Last updated: 2019-10-20

Checks: 6 1

Knit directory: csna_workflow/

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/projects/csna/csna_workflow/code/reconst_utils.R code/reconst_utils.R
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Gagnon_gm.RData data/Jackson_Lab_Gagnon/Gagnon_gm.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_missing_genotype_data.pdf data/Jackson_Lab_Gagnon/Percent_missing_genotype_data.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/percent_missing_id.RData data/Jackson_Lab_Gagnon/percent_missing_id.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrXint.csv data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrXint.csv
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrYint.csv data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrYint.csv
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/intensities.fst.RData data/Jackson_Lab_Gagnon/intensities.fst.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/summary.cg.RData data/Jackson_Lab_Gagnon/summary.cg.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_before_removal_of_bad_samples.pdf data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_before_removal_of_bad_samples.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_after_removal_of_bad_samples.pdf data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_after_removal_of_bad_samples.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/pr.RData data/Jackson_Lab_Gagnon/pr.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/m.RData data/Jackson_Lab_Gagnon/m.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/nxo.RData data/Jackson_Lab_Gagnon/nxo.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/number_crossover.pdf data/Jackson_Lab_Gagnon/number_crossover.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/e.RData data/Jackson_Lab_Gagnon/e.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/errors_ind.RData data/Jackson_Lab_Gagnon/errors_ind.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/snpg.RData data/Jackson_Lab_Gagnon/snpg.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_genotype_errors_obs_vs_predicted.pdf data/Jackson_Lab_Gagnon/Percent_genotype_errors_obs_vs_predicted.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_missing_genotype_data_per_marker.pdf data/Jackson_Lab_Gagnon/Percent_missing_genotype_data_per_marker.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/genotype_frequency_marker.pdf data/Jackson_Lab_Gagnon/genotype_frequency_marker.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/genotype_error_marker.pdf data/Jackson_Lab_Gagnon/genotype_error_marker.pdf
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/qc_info_bad_sample.RData data/Jackson_Lab_Gagnon/qc_info_bad_sample.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/bad_markers.RData data/Jackson_Lab_Gagnon/bad_markers.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/gm_DO2437_qc.RData data/Jackson_Lab_Gagnon/gm_DO2437_qc.RData
/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/e_g_snpg_qc.RData data/Jackson_Lab_Gagnon/e_g_snpg_qc.RData

Repository version: 884447c

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Unstaged changes:
    Modified:   README.md
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    Modified:   analysis/10_qtl_permu.R
    Modified:   analysis/run_10_qtl_permu.R
    Modified:   analysis/run_11_qtl_blup.R

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author: “Hao He” date: “2019-10-20” output: html_document —

Genotype diagnostics for diversity outbred mice

We first load the R/qtl2 package and the data. We’ll also load the R/broman package for some utilities and plotting functions, and R/qtlcharts for interactive graphs.

library

library(broman)
library(qtl2)
library(qtlcharts)
library(ggplot2)
library(ggrepel)
library(DOQTL)
library(mclust)
source("/projects/csna/csna_workflow/code/reconst_utils.R")

Missing data per sample

load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Gagnon_gm.RData")
gm
Object of class cross2 (crosstype "do")

Total individuals               384
No. genotyped individuals       384
No. phenotyped individuals      384
No. with both geno & pheno      384

No. phenotypes                    1
No. covariates                    3
No. phenotype covariates          0

