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")

Generate json file for all 11 batches

#total sample id
#load json file for the 11 batches
gm <- get(load("data/Jackson_Lab_11_batches/gm_11batches.RData"))

gm

Object of class cross2 (crosstype "do")

Total individuals              2898
No. genotyped individuals      2898
No. phenotyped individuals     2898
No. with both geno & pheno     2898

No. phenotypes                    1
No. covariates                    2
No. phenotype covariates          0

No. chromosomes                  20
Total markers                112729

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
8555 8666 6420 6615 6571 6444 6294 5677 5870 5447 6352 5167 5274 5039 4555 
  16   17   18   19    X 
4369 4330 4002 3108 3974 

Missing data per sample

gm

Object of class cross2 (crosstype "do")

Total individuals              2898
No. genotyped individuals      2898
No. phenotyped individuals     2898
No. with both geno & pheno     2898

No. phenotypes                    1
No. covariates                    2
No. phenotype covariates          0

No. chromosomes                  20
Total markers                112729

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
8555 8666 6420 6615 6571 6444 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, 80)))

#save into pdf
pdf(file = "output/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 

p

Past versions of “Missing data per sample-2.png”

save(percent_missing,
     file = "data/Jackson_Lab_11_batches/percent_missing_id.RData")

Sexes

xint <- read_csv_numer("data/Jackson_Lab_11_batches/Jackson_Lab_11_batches_qtl2_chrXint.csv", transpose=TRUE)
yint <- read_csv_numer("data/Jackson_Lab_11_batches/Jackson_Lab_11_batches_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)
#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$channel == "X",]
rownames(X) <- X$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
  Jackson_Lab_Bubier_MURGIGV01_20190108_16305_E1
  Jackson_Lab_Gagnon_MURGIGV01_20191011_21433_H9
 Jackson_Lab_Bubier_MURGIGV01_20190425_18160_C11
   Jackson_Lab_Bubier_MURGIGV01_20170904_8673_B4
  Jackson_Lab_Bubier_MURGIGIV01_20171001_9373_H7
  Jackson_Lab_Bubier_MURGIGIV01_20171001_9375_E7
 Jackson_Lab_Bubier_MURGIGIV01_20171001_9255_H11
  Jackson_Lab_Bubier_MURGIGIV01_20171001_9371_G8
  Jackson_Lab_Bubier_MURGIGV01_20180518_13885_B9
  Jackson_Lab_Bubier_MURGIGIV01_20171001_9258_F8
 Jackson_Lab_Bubier_MURGIGIV01_20171001_9254_F11
   Jackson_Lab_Bubier_MURGIGV01_20170904_8616_C4
   Jackson_Lab_Bubier_MURGIGV01_20170904_8618_E3
   Jackson_Lab_Bubier_MURGIGV01_20170904_8617_A4
  Jackson_Lab_Bubier_MURGIGV01_20161227_8664_E10
   Jackson_Lab_Bubier_MURGIGV01_20170904_9378_C8
   Jackson_Lab_Bubier_MURGIGV01_20161227_8585_B8
   Jackson_Lab_Bubier_MURGIGV01_20170904_8674_F3
  Jackson_Lab_Bubier_MURGIGV01_20160908_8184_E10
   Jackson_Lab_Bubier_MURGIGV01_20161227_8166_H3
  Jackson_Lab_Bubier_MURGIGV01_20160908_8167_F10
  Jackson_Lab_Bubier_MURGIGV01_20160908_8168_D10
  Jackson_Lab_Bubier_MURGIGV01_20160908_8183_G10
  Jackson_Lab_Bubier_MURGIGV01_20160908_8185_C10
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
   Jackson_Lab_Bubier_MURGIGV01_20160908_7996_D2
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20190425_18229_F7
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6
  Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10
  Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10
                                            ind2 prop_match n_mismatch
 Jackson_Lab_Bubier_MURGIGV01_20190108_16487_C10       1.00         30
 Jackson_Lab_Gagnon_MURGIGV01_20191011_21412_A10       1.00         35
 Jackson_Lab_Bubier_MURGIGV01_20190425_18162_E11       1.00         40
   Jackson_Lab_Bubier_MURGIGV01_20170904_8669_D5       1.00         46
  Jackson_Lab_Bubier_MURGIGV01_20181207_9373_G10       1.00         52
  Jackson_Lab_Bubier_MURGIGV01_20181207_9375_B10       1.00         57
  Jackson_Lab_Bubier_MURGIGV01_20181207_9255_D10       1.00         61
  Jackson_Lab_Bubier_MURGIGV01_20181207_9371_F10       1.00         62
  Jackson_Lab_Bubier_MURGIGV01_20181206_13885_A8       1.00         67
   Jackson_Lab_Bubier_MURGIGV01_20170904_9386_E9       1.00         79
  Jackson_Lab_Bubier_MURGIGV01_20181207_9254_C10       1.00         81
   Jackson_Lab_Bubier_MURGIGV01_20170904_8597_E5       1.00         85
   Jackson_Lab_Bubier_MURGIGV01_20170904_8614_A5       1.00         86
   Jackson_Lab_Bubier_MURGIGV01_20170904_8613_C5       1.00         94
   Jackson_Lab_Bubier_MURGIGV01_20170904_8664_C1       1.00        111
  Jackson_Lab_Bubier_MURGIGV01_20181207_9378_E10       1.00        168
   Jackson_Lab_Bubier_MURGIGV01_20170904_8585_B1       1.00        172
   Jackson_Lab_Bubier_MURGIGV01_20170904_8670_B5       1.00        246
   Jackson_Lab_Bubier_MURGIGV01_20161227_8184_F3       1.00        288
   Jackson_Lab_Bubier_MURGIGV01_20161227_8166_C1       1.00        294
   Jackson_Lab_Bubier_MURGIGV01_20161227_8167_F4       1.00        383
   Jackson_Lab_Bubier_MURGIGV01_20161227_8168_E3       0.99        666
   Jackson_Lab_Bubier_MURGIGV01_20161227_8183_G3       0.99        677
   Jackson_Lab_Bubier_MURGIGV01_20161227_8185_B4       0.99       1253
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.98       1999
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.98       2029
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4       0.98       2090
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.98       2136
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.98       2161
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.98       2172
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.98       2231
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.98       2239
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.98       2257
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.98       2256
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.98       2274
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.98       2261
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.98       2272
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2279
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2280
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4       0.97       2286
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3       0.97       2300
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.97       2341
