative	MGI_C57BL6J_3648356	Gm7466	NCBI_Gene:665053,ENSEMBL:ENSMUSG00000117359	MGI:3648356	pseudogene	predicted gene 7466
18	gene	6.788768	6.792137	positive	MGI_C57BL6J_6275412	Gm50081	ENSEMBL:ENSMUSG00000117461	MGI:6275412	lncRNA gene	predicted gene, 50081
18	gene	6.910459	6.936621	negative	MGI_C57BL6J_3826781	Gm2350	ENSEMBL:ENSMUSG00000117652	MGI:3826781	lncRNA gene	predicted gene 2350
18	gene	6.934518	7.004780	negative	MGI_C57BL6J_2687286	Mkx	NCBI_Gene:210719,ENSEMBL:ENSMUSG00000061013	MGI:2687286	protein coding gene	mohawk homeobox
18	gene	7.004963	7.066322	positive	MGI_C57BL6J_5826270	Gm46633	NCBI_Gene:108168391,ENSEMBL:ENSMUSG00000117356	MGI:5826270	lncRNA gene	predicted gene, 46633
18	pseudogene	7.041939	7.044195	negative	MGI_C57BL6J_3642728	Gm10350	NCBI_Gene:664778,ENSEMBL:ENSMUSG00000117381	MGI:3642728	pseudogene	predicted gene 10350
18	gene	7.088209	7.298304	negative	MGI_C57BL6J_1922184	Armc4	NCBI_Gene:74934,ENSEMBL:ENSMUSG00000061802	MGI:1922184	protein coding gene	armadillo repeat containing 4
18	pseudogene	7.107181	7.108183	negative	MGI_C57BL6J_5010766	Gm18581	NCBI_Gene:100417382,ENSEMBL:ENSMUSG00000117360	MGI:5010766	pseudogene	predicted gene, 18581
18	gene	7.150344	7.151777	negative	MGI_C57BL6J_1915234	4930415O11Rik	ENSEMBL:ENSMUSG00000117383	MGI:1915234	unclassified gene	RIKEN cDNA 4930415O11 gene
18	gene	7.332445	7.346299	negative	MGI_C57BL6J_5594138	Gm34979	NCBI_Gene:102638402	MGI:5594138	lncRNA gene	predicted gene, 34979
18	gene	7.347959	7.626893	negative	MGI_C57BL6J_1922989	Mpp7	NCBI_Gene:75739,ENSEMBL:ENSMUSG00000057440	MGI:1922989	protein coding gene	membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7)
18	pseudogene	7.548932	7.549781	positive	MGI_C57BL6J_5011133	Gm18948	NCBI_Gene:100418013,ENSEMBL:ENSMUSG00000117556	MGI:5011133	pseudogene	predicted gene, 18948
18	pseudogene	7.581482	7.582130	positive	MGI_C57BL6J_3644282	Fabp5l2	NCBI_Gene:622384,ENSEMBL:ENSMUSG00000094334	MGI:3644282	pseudogene	fatty acid binding protein 5-like 2
18	pseudogene	7.684076	7.693411	negative	MGI_C57BL6J_5010948	Gm18763	NCBI_Gene:100417683,ENSEMBL:ENSMUSG00000117542	MGI:5010948	pseudogene	predicted gene, 18763
18	pseudogene	7.798784	7.799900	negative	MGI_C57BL6J_3780506	Gm2336	NCBI_Gene:100039606	MGI:3780506	pseudogene	predicted gene 2336
18	gene	7.835177	7.835283	positive	MGI_C57BL6J_5454069	Gm24292	ENSEMBL:ENSMUSG00000064822	MGI:5454069	snRNA gene	predicted gene, 24292
18	pseudogene	7.844684	7.844935	negative	MGI_C57BL6J_6275329	Gm50027	ENSEMBL:ENSMUSG00000117462	MGI:6275329	pseudogene	predicted gene, 50027
18	gene	7.868018	7.869113	negative	MGI_C57BL6J_3641793	Gm9993	ENSEMBL:ENSMUSG00000117505	MGI:3641793	lncRNA gene	predicted gene 9993
18	gene	7.868832	7.929028	positive	MGI_C57BL6J_2387357	Wac	NCBI_Gene:225131,ENSEMBL:ENSMUSG00000024283	MGI:2387357	protein coding gene	WW domain containing adaptor with coiled-coil
18	gene	7.894300	7.895792	positive	MGI_C57BL6J_1924412	A430102J17Rik	NA	NA	unclassified gene	RIKEN cDNA A430102J17 gene
18	pseudogene	7.932728	7.933556	positive	MGI_C57BL6J_3643245	Gm7502	NCBI_Gene:665121,ENSEMBL:ENSMUSG00000091019	MGI:3643245	pseudogene	predicted gene 7502

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                 20
Total markers                32690

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064  940 
  17   18   19    X 
 401 1095 1067  575 

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

cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr3-4.csv",alleles=c("A","B"))

 --Read the following data:
     138  individuals
     32690  markers
     5  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    138 

    No. phenotypes:     5 
    Percent phenotyped: 100 100 100 100 100 

    No. chromosomes:    20 
        Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
        X chr:          X 

    Total markers:      32690 
    No. markers:        2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 
                        1396 1628 1692 1064 940 401 1095 1067 575 
    Percent genotyped:  99.5 
    Genotypes (%):    
          Autosomes:    AA:54.3  AB:45.7 
       X chromosome:    AA:52.7  AB:47.3 

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

print("method == hk")

[1] "method == hk"

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

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 = 10, 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 (10 permutations)
     lod
1%  4.86
5%  4.49
10% 4.03

X chromosome LOD thresholds (183 permutations)
     lod
1%  4.15
5%  3.62
10% 2.94

#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
UNCHS000141     1  4.439109 1.4810382 0.7191035
UNC2981117      2 27.613431 0.8724495 1.0000000
JAX00521745     3 20.613360 0.7586221 1.0000000
JAX00556948     4 41.038754 0.7726880 1.0000000
UNC8675939      5  6.193174 2.0684019 0.5185858
UNC10923091     6 18.321306 0.6856206 1.0000000
UNCHS020066     7 34.113657 1.2936681 0.8168395
UNC15524531     8 59.462590 1.9315437 0.5185858
UNC17203597     9 68.527929 0.6928610 1.0000000
UNCHS028001    10 21.783362 1.0219015 0.9118239
UNC20465557    11 80.904972 0.7841081 1.0000000
UNC21787084    12 53.288332 0.7505664 1.0000000
JAX00351755    13  7.044200 0.9809396 1.0000000
UNC24056202    14 30.156730 1.8824837 0.5185858
UNC25275535    15 11.967146 0.8880487 1.0000000
UNCHS042071    16 13.176096 0.7730967 1.0000000
UNCrs47191360  17 18.433013 1.2636005 0.8168395
UNCHS045343    18  2.934001 3.3781249 0.2096965
UNC29920552    19  8.810092 1.9159935 0.5185858
UNCHS048314     X  8.974041 1.9849011 0.6619190

