Genotype Frequencies - PBS Myocarditis Yes vs PBS Myocarditis No

workflowr

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Last updated: 2023-04-16

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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Repository version: 51e3845

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    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
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_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
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    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
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.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_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
    Untracked:  data/mean.differences_age.of.onset_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.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_sample.outliers.removed_7.batches_myo.csv
    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
    Untracked:  data/mean.differences_age.of.onset_ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.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_7.batches_myo_mis.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.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
    Untracked:  data/mean.differences_age.of.onset_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.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_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_age.of.onset_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    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.csv
    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
    Untracked:  data/mean.differences_group_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_group_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_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
    Untracked:  data/mean.differences_group_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    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.csv
    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
    Untracked:  data/mean.differences_group_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/mean.differences_group_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/mean.differences_group_myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    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
    Untracked:  data/mean.differences_group_pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.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_sample.outliers.removed_7.batches_myo.csv
    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
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
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Allele Frequencies

All variants (before snp qc)

After SNP QC

with sample outliers

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_7.batches_myo.rds")
#pr <- readRDS("data/serreze_probs.rds")

geno <- read.csv("data/sample_geno_bc_7.batches_myo.csv", as.is=T, check.names=FALSE)
#names(geno) <- gsub("^X","",names(geno))
#names(geno) <- gsub("\.","-",names(geno))
#names(geno)[2:ncol(geno)] <- samples$Unique.Sample.ID
rownames(geno) <- geno$marker

## 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)

covars <- read_csv("data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_7.batches_myo.csv")
# removing any missing info
#covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)

[1] 96

table(covars$"Myocarditis Status")

 NO YES 
 17  79 

table(covars$"Murine MHC KO Status")

HOM 
 96 

table(covars$"Drug Treatment")

PBS 
 96 

table(covars$"clinical pheno")

 EOI SICK 
  64   32 

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

Object of class cross2 (crosstype "bc")

Total individuals               96
No. genotyped individuals       96
No. phenotyped individuals      96
No. with both geno & pheno      96

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 
 17  79 

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

HOM 
 96 

table(gm$covar$"Drug Treatment")

PBS 
 96 

table(gm$covar$"clinical pheno")

 EOI SICK 
  64   32 

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

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)

[1] 32660    96

## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]

### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
#    genoi <- geno[i,]
#    freqf <- table(factor(geno[i,], c("-","AA","AB")))
#    genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
#    genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
#    genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
#    freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
#    freqf <- as.data.frame(t(freqf[1,]))
#    rownames(freqf) <- rownames(genoi)
#    colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
#    genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
#    genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)

### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)

### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)

[1] 32660     8

# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)

FALSE  TRUE 
24849  7811 

## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)

[1] 26651     9

table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
18840  7811 

gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)

[1] 27553     9

table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
19754  7799 

## merging with sample_genos
filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)

[1] 26651   106

filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
names(filtered_gf_mar_probs_sample)[1] <- c("marker")
dim(filtered_gf_mar_probs_sample)

[1] 27553   106

## saving files
write.csv(filtered_gf_mar_geno_sample, "data/pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs_sample, "data/pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv", quote=F)

write.csv(filtered_gf_mar_geno, "data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv", quote=F)

sample outliers removed

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_7.batches_myo.rds")
#pr <- readRDS("data/serreze_probs.rds")

geno <- read.csv("data/sample_geno_bc_7.batches_myo.csv", as.is=T, check.names=FALSE)
#names(geno) <- gsub("^X","",names(geno))
#names(geno) <- gsub("\.","-",names(geno))
#names(geno)[2:ncol(geno)] <- samples$Unique.Sample.ID
rownames(geno) <- geno$marker

## 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)

covars <- read_csv("data/covar_corrected.cleaned_pbs-myo-yes.vs.pbs-myo-no_7.batches_myo.csv")
# removing any missing info
covars <- subset(covars, covars$out.age.of.onset=='Keep' & covars$out.rz.age=='Keep')
nrow(covars)

[1] 92

table(covars$"Myocarditis Status")

 NO YES 
 16  76 

table(covars$"Murine MHC KO Status")

HOM 
 92 

table(covars$"Drug Treatment")

