Last updated: 2022-04-15

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Knit directory: Serreze-T1D_Workflow/

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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNC18311938_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028536_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-10_peak.marker-UNCHS028536_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC19970181_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC19970181_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-11_peak.marker-UNC20090524_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-12_peak.marker-ICR499_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-13_peak.marker-UNCHS036773_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-13_peak.marker-UNCHS036773_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-14_peak.marker-UNC24056202_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-15_peak.marker-UNC26070435_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-15_peak.marker-UNC26070435_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCHS044241_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCHS044241_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCJPD006614_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-17_peak.marker-UNCrs47191360_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNC4609527_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-2_peak.marker-UNC4609527_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00516812_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00517674_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00517674_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00518911r_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00518911r_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00522922_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00524205_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00529707r_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00529707r_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-JAX00535764_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4809586_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4809586_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4813186_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4813186_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4813186_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-UNC4836550_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4836550_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4836550_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4875653_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4875653_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4901083_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4901083_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4909519_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4909519_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912039_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912039_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912288_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4912288_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4915446_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4915446_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4923398_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4923398_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4924377_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4938565_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4938565_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4945186_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4947492_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4956618_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4956618_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-UNC4957168_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4957859_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4959266_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC4960612_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-UNC4961542_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-UNC4979914_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-UNC5083168_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-UNC5213888_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5213888_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNC5329171_lod.drop-1.5_5.batches_mis_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008219_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008219_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008229_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008260_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008271_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008281_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008286_lod.drop-1.5_5.batches_0.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008338_lod.drop-1.5_5.batches_mis.csv
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    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008363_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008363_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008379_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008379_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008409_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008432_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008487_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008511_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008511_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008609_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008613_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008613_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008614_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008627_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008659_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008725_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS008815_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009066_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009067_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009068_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009112_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009224_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009224_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009383_lod.drop-1.5_5.batches_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009383_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009693_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCHS009821_lod.drop-1.5_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001170_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001170_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001198_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001198_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-UNCJPD001276_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-sanger2496q_lod.drop-1.5_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-3_peak.marker-sanger2496q_lod.drop-1.5_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8250659_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8439633_lod.drop-1.5_snpsqc_5.batches_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNC8439633_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-4_peak.marker-UNCHS012955_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-6_peak.marker-UNC11108920_lod.drop-1.5_snpsqc_5.batches.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-6_peak.marker-UNC11108920_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-7_peak.marker-UNCHS020066_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis_0.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_5.batches_mis_52.csv
    Untracked:  data/ici.vs.eoi_age.of.onset-no.covariates_blup_sub_chr-8_peak.marker-UNC15524531_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_0.csv
    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
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    Untracked:  data/ici.vs.pbs_blup_chr-1_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-5_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_blup_chr-6_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-9_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-9_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_blup_chr-X_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-X_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_blup_sub_chr-13_peak.marker-UNC22384241_lod.drop-1.5_snpsqc_snpsqc_5.batches_peaks.csv
    Untracked:  data/ici.vs.pbs_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
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    Untracked:  data/ici.vs.pbs_blup_sub_chr-8_peak.marker-UNCHS023353_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis_peaks.csv
    Untracked:  data/ici.vs.pbs_blup_sub_chr-8_peak.marker-UNCHS023353_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_peaks.csv
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    Untracked:  data/ici.vs.pbs_fitqtl_peaks_additive_snpsqc_dis_no-x_updated_5.batches_mis_peaks.txt
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    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_additive_snpsqc_dis_no-x_updated_5.batches_peaks.txt
    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_additive_snpsqc_snpsqc_5.batches_peaks.txt
    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_interacting_snpsqc_dis_no-x_updated_5.batches_mis_peaks.txt
    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_interacting_snpsqc_dis_no-x_updated_5.batches_peaks.txt
    Untracked:  data/ici.vs.pbs_fitqtl_peaks_sex_interacting_snpsqc_snpsqc_5.batches_peaks.txt
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    Untracked:  data/ici.vs.pbs_genes_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
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    Untracked:  data/ici.vs.pbs_gm_qtl_5.batches.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-JAX00153527_lod.drop-1.5_5.batches.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-JAX00153527_lod.drop-1.5_snpsqc_5.batches.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_blup_sub_chr-7_peak.marker-UNCHS020486_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-JAX00153527_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020443_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_rz.age-additive.covariates_genes_chr-7_peak.marker-UNCHS020486_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_scanone_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_5.batches_mis.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_5.batches_mis.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches_mis.Rdata
    Untracked:  data/percent_missing_id_3.batches.RData
    Untracked:  data/percent_missing_id_4.batches.RData
    Untracked:  data/percent_missing_id_4.batches_bc.RData
    Untracked:  data/percent_missing_id_5.batches.RData
    Untracked:  data/percent_missing_marker_4.batches.RData
    Untracked:  data/percent_missing_marker_5.batches.RData
    Untracked:  data/pheno.csv
    Untracked:  data/pheno_BC312.csv
    Untracked:  data/physical_map.csv
    Untracked:  data/physical_map_BC312.csv
    Untracked:  data/qc_info_bad_sample_3.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches_bc.RData
    Untracked:  data/qc_info_bad_sample_5.batches.RData
    Untracked:  data/remaining.markers_geno.freq.xlsx
    Untracked:  data/sample_geno.csv
    Untracked:  data/sample_geno_AHB_BC312.csv
    Untracked:  data/sample_geno_bc.csv
    Untracked:  data/sample_geno_bc_BC312.csv
    Untracked:  data/serreze_probs.rds
    Untracked:  data/serreze_probs_BC312.rds
    Untracked:  data/serreze_probs_allqc.rds
    Untracked:  data/serreze_probs_allqc_5.batches.rds
    Untracked:  data/serreze_probs_allqc_5.batches_mis.rds
    Untracked:  data/summary.cg_3.batches.RData
    Untracked:  data/summary.cg_4.batches.RData
    Untracked:  data/summary.cg_4.batches_bc.RData
    Untracked:  data/summary.cg_5.batches.RData
    Untracked:  output/Percent_missing_genotype_data_5.batches.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_5.batches.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf
    Untracked:  output/genotype_error_marker_5.batches.pdf
    Untracked:  output/genotype_frequency_marker_5.batches.pdf