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 = 10, 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")))

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

Genome-wide scan

#gm

Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]

addcovar = model.matrix(~UNCHS008487+UNC18240977, 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$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$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	2.997377	2.972464
0.05	2.792809	2.690815
0.1	2.536468	2.322500

The figures below show QTL maps for each phenotype

out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, 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, "html") 
#  %>% kable_styling("striped", full_width = T) %>%
#  column_spec(1, bold=TRUE)
#  )

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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	2.934	3.467491	1.972	4.621	UNCHS045343

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

print(kable(peaks_mba, escape = F, align = c("cccccccc"), "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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	4.861199	3.467491	3.01212	7.97679	UNCHS045343

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_5.batches_mis_conditional_2-peaks-chr3-10.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_5.batches_mis_conditional_2-peaks-chr3-10.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]"))
}

chr	type	start	stop	strand	ID	Name	Dbxref	gene_id	mgi_type	description
18	pseudogene	3.015908	3.016159	positive	MGI_C57BL6J_6275399	Gm50072	ENSEMBL:ENSMUSG00000117547	MGI:6275399	pseudogene	predicted gene, 50072
18	pseudogene	3.026901	3.027882	negative	MGI_C57BL6J_3852490	Vmn1r-ps151	NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774	MGI:3852490	pseudogene	vomeronasal 1 receptor, pseudogene 151
18	pseudogene	3.080778	3.081476	negative	MGI_C57BL6J_3852491	Vmn1r-ps152	NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444	MGI:3852491	pseudogene	vomeronasal 1 receptor, pseudogene 152
18	pseudogene	3.105167	3.105396	negative	MGI_C57BL6J_6275400	Gm50073	ENSEMBL:ENSMUSG00000117531	MGI:6275400	pseudogene	predicted gene, 50073
18	gene	3.122492	3.130012	negative	MGI_C57BL6J_3852494	Vmn1r238	NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539	MGI:3852494	protein coding gene	vomeronasal 1 receptor, 238
18	pseudogene	3.139032	3.139305	negative	MGI_C57BL6J_6275401	Gm50074	ENSEMBL:ENSMUSG00000117353	MGI:6275401	pseudogene	predicted gene, 50074
18	pseudogene	3.171447	3.171932	negative	MGI_C57BL6J_6275402	Gm50075	ENSEMBL:ENSMUSG00000117464	MGI:6275402	pseudogene	predicted gene, 50075
18	gene	3.266048	3.338176	negative	MGI_C57BL6J_88495	Crem	NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889	MGI:88495	protein coding gene	cAMP responsive element modulator
18	gene	3.336416	3.366863	positive	MGI_C57BL6J_3643252	Gm6225	NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746	MGI:3643252	lncRNA gene	predicted gene 6225
18	gene	3.382970	3.436700	positive	MGI_C57BL6J_1918995	Cul2	NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231	MGI:1918995	protein coding gene	cullin 2
18	pseudogene	3.446654	3.447503	positive	MGI_C57BL6J_3648491	Gm6235	NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370	MGI:3648491	pseudogene	predicted gene 6235
18	pseudogene	3.466371	3.466502	negative	MGI_C57BL6J_6275423	Gm50088	ENSEMBL:ENSMUSG00000117389	MGI:6275423	pseudogene	predicted gene, 50088
18	gene	3.471630	3.474315	positive	MGI_C57BL6J_3645649	G430049J08Rik	ENSEMBL:ENSMUSG00000096528	MGI:3645649	unclassified gene	RIKEN cDNA G430049J08 gene
18	pseudogene	3.484218	3.485193	positive	MGI_C57BL6J_6275424	Gm50089	ENSEMBL:ENSMUSG00000117469	MGI:6275424	pseudogene	predicted gene, 50089
18	gene	3.507923	3.516404	positive	MGI_C57BL6J_1915260	Bambi	NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232	MGI:1915260	protein coding gene	BMP and activin membrane-bound inhibitor
18	pseudogene	3.558675	3.560037	positive	MGI_C57BL6J_6275425	Gm50090	ENSEMBL:ENSMUSG00000117454	MGI:6275425	pseudogene	predicted gene, 50090
18	gene	3.616281	3.618242	positive	MGI_C57BL6J_6275426	Gm50091	ENSEMBL:ENSMUSG00000117506	MGI:6275426	unclassified gene	predicted gene, 50091
18	gene	3.770741	3.770804	negative	MGI_C57BL6J_5453420	Gm23643	ENSEMBL:ENSMUSG00000088480	MGI:5453420	snRNA gene	predicted gene, 23643
18	pseudogene	3.857730	3.858585	negative	MGI_C57BL6J_3647465	Rpl7a-ps6	NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463	MGI:3647465	pseudogene	ribosomal protein L7A, pseudogene 6
18	gene	3.860108	3.860430	positive	MGI_C57BL6J_5454409	Gm24632	ENSEMBL:ENSMUSG00000084719	MGI:5454409	unclassified non-coding RNA gene	predicted gene, 24632
18	pseudogene	4.010143	4.011335	positive	MGI_C57BL6J_3646622	Gm7378	NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395	MGI:3646622	pseudogene	predicted gene 7378
18	pseudogene	4.052351	4.052771	negative	MGI_C57BL6J_3644899	Gm6248	NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565	MGI:3644899	pseudogene	predicted gene 6248
18	gene	4.165832	4.182236	positive	MGI_C57BL6J_1914578	Lyzl1	NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233	MGI:1914578	protein coding gene	lysozyme-like 1
18	pseudogene	4.198320	4.199135	negative	MGI_C57BL6J_3708638	Gm10557	NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647	MGI:3708638	pseudogene	predicted gene 10557