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4       0.97       2355
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2358
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2       0.97       2360
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2405
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2402
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2384
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2406
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2444
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2462
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2439
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2460
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2502
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.97       2498
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2528
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3       0.97       2510
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2528
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2543
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2551
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.97       2560
   Jackson_Lab_Bubier_MURGIGV01_20161227_7996_B1       0.97       2959
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2575
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2552
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2556
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.97       2592
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2602
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2620
  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5       0.97       2609
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4       0.97       2624
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2612
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2637
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2655
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2       0.97       2670
  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4       0.97       2711
  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2       0.97       2707
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2767
  Jackson_Lab_Bubier_MURGIGV01_20190425_18231_H7       0.97       3197
  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9       0.97       2827
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.97       2872
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2872
  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8       0.97       2855
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3       0.97       2867
  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3       0.97       2861
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1       0.97       2890
  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1       0.97       2940
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2935
 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11       0.97       2960
  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6       0.97       2972
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2981
  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7       0.97       2987
 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11       0.96       3283
   Jackson_Lab_Bubier_MURGIGV01_20161227_8166_C1       0.95       5134
   Jackson_Lab_Bubier_MURGIGV01_20161227_8166_H3       0.95       5480
 n_typed n_match index1 index2
  112143  112113   1560   1630
  112027  111992   2874   2875
  112012  111972   2118   2120
  111435  111389    509    517
  112133  112081    485   1514
  111952  111895    482   1509
  112077  112016    481   1511
  112153  112091    488   1513
  112005  111938    711   1206
  111431  111352    487    542
  111927  111846    479   1510
  110987  110902    510    518
  110927  110841    506    514
  110546  110452    508    516
  111653  111542    459    492
  111837  111669    534   1512
  111428  111256    440    491
  109186  108940    507    515
  110967  110679    268    308
  109711  109417    310    385
  110540  110157    269    316
  109746  109080    267    307
  109152  108475    270    309
  108741  107488    266    312
   91336   89337   1460   1468
   90922   88893   1444   1468
   90690   88600   1444   1460
   91521   89385   1468   1500
   91299   89138   1468   1492
   90443   88271   1452   1468
   91320   89089   1460   1500
   91033   88794   1460   1492
   91459   89202   1468   1476
   90796   88540   1444   1500
   91243   88969   1460   1476
   90653   88392   1468   1516
   91068   88796   1436   1468
   90846   88567   1468   1484
   90537   88257   1444   1492
   90241   87955   1452   1460
   89711   87411   1444   1452
   90689   88348   1444   1476
   90856   88501   1436   1460
   90478   88120   1460   1516
   90309   87949   1436   1444
   91116   88711   1492   1500
   90592   88190   1460   1484
   89846   87462   1444   1516
   90057   87651   1452   1492
   91035   88591   1476   1492
   91329   88867   1476   1500
   90122   87683   1444   1484
   90276   87816   1452   1500
   91009   88507   1436   1500
   90197   87699   1452   1476
   90564   88036   1500   1516
   89898   87388   1436   1452
   90274   87746   1492   1516
   90723   88180   1436   1492
   90781   88230   1484   1500
   90882   88322   1436   1476
  104715  101756    203    384
   90506   87931   1484   1492
   89470   86918   1452   1516
   89608   87052   1452   1484
   90854   88262   1137   1468
   90487   87885   1476   1516
   90661   88041   1476   1484
   90220   87611   1097   1468
   90599   87975   1137   1460
   90095   87483   1436   1516
   90322   87685   1436   1484
   89799   87144   1484   1516
   90056   87386   1137   1444
   90055   87344   1097   1460
   89568   86861   1097   1444
   90715   87948   1137   1500
  104191  100994   2185   2187
   90169   87342   1097   1500
   90585   87713   1137   1476
   90454   87582   1137   1492
   89841   86986   1097   1492
   89627   86760   1137   1452
   89078   86217   1097   1452
   89756   86866   1097   1436
   90250   87310   1137   1436
   89297   86362   1097   1516
   89885   86925   1137   1516
   90108   87136   1097   1476
   89937   86956   1137   1484
   89421   86434   1097   1484
   89510   86227   1097   1137
  101152   96018    271    385
  101684   96204    271    310

