Last updated: 2023-04-16

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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    Untracked:  data/het-ici.vs.het-pbs_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_gm_qtl_snpsqc_dis_no-x_updated_no-sex_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_gm_qtl_snpsqc_dis_no-x_updated_no-sex_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_no-sex_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_no-sex_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNC26069905_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNCHS041223_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNC28542319_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCJPD006670_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC28776739_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNCHS045594_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30414168_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30426276_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNC2031646_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS003700_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-ICR5131_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNCHS006420_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC6759992_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC6765178_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9889957_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9900273_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC10800126_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC10832076_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS020903_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS021163_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC15471847_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC15548888_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC26069905_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS041223_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNC28542319_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCJPD006670_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC28776739_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNCHS045594_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30414168_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30426276_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNC2031646_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS003700_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-ICR5131_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNCHS006420_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC6759992_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC6765178_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9889957_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9900273_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC10800126_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC10832076_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS020903_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS021163_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC15471847_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC15548888_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
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    Untracked:  data/summary.cg_7.batches_myo.RData
    Untracked:  data/summary.counts_EOI_ici.sick_vs_ici.eoi
    Untracked:  data/summary.counts_SICK_ici.sick_vs_ici.eoi
    Untracked:  data/summary.frequency_EOI_ici.sick_vs_ici.eoi
    Untracked:  data/summary.frequency_SICK_ici.sick_vs_ici.eoi

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/index.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.

Data Information

Loading Data

We will load the data and subset indivials out that are in the groups of interest.

load("data/gm_allqc_7.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm
Object of class cross2 (crosstype "bc")

Total individuals              357
No. genotyped individuals      357
No. phenotyped individuals     357
No. with both geno & pheno     357

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                32660

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2482 2412 1742 1772 1648 1842 1541 1517 1769 1092 1754 1230 1441 1505 1128  842 
  17   18   19    X 
 655  820  945 4523 
#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")

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

#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)

#gm$covar$myo.yes_vs_myo.no <- ifelse(gm$covar$group == "PBS", 0, 1)
#gm.full <- gm

covars <- read_csv("data/covar_corrected_myo-yes.vs.myo-no_7.batches_myo.csv")
#removing any missing info
#covars <- subset(covars, covars$myo.yes_vs_myo.no!='')
nrow(covars)
[1] 241
table(covars$"Myocarditis Status")

 NO YES 
 29 212 
table(covars$"Murine MHC KO Status")

HOM 
241 
table(covars$"Drug Treatment")

ICI PBS 
145  96 
table(covars$"clinical pheno")

 EOI SICK 
  95  146 
#keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm
Object of class cross2 (crosstype "bc")

Total individuals              241
No. genotyped individuals      241
No. phenotyped individuals     241
No. with both geno & pheno     241

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                32660

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2482 2412 1742 1772 1648 1842 1541 1517 1769 1092 1754 1230 1441 1505 1128  842 
  17   18   19    X 
 655  820  945 4523 
table(gm$covar$"Myocarditis Status")

 NO YES 
 29 212 
table(gm$covar$"Murine MHC KO Status")

HOM 
241 
table(gm$covar$"Drug Treatment")

ICI PBS 
145  96 
table(gm$covar$"clinical pheno")

 EOI SICK 
  95  146 
#pr.qc.ids <- pr
#for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

##removing problmetic marker

#gm <- drop_markers(gm, "UNCHS013106")

##dropping monomorphic markers within the dataset

g <- do.call("cbind", gm$geno)

gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
#gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))))

gf_mar <- gf_mar[gf_mar[,2] != "NaN",]

count <- rowSums(gf_mar <=0.05)
low_freq_df <- merge(as.data.frame(gf_mar),as.data.frame(count), by="row.names",all=T)
low_freq_df[is.na(low_freq_df)] <- ''
low_freq_df <- low_freq_df[low_freq_df$count == 1,]
rownames(low_freq_df) <- low_freq_df$Row.names

low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)

nrow(low_freq)
[1] 6169
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 3929
gfmar_mar_1 4156
gfmar_mar_5 6167
gfmar_mar_10 6516
gfmar_mar_15 6544
gfmar_mar_25 6625
gfmar_mar_50 32274
total_snps 32660 
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              241
No. genotyped individuals      241
No. phenotyped individuals     241
No. with both geno & pheno     241

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                26491

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2326 2278 1598 1656 1520 1685 1453 1443 1665  958 1657 1120 1366 1408 1036  745 
  17   18   19    X 
 565  742  897  373 
## dropping disproportionate markers
#dismark <- read.csv("data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv")
#nrow(dismark)
#ames(dismark)[1] <- c("marker")
#dismark <- dismark[!dismark$Include,]
#nrow(dismark)