18	gene	4.244121	4.250779	negative	MGI_C57BL6J_5624542	Gm41657	NCBI_Gene:105246358	MGI:5624542	lncRNA gene	predicted gene, 41657
18	pseudogene	4.293097	4.294538	negative	MGI_C57BL6J_3647488	Gm7400	NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482	MGI:3647488	pseudogene	predicted gene 7400
18	gene	4.331325	4.353412	negative	MGI_C57BL6J_1346878	Map3k8	NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235	MGI:1346878	protein coding gene	mitogen-activated protein kinase kinase kinase 8
18	gene	4.353547	4.368945	positive	MGI_C57BL6J_1921165	4833419F23Rik	NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401	MGI:1921165	lncRNA gene	RIKEN cDNA 4833419F23 gene
18	gene	4.353644	4.380723	positive	MGI_C57BL6J_6275434	Gm50096	ENSEMBL:ENSMUSG00000117594	MGI:6275434	lncRNA gene	predicted gene, 50096
18	gene	4.373669	4.379985	negative	MGI_C57BL6J_5477359	Gm26865	ENSEMBL:ENSMUSG00000097641	MGI:5477359	lncRNA gene	predicted gene, 26865
18	gene	4.375592	4.398798	positive	MGI_C57BL6J_1914690	Mtpap	NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234	MGI:1914690	protein coding gene	mitochondrial poly(A) polymerase
18	gene	4.399328	4.400910	positive	MGI_C57BL6J_6275321	Gm50023	ENSEMBL:ENSMUSG00000117579	MGI:6275321	lncRNA gene	predicted gene, 50023
18	pseudogene	4.484469	4.485501	positive	MGI_C57BL6J_3644066	Gm7411	NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640	MGI:3644066	pseudogene	predicted gene 7411
18	gene	4.541612	4.562599	negative	MGI_C57BL6J_5624543	Gm41658	NCBI_Gene:105246359	MGI:5624543	lncRNA gene	predicted gene, 41658
18	gene	4.590763	4.616064	positive	MGI_C57BL6J_6275323	Gm50024	ENSEMBL:ENSMUSG00000117521	MGI:6275323	lncRNA gene	predicted gene, 50024
18	gene	4.590792	4.682869	positive	MGI_C57BL6J_2685174	Jcad	NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960	MGI:2685174	protein coding gene	junctional cadherin 5 associated
18	pseudogene	4.743061	4.743876	positive	MGI_C57BL6J_3645969	Gm5047	NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585	MGI:3645969	pseudogene	predicted gene 5047
18	gene	4.812486	4.850959	positive	MGI_C57BL6J_3642809	Gm10556	NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043	MGI:3642809	lncRNA gene	predicted gene 10556
18	gene	4.820303	4.826659	negative	MGI_C57BL6J_5593428	Gm34269	NCBI_Gene:102637466	MGI:5593428	lncRNA gene	predicted gene, 34269
18	gene	4.869777	4.912263	positive	MGI_C57BL6J_5624544	Gm41659	NCBI_Gene:105246360	MGI:5624544	lncRNA gene	predicted gene, 41659
18	gene	4.920245	5.119299	positive	MGI_C57BL6J_2147319	Svil	NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236	MGI:2147319	protein coding gene	supervillin
18	gene	4.959636	4.960300	positive	MGI_C57BL6J_3642287	BC025933	NA	NA	protein coding gene	cDNA sequence BC025933
18	gene	5.042818	5.048867	negative	MGI_C57BL6J_5593485	Gm34326	NCBI_Gene:102637544	MGI:5593485	lncRNA gene	predicted gene, 34326
18	gene	5.139349	5.142068	positive	MGI_C57BL6J_5593700	Gm34541	NCBI_Gene:102637831	MGI:5593700	lncRNA gene	predicted gene, 34541
18	gene	5.141762	5.165731	negative	MGI_C57BL6J_5477176	Gm26682	NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888	MGI:5477176	lncRNA gene	predicted gene, 26682
18	gene	5.210027	5.334963	negative	MGI_C57BL6J_2444919	Zfp438	NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945	MGI:2444919	protein coding gene	zinc finger protein 438
18	gene	5.348258	5.361861	negative	MGI_C57BL6J_6275385	Gm50064	ENSEMBL:ENSMUSG00000117520	MGI:6275385	lncRNA gene	predicted gene, 50064
18	gene	5.368541	5.370482	negative	MGI_C57BL6J_6275387	Gm50065	ENSEMBL:ENSMUSG00000117617	MGI:6275387	lncRNA gene	predicted gene, 50065
18	gene	5.389802	5.402870	negative	MGI_C57BL6J_5593789	Gm34630	NCBI_Gene:102637944	MGI:5593789	lncRNA gene	predicted gene, 34630
18	gene	5.491501	5.593505	negative	MGI_C57BL6J_3642044	Gm10125	NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087	MGI:3642044	lncRNA gene	predicted gene 10125
18	gene	5.572824	5.575055	negative	MGI_C57BL6J_5477069	Gm26575	ENSEMBL:ENSMUSG00000097926	MGI:5477069	lncRNA gene	predicted gene, 26575
18	gene	5.588043	5.591711	negative	MGI_C57BL6J_3780408	Gm2238	ENSEMBL:ENSMUSG00000103138	MGI:3780408	unclassified gene	predicted gene 2238
18	gene	5.591860	5.775468	positive	MGI_C57BL6J_1344313	Zeb1	NCBI_Gene:21417,ENSEMBL:ENSMUSG00000024238	MGI:1344313	protein coding gene	zinc finger E-box binding homeobox 1
18	pseudogene	5.611558	5.611930	negative	MGI_C57BL6J_3801802	Gm16147	ENSEMBL:ENSMUSG00000090078	MGI:3801802	pseudogene	predicted gene 16147
18	gene	5.614473	5.614583	positive	MGI_C57BL6J_5452486	Gm22709	ENSEMBL:ENSMUSG00000080543	MGI:5452486	miRNA gene	predicted gene, 22709
18	gene	5.885791	5.889876	negative	MGI_C57BL6J_5593899	Gm34740	NCBI_Gene:102638095	MGI:5593899	lncRNA gene	predicted gene, 34740
18	gene	5.945183	5.966908	positive	MGI_C57BL6J_5593963	Gm34804	NCBI_Gene:102638178,ENSEMBL:ENSMUSG00000117508	MGI:5593963	lncRNA gene	predicted gene, 34804
18	gene	6.004578	6.015278	positive	MGI_C57BL6J_5624548	Gm41663	NCBI_Gene:105246364	MGI:5624548	lncRNA gene	predicted gene, 41663
18	gene	6.014253	6.024013	negative	MGI_C57BL6J_5624547	Gm41662	NCBI_Gene:105246363,ENSEMBL:ENSMUSG00000117548	MGI:5624547	lncRNA gene	predicted gene, 41662
18	gene	6.024427	6.136098	negative	MGI_C57BL6J_1922665	Arhgap12	NCBI_Gene:75415,ENSEMBL:ENSMUSG00000041225	MGI:1922665	protein coding gene	Rho GTPase activating protein 12