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] "Jackson_Lab_Bubier_MURGIGV01_20190108_16305_E1" 
 [2] "Jackson_Lab_Gagnon_MURGIGV01_20191011_21412_A10"
 [3] "Jackson_Lab_Bubier_MURGIGV01_20190425_18162_E11"
 [4] "Jackson_Lab_Bubier_MURGIGV01_20170904_8669_D5"  
 [5] "Jackson_Lab_Bubier_MURGIGIV01_20171001_9373_H7" 
 [6] "Jackson_Lab_Bubier_MURGIGIV01_20171001_9375_E7" 
 [7] "Jackson_Lab_Bubier_MURGIGV01_20181207_9255_D10" 
 [8] "Jackson_Lab_Bubier_MURGIGIV01_20171001_9371_G8" 
 [9] "Jackson_Lab_Bubier_MURGIGV01_20181206_13885_A8" 
[10] "Jackson_Lab_Bubier_MURGIGV01_20170904_9386_E9"  
[11] "Jackson_Lab_Bubier_MURGIGV01_20181207_9254_C10" 
[12] "Jackson_Lab_Bubier_MURGIGV01_20170904_8597_E5"  
[13] "Jackson_Lab_Bubier_MURGIGV01_20170904_8618_E3"  
[14] "Jackson_Lab_Bubier_MURGIGV01_20170904_8613_C5"  
[15] "Jackson_Lab_Bubier_MURGIGV01_20170904_8664_C1"  
[16] "Jackson_Lab_Bubier_MURGIGV01_20170904_9378_C8"  
[17] "Jackson_Lab_Bubier_MURGIGV01_20161227_8585_B8"  
[18] "Jackson_Lab_Bubier_MURGIGV01_20170904_8670_B5"  
[19] "Jackson_Lab_Bubier_MURGIGV01_20160908_8184_E10" 
[20] "Jackson_Lab_Bubier_MURGIGV01_20161227_8166_C1"  
[21] "Jackson_Lab_Bubier_MURGIGV01_20160908_8167_F10" 
[22] "Jackson_Lab_Bubier_MURGIGV01_20160908_8168_D10" 
[23] "Jackson_Lab_Bubier_MURGIGV01_20160908_8183_G10" 
[24] "Jackson_Lab_Bubier_MURGIGV01_20160908_8185_C10" 
[25] "Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4" 
[26] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[27] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[28] "Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5" 
[29] "Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8" 
[30] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[31] "Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4" 
[32] "Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8" 
[33] "Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6" 
[34] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[35] "Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4" 
[36] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[37] "Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1" 
[38] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[39] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[40] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[41] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[42] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[43] "Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1" 
[44] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[45] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[46] "Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8" 
[47] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[48] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[49] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[50] "Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8" 
[51] "Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6" 
[52] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[53] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[54] "Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1" 
[55] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[56] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[57] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[58] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[59] "Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1" 
[60] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[61] "Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1" 
[62] "Jackson_Lab_Bubier_MURGIGV01_20160908_7996_D2"  
[63] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[64] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[65] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[66] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[67] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[68] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[69] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[70] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[71] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[72] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[73] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[74] "Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2" 
[75] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[76] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[77] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[78] "Jackson_Lab_Bubier_MURGIGV01_20190425_18231_H7" 
[79] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[80] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[81] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[82] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[83] "Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3" 
[84] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[85] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[86] "Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11"
[87] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[88] "Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11"
[89] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[90] "Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7" 
[91] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[92] "Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6" 
[93] "Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10" 
[94] "Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10" 

save(summary.cg,
     file = "data/Jackson_Lab_11_batches/summary.cg.RData")

pdf(file = "output/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)])

Past versions of “Sample duplicates-1.png”

pdf(file = "output/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)])

Past versions of “Sample duplicates-2.png”