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

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Rmd a8c2d1a Belinda Cornes 2022-04-13 additonal analysis - peaks and continuous variables
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Loading Data

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

load("data/gm_allqc_5.batches_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 
#removing ici-late
table(gm$covar$group)

 (A2 Parental) (DQ8 Parental) B6.g7 Parental            EOI             F1 
             2              2              1            164              1 
           ICI            PBS 
           104             34 
ici.late_ids <- gm$covar[gm$covar$"diabetic status" == "T1D>17",]$id
length(ici.late_ids)
[1] 17
ici.late_ids
 [1] "NG00198-ICI-LATE" "NG00329-ICI-LATE" "NG00343-ICI-LATE" "NG00386-ICI-LATE"
 [5] "NG00442-ICI-LATE" "NG00443-ICI-LATE" "NG00526-ICI-LATE" "NG00522-ICI-LATE"
 [9] "NG00790-ICI-LATE" "NG00874-ICI-LATE" "NG00929-ICI-LATE" "NG01225-ICI-LATE"
[13] "NG01303-ICI-LATE" "NG01020-ICI-LATE" "NG01397-ICI-LATE" "NG01389-ICI-LATE"
[17] "NG01548-ICI-LATE"
gm <- gm[paste0("-",as.character(ici.late_ids)),]

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

ICI PBS 
 87  34 
mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm
Object of class cross2 (crosstype "bc")

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

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

No. chromosomes                  20
Total markers                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-Early <- ifelse(gm$covar$group == "ICI-Early", 1, 0)
#bin_pheno <- as.data.frame(bin_pheno)
#rownames(bin_pheno) <- rownames(gm$covar)

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

##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] 98401
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 88934
gfmar_mar_1 92474
gfmar_mar_5 98130
gfmar_mar_10 99266
gfmar_mar_15 99370
gfmar_mar_25 100369
gfmar_mar_50 131102
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              121
No. genotyped individuals      121
No. phenotyped individuals     121
No. with both geno & pheno     121

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

No. chromosomes                 20
Total markers                32954

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2965 2901 2075 2085 1970 2072 1878 1716 2023 1239 2082 1409 1637 1708 1063  957 
  17   18   19    X 
 410 1106 1078  580 
markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]

pr.qc <- calc_genoprob(gm)

Genome-wide scan

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

The table shows the estimated significance thresholds from permutation test.