18	pseudogene	6.029179	6.030456	negative	MGI_C57BL6J_5011425	Gm19240	NCBI_Gene:100418482	MGI:5011425	pseudogene	predicted gene, 19240
18	gene	6.201002	6.242174	negative	MGI_C57BL6J_1098268	Kif5b	NCBI_Gene:16573,ENSEMBL:ENSMUSG00000006740	MGI:1098268	protein coding gene	kinesin family member 5B
18	gene	6.213913	6.227324	positive	MGI_C57BL6J_4937863	Gm17036	ENSEMBL:ENSMUSG00000091165	MGI:4937863	lncRNA gene	predicted gene 17036
18	gene	6.234853	6.326693	positive	MGI_C57BL6J_5624549	Gm41664	NCBI_Gene:105246365,ENSEMBL:ENSMUSG00000117519	MGI:5624549	lncRNA gene	predicted gene, 41664
18	pseudogene	6.332591	6.333082	positive	MGI_C57BL6J_3646174	Rpl27-ps3	NCBI_Gene:621100,ENSEMBL:ENSMUSG00000073640	MGI:3646174	pseudogene	ribosomal protein L27, pseudogene 3
18	pseudogene	6.368360	6.368797	positive	MGI_C57BL6J_6275267	Gm49986	ENSEMBL:ENSMUSG00000117387	MGI:6275267	pseudogene	predicted gene, 49986
18	pseudogene	6.371352	6.371660	negative	MGI_C57BL6J_3648803	Gm6291	NCBI_Gene:102637200,ENSEMBL:ENSMUSG00000095614	MGI:3648803	pseudogene	predicted gene 6291
18	gene	6.435951	6.516108	negative	MGI_C57BL6J_1278322	Epc1	NCBI_Gene:13831,ENSEMBL:ENSMUSG00000024240	MGI:1278322	protein coding gene	enhancer of polycomb homolog 1
18	gene	6.490143	6.496962	positive	MGI_C57BL6J_5579235	Gm28529	ENSEMBL:ENSMUSG00000101320	MGI:5579235	lncRNA gene	predicted gene 28529
18	gene	6.490564	6.490646	negative	MGI_C57BL6J_3811424	Mir1893	miRBase:MI0008327,NCBI_Gene:100316773,ENSEMBL:ENSMUSG00000084514	MGI:3811424	miRNA gene	microRNA 1893
18	pseudogene	6.557130	6.557796	positive	MGI_C57BL6J_3645335	Gm7464	NCBI_Gene:665047,ENSEMBL:ENSMUSG00000117425	MGI:3645335	pseudogene	predicted gene 7464
18	gene	6.603629	6.640435	positive	MGI_C57BL6J_1918151	4921524L21Rik	NCBI_Gene:70901,ENSEMBL:ENSMUSG00000039540	MGI:1918151	protein coding gene	RIKEN cDNA 4921524L21 gene
18	pseudogene	6.687843	6.691317	negative	MGI_C57BL6J_6275405	Gm50077	ENSEMBL:ENSMUSG00000117620	MGI:6275405	pseudogene	predicted gene, 50077
18	gene	6.733905	6.794429	positive	MGI_C57BL6J_102790	Rab18	NCBI_Gene:19330,ENSEMBL:ENSMUSG00000073639	MGI:102790	protein coding gene	RAB18, member RAS oncogene family
18	pseudogene	6.760073	6.760959	negative	MGI_C57BL6J_3648356	Gm7466	NCBI_Gene:665053,ENSEMBL:ENSMUSG00000117359	MGI:3648356	pseudogene	predicted gene 7466
18	gene	6.788768	6.792137	positive	MGI_C57BL6J_6275412	Gm50081	ENSEMBL:ENSMUSG00000117461	MGI:6275412	lncRNA gene	predicted gene, 50081
18	gene	6.910459	6.936621	negative	MGI_C57BL6J_3826781	Gm2350	ENSEMBL:ENSMUSG00000117652	MGI:3826781	lncRNA gene	predicted gene 2350
18	gene	6.934518	7.004780	negative	MGI_C57BL6J_2687286	Mkx	NCBI_Gene:210719,ENSEMBL:ENSMUSG00000061013	MGI:2687286	protein coding gene	mohawk homeobox
18	gene	7.004963	7.066322	positive	MGI_C57BL6J_5826270	Gm46633	NCBI_Gene:108168391,ENSEMBL:ENSMUSG00000117356	MGI:5826270	lncRNA gene	predicted gene, 46633
18	pseudogene	7.041939	7.044195	negative	MGI_C57BL6J_3642728	Gm10350	NCBI_Gene:664778,ENSEMBL:ENSMUSG00000117381	MGI:3642728	pseudogene	predicted gene 10350
18	gene	7.088209	7.298304	negative	MGI_C57BL6J_1922184	Armc4	NCBI_Gene:74934,ENSEMBL:ENSMUSG00000061802	MGI:1922184	protein coding gene	armadillo repeat containing 4
18	pseudogene	7.107181	7.108183	negative	MGI_C57BL6J_5010766	Gm18581	NCBI_Gene:100417382,ENSEMBL:ENSMUSG00000117360	MGI:5010766	pseudogene	predicted gene, 18581
18	gene	7.150344	7.151777	negative	MGI_C57BL6J_1915234	4930415O11Rik	ENSEMBL:ENSMUSG00000117383	MGI:1915234	unclassified gene	RIKEN cDNA 4930415O11 gene
18	gene	7.332445	7.346299	negative	MGI_C57BL6J_5594138	Gm34979	NCBI_Gene:102638402	MGI:5594138	lncRNA gene	predicted gene, 34979
18	gene	7.347959	7.626893	negative	MGI_C57BL6J_1922989	Mpp7	NCBI_Gene:75739,ENSEMBL:ENSMUSG00000057440	MGI:1922989	protein coding gene	membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7)
18	pseudogene	7.548932	7.549781	positive	MGI_C57BL6J_5011133	Gm18948	NCBI_Gene:100418013,ENSEMBL:ENSMUSG00000117556	MGI:5011133	pseudogene	predicted gene, 18948
18	pseudogene	7.581482	7.582130	positive	MGI_C57BL6J_3644282	Fabp5l2	NCBI_Gene:622384,ENSEMBL:ENSMUSG00000094334	MGI:3644282	pseudogene	fatty acid binding protein 5-like 2
18	pseudogene	7.684076	7.693411	negative	MGI_C57BL6J_5010948	Gm18763	NCBI_Gene:100417683,ENSEMBL:ENSMUSG00000117542	MGI:5010948	pseudogene	predicted gene, 18763
18	pseudogene	7.798784	7.799900	negative	MGI_C57BL6J_3780506	Gm2336	NCBI_Gene:100039606	MGI:3780506	pseudogene	predicted gene 2336
18	gene	7.835177	7.835283	positive	MGI_C57BL6J_5454069	Gm24292	ENSEMBL:ENSMUSG00000064822	MGI:5454069	snRNA gene	predicted gene, 24292
18	pseudogene	7.844684	7.844935	negative	MGI_C57BL6J_6275329	Gm50027	ENSEMBL:ENSMUSG00000117462	MGI:6275329	pseudogene	predicted gene, 50027
18	gene	7.868018	7.869113	negative	MGI_C57BL6J_3641793	Gm9993	ENSEMBL:ENSMUSG00000117505	MGI:3641793	lncRNA gene	predicted gene 9993
18	gene	7.868832	7.929028	positive	MGI_C57BL6J_2387357	Wac	NCBI_Gene:225131,ENSEMBL:ENSMUSG00000024283	MGI:2387357	protein coding gene	WW domain containing adaptor with coiled-coil
18	gene	7.894300	7.895792	positive	MGI_C57BL6J_1924412	A430102J17Rik	NA	NA	unclassified gene	RIKEN cDNA A430102J17 gene
18	pseudogene	7.932728	7.933556	positive	MGI_C57BL6J_3643245	Gm7502	NCBI_Gene:665121,ENSEMBL:ENSMUSG00000091019	MGI:3643245	pseudogene	predicted gene 7502