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

Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11 
                                           68.1 
 Jackson_Lab_Bubier_MURGIGV01_20181207_14973_A3 
                                           39.7 
 Jackson_Lab_Bubier_MURGIGV01_20190425_19037_H6 
                                           36.8 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18790_A1 
                                           21.3 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18839_B7 
                                           19.2 
 Jackson_Lab_Bubier_MURGIGV01_20181207_9243_A10 
                                           19.0 
  Jackson_Lab_Bubier_MURGIGV01_20160908_7649_H5 
                                           18.9 
 Jackson_Lab_Bubier_MURGIGV01_20160908_7747_F12 
                                           18.8 
 Jackson_Lab_Bubier_MURGIGV01_20181206_15992_D5 
                                           18.6 
Jackson_Lab_Bubier_MURGIGV01_20181207_14788_A12 
                                           18.0 
Jackson_Lab_Bubier_MURGIGV01_20190108_17008_F11 
                                           17.9 
 Jackson_Lab_Bubier_MURGIGV01_20190108_16307_G1 
                                           17.4 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18933_G6 
                                           17.3 
Jackson_Lab_Bubier_MURGIGV01_20190108_17150_H12 
                                           17.0 
 Jackson_Lab_Bubier_MURGIGV01_20181207_14997_E4 
                                           16.3 
Jackson_Lab_Bubier_MURGIGV01_20190108_14718_G12 
                                           15.6 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18850_E8 
                                           15.4 
 Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6 
                                           14.4 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18890_D1 
                                           13.1 
 Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7 
                                           12.9 

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))
    }
  }
}

Past versions of “Array intensities and Genotype frequencies-2.png”

Crossover counts and Genotyping error LOD scores

#load pre-caluated results
load("data/Jackson_Lab_11_batches/pr.RData")
load("data/Jackson_Lab_11_batches/m.RData")
load("data/Jackson_Lab_11_batches/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 = "output/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

Past versions of “Crossover counts and Genotyping error LOD scores-2.png”

#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 total_xo
Jackson_Lab_Bubier_MURGIGV01_20181206_16218_G7             5.21      574
Jackson_Lab_Bubier_MURGIGV01_20181206_16219_H7             5.23      590
Jackson_Lab_Bubier_MURGIGV01_20181207_14695_F7             5.33      558
Jackson_Lab_Bubier_MURGIGV01_20190108_17018_A12            5.44      587
Jackson_Lab_Gagnon_MURGIGV01_20191011_20658_B1             5.51      618
Jackson_Lab_Bubier_MURGIGV01_20190108_17143_F1             5.53      582
Jackson_Lab_Bubier_MURGIGV01_20190425_18256_A11            5.66      564
Jackson_Lab_Bubier_MURGIGV01_20181207_15071_B12            5.67      599
Jackson_Lab_Bubier_MURGIGV01_20190108_17142_E1             5.71      592
Jackson_Lab_Bubier_MURGIGV01_20190108_16866_G12            6.06      617
Jackson_Lab_Gagnon_MURGIGV01_20191011_20649_A1             6.20      604
Jackson_Lab_Bubier_MURGIGV01_20181207_16256_G1             6.22      558
Jackson_Lab_Bubier_MURGIGV01_20190108_16961_G6             6.54      605
Jackson_Lab_Bubier_MURGIGV01_20160908_7996_D2              6.60      474
Jackson_Lab_Bubier_MURGIGV01_20181206_15949_H1             6.78      602
Jackson_Lab_Bubier_MURGIGV01_20160908_7689_A12             6.88      439
Jackson_Lab_Bubier_MURGIGV01_20181206_15318_G1             6.94      585
Jackson_Lab_Bubier_MURGIGV01_20190425_18799_B2             7.23      694