No. chromosomes                  20
Total markers                112728

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
8555 8666 6420 6615 6571 6443 6294 5677 5870 5447 6352 5167 5274 5039 4555 
  16   17   18   19    X 
4369 4330 4002 3108 3974 
percent_missing <- n_missing(gm, "ind", "prop")*100
setScreenSize(height=100, width=300)
Set screen size to height=100 x width=300
labels <- paste0(names(percent_missing), " (", round(percent_missing,2), "%)")
iplot(seq_along(percent_missing), percent_missing, indID=labels,
      chartOpts=list(xlab="Mouse", ylab="Percent missing genotype data",
                     ylim=c(0, 100)))
#save into pdf
pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_missing_genotype_data.pdf", width = 20, height = 20)
labels <- as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "V01_"))[,2])
labels[percent_missing < 5] = ""
# Change point shapes and colors
p <- ggplot(data = data.frame(Mouse=seq_along(percent_missing),  
                         Percent_missing_genotype_data = percent_missing,
                         batch = factor(as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,6]))), 
        aes(x=Mouse, y=Percent_missing_genotype_data, color = batch)) +
  geom_point() +
  geom_hline(yintercept=5, linetype="solid", color = "red") +
  geom_text_repel(aes(label=labels), vjust = 0, nudge_y = 0.01, show.legend = FALSE, size=3) +
  theme(text = element_text(size = 20))
p
dev.off()
png 
  2 
p

Version Author Date
a6d6886 xhyuo 2019-10-20 
save(percent_missing,
     file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/percent_missing_id.RData")

Sexes

xint <- read_csv_numer("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrXint.csv", transpose=TRUE)
yint <- read_csv_numer("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Jackson_Lab_Gagnon_qtl2_chrYint.csv", transpose=TRUE)

# Gigamuga marker annotation file from UNC.
gm_marker_file = "http://csbio.unc.edu/MUGA/snps.gigamuga.Rdata" #FIXED
# Read in the UNC GigaMUGA SNPs and clusters.
load(url(gm_marker_file))
#subset down to gm
snps$marker = as.character(snps$marker)

#load the intensities.fst.RData
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/intensities.fst.RData")
#X and Y channel
X <- result[result$channel == "X",]
rownames(X) <- X$snp
X <- X[,c(-1,-2)]

Y <- result[result$channel == "Y",]
rownames(Y) <- Y$snp
Y <- Y[,c(-1,-2)]

#determine sex
sex = determine_sex_chry_m(x = X, y = Y, markers = snps)$sex

gm$covar <- merge(data.frame(id = names(sex),
                             predict.sex = sex,stringsAsFactors = F),
                   gm$covar,
                   by.x = "id", 
                   by.y = "row.names")
rownames(gm$covar) <- gm$covar$id

#sex order
sex <- gm$covar[rownames(xint),"predict.sex"]

x_pval <- apply(xint, 2, function(a) t.test(a ~ sex)$p.value)
y_pval <- apply(yint, 2, function(a) t.test(a ~ sex)$p.value)

xint_ave <- rowMeans(xint[, x_pval < 0.05/length(x_pval)], na.rm=TRUE)
yint_ave <- rowMeans(yint[, y_pval < 0.05/length(y_pval)], na.rm=TRUE)

point_colors <- as.character( brocolors("web")[c("green", "purple")] )
labels <- paste0(names(xint_ave))
iplot(xint_ave, yint_ave, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Average Y chr intensity"))
phetX <- rowSums(gm$geno$X == 2)/rowSums(gm$geno$X != 0)
phetX <- phetX[names(phetX) %in% names(xint_ave)]
iplot(xint_ave, phetX, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Proportion het on X chr"))

Sample duplicates

cg <- compare_geno(gm, cores=10)
summary.cg <- summary(cg)
summary.cg
                                               ind1
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21433_H9
                                                ind2 prop_match n_mismatch
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21412_A10          1         35
 n_typed n_match index1 index2
  112027  111992    360    361
summary.cg$Name.ind1 <- as.character(do.call(rbind.data.frame, strsplit(as.character(summary.cg$ind1), "_"))[,6])
summary.cg$Name.ind2 <- as.character(do.call(rbind.data.frame, strsplit(as.character(summary.cg$ind2), "_"))[,6])
summary.cg$miss.ind1 <- percent_missing[match(summary.cg$ind1, names(percent_missing))]
summary.cg$miss.ind2 <- percent_missing[match(summary.cg$ind2, names(percent_missing))]
summary.cg$remove.id <- ifelse(summary.cg$miss.ind1 > summary.cg$miss.ind2, summary.cg$ind1, summary.cg$ind2)
summary.cg$remove.id  
[1] "The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21412_A10"
save(summary.cg,
     file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/summary.cg.RData")

pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_before_removal_of_bad_samples.pdf", width = 20, height = 20) 
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])
dev.off()
png 
  2 
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])