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                 20
Total markers                32690

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064  940 
  17   18   19    X 
 401 1095 1067  575 

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

cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr3-10.csv",alleles=c("A","B"))

 --Read the following data:
     138  individuals
     32690  markers
     5  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    138 

    No. phenotypes:     5 
    Percent phenotyped: 100 100 100 100 100 

    No. chromosomes:    20 
        Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
        X chr:          X 

    Total markers:      32690 
    No. markers:        2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 
                        1396 1628 1692 1064 940 401 1095 1067 575 
    Percent genotyped:  99.5 
    Genotypes (%):    
          Autosomes:    AA:54.3  AB:45.7 
       X chromosome:    AA:52.7  AB:47.3 

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

print("method == hk")

[1] "method == hk"

add.covars = qtl::pull.pheno(cross.probs, c("UNCHS008487","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 = 10, 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 (10 permutations)
     lod
1%  3.62
5%  3.43
10% 3.19

X chromosome LOD thresholds (183 permutations)
     lod
1%  2.57
5%  2.53
10% 2.49

#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
UNCHS000141     1  4.439109 1.4810382 0.9118239
UNC2981117      2 27.613431 0.8724495 1.0000000
JAX00521745     3 20.613360 0.7586221 1.0000000
JAX00556948     4 41.038754 0.7726880 1.0000000
UNC8675939      5  6.193174 2.0684019 0.7191035
UNC10923091     6 18.321306 0.6856206 1.0000000
UNCHS020066     7 34.113657 1.2936681 1.0000000
UNC15524531     8 59.462590 1.9315437 0.8168395
UNC17203597     9 68.527929 0.6928610 1.0000000
UNCHS028001    10 21.783362 1.0219015 1.0000000
UNC20465557    11 80.904972 0.7841081 1.0000000
UNC21787084    12 53.288332 0.7505664 1.0000000
JAX00351755    13  7.044200 0.9809396 1.0000000
UNC24056202    14 30.156730 1.8824837 0.8168395
UNC25275535    15 11.967146 0.8880487 1.0000000
UNCHS042071    16 13.176096 0.7730967 1.0000000
UNCrs47191360  17 18.433013 1.2636005 1.0000000
UNCHS045343    18  2.934001 3.3781249 0.1051672
UNC29920552    19  8.810092 1.9159935 0.8168395
UNCHS048314     X  8.974041 1.9849011 0.5288989

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 = 10, 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")))

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

Genome-wide scan

#gm

Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]

addcovar = model.matrix(~UNC8250659+UNC18240977, 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$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$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	2.966565	3.164031
0.05	2.727639	2.959587
0.1	2.428245	2.692235

The figures below show QTL maps for each phenotype

out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, 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, "html") 
#  %>% kable_styling("striped", full_width = T) %>%
#  column_spec(1, bold=TRUE)
#  )

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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	2.934	3.75378	1.972	4.621	UNCHS045343

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

print(kable(peaks_mba, escape = F, align = c("cccccccc"), "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]"))
}

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.PBS	18	4.861199	3.75378	3.01212	7.97679	UNCHS045343

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_5.batches_mis_conditional_2-peaks-chr4-10.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_5.batches_mis_conditional_2-peaks-chr4-10.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]"))
}

