After genotype diagnostics for diversity outbred mice
library
Missing data per sample
Sexes
Sample duplicates
Array intensities and Genotype frequencies
Crossover counts and Genotyping error LOD scores

Last updated: 2019-12-03

Checks: 7 0

Knit directory: csna_workflow/

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Unstaged changes:
    Modified:   README.md
    Modified:   _workflowr.yml
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    Modified:   analysis/run_05_after_diagnosis_qc_gigamuga_nine_batches.R
    Modified:   analysis/run_10_qtl_permu.R
    Modified:   analysis/run_11_qtl_blup.R

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File	Version	Author	Date	Message
Rmd	4aeb087	xhyuo	2019-12-03	05_after_diagnosis_qc_gigamuga_11_batches.Rmd

After 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("code/reconst_utils.R")

Missing data per sample

load("data/Jackson_Lab_11_batches/gm_DO2816_qc.RData")
gm <- gm_DO2816_qc
gm

Object of class cross2 (crosstype "do")

Total individuals              2816
No. genotyped individuals      2816
No. phenotyped individuals     2816
No. with both geno & pheno     2816

No. phenotypes                    1
No. covariates                    4
No. phenotype covariates          0

No. chromosomes                  20
Total markers                112455

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
8530 8649 6403 6600 6559 6427 6281 5660 5854 5439 6338 5151 5257 5025 4547 
  16   17   18   19    X 
4355 4321 3986 3102 3971

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, 60)))

#save into pdf
pdf(file = "output/AfterQC_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), "_"))[,5]))), 
        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

Sexes

xint <- read_csv_numer("data/Jackson_Lab_Bubier_MURGIGV01/Jackson_Lab_Bubier_MURGIGV01_qtl2_chrXint.csv", transpose=TRUE)
yint <- read_csv_numer("data/Jackson_Lab_Bubier_MURGIGV01/Jackson_Lab_Bubier_MURGIGV01_qtl2_chrYint.csv", transpose=TRUE)

#subset to gm subject name
xint <- xint[rownames(xint) %in% rownames(gm$covar),]
yint <- yint[rownames(yint) %in% rownames(gm$covar),]

# 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)
#snp <- snps[snps$marker %in% marker_names(gm),]

#load the intensities.fst.RData
load("data/Jackson_Lab_11_batches/intensities.fst.RData")
#X and Y channel
X <- result[result$channel == "X",c("snp","channel",rownames(gm$covar))]
rownames(X) <- X$snp
X <- X[,c(-1,-2)]

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

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

#sex order
sex <- sex[rownames(xint)]

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=20)
summary.cg <- summary(cg)
summary.cg
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  

pdf(file = "output/AfterQC_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()

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)])

pdf(file = "output/AfterQC_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()

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)])

#show top 20 samples with missing genotypes
percent_missing <- n_missing(gm, "ind", "prop")*100
round(sort(percent_missing, decreasing=TRUE)[1:20], 1)

Array intensities and Genotype frequencies

int <- result[,c("snp","channel",rownames(gm$covar))]
#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))
    }
  }
}

Crossover counts and Genotyping error LOD scores

#load pre-caluated results
load("data/Jackson_Lab_11_batches/nxo.RData")

#crossover
totxo <- rowSums(nxo)[names(rowSums(nxo)) %in% 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 = "output/AfterQC_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()

p

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] htmlwidgets_1.3          rlang_0.4.0             
[35] RSQLite_2.1.1            jsonlite_1.6            
[37] hwriter_1.3.2            gtools_3.5.0            
[39] BiocParallel_1.8.2       dplyr_0.8.3             
[41] RCurl_1.95-4.12          magrittr_1.5            
[43] modeltools_0.2-21        qtl_1.41-6              
[45] Matrix_1.2-14            Rcpp_1.0.2              
[47] munsell_0.5.0            stringi_1.2.4           
[49] whisker_0.3-2            yaml_2.2.0              
[51] MASS_7.3-50              zlibbioc_1.20.0         
[53] rhdf5_2.18.0             flexmix_2.3-13          
[55] plyr_1.8.4               grid_3.3.2              
[57] blob_1.1.1               gdata_2.18.0            
[59] crayon_1.3.4             lattice_0.20-35         
[61] GenomicFeatures_1.26.2   annotate_1.52.1         
[63] knitr_1.20               pillar_1.3.1            
[65] RUnit_0.4.31             fpc_2.1-10              
[67] corpcor_1.6.9            codetools_0.2-15        
[69] biomaRt_2.30.0           XML_3.98-1.16           
[71] glue_1.3.1               evaluate_0.10           
[73] data.table_1.11.4        foreach_1.4.4           
[75] gtable_0.2.0             purrr_0.3.2             
[77] kernlab_0.9-25           assertthat_0.2.1        
[79] xtable_1.8-2             class_7.3-14            
[81] tibble_2.1.3             iterators_1.0.10        
[83] GenomicAlignments_1.10.1 AnnotationDbi_1.36.2    
[85] memoise_1.1.0            workflowr_1.4.0         
[87] cluster_2.0.7-1

after_diagnosis_qc_gigamuga_11_batches

Hao He

2019-12-03