Version Author Date
a6d6886 xhyuo 2019-10-20 
pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Proportion_matching_genotypes_after_removal_of_bad_samples.pdf",width = 20, height = 20) 
cgsub <- cg[percent_missing < 5, percent_missing < 5]
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cgsub[upper.tri(cgsub)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cgsub[upper.tri(cgsub)])
dev.off()
png 
  2 
cgsub <- cg[percent_missing < 5, percent_missing < 5]
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cgsub[upper.tri(cgsub)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cgsub[upper.tri(cgsub)])

Version Author Date
a6d6886 xhyuo 2019-10-20 
#show top 20 samples with missing genotypes
percent_missing <- n_missing(gm, "ind", "prop")*100
round(sort(percent_missing, decreasing=TRUE)[1:20], 1)
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11 
                                               68.1 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6 
                                               14.4 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7 
                                               12.9 
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21534_A11 
                                               12.6 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20649_A1 
                                                6.2 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20658_B1 
                                                5.5 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21440_A7 
                                                3.8 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20692_B7 
                                                3.4 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20656_F1 
                                                2.0 
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_22396_A12 
                                                1.9 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20660_A2 
                                                1.8 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21526_B2 
                                                1.6 
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_22637_G11 
                                                1.4 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21411_B4 
                                                1.2 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21490_E1 
                                                1.1 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20716_B1 
                                                0.9 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21516_G6 
                                                0.9 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21405_F7 
                                                0.9 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21535_C1 
                                                0.9 
 The_Jackson_Lab_Gagnon_MURGIGV01_20191010_22640_B7 
                                                0.8

Array intensities and Genotype frequencies

int <- result
rm(result)
int <- int[seq(1, nrow(int), by=2),-(1:2)] + int[-seq(1, nrow(int), by=2),-(1:2)]
int <- int[,intersect(ind_ids(gm), colnames(int))]
n <- names(sort(percent_missing[intersect(ind_ids(gm), colnames(int))], decreasing=TRUE))
iboxplot(log10(t(int[,n])+1), orderByMedian=FALSE, chartOpts=list(ylab="log10(SNP intensity + 1)"))
# Genotype frequencies
g <- do.call("cbind", gm$geno[1:19])
fg <- do.call("cbind", gm$founder_geno[1:19])
g <- g[,colSums(fg==0)==0]
fg <- fg[,colSums(fg==0)==0]
fgn <- colSums(fg==3)

gf_ind <- vector("list", 4)
for(i in 1:4) {
  gf_ind[[i]] <- t(apply(g[,fgn==i], 1, function(a) table(factor(a, 1:3))/sum(a != 0)))
}

par(mfrow=c(4,1), mar=c(0.6, 0.6, 2.6, 0.6))
for(i in 1:4) {
  triplot(c("AA", "AB", "BB"), main=paste0("MAF = ", i, "/8"))
  tripoints(gf_ind[[i]], pch=21, bg="lightblue")
  tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
  
  if(i>=3) { # label mouse with lowest het
    wh <- which(gf_ind[[i]][,2] == min(gf_ind[[i]][,2]))
    tritext(gf_ind[[i]][wh,,drop=FALSE] + c(0.02, -0.02, 0),
            names(wh), adj=c(0, 1))
  }
  
  # label other mice
  if(i==1) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.3]
  }
  else if(i==2) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.48]
  }
  else if(i==3) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.51]
  }
  else if(i==4) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.6]
  }
  
  for(ind in lab) {
    if(grepl("^F", ind) && i != 3) {
      tritext(gf_ind[[i]][ind,,drop=FALSE] + c(-0.01, 0, +0.01), ind, adj=c(1,0.5))
    } else {
      tritext(gf_ind[[i]][ind,,drop=FALSE] + c(0.01, 0, -0.01), ind, adj=c(0,0.5))
    }
  }
}