chr	type	start	stop	strand	ID	Name	Dbxref	gene_id	mgi_type	description
18	pseudogene	3.015908	3.016159	positive	MGI_C57BL6J_6275399	Gm50072	ENSEMBL:ENSMUSG00000117547	MGI:6275399	pseudogene	predicted gene, 50072
18	pseudogene	3.026901	3.027882	negative	MGI_C57BL6J_3852490	Vmn1r-ps151	NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774	MGI:3852490	pseudogene	vomeronasal 1 receptor, pseudogene 151
18	pseudogene	3.080778	3.081476	negative	MGI_C57BL6J_3852491	Vmn1r-ps152	NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444	MGI:3852491	pseudogene	vomeronasal 1 receptor, pseudogene 152
18	pseudogene	3.105167	3.105396	negative	MGI_C57BL6J_6275400	Gm50073	ENSEMBL:ENSMUSG00000117531	MGI:6275400	pseudogene	predicted gene, 50073
18	gene	3.122492	3.130012	negative	MGI_C57BL6J_3852494	Vmn1r238	NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539	MGI:3852494	protein coding gene	vomeronasal 1 receptor, 238
18	pseudogene	3.139032	3.139305	negative	MGI_C57BL6J_6275401	Gm50074	ENSEMBL:ENSMUSG00000117353	MGI:6275401	pseudogene	predicted gene, 50074
18	pseudogene	3.171447	3.171932	negative	MGI_C57BL6J_6275402	Gm50075	ENSEMBL:ENSMUSG00000117464	MGI:6275402	pseudogene	predicted gene, 50075
18	gene	3.266048	3.338176	negative	MGI_C57BL6J_88495	Crem	NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889	MGI:88495	protein coding gene	cAMP responsive element modulator
18	gene	3.336416	3.366863	positive	MGI_C57BL6J_3643252	Gm6225	NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746	MGI:3643252	lncRNA gene	predicted gene 6225
18	gene	3.382970	3.436700	positive	MGI_C57BL6J_1918995	Cul2	NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231	MGI:1918995	protein coding gene	cullin 2
18	pseudogene	3.446654	3.447503	positive	MGI_C57BL6J_3648491	Gm6235	NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370	MGI:3648491	pseudogene	predicted gene 6235
18	pseudogene	3.466371	3.466502	negative	MGI_C57BL6J_6275423	Gm50088	ENSEMBL:ENSMUSG00000117389	MGI:6275423	pseudogene	predicted gene, 50088
18	gene	3.471630	3.474315	positive	MGI_C57BL6J_3645649	G430049J08Rik	ENSEMBL:ENSMUSG00000096528	MGI:3645649	unclassified gene	RIKEN cDNA G430049J08 gene
18	pseudogene	3.484218	3.485193	positive	MGI_C57BL6J_6275424	Gm50089	ENSEMBL:ENSMUSG00000117469	MGI:6275424	pseudogene	predicted gene, 50089
18	gene	3.507923	3.516404	positive	MGI_C57BL6J_1915260	Bambi	NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232	MGI:1915260	protein coding gene	BMP and activin membrane-bound inhibitor
18	pseudogene	3.558675	3.560037	positive	MGI_C57BL6J_6275425	Gm50090	ENSEMBL:ENSMUSG00000117454	MGI:6275425	pseudogene	predicted gene, 50090
18	gene	3.616281	3.618242	positive	MGI_C57BL6J_6275426	Gm50091	ENSEMBL:ENSMUSG00000117506	MGI:6275426	unclassified gene	predicted gene, 50091
18	gene	3.770741	3.770804	negative	MGI_C57BL6J_5453420	Gm23643	ENSEMBL:ENSMUSG00000088480	MGI:5453420	snRNA gene	predicted gene, 23643
18	pseudogene	3.857730	3.858585	negative	MGI_C57BL6J_3647465	Rpl7a-ps6	NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463	MGI:3647465	pseudogene	ribosomal protein L7A, pseudogene 6
18	gene	3.860108	3.860430	positive	MGI_C57BL6J_5454409	Gm24632	ENSEMBL:ENSMUSG00000084719	MGI:5454409	unclassified non-coding RNA gene	predicted gene, 24632
18	pseudogene	4.010143	4.011335	positive	MGI_C57BL6J_3646622	Gm7378	NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395	MGI:3646622	pseudogene	predicted gene 7378
18	pseudogene	4.052351	4.052771	negative	MGI_C57BL6J_3644899	Gm6248	NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565	MGI:3644899	pseudogene	predicted gene 6248
18	gene	4.165832	4.182236	positive	MGI_C57BL6J_1914578	Lyzl1	NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233	MGI:1914578	protein coding gene	lysozyme-like 1
18	pseudogene	4.198320	4.199135	negative	MGI_C57BL6J_3708638	Gm10557	NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647	MGI:3708638	pseudogene	predicted gene 10557
18	gene	4.244121	4.250779	negative	MGI_C57BL6J_5624542	Gm41657	NCBI_Gene:105246358	MGI:5624542	lncRNA gene	predicted gene, 41657
18	pseudogene	4.293097	4.294538	negative	MGI_C57BL6J_3647488	Gm7400	NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482	MGI:3647488	pseudogene	predicted gene 7400
18	gene	4.331325	4.353412	negative	MGI_C57BL6J_1346878	Map3k8	NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235	MGI:1346878	protein coding gene	mitogen-activated protein kinase kinase kinase 8
18	gene	4.353547	4.368945	positive	MGI_C57BL6J_1921165	4833419F23Rik	NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401	MGI:1921165	lncRNA gene	RIKEN cDNA 4833419F23 gene
18	gene	4.353644	4.380723	positive	MGI_C57BL6J_6275434	Gm50096	ENSEMBL:ENSMUSG00000117594	MGI:6275434	lncRNA gene	predicted gene, 50096
18	gene	4.373669	4.379985	negative	MGI_C57BL6J_5477359	Gm26865	ENSEMBL:ENSMUSG00000097641	MGI:5477359	lncRNA gene	predicted gene, 26865
18	gene	4.375592	4.398798	positive	MGI_C57BL6J_1914690	Mtpap	NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234	MGI:1914690	protein coding gene	mitochondrial poly(A) polymerase
18	gene	4.399328	4.400910	positive	MGI_C57BL6J_6275321	Gm50023	ENSEMBL:ENSMUSG00000117579	MGI:6275321	lncRNA gene	predicted gene, 50023
18	pseudogene	4.484469	4.485501	positive	MGI_C57BL6J_3644066	Gm7411	NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640	MGI:3644066	pseudogene	predicted gene 7411
18	gene	4.541612	4.562599	negative	MGI_C57BL6J_5624543	Gm41658	NCBI_Gene:105246359	MGI:5624543	lncRNA gene	predicted gene, 41658
18	gene	4.590763	4.616064	positive	MGI_C57BL6J_6275323	Gm50024	ENSEMBL:ENSMUSG00000117521	MGI:6275323	lncRNA gene	predicted gene, 50024
18	gene	4.590792	4.682869	positive	MGI_C57BL6J_2685174	Jcad	NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960	MGI:2685174	protein coding gene	junctional cadherin 5 associated
18	pseudogene	4.743061	4.743876	positive	MGI_C57BL6J_3645969	Gm5047	NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585	MGI:3645969	pseudogene	predicted gene 5047
18	gene	4.812486	4.850959	positive	MGI_C57BL6J_3642809	Gm10556	NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043	MGI:3642809	lncRNA gene	predicted gene 10556
18	gene	4.820303	4.826659	negative	MGI_C57BL6J_5593428	Gm34269	NCBI_Gene:102637466	MGI:5593428	lncRNA gene	predicted gene, 34269
18	gene	4.869777	4.912263	positive	MGI_C57BL6J_5624544	Gm41659	NCBI_Gene:105246360	MGI:5624544	lncRNA gene	predicted gene, 41659
18	gene	4.920245	5.119299	positive	MGI_C57BL6J_2147319	Svil	NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236	MGI:2147319	protein coding gene	supervillin
18	gene	4.959636	4.960300	positive	MGI_C57BL6J_3642287	BC025933	NA	NA	protein coding gene	cDNA sequence BC025933
18	gene	5.042818	5.048867	negative	MGI_C57BL6J_5593485	Gm34326	NCBI_Gene:102637544	MGI:5593485	lncRNA gene	predicted gene, 34326
18	gene	5.139349	5.142068	positive	MGI_C57BL6J_5593700	Gm34541	NCBI_Gene:102637831	MGI:5593700	lncRNA gene	predicted gene, 34541
18	gene	5.141762	5.165731	negative	MGI_C57BL6J_5477176	Gm26682	NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888	MGI:5477176	lncRNA gene	predicted gene, 26682
18	gene	5.210027	5.334963	negative	MGI_C57BL6J_2444919	Zfp438	NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945	MGI:2444919	protein coding gene	zinc finger protein 438