PBS 
 92 

table(covars$"clinical pheno")

 EOI SICK 
  64   28 

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

Object of class cross2 (crosstype "bc")

Total individuals               92
No. genotyped individuals       92
No. phenotyped individuals      92
No. with both geno & pheno      92

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 
 16  76 

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

HOM 
 92 

table(gm$covar$"Drug Treatment")

PBS 
 92 

table(gm$covar$"clinical pheno")

 EOI SICK 
  64   28 

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

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)

[1] 32660    92

## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]

### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
#    genoi <- geno[i,]
#    freqf <- table(factor(geno[i,], c("-","AA","AB")))
#    genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
#    genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
#    genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
#    freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
#    freqf <- as.data.frame(t(freqf[1,]))
#    rownames(freqf) <- rownames(genoi)
#    colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
#    genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
#    genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)

### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)

### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)

[1] 32660     8

# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)

FALSE  TRUE 
25079  7581 

## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)

[1] 26649     9

table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
19068  7581 

gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)

[1] 27576     9

table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
20006  7570 

## merging with sample_genos
filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)

[1] 26649   102

filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
names(filtered_gf_mar_probs_sample)[1] <- c("marker")
dim(filtered_gf_mar_probs_sample)

[1] 27576   102

## saving files
write.csv(filtered_gf_mar_geno_sample, "data/pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs_sample, "data/pbs-myo-yes.vs.pbs-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv", quote=F)

write.csv(filtered_gf_mar_geno, "data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv", quote=F)

Session information

R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] ggdark_0.2.1      ggthemes_4.2.4    cowplot_1.1.1     ggnewscale_0.4.8 
 [5] abind_1.4-5       qtl2_0.30         reshape2_1.4.4    ggplot2_3.4.0    
 [9] tibble_3.1.8      psych_2.2.9       readxl_1.4.1      cluster_2.1.4    
[13] dplyr_1.0.10      optparse_1.7.3    rhdf5_2.40.0      mclust_6.0.0     
[17] tidyr_1.2.1       data.table_1.14.6 knitr_1.42        kableExtra_1.3.4 
[21] workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] httr_1.4.4         sass_0.4.5         bit64_4.0.5        jsonlite_1.8.4    
 [5] viridisLite_0.4.1  bslib_0.4.2        assertthat_0.2.1   getPass_0.2-2     
 [9] highr_0.10         blob_1.2.3         cellranger_1.1.0   yaml_2.3.7        
[13] pillar_1.8.1       RSQLite_2.2.19     lattice_0.20-45    glue_1.6.2        
[17] digest_0.6.31      promises_1.2.0.1   rvest_1.0.3        colorspace_2.0-3  
[21] htmltools_0.5.5    httpuv_1.6.6       plyr_1.8.8         pkgconfig_2.0.3   
[25] purrr_0.3.5        scales_1.2.1       webshot_0.5.4      processx_3.8.0    
[29] svglite_2.1.0      whisker_0.4.1      getopt_1.20.3      later_1.3.0       
[33] git2r_0.30.1       farver_2.1.1       generics_0.1.3     cachem_1.0.7      
[37] withr_2.5.0        cli_3.6.1          mnormt_2.1.1       magrittr_2.0.3    
[41] memoise_2.0.1      evaluate_0.20      ps_1.7.2           fs_1.6.1          
[45] fansi_1.0.3        nlme_3.1-161       xml2_1.3.3         tools_4.2.2       
[49] lifecycle_1.0.3    stringr_1.5.0      Rhdf5lib_1.18.2    munsell_0.5.0     
[53] callr_3.7.3        compiler_4.2.2     jquerylib_0.1.4    systemfonts_1.0.4 
[57] rlang_1.1.0        grid_4.2.2         rhdf5filters_1.8.0 rstudioapi_0.14   
[61] rmarkdown_2.21.2   gtable_0.3.1       DBI_1.1.3          R6_2.5.1          
[65] bit_4.0.5          fastmap_1.1.1      utf8_1.2.2         rprojroot_2.0.3   
[69] stringi_1.7.12     parallel_4.2.2     Rcpp_1.0.9         vctrs_0.6.1       
[73] tidyselect_1.2.0   xfun_0.38