Version Author Date
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Crossover counts and Genotyping error LOD scores

# qsub qsub pr_cross_over.sh to get the pr, m and nxo
#load pre-caluated results
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/pr.RData")
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/m.RData")
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/nxo.RData")

#crossover
totxo <- rowSums(nxo)[rownames(gm$covar)]
iplot(seq_along(totxo),
      totxo,
      group=gm$covar$ngen,
      chartOpts=list(xlab="Mouse", ylab="Number of crossovers", 
                     margin=list(left=80,top=40,right=40,bottom=40,inner=5),
                     axispos=list(xtitle=25,ytitle=50,xlabel=5,ylabel=5)))
#save crossover into pdf
pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/number_crossover.pdf")
cross_over <- data.frame(Mouse = seq_along(totxo), Number_crossovers = totxo, generation = gm$covar$ngen)
names(totxo) <- as.character(do.call(rbind.data.frame, strsplit(names(totxo), "V01_"))[,2])
names(totxo)[totxo <= 800] = ""
# Change point shapes and colors
p <-ggplot(cross_over, aes(x=Mouse, y=Number_crossovers, fill = generation, color=generation)) +
  geom_point() +
  geom_text_repel(aes(label=names(totxo),hjust=0,vjust=0), show.legend = FALSE)
p
dev.off()
png 
  2 
p

Version Author Date
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#Here are the crossover counts for those  mice:
tmp <- cbind(percent_missing=round(percent_missing,2), total_xo=totxo)[percent_missing >= 5,]
tmp[order(tmp[,1]),]
                                                    percent_missing
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20658_B1             5.51
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20649_A1             6.20
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21534_A11           12.61
The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7            12.94
The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6            14.37
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11           68.07
                                                    total_xo
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20658_B1       618
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20649_A1       604
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_21534_A11      624
The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7       584
The_Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6       610
The_Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11      639
# Genotyping error LOD scores
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/e.RData")
errors_ind <- rowSums(e>2)/n_typed(gm)*100
lab <- paste0(names(errors_ind), " (", myround(percent_missing,1), "%)")
iplot(seq_along(errors_ind), errors_ind, indID=lab,
      chartOpts=list(xlab="Mouse", ylab="Percent genotyping errors", ylim=c(0, 8),
                     axispos=list(xtitle=25, ytitle=50, xlabel=5, ylabel=5)))
save(errors_ind, file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/errors_ind.RData")

# Apparent genotyping errors
load("/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/snpg.RData")

gobs <- do.call("cbind", gm$geno)
gobs[gobs==0] <- NA

par(pty="s")
err_direct <- rowMeans(snpg != gobs, na.rm=TRUE)*100
errors_ind_0 <- rowSums(e > 0)/n_typed(gm)*100
par(mar=c(4.1,4.1,0.6, 0.6))
grayplot(errors_ind_0, err_direct,
         xlab="Percent errors (error LOD > 0)",
         ylab="Percent errors (obs vs predicted)",
         xlim=c(0, 2), ylim=c(0, 2))
abline(0,1,lty=2, col="gray60")

Version Author Date
a6d6886 xhyuo 2019-10-20 
pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_genotype_errors_obs_vs_predicted.pdf",width = 20, height = 20) 
par(pty="s")
err_direct <- rowMeans(snpg != gobs, na.rm=TRUE)*100
errors_ind_0 <- rowSums(e > 0)/n_typed(gm)*100
par(mar=c(4.1,4.1,0.6, 0.6))
grayplot(errors_ind_0, err_direct,
         xlab="Percent errors (error LOD > 0)",
         ylab="Percent errors (obs vs predicted)",
         xlim=c(0, 2), ylim=c(0, 2))
abline(0,1,lty=2, col="gray60")
dev.off()
png 
  2

