Step 2: SNP QC [4.batches_myo]

workflowr

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Last updated: 2023-01-11

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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Seed: set.seed(20220210)

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File paths: absolute

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absolute	relative
/Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow	.

Repository version: c9fc66b

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    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.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_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNCHS006135_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.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_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/percent_missing_id_4.batches_myo.RData
    Untracked:  data/percent_missing_marker_4.batches_myo.RData
    Untracked:  data/pheno_4.batches_myo.csv
    Untracked:  data/physical_map_4.batches_myo.csv
    Untracked:  data/qc_info_bad_sample_4.batches_myo.RData
    Untracked:  data/sample_geno_AHB_4.batches_myo.csv
    Untracked:  data/sample_geno_bc_4.batches_myo.csv
    Untracked:  data/serreze_probs_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo_mis.rds
    Untracked:  data/summary.cg_4.batches_myo.RData
    Untracked:  output/Percent_missing_genotype_data_4.batches_myo.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_4.batches_myo.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches_myo.pdf
    Untracked:  output/genotype_error_marker_4.batches_myo.pdf
    Untracked:  output/genotype_frequency_marker_4.batches_myo.pdf

Unstaged changes:
    Modified:   analysis/index_5.batches.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

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

---

Loading Project

load("data/e_snpg_samqc_4.batches_myo.RData")
gm <- get(load("data/gm_samqc_4.batches_myo.RData"))

gm

Warning in check_cross2(object): 1249 invalid genotypes in cross

Object of class cross2 (crosstype "bc")

Total individuals               208
No. genotyped individuals       208
No. phenotyped individuals      208
No. with both geno & pheno      208

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

No. chromosomes                  20
Total markers                133716

No. markers by chr:
    1     2     3     4     5     6     7     8     9    10    11    12    13 
10159 10172  7987  7736  7778  7911  7548  6561  6823  6471  7276  6226  6177 
   14    15    16    17    18    19     X 
 6082  5421  5075  5162  4682  3612  4857 

It can also be useful to look at the proportion of missing genotypes by marker. Markers with a lot of missing data were likely difficult to call, and so the genotypes that were called may contain a lot of errors.

Marker Missing Data

pmis_mar <- n_missing(gm, "marker", "proportion")*100
save(pmis_mar, file = "data/percent_missing_marker_4.batches_myo.RData")

par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
     main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)

pdf(file = "output/Percent_missing_genotype_data_per_marker_4.batches_myo.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
     main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
dev.off()

quartz_off_screen 
                2 

	count
pmis_mar_5	6919
pmis_mar_10	3772
pmis_mar_15	2302
pmis_mar_25	1156
pmis_mar_50	289
pmis_mar_75	26
total_snps	133716

Marker Genotype Frequencies

g <- do.call("cbind", gm$geno[1:19])
#fg <- do.call("cbind", gm$founder_geno[1:19])
#g <- g[,colSums(g)!=0]
#fg <- fg[,colSums(fg==0)==0]
#fgn <- colSums(g==2)

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",]
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),]

pdf(file = "output/genotype_frequency_marker_4.batches_myo.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)
dev.off()

quartz_off_screen 
                2 

par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)

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	87225
gfmar_mar_1	91198
gfmar_mar_5	94284
gfmar_mar_10	95166
gfmar_mar_15	95539
gfmar_mar_25	95988
gfmar_mar_50	128857
total_snps	128857

save(gf_mar, file = "data/genotype_freq_marker_4.batches_myo.RData")

Marker Genotype Errors

errors_mar <- colSums(e>2)/n_typed(gm, "marker")*100

grayplot(pmis_mar, errors_mar,
         xlab="Proportion missing", ylab="Proportion genotyping errors")

pdf(file = "output/genotype_error_marker_4.batches_myo.pdf")
grayplot(pmis_mar, errors_mar,
         xlab="Proportion missing", ylab="Proportion genotyping errors")
dev.off()

quartz_off_screen 
                2 

save(errors_mar, file = "data/genotype_errors_marker_4.batches_myo.RData")

Markers with higher rates of missing genotypes tend to show higher errors rates.

Removing Markers

Missingness

#Missingness

length(pmis_mar[pmis_mar >= 10])

[1] 3772

high_miss <- find_markerpos(gm, names(pmis_mar[pmis_mar >= 10]))
high_miss$id <- rownames(high_miss)
high_miss_df <- as.data.frame(pmis_mar[pmis_mar >= 10])
high_miss_df$index = 1: nrow(high_miss_df)
high_miss_df$id <- rownames(high_miss_df)

high_miss_bad <- merge(high_miss,high_miss_df, by=c("id"),all=T)
names(high_miss_bad)[5] <- c("high_miss")
names(high_miss_bad)[1] <- c("marker")
high_miss_bad <- high_miss_bad[order(high_miss_bad$index),]

Monomorphic/Low Frequency markers

#Monomorphic/Low Frequency markers

#low_freq_df <- as.data.frame(gf_mar)
count <- rowSums(gf_mar <= 0.01)
#count <- as.data.frame(count)
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_df$id <- rownames(low_freq_df)
#low_freq_df$index = 1: nrow(low_freq_df)
low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)

nrow(low_freq)