Jackson_Lab_Bubier_MURGIGV01_20190425_18231_H7             7.32      620
Jackson_Lab_Bubier_MURGIGV01_20190108_16485_A6             7.39      599
Jackson_Lab_Bubier_MURGIGV01_20190425_18842_E7             7.60      649
Jackson_Lab_Bubier_MURGIGV01_20181207_13744_F1             7.85      551
Jackson_Lab_Bubier_MURGIGV01_20190425_18798_A2             8.08      633
Jackson_Lab_Bubier_MURGIGV01_20190425_18927_A6             8.47      580
Jackson_Lab_Bubier_MURGIGV01_20190425_18903_A3             8.78      614
Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10             8.94      448
Jackson_Lab_Bubier_MURGIGV01_20181207_13702_E1             9.12      556
Jackson_Lab_Bubier_MURGIGV01_20190425_17013_H1             9.39      601
Jackson_Lab_Bubier_MURGIGV01_20190425_18196_E3             9.61      607
Jackson_Lab_Bubier_MURGIGV01_20190425_18169_D12            9.70      592
Jackson_Lab_Bubier_MURGIGV01_20190425_18791_B1            10.10     3245
Jackson_Lab_Bubier_MURGIGV01_20190425_18940_E7            10.42     2528
Jackson_Lab_Bubier_MURGIGV01_20190425_18938_D7            10.43      600
Jackson_Lab_Bubier_MURGIGV01_20190425_18132_G7            10.63      648
Jackson_Lab_Bubier_MURGIGV01_20190425_18133_H7            10.88      881
Jackson_Lab_Bubier_MURGIGV01_20190425_18929_C6            11.41      607
Jackson_Lab_Bubier_MURGIGV01_20190425_18124_G6            11.61      650
Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9            11.68      538
Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5            11.69      562
Jackson_Lab_Bubier_MURGIGV01_20190425_18838_A7            11.72      648
Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4            11.85      578
Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6            11.85      566
Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8            12.00      584
Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1            12.07      549
Jackson_Lab_Bubier_MURGIGV01_20190425_18896_B2            12.15      622
Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11           12.28      565
Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7            12.34      567
Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2            12.40      560
Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11           12.44      593
Jackson_Lab_Gagnon_MURGIGV01_20191011_21534_A11           12.61      624
Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3            12.85      620
Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6            12.89      601
Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7            12.94      584
Jackson_Lab_Bubier_MURGIGV01_20190425_18890_D1            13.08      654
Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6            14.37      610
Jackson_Lab_Bubier_MURGIGV01_20190425_18850_E8            15.40      638
Jackson_Lab_Bubier_MURGIGV01_20190108_14718_G12           15.63      593
Jackson_Lab_Bubier_MURGIGV01_20181207_14997_E4            16.29      596
Jackson_Lab_Bubier_MURGIGV01_20190108_17150_H12           16.96      621
Jackson_Lab_Bubier_MURGIGV01_20190425_18933_G6            17.28      607
Jackson_Lab_Bubier_MURGIGV01_20190108_16307_G1            17.36      570
Jackson_Lab_Bubier_MURGIGV01_20190108_17008_F11           17.91      623
Jackson_Lab_Bubier_MURGIGV01_20181207_14788_A12           18.04      591
Jackson_Lab_Bubier_MURGIGV01_20181206_15992_D5            18.56      567
Jackson_Lab_Bubier_MURGIGV01_20160908_7747_F12            18.85      447
Jackson_Lab_Bubier_MURGIGV01_20160908_7649_H5             18.88      412
Jackson_Lab_Bubier_MURGIGV01_20181207_9243_A10            19.02      611
Jackson_Lab_Bubier_MURGIGV01_20190425_18839_B7            19.16      907
Jackson_Lab_Bubier_MURGIGV01_20190425_18790_A1            21.29      602
Jackson_Lab_Bubier_MURGIGV01_20190425_19037_H6            36.75      634
Jackson_Lab_Bubier_MURGIGV01_20181207_14973_A3            39.72      619
Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11           68.07      639