Missing data in Markers and Genotype frequencies Markers

#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.
pmis_mar <- n_missing(gm, "marker", "proportion")*100

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)

Version Author Date
a6d6886 xhyuo 2019-10-20 
pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/Percent_missing_genotype_data_per_marker.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()
png 
  2 
# Genotype frequencies Markers
gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:3))/sum(a != 0)))
gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:3))))

pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/genotype_frequency_marker.pdf")
par(mfrow=c(2,2), mar=c(0.6, 0.6, 2.6, 0.6))
for(i in 1:4) {
  triplot(c("AA", "AB", "BB"), main=paste0("MAF = ", i, "/8"))
  z <- gf_mar[fgn==i,]
  z <- z[rowSums(is.na(z)) < 3,]
  tripoints(z, pch=21, bg="gray80", cex=0.6)
  tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
}
dev.off()
png 
  2 
par(mfrow=c(2,2), mar=c(0.6, 0.6, 2.6, 0.6))
for(i in 1:4) {
  triplot(c("AA", "AB", "BB"), main=paste0("MAF = ", i, "/8"))
  z <- gf_mar[fgn==i,]
  z <- z[rowSums(is.na(z)) < 3,]
  tripoints(z, pch=21, bg="gray80", cex=0.6)
  tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
}

Version Author Date
a6d6886 xhyuo 2019-10-20 
# Genotype errors Markers
errors_mar <- colSums(e>2)/n_typed(gm, "marker")*100

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

pdf(file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/genotype_error_marker.pdf")
grayplot(pmis_mar, errors_mar,
         xlab="Proportion missing", ylab="Proportion genotyping errors")
dev.off()
png 
  2

Remove bad samples

#percent missing
qc_info <- merge(gm$covar,
                 data.frame(id = names(percent_missing),
                            percent_missing = percent_missing,stringsAsFactors = F),by = "id")
#cross_over
qc_info <- merge(qc_info,
                 data.frame(id = rownames(cross_over),
                            Number_crossovers = cross_over$Number_crossovers,stringsAsFactors = F),by = "id")

#missing sex
qc_info[qc_info$id %in% tot.id.covar.csna.sexna$ID, "sex"] <- NA
qc_info$sex.match <- ifelse(qc_info$predict.sex == qc_info$sex, TRUE, FALSE)

#genotype errors
qc_info <- merge(qc_info,
                 data.frame(id = names(errors_ind),
                            genotype_erros = errors_ind,stringsAsFactors = F),by = "id")
#duplicated id to be remove
qc_info$remove.id.duplicated <- ifelse(qc_info$id %in% summary.cg$remove.id, TRUE,FALSE)

bad.sample <- qc_info[qc_info$ngen ==1 | qc_info$Number_crossovers <= 200 | qc_info$Number_crossovers >=1000 | qc_info$percent_missing >= 10 | qc_info$genotype_erros >= 1 | qc_info$remove.id.duplicated == TRUE,]

save(qc_info, bad.sample, file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/qc_info_bad_sample.RData")

#remove 77 bad samples
gm.no.bad <- gm[paste0("-",as.character(bad.sample$id)),]

#fix sex mismatch
gm.no.bad$covar[gm.no.bad$covar$id %in% names(yint_ave[!names(yint_ave) %in% bad.sample$id][yint_ave[!names(yint_ave) %in% bad.sample$id] <= 0.1]),"predict.sex"] <- "F"

gm.no.bad

#2437 subjects
# update other stuff
e <- e[ind_ids(gm.no.bad),]
g <- g[ind_ids(gm.no.bad),]
snpg <- snpg[ind_ids(gm.no.bad),]

length(errors_mar[errors_mar > 5])
# omit the 273 markers with error rates >5%.
bad_markers <- find_markerpos(gm.no.bad, names(errors_mar[errors_mar > 5]))
save(bad_markers, file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/bad_markers.RData")
#drop bad markers
gm_DO2437_qc <- drop_markers(gm.no.bad, names(errors_mar)[errors_mar > 5])

gm_DO2437_qc

save(gm_DO2437_qc, file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/gm_DO2437_qc.RData")
save(e,g,snpg, file = "/projects/csna/csna_workflow/data/Jackson_Lab_Gagnon/e_g_snpg_qc.RData")

sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.5 (Final)