18	gene	5.348258	5.361861	negative	MGI_C57BL6J_6275385	Gm50064	ENSEMBL:ENSMUSG00000117520	MGI:6275385	lncRNA gene	predicted gene, 50064
18	gene	5.368541	5.370482	negative	MGI_C57BL6J_6275387	Gm50065	ENSEMBL:ENSMUSG00000117617	MGI:6275387	lncRNA gene	predicted gene, 50065
18	gene	5.389802	5.402870	negative	MGI_C57BL6J_5593789	Gm34630	NCBI_Gene:102637944	MGI:5593789	lncRNA gene	predicted gene, 34630
18	gene	5.491501	5.593505	negative	MGI_C57BL6J_3642044	Gm10125	NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087	MGI:3642044	lncRNA gene	predicted gene 10125
18	gene	5.572824	5.575055	negative	MGI_C57BL6J_5477069	Gm26575	ENSEMBL:ENSMUSG00000097926	MGI:5477069	lncRNA gene	predicted gene, 26575
18	gene	5.588043	5.591711	negative	MGI_C57BL6J_3780408	Gm2238	ENSEMBL:ENSMUSG00000103138	MGI:3780408	unclassified gene	predicted gene 2238
18	gene	5.591860	5.775468	positive	MGI_C57BL6J_1344313	Zeb1	NCBI_Gene:21417,ENSEMBL:ENSMUSG00000024238	MGI:1344313	protein coding gene	zinc finger E-box binding homeobox 1
18	pseudogene	5.611558	5.611930	negative	MGI_C57BL6J_3801802	Gm16147	ENSEMBL:ENSMUSG00000090078	MGI:3801802	pseudogene	predicted gene 16147
18	gene	5.614473	5.614583	positive	MGI_C57BL6J_5452486	Gm22709	ENSEMBL:ENSMUSG00000080543	MGI:5452486	miRNA gene	predicted gene, 22709
18	gene	5.885791	5.889876	negative	MGI_C57BL6J_5593899	Gm34740	NCBI_Gene:102638095	MGI:5593899	lncRNA gene	predicted gene, 34740
18	gene	5.945183	5.966908	positive	MGI_C57BL6J_5593963	Gm34804	NCBI_Gene:102638178,ENSEMBL:ENSMUSG00000117508	MGI:5593963	lncRNA gene	predicted gene, 34804
18	gene	6.004578	6.015278	positive	MGI_C57BL6J_5624548	Gm41663	NCBI_Gene:105246364	MGI:5624548	lncRNA gene	predicted gene, 41663
18	gene	6.014253	6.024013	negative	MGI_C57BL6J_5624547	Gm41662	NCBI_Gene:105246363,ENSEMBL:ENSMUSG00000117548	MGI:5624547	lncRNA gene	predicted gene, 41662
18	gene	6.024427	6.136098	negative	MGI_C57BL6J_1922665	Arhgap12	NCBI_Gene:75415,ENSEMBL:ENSMUSG00000041225	MGI:1922665	protein coding gene	Rho GTPase activating protein 12
18	pseudogene	6.029179	6.030456	negative	MGI_C57BL6J_5011425	Gm19240	NCBI_Gene:100418482	MGI:5011425	pseudogene	predicted gene, 19240
18	gene	6.201002	6.242174	negative	MGI_C57BL6J_1098268	Kif5b	NCBI_Gene:16573,ENSEMBL:ENSMUSG00000006740	MGI:1098268	protein coding gene	kinesin family member 5B
18	gene	6.213913	6.227324	positive	MGI_C57BL6J_4937863	Gm17036	ENSEMBL:ENSMUSG00000091165	MGI:4937863	lncRNA gene	predicted gene 17036
18	gene	6.234853	6.326693	positive	MGI_C57BL6J_5624549	Gm41664	NCBI_Gene:105246365,ENSEMBL:ENSMUSG00000117519	MGI:5624549	lncRNA gene	predicted gene, 41664
18	pseudogene	6.332591	6.333082	positive	MGI_C57BL6J_3646174	Rpl27-ps3	NCBI_Gene:621100,ENSEMBL:ENSMUSG00000073640	MGI:3646174	pseudogene	ribosomal protein L27, pseudogene 3
18	pseudogene	6.368360	6.368797	positive	MGI_C57BL6J_6275267	Gm49986	ENSEMBL:ENSMUSG00000117387	MGI:6275267	pseudogene	predicted gene, 49986
18	pseudogene	6.371352	6.371660	negative	MGI_C57BL6J_3648803	Gm6291	NCBI_Gene:102637200,ENSEMBL:ENSMUSG00000095614	MGI:3648803	pseudogene	predicted gene 6291
18	gene	6.435951	6.516108	negative	MGI_C57BL6J_1278322	Epc1	NCBI_Gene:13831,ENSEMBL:ENSMUSG00000024240	MGI:1278322	protein coding gene	enhancer of polycomb homolog 1
18	gene	6.490143	6.496962	positive	MGI_C57BL6J_5579235	Gm28529	ENSEMBL:ENSMUSG00000101320	MGI:5579235	lncRNA gene	predicted gene 28529
18	gene	6.490564	6.490646	negative	MGI_C57BL6J_3811424	Mir1893	miRBase:MI0008327,NCBI_Gene:100316773,ENSEMBL:ENSMUSG00000084514	MGI:3811424	miRNA gene	microRNA 1893
18	pseudogene	6.557130	6.557796	positive	MGI_C57BL6J_3645335	Gm7464	NCBI_Gene:665047,ENSEMBL:ENSMUSG00000117425	MGI:3645335	pseudogene	predicted gene 7464
18	gene	6.603629	6.640435	positive	MGI_C57BL6J_1918151	4921524L21Rik	NCBI_Gene:70901,ENSEMBL:ENSMUSG00000039540	MGI:1918151	protein coding gene	RIKEN cDNA 4921524L21 gene
18	pseudogene	6.687843	6.691317	negative	MGI_C57BL6J_6275405	Gm50077	ENSEMBL:ENSMUSG00000117620	MGI:6275405	pseudogene	predicted gene, 50077
18	gene	6.733905	6.794429	positive	MGI_C57BL6J_102790	Rab18	NCBI_Gene:19330,ENSEMBL:ENSMUSG00000073639	MGI:102790	protein coding gene	RAB18, member RAS oncogene family
18	pseudogene	6.760073	6.760959	negative	MGI_C57BL6J_3648356	Gm7466	NCBI_Gene:665053,ENSEMBL:ENSMUSG00000117359	MGI:3648356	pseudogene	predicted gene 7466
18	gene	6.788768	6.792137	positive	MGI_C57BL6J_6275412	Gm50081	ENSEMBL:ENSMUSG00000117461	MGI:6275412	lncRNA gene	predicted gene, 50081
18	gene	6.910459	6.936621	negative	MGI_C57BL6J_3826781	Gm2350	ENSEMBL:ENSMUSG00000117652	MGI:3826781	lncRNA gene	predicted gene 2350
18	gene	6.934518	7.004780	negative	MGI_C57BL6J_2687286	Mkx	NCBI_Gene:210719,ENSEMBL:ENSMUSG00000061013	MGI:2687286	protein coding gene	mohawk homeobox
18	gene	7.004963	7.066322	positive	MGI_C57BL6J_5826270	Gm46633	NCBI_Gene:108168391,ENSEMBL:ENSMUSG00000117356	MGI:5826270	lncRNA gene	predicted gene, 46633
18	pseudogene	7.041939	7.044195	negative	MGI_C57BL6J_3642728	Gm10350	NCBI_Gene:664778,ENSEMBL:ENSMUSG00000117381	MGI:3642728	pseudogene	predicted gene 10350
18	gene	7.088209	7.298304	negative	MGI_C57BL6J_1922184	Armc4	NCBI_Gene:74934,ENSEMBL:ENSMUSG00000061802	MGI:1922184	protein coding gene	armadillo repeat containing 4
18	pseudogene	7.107181	7.108183	negative	MGI_C57BL6J_5010766	Gm18581	NCBI_Gene:100417382,ENSEMBL:ENSMUSG00000117360	MGI:5010766	pseudogene	predicted gene, 18581
18	gene	7.150344	7.151777	negative	MGI_C57BL6J_1915234	4930415O11Rik	ENSEMBL:ENSMUSG00000117383	MGI:1915234	unclassified gene	RIKEN cDNA 4930415O11 gene
18	gene	7.332445	7.346299	negative	MGI_C57BL6J_5594138	Gm34979	NCBI_Gene:102638402	MGI:5594138	lncRNA gene	predicted gene, 34979
18	gene	7.347959	7.626893	negative	MGI_C57BL6J_1922989	Mpp7	NCBI_Gene:75739,ENSEMBL:ENSMUSG00000057440	MGI:1922989	protein coding gene	membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7)
18	pseudogene	7.548932	7.549781	positive	MGI_C57BL6J_5011133	Gm18948	NCBI_Gene:100418013,ENSEMBL:ENSMUSG00000117556	MGI:5011133	pseudogene	predicted gene, 18948
18	pseudogene	7.581482	7.582130	positive	MGI_C57BL6J_3644282	Fabp5l2	NCBI_Gene:622384,ENSEMBL:ENSMUSG00000094334	MGI:3644282	pseudogene	fatty acid binding protein 5-like 2
18	pseudogene	7.684076	7.693411	negative	MGI_C57BL6J_5010948	Gm18763	NCBI_Gene:100417683,ENSEMBL:ENSMUSG00000117542	MGI:5010948	pseudogene	predicted gene, 18763
18	pseudogene	7.798784	7.799900	negative	MGI_C57BL6J_3780506	Gm2336	NCBI_Gene:100039606	MGI:3780506	pseudogene	predicted gene 2336
18	gene	7.835177	7.835283	positive	MGI_C57BL6J_5454069	Gm24292	ENSEMBL:ENSMUSG00000064822	MGI:5454069	snRNA gene	predicted gene, 24292
18	pseudogene	7.844684	7.844935	negative	MGI_C57BL6J_6275329	Gm50027	ENSEMBL:ENSMUSG00000117462	MGI:6275329	pseudogene	predicted gene, 50027
18	gene	7.868018	7.869113	negative	MGI_C57BL6J_3641793	Gm9993	ENSEMBL:ENSMUSG00000117505	MGI:3641793	lncRNA gene	predicted gene 9993
18	gene	7.868832	7.929028	positive	MGI_C57BL6J_2387357	Wac	NCBI_Gene:225131,ENSEMBL:ENSMUSG00000024283	MGI:2387357	protein coding gene	WW domain containing adaptor with coiled-coil
18	gene	7.894300	7.895792	positive	MGI_C57BL6J_1924412	A430102J17Rik	NA	NA	unclassified gene	RIKEN cDNA A430102J17 gene
18	pseudogene	7.932728	7.933556	positive	MGI_C57BL6J_3643245	Gm7502	NCBI_Gene:665121,ENSEMBL:ENSMUSG00000091019	MGI:3643245	pseudogene	predicted gene 7502