# Genotyping error LOD scores
load("data/Jackson_Lab_11_batches/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 = "data/Jackson_Lab_11_batches/errors_ind.RData")

# Apparent genotyping errors
load("data/Jackson_Lab_11_batches/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")

Past versions of “Crossover counts and Genotyping error LOD scores-4.png”

pdf(file = "output/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)

Past versions of “Missing data in Markers and Genotype frequencies Markers-1.png”

pdf(file = "output/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 = "output/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")
}

Past versions of “Missing data in Markers and Genotype frequencies Markers-2.png”

# 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 = "output/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$sex.match <- ifelse(qc_info$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,]
bad.sample

                                                  id predict.sex sex ngen
1     Jackson_Lab_Bubier_MURGIGIV01_20171001_9228_E8           M   M   25
2     Jackson_Lab_Bubier_MURGIGIV01_20171001_9243_F7           F   F   25
9     Jackson_Lab_Bubier_MURGIGIV01_20171001_9371_G8           F   F   25
10    Jackson_Lab_Bubier_MURGIGIV01_20171001_9373_H7           F   F   25
11    Jackson_Lab_Bubier_MURGIGIV01_20171001_9375_E7           F   F   25
59     Jackson_Lab_Bubier_MURGIGV01_20160908_7629_F8           M   M   21
79     Jackson_Lab_Bubier_MURGIGV01_20160908_7649_H5           F   F    1
142   Jackson_Lab_Bubier_MURGIGV01_20160908_7747_F12           M   F    1
192    Jackson_Lab_Bubier_MURGIGV01_20160908_7996_D2           M   F    1
270   Jackson_Lab_Bubier_MURGIGV01_20160908_8166_H10           M   F    1
271   Jackson_Lab_Bubier_MURGIGV01_20160908_8167_F10           M   M   22
272   Jackson_Lab_Bubier_MURGIGV01_20160908_8168_D10           M   M   22
284   Jackson_Lab_Bubier_MURGIGV01_20160908_8183_G10           M   F   22
285   Jackson_Lab_Bubier_MURGIGV01_20160908_8184_E10           F   F   22
286   Jackson_Lab_Bubier_MURGIGV01_20160908_8185_C10           M   F   22
299   Jackson_Lab_Bubier_MURGIGV01_20161227_7649_H12           M   F    1
300    Jackson_Lab_Bubier_MURGIGV01_20161227_7747_A1           F   F    1
301    Jackson_Lab_Bubier_MURGIGV01_20161227_7996_B1           M   F    1
324    Jackson_Lab_Bubier_MURGIGV01_20161227_8166_C1           M   F    1
325    Jackson_Lab_Bubier_MURGIGV01_20161227_8166_H3           M   F    1
435    Jackson_Lab_Bubier_MURGIGV01_20161227_8585_B8           M   M   23
491    Jackson_Lab_Bubier_MURGIGV01_20170904_8597_E5           M   M   23
497    Jackson_Lab_Bubier_MURGIGV01_20170904_8613_C5           M   M   23
502    Jackson_Lab_Bubier_MURGIGV01_20170904_8618_E3           M   M   23
513    Jackson_Lab_Bubier_MURGIGV01_20170904_8664_C1           F   F   23
518    Jackson_Lab_Bubier_MURGIGV01_20170904_8669_D5           F   F   23
519    Jackson_Lab_Bubier_MURGIGV01_20170904_8670_B5           F   F   23
541    Jackson_Lab_Bubier_MURGIGV01_20170904_9378_C8           F   F   25
549    Jackson_Lab_Bubier_MURGIGV01_20170904_9386_E9           M   M   25
870   Jackson_Lab_Bubier_MURGIGV01_20181206_13885_A8           M   M   29
943   Jackson_Lab_Bubier_MURGIGV01_20181206_15318_G1           F   F    1
1044  Jackson_Lab_Bubier_MURGIGV01_20181206_15949_H1           F   F    1
1086  Jackson_Lab_Bubier_MURGIGV01_20181206_15992_D5           M   F    1
1094  Jackson_Lab_Bubier_MURGIGV01_20181206_16000_D6           M   F    1
1134 Jackson_Lab_Bubier_MURGIGV01_20181206_16041_D11           M   F    1
1249  Jackson_Lab_Bubier_MURGIGV01_20181207_13702_E1           F   F    1
1253  Jackson_Lab_Bubier_MURGIGV01_20181207_13744_F1           F   F    1
1310 Jackson_Lab_Bubier_MURGIGV01_20181207_14788_A12           M   F    1
1322  Jackson_Lab_Bubier_MURGIGV01_20181207_14973_A3           M   F    1
1331  Jackson_Lab_Bubier_MURGIGV01_20181207_14982_A3           M   F    1
1346  Jackson_Lab_Bubier_MURGIGV01_20181207_14997_E4           M   F    1
1347  Jackson_Lab_Bubier_MURGIGV01_20181207_14998_A4           M   F    1
1363  Jackson_Lab_Bubier_MURGIGV01_20181207_15014_A5           M   F    1
1389  Jackson_Lab_Bubier_MURGIGV01_20181207_15040_A1           M   F    1
1403  Jackson_Lab_Bubier_MURGIGV01_20181207_15054_A2           M   F    1
1465  Jackson_Lab_Bubier_MURGIGV01_20181207_15117_A6           M   F    1
1473  Jackson_Lab_Bubier_MURGIGV01_20181207_15125_A7           M   F    1
1481  Jackson_Lab_Bubier_MURGIGV01_20181207_15133_A8           M   F    1
1489  Jackson_Lab_Bubier_MURGIGV01_20181207_15141_A9           M   F    1
1540 Jackson_Lab_Bubier_MURGIGV01_20181207_16291_A11           M   F    1
1549  Jackson_Lab_Bubier_MURGIGV01_20181207_9243_A10           M   F   25
1550  Jackson_Lab_Bubier_MURGIGV01_20181207_9254_C10           M   M   25
1551  Jackson_Lab_Bubier_MURGIGV01_20181207_9255_D10           M   M   25
1556 Jackson_Lab_Bubier_MURGIGV01_20190108_14718_G12           M   F   30
1562  Jackson_Lab_Bubier_MURGIGV01_20190108_16305_E1           M   M   31
1564  Jackson_Lab_Bubier_MURGIGV01_20190108_16307_G1           M   F   31
1630  Jackson_Lab_Bubier_MURGIGV01_20190108_16485_A6           M   F   31