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] stats4    parallel  methods   stats     graphics  grDevices utils    
[8] datasets  base     

other attached packages:
 [1] mclust_5.2.1                       DOQTL_1.10.0                      
 [3] VariantAnnotation_1.20.3           Rsamtools_1.26.2                  
 [5] SummarizedExperiment_1.4.0         Biobase_2.34.0                    
 [7] BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.42.0                   
 [9] rtracklayer_1.34.2                 Biostrings_2.42.1                 
[11] XVector_0.14.1                     GenomicRanges_1.26.4              
[13] GenomeInfoDb_1.10.3                IRanges_2.8.2                     
[15] S4Vectors_0.12.2                   BiocGenerics_0.20.0               
[17] ggrepel_0.8.1                      ggplot2_3.1.0                     
[19] qtlcharts_0.9-6                    qtl2_0.18                         
[21] broman_0.68-2                     

loaded via a namespace (and not attached):
 [1] bitops_1.0-6             fs_1.2.6                
 [3] bit64_0.9-7              doParallel_1.0.10       
 [5] rprojroot_1.3-2          prabclus_2.2-6          
 [7] regress_1.3-15           tools_3.3.2             
 [9] backports_1.1.2          R6_2.4.0                
[11] DBI_1.0.0                lazyeval_0.2.1          
[13] colorspace_1.4-0         trimcluster_0.1-2       
[15] annotationTools_1.48.0   nnet_7.3-12             
[17] withr_2.1.2              tidyselect_0.2.5        
[19] bit_1.1-14               git2r_0.23.0            
[21] labeling_0.3             diptest_0.75-7          
[23] scales_1.0.0             QTLRel_1.0              
[25] DEoptimR_1.0-8           mvtnorm_1.0-5           
[27] robustbase_0.92-7        stringr_1.3.1           
[29] digest_0.6.18            rmarkdown_1.11          
[31] pkgconfig_2.0.1          htmltools_0.3.6         
[33] highr_0.6                htmlwidgets_1.3         
[35] rlang_0.4.0              RSQLite_2.1.1           
[37] jsonlite_1.6             hwriter_1.3.2           
[39] gtools_3.5.0             BiocParallel_1.8.2      
[41] dplyr_0.8.3              RCurl_1.95-4.12         
[43] magrittr_1.5             modeltools_0.2-21       
[45] qtl_1.41-6               Matrix_1.2-14           
[47] Rcpp_1.0.2               munsell_0.5.0           
[49] stringi_1.2.4            whisker_0.3-2           
[51] yaml_2.2.0               MASS_7.3-50             
[53] zlibbioc_1.20.0          rhdf5_2.18.0            
[55] flexmix_2.3-13           plyr_1.8.4              
[57] grid_3.3.2               blob_1.1.1              
[59] gdata_2.18.0             crayon_1.3.4            
[61] lattice_0.20-35          GenomicFeatures_1.26.2  
[63] annotate_1.52.1          knitr_1.20              
[65] pillar_1.3.1             RUnit_0.4.31            
[67] fpc_2.1-10               corpcor_1.6.9           
[69] codetools_0.2-15         biomaRt_2.30.0          
[71] XML_3.98-1.16            glue_1.3.1              
[73] evaluate_0.10            data.table_1.11.4       
[75] foreach_1.4.4            gtable_0.2.0            
[77] purrr_0.3.2              kernlab_0.9-25          
[79] assertthat_0.2.1         xtable_1.8-2            
[81] class_7.3-14             tibble_2.1.3            
[83] iterators_1.0.10         GenomicAlignments_1.10.1
[85] AnnotationDbi_1.36.2     memoise_1.1.0           
[87] workflowr_1.4.0          cluster_2.0.7-1