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

#gm

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

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

#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI_Early.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))

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

rownames(summary_table) <- NULL

summary_table[c(3,1:2)] %>%
  kable(escape = F,align = c("ccc")) %>%
  kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)
significance.level autosomes X
0.01 2.971957 3.259453
0.05 2.934750 3.054153
0.1 2.888128 2.785681

The figures below show QTL maps for each phenotype

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

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

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

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

plot_lod(out,gm$gmap)

LOD peaks

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

Centimorgan (cM)

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

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

rownames(peaks) <- NULL

#print(kable(peaks, "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_Early.vs.PBS 18 2.934 3.62562 1.972 3.857 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_Early.vs.PBS 18 4.861199 3.62562 3.01212 5.584328 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$phenotype[i]])
  blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]])

  write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis.csv"), quote=F)

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

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


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

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


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


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

  genesgss = query_genes(chr, start, end)

  write.csv(genesgss, file=paste0("data/ici-early.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis.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

R/qtl

scanone

gm
Object of class cross2 (crosstype "bc")

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

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

No. chromosomes                 20
Total markers                32954

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2965 2901 2075 2085 1970 2072 1878 1716 2023 1239 2082 1409 1637 1708 1063  957 
  17   18   19    X 
 410 1106 1078  580 
#detach("package:qtl2", unload=TRUE)
#library(qtl)

cross <- qtl::read.cross("csv", file = "data/ici-early.vs.pbs_gm_qtl_snpsqc_5.batches_mis.csv",alleles=c("A","B"))
 --Read the following data:
     121  individuals
     32954  markers
     3  phenotypes
 --Cross type: bc 
cross <- qtl::jittermap(cross)

summary(cross)
    Backcross

    No. individuals:    121 

    No. phenotypes:     3 
    Percent phenotyped: 100 100 100 

    No. chromosomes:    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:      32954 
    No. markers:        2965 2901 2075 2085 1970 2072 1878 1716 2023 1239 2082 
                        1409 1637 1708 1063 957 410 1106 1078 580 
    Percent genotyped:  99.4 
    Genotypes (%):    
          Autosomes:    AA:54.8  AB:45.2 
       X chromosome:    AA:53.2  AB:46.8 
cross.probs <- qtl::calc.genoprob(cross)

print("method == hk")
[1] "method == hk"
scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI_Early.vs.PBS" , model="binary", method="hk")
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI_Early.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
plot(operm.hk)

print(summary(operm.hk, alpha=c(0.01,  0.05, 0.1)))
Autosome LOD thresholds (10 permutations)
     lod
1%  3.45
5%  3.12
10% 2.71

X chromosome LOD thresholds (184 permutations)
     lod
1%  2.83
5%  2.57
10% 2.23
#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 1.1861632 1.0000000
ICR5010         2 27.502430 1.2408847 1.0000000
JAX00521745     3 20.613364 0.5860556 1.0000000
UNCHS011877     4 40.235758 0.6850160 1.0000000
UNC8675939      5  6.193174 1.9652772 0.3135081
UNC10749955     6 11.546210 1.0126017 1.0000000
UNCHS020066     7 34.113662 1.4825014 1.0000000
UNC15524531     8 59.462598 1.7049819 0.8168345
UNCHS026344     9 42.356213 0.4902771 1.0000000
UNCHS028001    10 21.783364 1.1374333 1.0000000
UNCHS030712    11 32.135956 0.8939714 1.0000000
UNCHS034425    12 48.225301 0.5631440 1.0000000
JAX00351755    13  7.044201 0.7017565 1.0000000
UNC24056202    14 30.156741 2.2724632 0.2096934
UNC25275535    15 11.967147 1.4217875 1.0000000
UNCHS042071    16 13.176103 0.5983820 1.0000000
UNCrs47191360  17 18.433016 1.2741144 1.0000000
UNCHS045343    18  2.934001 3.6256205 0.0000000
UNC29920552    19  8.810095 1.7850063 0.7190977
UNCHS048314     X  8.974042 2.0610212 0.3457635
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 3.62562    0
#print("method == ehk")

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

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

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

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

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

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

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

other attached packages:
 [1] abind_1.4-5       qtl2_0.22         reshape2_1.4.4    ggplot2_3.3.5    
 [5] tibble_3.1.2      psych_2.0.7       readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.8       optparse_1.6.6    rhdf5_2.28.1      mclust_5.4.6     
[13] tidyr_1.0.2       data.table_1.14.0 knitr_1.33        kableExtra_1.1.0 
[17] workflowr_1.6.2  

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