[1] 91241

low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
#names(low_freq_bad)[5] <- c("AA_freq")
#names(low_freq_bad)[6] <- c("AB_freq")
#names(low_freq_bad)[7] <- c("BB_freq")
names(low_freq_bad)[1] <- c("marker")
#low_freq_bad <- low_freq_bad[order(low_freq_bad$index),]

Genotyping Error

##Genotyping Error

length(errors_mar[errors_mar > 5])

[1] 12174

error_markers_names <- names(errors_mar[errors_mar > 5])
error_markers_names <- error_markers_names[complete.cases(error_markers_names)]

error_markers <- find_markerpos(gm, error_markers_names)
error_markers$id <- rownames(error_markers)
#rne <- rownames(as.data.frame(errors_mar))
error_mars_df <- as.data.frame(errors_mar[errors_mar > 5])
error_mars_df <- error_mars_df[complete.cases(error_mars_df$"errors_mar[errors_mar > 5]"),]
error_mars_df <- as.data.frame(error_mars_df)
#error_mars_df$id = rownames(error_mars_df)
error_mars_df$index = 1: nrow(error_mars_df)

#error_markers_bad <- merge(error_markers,error_mars_df, by=c("id"),all=T)
error_markers_bad <- cbind(error_markers,error_mars_df)
names(error_markers_bad)[5] <- c("error_mars")
names(error_markers_bad)[4] <- c("marker")
error_markers_bad <- error_markers_bad[order(error_markers_bad$index),]

Total

### merge all

bad_markers <- rbind(high_miss_bad[c("marker","chr","gmap","pmap")], low_freq_bad[c("marker","chr","gmap","pmap")], error_markers_bad[c("marker","chr","gmap","pmap")])
#nrow(bad_markers)

duplicate <- bad_markers[duplicated(bad_markers),]

bad_markers <- bad_markers[!duplicated(bad_markers),]
nrow(bad_markers)

[1] 101106

save(bad_markers, file = "data/bad_markers_all_4.batches_myo.RData")

Only removing markers that are missing in at least 10% of the samples as well those that come as genotyping errors and have a allele frequency of less than 1%

#missing in at least 10% of the samples

gm_allqc2 <- drop_markers(gm_samqc, bad_markers$marker)
gm_allqc <- drop_nullmarkers(gm_allqc2)


gm_allqc

Warning in check_cross2(object): 448 invalid genotypes in cross

Object of class cross2 (crosstype "bc")

Total individuals              208
No. genotyped individuals      208
No. phenotyped individuals     208
No. with both geno & pheno     208

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

No. chromosomes                 20
Total markers                32610

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109  835 
  17   18   19    X 
 674  813  940 4501 

save(gm_allqc, file = "data/gm_allqc_4.batches_myo.RData")

Session information

sessionInfo()

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] fst_0.9.8        knitr_1.41       kableExtra_1.3.4 mclust_6.0.0    
 [5] ggrepel_0.9.2    ggplot2_3.4.0    qtlcharts_0.16   qtl2_0.30       
 [9] broman_0.80      workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9        svglite_2.1.0     getPass_0.2-2     ps_1.7.2         
 [5] assertthat_0.2.1  rprojroot_2.0.3   digest_0.6.30     utf8_1.2.2       
 [9] R6_2.5.1          RSQLite_2.2.19    evaluate_0.18     httr_1.4.4       
[13] highr_0.9         pillar_1.8.1      rlang_1.0.6       rstudioapi_0.14  
[17] data.table_1.14.6 whisker_0.4.1     callr_3.7.3       jquerylib_0.1.4  
[21] blob_1.2.3        fstcore_0.9.12    rmarkdown_2.18    webshot_0.5.4    
[25] qtl_1.54          stringr_1.5.0     bit_4.0.5         munsell_0.5.0    
[29] compiler_4.2.2    httpuv_1.6.6      xfun_0.35         systemfonts_1.0.4
[33] pkgconfig_2.0.3   htmltools_0.5.3   tidyselect_1.2.0  tibble_3.1.8     
[37] viridisLite_0.4.1 fansi_1.0.3       dplyr_1.0.10      withr_2.5.0      
[41] later_1.3.0       grid_4.2.2        jsonlite_1.8.4    gtable_0.3.1     
[45] lifecycle_1.0.3   DBI_1.1.3         git2r_0.30.1      magrittr_2.0.3   
[49] scales_1.2.1      cli_3.4.1         stringi_1.7.8     cachem_1.0.6     
[53] fs_1.5.2          promises_1.2.0.1  xml2_1.3.3        bslib_0.4.1      
[57] vctrs_0.5.1       generics_0.1.3    tools_4.2.2       bit64_4.0.5      
[61] glue_1.6.2        processx_3.8.0    parallel_4.2.2    fastmap_1.1.0    
[65] yaml_2.3.6        colorspace_2.0-3  rvest_1.0.3       memoise_2.0.1    
[69] sass_0.4.4