1889 Jackson_Lab_Bubier_MURGIGV01_20190108_17008_F11           M   F   32
1976 Jackson_Lab_Bubier_MURGIGV01_20190108_17150_H12           M   F   32
2040  Jackson_Lab_Bubier_MURGIGV01_20190425_17013_H1           F   M   32
2083  Jackson_Lab_Bubier_MURGIGV01_20190425_18124_G6           M   M   33
2091  Jackson_Lab_Bubier_MURGIGV01_20190425_18132_G7           M   M   33
2092  Jackson_Lab_Bubier_MURGIGV01_20190425_18133_H7           M   M   33
2121 Jackson_Lab_Bubier_MURGIGV01_20190425_18162_E11           M   M   33
2188  Jackson_Lab_Bubier_MURGIGV01_20190425_18231_H7           F   F   33
2229  Jackson_Lab_Bubier_MURGIGV01_20190425_18790_A1           F   F   33
2230  Jackson_Lab_Bubier_MURGIGV01_20190425_18791_B1           F   F   33
2277  Jackson_Lab_Bubier_MURGIGV01_20190425_18838_A7           M   F   33
2278  Jackson_Lab_Bubier_MURGIGV01_20190425_18839_B7           M   F   33
2289  Jackson_Lab_Bubier_MURGIGV01_20190425_18850_E8           F   F   33
2328  Jackson_Lab_Bubier_MURGIGV01_20190425_18890_D1           F   F   33
2334  Jackson_Lab_Bubier_MURGIGV01_20190425_18896_B2           F   F   33
2367  Jackson_Lab_Bubier_MURGIGV01_20190425_18929_C6           F   F   33
2370  Jackson_Lab_Bubier_MURGIGV01_20190425_18933_G6           F   F   33
2375  Jackson_Lab_Bubier_MURGIGV01_20190425_18938_D7           M   M   33
2376  Jackson_Lab_Bubier_MURGIGV01_20190425_18940_E7           M   M   33
2468  Jackson_Lab_Bubier_MURGIGV01_20190425_19037_H6           M   M   33
2526  Jackson_Lab_Gagnon_MURGIGV01_20191010_20564_A6           F   F   34
2536  Jackson_Lab_Gagnon_MURGIGV01_20191010_20577_C7           F   F   34
2738 Jackson_Lab_Gagnon_MURGIGV01_20191011_20641_D11           M   F   34
2792 Jackson_Lab_Gagnon_MURGIGV01_20191011_21412_A10           F   F   34
2878 Jackson_Lab_Gagnon_MURGIGV01_20191011_21534_A11           M   M   34
     percent_missing Number_crossovers sex.match genotype_erros
1         0.46039617               183      TRUE    0.024062027
2         0.36459119               147      TRUE    0.038284157
9         0.39563910               518      TRUE    0.058780047
10        0.43112243               510      TRUE    0.041873435
11        0.48168617               504      TRUE    0.059722247
59        0.08161165                 4      TRUE    0.005326846
79       18.88333969               931      TRUE    0.844251001
142      18.84785636              2826     FALSE    3.981111038
192       6.59546346               554     FALSE    1.683856630
270       8.94179847               656     FALSE    2.734561467
271       1.66771638               448      TRUE    0.120885168
272       2.29044878               447      TRUE    0.194285818
284       2.55391248               453     FALSE    0.195721438
285       1.26231937               483      TRUE    0.082655023
286       3.28930444               512     FALSE    0.351308464
299       1.30135103               486     FALSE    0.193237583
300       0.72829529               463      TRUE    0.130464310
301       0.82410028               503     FALSE    0.131484794
324       1.95779258               474     FALSE    0.276867954
325       1.25965812               473     FALSE    0.164407191
435       0.76643987               464      TRUE    0.043802798
491       1.14699855               483      TRUE    0.070892710
497       1.59852389               476      TRUE    0.099164315
502       1.17449813               487      TRUE    0.077195817
513       0.65378030               496      TRUE    0.058039860
518       0.94474359               459      TRUE    0.064479152
519       2.34189960               464      TRUE    0.159870650
541       0.63692572               488      TRUE    0.078563712
549       0.99885566               523      TRUE    0.061826295
870       0.47458950               565      TRUE    0.035652530
943       6.93876465               624      TRUE    1.698647373
1044      6.78263801               612      TRUE    1.637753014
1086     18.56398975              2856     FALSE    2.703644801
1094     12.88931863              3275     FALSE    2.088615974
1134     12.27723124              3261     FALSE    2.097301014
1249      9.11655386               673      TRUE    2.251786202
1253      7.85246033               581      TRUE    1.920540639
1310     18.04238483              2761     FALSE    4.540534690
1322     39.72181071              3043     FALSE    6.715132963
1331     12.84762572              3331     FALSE    1.967510128
1346     16.28862138              5203     FALSE   17.391672937
1347     11.84788298              3326     FALSE    1.936139595
1363     11.68732092              3298     FALSE    1.962753882
1389     12.07320210              3355     FALSE    1.950181095
1403     12.40319705              3272     FALSE    1.965629336
1465     11.84522173              3290     FALSE    1.967275801
1473     12.33755289              3339     FALSE    2.014753949
1481     11.99957420              3276     FALSE    1.988871192
1489     11.68111134              3333     FALSE    1.961611474
1540     12.44134162              3262     FALSE    1.995866429
1549     19.02349883              3471     FALSE    1.634459489
