QTL Analysis - binary [Hetero: ICI Myocarditis Yes vs ICI Myocarditis No]

Data Information
Loading Data
Genome-wide scan
LOD peaks
- Centimorgan (cM)
- Megabase (MB)
QTL effects
R/qtl
- scanone

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_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_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_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
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
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    Untracked:  data/ici.vs.pbs_blup_sub_chr-7_peak.marker-UNCHS020711_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_blup_sub_chr-7_peak.marker-UNCHS020711_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_genes_chr-7_peak.marker-UNCHS020711_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
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    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_age.of.onset_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_age.of.onset_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_age.of.onset_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_age.of.onset_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
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    Untracked:  data/mean.differences_age.of.onset_ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_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/mean.differences_age.of.onset_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_age.of.onset_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_age.of.onset_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_age.of.onset_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
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    Untracked:  data/mean.differences_group_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_group_ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
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    Untracked:  data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_group_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
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    Untracked:  data/mean.differences_group_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
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    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
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    Untracked:  data/percent_missing_id_7.batches_myo.RData
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    Untracked:  data/serreze_probs_7.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_7.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_7.batches_myo_mis.rds
    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

Myocarditis Status: Yes, No
Murine MHC KO Status: Het
Drug Treatment(s): ICI
Clinical Phenotype(s): Sick, EOI
Covariates (Myocarditis Score, sex)

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_mis.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                130890

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9927 9959 7817 7558 7564 7722 7374 6434 6686 6356 7128 6091 6053 5944 5322 5003 
  17   18   19    X 
5053 4590 3547 4762

#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")

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

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

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

covars <- read_csv("data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_7.batches_myo_mis.csv")
#removing any missing info
#covars <- subset(covars, covars$het.ici.myo.yes_vs_het.ici.myo.no!='')
nrow(covars)

[1] 39

table(covars$"Myocarditis Status")


 NO YES 
 32   7

table(covars$"Murine MHC KO Status")


HET 
 39

table(covars$"Drug Treatment")


ICI 
 39

table(covars$"clinical pheno")


 EOI SICK 
  25   14

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

Object of class cross2 (crosstype "bc")

Total individuals                39
No. genotyped individuals        39
No. phenotyped individuals       39
No. with both geno & pheno       39

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

No. chromosomes                  20
Total markers                130890

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9927 9959 7817 7558 7564 7722 7374 6434 6686 6356 7128 6091 6053 5944 5322 5003 
  17   18   19    X 
5053 4590 3547 4762

table(gm$covar$"Myocarditis Status")


 NO YES 
 32   7

table(gm$covar$"Murine MHC KO Status")


HET 
 39

table(gm$covar$"Drug Treatment")


ICI 
 39

table(gm$covar$"clinical pheno")


 EOI SICK 
  25   14

#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] 98070

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	95063
gfmar_mar_1	95063
gfmar_mar_5	98070
gfmar_mar_10	98478
gfmar_mar_15	98551
gfmar_mar_25	99022
gfmar_mar_50	130475
total_snps	130890

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               39
No. genotyped individuals       39
No. phenotyped individuals      39
No. with both geno & pheno      39

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

No. chromosomes                 20
Total markers                32820

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2923 2765 2051 2045 1920 2051 1850 1674 1977 1219 2062 1402 1619 1690 1263  938 
  17   18   19    X 
 682 1082 1044  563

### dropping disproportionate markers
#dismark <- read.csv("data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv")
#nrow(dismark)
#names(dismark)[1] <- c("marker")
#ismark <- 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(~sex+Histology.Score, data = covars)[,-1]
covars$het.ici.myo.yes_vs_het.ici.myo.no= as.numeric(covars$het.ici.myo.yes_vs_het.ici.myo.no)

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

operm <- scan1perm(pr.qc, covars["het.ici.myo.yes_vs_het.ici.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["het.ici.myo.yes_vs_het.ici.myo.no"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, covars["het.ici.myo.yes_vs_het.ici.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/het-ici-myo-yes.vs.het-ici-myo-no_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_7.batches_myo_mis.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/het-ici-myo-yes.vs.het-ici-myo-no_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_7.batches_myo_mis.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

QTL Analysis - binary [Hetero: ICI Myocarditis Yes vs ICI Myocarditis No]

Belinda Cornes

2023-04-16

Data Information

Loading Data

Genome-wide scan

LOD peaks

Centimorgan (cM)

Megabase (MB)

QTL effects

R/qtl

scanone