#gm_qc_dis <- drop_markers(gm_qc, dismark$marker)
#gm_qc_dis <- drop_nullmarkers(gm_qc_dis)

#gm = gm_qc_dis
#gm

markers <- marker_names(gm)
gmapdf <- read.csv("data/genetic_map_7.batches_myo.csv")
pmapdf <- read.csv("data/physical_map_7.batches_myo.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)

colnames(covars) <- gsub(" ", ".", colnames(covars))

Genome-wide scan

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

The table shows the estimated significance thresholds from permutation test.

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

#Xcovar <- get_x_covar(gm)
addcovar = model.matrix(~Histology.Score, data = covars)[,-1]
covars$myo.yes_vs_myo.no= as.numeric(covars$myo.yes_vs_myo.no)

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

operm <- scan1perm(pr.qc, covars["myo.yes_vs_myo.no"], model="binary", addcovar=addcovar, n_perm=1000, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))

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

rownames(summary_table) <- NULL

summary_table[c(3,1:2)] %>%
  kable(escape = F,align = c("ccc")) %>%
  kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)
significance.level autosomes X
0.01 0 0
0.05 0 0
0.1 0 0

The figures below show QTL maps for each phenotype

#out <- scan1(pr.qc, covars["myo.yes_vs_myo.no"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, covars["myo.yes_vs_myo.no"], model="binary",addcovar=addcovar)

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


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

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

plot_lod(out,gm$gmap)

LOD peaks

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

Centimorgan (cM)

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

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

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

#plot only peak chromosomes

plot_lod_chr<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    
    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

  }
}


for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
  #plot_lod_chr(out,gm$gmap, peaks$chr[i])
  plot_lod_chr(out,gm$gmap, i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

Megabase (MB)

print("peaks in MB positions")

[1] “peaks in MB positions”

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

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


rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

plot_lod_chr_mb<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')


  }
}

for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
  #plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
  plot_lod_chr_mb(out,gm$pmap,i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("code/cc_variants.sqlite")
query_genes <- create_gene_query_func("code/mouse_genes_mgi.sqlite")

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


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

  write.csv(as.data.frame(blup_sub), paste0("data/myo-yes.vs.myo-no_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_7.batches_myo.csv"), quote=F)

  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])")
       )

  #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/myo-yes.vs.myo-no_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_7.batches_myo.csv"), quote=F)

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

  print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))


}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

R/qtl

scanone


R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] abind_1.4-5       qtl2_0.24         reshape2_1.4.4    ggplot2_3.3.3    
 [5] tibble_3.0.6      psych_2.0.12      readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.4       optparse_1.6.6    rhdf5_2.34.0      mclust_5.4.7     
[13] tidyr_1.1.2       data.table_1.13.6 knitr_1.31        kableExtra_1.3.1 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10        lattice_0.20-41    assertthat_0.2.1   rprojroot_2.0.2   
 [5] digest_0.6.27      R6_2.5.0           cellranger_1.1.0   plyr_1.8.6        
 [9] RSQLite_2.2.3      evaluate_0.14      httr_1.4.2         highr_0.8         
[13] pillar_1.4.7       rlang_0.4.10       rstudioapi_0.13    blob_1.2.1        
[17] rmarkdown_2.6      webshot_0.5.2      stringr_1.4.0      bit_4.0.4         
[21] munsell_0.5.0      compiler_4.0.3     httpuv_1.5.5       xfun_0.21         
[25] pkgconfig_2.0.3    mnormt_2.0.2       tmvnsim_1.0-2      htmltools_0.5.1.1 
[29] tidyselect_1.1.0   viridisLite_0.3.0  crayon_1.4.1       withr_2.4.1       
[33] later_1.1.0.1      rhdf5filters_1.2.1 grid_4.0.3         nlme_3.1-152      
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.1          git2r_0.28.0      
[41] magrittr_2.0.1     scales_1.1.1       cachem_1.0.3       stringi_1.5.3     
[45] fs_1.5.0           promises_1.1.1     getopt_1.20.3      xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6        Rhdf5lib_1.12.1   
[53] tools_4.0.3        bit64_4.0.5        glue_1.4.2         purrr_0.3.4       
[57] fastmap_1.1.0      parallel_4.0.3     yaml_2.2.1         colorspace_2.0-0  
[61] rvest_0.3.6        memoise_2.0.0