1550      0.51007283               482      TRUE    0.045473189
1551      0.39386493               481      TRUE    0.040076591
1556     15.63217983              2874     FALSE    1.783254650
1562      0.38854243               570      TRUE    0.034731189
1564     17.35578245              3011     FALSE    1.948177408
1630      7.39117707              1041     FALSE    0.223186490
1889     17.90666111              1033     FALSE    0.176134338
1976     16.96014335              1108     FALSE    0.216857173
2040      9.38888840              2993     FALSE    1.421508640
2083     11.60925760              1581      TRUE    1.067822806
2091     10.62814360               881      TRUE    0.854607536
2092     10.88184939               855      TRUE    0.757500348
2121      0.49499242               594      TRUE    0.049032281
2188      7.31577500               689      TRUE    0.558947953
2229     21.28644803              3245      TRUE    3.015788940
2230     10.09500661               883      TRUE    0.841646193
2277     11.71570758               907     FALSE    0.867144953
2278     19.15744839              1771     FALSE    1.419902779
2289     15.39887695              1348      TRUE    0.213903743
2328     13.07560610              2817      TRUE    1.649164702
2334     12.15037834              2212      TRUE    1.010784393
2367     11.41498638               826      TRUE    0.747038383
2370     17.27860621               985      TRUE    0.145842940
2375     10.42943697              2528      TRUE    1.193400151
2376     10.41967905               754      TRUE    0.712001030
2468     36.75363039              2874      TRUE    2.106680505
2526     14.36631213              1468      TRUE    0.247581163
2536     12.94165654              1499      TRUE    0.204809456
2738     68.07032795              1692     FALSE    2.039228760
2792      0.48523450               592      TRUE    0.040113387
2878     12.60988743              1437      TRUE    0.240574944
     remove.id.duplicated
1                   FALSE
2                   FALSE
9                    TRUE
10                   TRUE
11                   TRUE
59                  FALSE
79                  FALSE
142                 FALSE
192                  TRUE
270                  TRUE
271                  TRUE
272                  TRUE
284                  TRUE
285                  TRUE
286                  TRUE
299                 FALSE
300                 FALSE
301                 FALSE
324                  TRUE
325                 FALSE
435                  TRUE
491                  TRUE
497                  TRUE
502                  TRUE
513                  TRUE
518                  TRUE
519                  TRUE
541                  TRUE
549                  TRUE
870                  TRUE
943                 FALSE
1044                FALSE
1086                FALSE
1094                 TRUE
1134                 TRUE
1249                FALSE
1253                FALSE
1310                FALSE
1322                FALSE
1331                 TRUE
1346                FALSE
1347                 TRUE
1363                 TRUE
1389                 TRUE
1403                 TRUE
1465                 TRUE
1473                 TRUE
1481                 TRUE
1489                FALSE
1540                 TRUE
1549                FALSE
1550                 TRUE
1551                 TRUE
1556                FALSE
1562                 TRUE
1564                FALSE
1630                FALSE
1889                FALSE
1976                FALSE
2040                FALSE
2083                FALSE
2091                FALSE
2092                FALSE
2121                 TRUE
2188                 TRUE
2229                FALSE
2230                FALSE
2277                FALSE
2278                FALSE
2289                FALSE
2328                FALSE
2334                FALSE
2367                FALSE
2370                FALSE
2375                FALSE
2376                FALSE
2468                FALSE
2526                FALSE
2536                FALSE
2738                FALSE
2792                 TRUE
2878                FALSE

save(qc_info, bad.sample, file = "data/Jackson_Lab_11_batches/qc_info_bad_sample.RData")

#remove 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

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                112729

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
8555 8666 6420 6615 6571 6444 6294 5677 5870 5447 6352 5167 5274 5039 4555 
  16   17   18   19    X 
4369 4330 4002 3108 3974 

# 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])

[1] 274

# omit the  markers with error rates >5%.
bad_markers <- find_markerpos(gm.no.bad, names(errors_mar[errors_mar > 5]))
save(bad_markers, file = "data/Jackson_Lab_11_batches/bad_markers.RData")
#drop bad markers
gm_after_qc <- drop_markers(gm.no.bad, names(errors_mar)[errors_mar > 5])

gm_after_qc

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 

gm_DO2816_qc <- gm_after_qc

save(gm_DO2816_qc, file = "data/Jackson_Lab_11_batches/gm_DO2816_qc.RData")
save(e,g,snpg, file = "data/Jackson_Lab_11_batches/e_g_snpg_qc.RData")

Session information

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