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                 20
Total markers                32690

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064  940 
  17   18   19    X 
 401 1095 1067  575 

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

cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr4-10.csv",alleles=c("A","B"))

 --Read the following data:
     138  individuals
     32690  markers
     5  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    138 

    No. phenotypes:     5 
    Percent phenotyped: 100 100 100 100 100 

    No. chromosomes:    20 
        Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
        X chr:          X 

    Total markers:      32690 
    No. markers:        2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 
                        1396 1628 1692 1064 940 401 1095 1067 575 
    Percent genotyped:  99.5 
    Genotypes (%):    
          Autosomes:    AA:54.3  AB:45.7 
       X chromosome:    AA:52.7  AB:47.3 

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

print("method == hk")

[1] "method == hk"

add.covars = qtl::pull.pheno(cross.probs, c("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 = 10, 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 (10 permutations)
     lod
1%  2.51
5%  2.51
10% 2.50

X chromosome LOD thresholds (183 permutations)
     lod
1%  3.17
5%  2.82
10% 2.37

#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
UNCHS000141     1  4.439109 1.4810382 1.0000000
UNC2981117      2 27.613431 0.8724495 1.0000000
JAX00521745     3 20.613360 0.7586221 1.0000000
JAX00556948     4 41.038754 0.7726880 1.0000000
UNC8675939      5  6.193174 2.0684019 0.4165181
UNC10923091     6 18.321306 0.6856206 1.0000000
UNCHS020066     7 34.113657 1.2936681 1.0000000
UNC15524531     8 59.462590 1.9315437 0.5185858
UNC17203597     9 68.527929 0.6928610 1.0000000
UNCHS028001    10 21.783362 1.0219015 1.0000000
UNC20465557    11 80.904972 0.7841081 1.0000000
UNC21787084    12 53.288332 0.7505664 1.0000000
JAX00351755    13  7.044200 0.9809396 1.0000000
UNC24056202    14 30.156730 1.8824837 0.6195366
UNC25275535    15 11.967146 0.8880487 1.0000000
UNCHS042071    16 13.176096 0.7730967 1.0000000
UNCrs47191360  17 18.433013 1.2636005 1.0000000
UNCHS045343    18  2.934001 3.3781249 0.0000000
UNC29920552    19  8.810092 1.9159935 0.5185858
UNCHS048314     X  8.974041 1.9849011 0.3472122

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")))

            chr      pos      lod pval
UNCHS045343  18 2.934001 3.378125    0

#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 = 10, 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