Step 2: SNP QC [5 batches]
Belinda Cornes
2022-04-06
Last updated: 2022-04-06
Checks: 5 2
Knit directory: Serreze-T1D_Workflow/
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Unstaged changes:
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
Loading Project
load("data/e_snpg_samqc_5.batches.RData")
gm <- get(load("data/gm_samqc_5.batches.RData"))
gmObject of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 133716
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
10159 10172 7987 7736 7778 7911 7548 6561 6823 6472 7276 6226 6177
14 15 16 17 18 19 X
6082 5421 5075 5161 4682 3612 4857 It can also be useful to look at the proportion of missing genotypes by marker. Markers with a lot of missing data were likely difficult to call, and so the genotypes that were called may contain a lot of errors.
Marker Missing Data
pmis_mar <- n_missing(gm, "marker", "proportion")*100
save(pmis_mar, file = "data/percent_missing_marker_5.batches.RData")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
pdf(file = "output/Percent_missing_genotype_data_per_marker_5.batches.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
dev.off()quartz_off_screen
2 | count | |
|---|---|
| pmis_mar_5 | 3362 |
| pmis_mar_10 | 2360 |
| pmis_mar_15 | 1840 |
| pmis_mar_25 | 1208 |
| pmis_mar_50 | 334 |
| pmis_mar_75 | 65 |
| total_snps | 133716 |
Marker Genotype Frequencies
g <- do.call("cbind", gm$geno[1:19])
#fg <- do.call("cbind", gm$founder_geno[1:19])
#g <- g[,colSums(g)!=0]
#fg <- fg[,colSums(fg==0)==0]
#fgn <- colSums(g==2)
gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))))
gf_mar <- gf_mar[gf_mar[,2] != "NaN",]
MAF <- apply(gf_mar, 1, function(x) min(x))
MAF <- as.data.frame(MAF)
MAF$index <- 1:nrow(gf_mar)
gf_mar_maf <- merge(gf_mar,as.data.frame(MAF), by="row.names")
gf_mar_maf <- gf_mar_maf[order(gf_mar_maf$index),]
pdf(file = "output/genotype_frequency_marker_5.batches.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)
dev.off()quartz_off_screen
2 par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)
gfmar <- NULL
gfmar$gfmar_mar_0 <- sum(gf_mar_maf$MAF==0)
gfmar$gfmar_mar_1 <- sum(gf_mar_maf$MAF< 0.01)
gfmar$gfmar_mar_5 <- sum(gf_mar_maf$MAF< 0.05)
gfmar$gfmar_mar_10 <- sum(gf_mar_maf$MAF< 0.10)
gfmar$gfmar_mar_15 <- sum(gf_mar_maf$MAF< 0.15)
gfmar$gfmar_mar_25 <- sum(gf_mar_maf$MAF< 0.25)
gfmar$gfmar_mar_50 <- sum(gf_mar_maf$MAF<= 0.50)
gfmar$total_snps <- nrow(as.data.frame(gf_mar_maf))
gfmar <- t(as.data.frame(gfmar))
gfmar <- as.data.frame(gfmar)
gfmar$count <- gfmar$V1
gfmar[c(2)] %>%
kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
kable_styling(full_width = F) %>%
kable_styling("striped", full_width = F) %>%
row_spec(8 ,bold=T,color= "white",background = "black")| count | |
|---|---|
| gfmar_mar_0 | 84737 |
| gfmar_mar_1 | 91037 |
| gfmar_mar_5 | 95845 |
| gfmar_mar_10 | 96139 |
| gfmar_mar_15 | 96351 |
| gfmar_mar_25 | 97125 |
| gfmar_mar_50 | 128857 |
| total_snps | 128857 |
save(gf_mar, file = "data/genotype_freq_marker_5.batches.RData")Marker Genotype Errors
errors_mar <- colSums(e>2)/n_typed(gm, "marker")*100
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
pdf(file = "output/genotype_error_marker_5.batches.pdf")
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
dev.off()quartz_off_screen
2 save(errors_mar, file = "data/genotype_errors_marker_5.batches.RData")Markers with higher rates of missing genotypes tend to show higher errors rates.
Removing Markers
Missingness
#Missingness
length(pmis_mar[pmis_mar >= 10])[1] 2360high_miss <- find_markerpos(gm, names(pmis_mar[pmis_mar >= 10]))
high_miss$id <- rownames(high_miss)
high_miss_df <- as.data.frame(pmis_mar[pmis_mar >= 10])
high_miss_df$index = 1: nrow(high_miss_df)
high_miss_df$id <- rownames(high_miss_df)
high_miss_bad <- merge(high_miss,high_miss_df, by=c("id"),all=T)
names(high_miss_bad)[5] <- c("high_miss")
names(high_miss_bad)[1] <- c("marker")
high_miss_bad <- high_miss_bad[order(high_miss_bad$index),]Monomorphic/Low Frequency markers
#Monomorphic/Low Frequency markers
#low_freq_df <- as.data.frame(gf_mar)
count <- rowSums(gf_mar <= 0.01)
#count <- as.data.frame(count)
low_freq_df <- merge(as.data.frame(gf_mar),as.data.frame(count), by="row.names",all=T)
low_freq_df[is.na(low_freq_df)] <- ''
low_freq_df <- low_freq_df[low_freq_df$count == 1,]
rownames(low_freq_df) <- low_freq_df$Row.names
#low_freq_df$id <- rownames(low_freq_df)
#low_freq_df$index = 1: nrow(low_freq_df)
low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)
nrow(low_freq)[1] 91075low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
#names(low_freq_bad)[5] <- c("AA_freq")
#names(low_freq_bad)[6] <- c("AB_freq")
#names(low_freq_bad)[7] <- c("BB_freq")
names(low_freq_bad)[1] <- c("marker")
#low_freq_bad <- low_freq_bad[order(low_freq_bad$index),]Genotyping Error
##Genotyping Error
length(errors_mar[errors_mar > 5])[1] 10754error_markers_names <- names(errors_mar[errors_mar > 5])
error_markers_names <- error_markers_names[complete.cases(error_markers_names)]
error_markers <- find_markerpos(gm, error_markers_names)
error_markers$id <- rownames(error_markers)
#rne <- rownames(as.data.frame(errors_mar))
error_mars_df <- as.data.frame(errors_mar[errors_mar > 5])
error_mars_df <- error_mars_df[complete.cases(error_mars_df$"errors_mar[errors_mar > 5]"),]
error_mars_df <- as.data.frame(error_mars_df)
#error_mars_df$id = rownames(error_mars_df)
error_mars_df$index = 1: nrow(error_mars_df)
#error_markers_bad <- merge(error_markers,error_mars_df, by=c("id"),all=T)
error_markers_bad <- cbind(error_markers,error_mars_df)
names(error_markers_bad)[5] <- c("error_mars")
names(error_markers_bad)[4] <- c("marker")
error_markers_bad <- error_markers_bad[order(error_markers_bad$index),]Total
### merge all
bad_markers <- rbind(high_miss_bad[c("marker","chr","gmap","pmap")], low_freq_bad[c("marker","chr","gmap","pmap")], error_markers_bad[c("marker","chr","gmap","pmap")])
#nrow(bad_markers)
duplicate <- bad_markers[duplicated(bad_markers),]
bad_markers <- bad_markers[!duplicated(bad_markers),]
nrow(bad_markers)[1] 99179save(bad_markers, file = "data/bad_markers_all_5.batches.RData")Only removing markers that are missing in at least 10% of the samples as well those that come as genotyping errors and have a allele frequency of less than 1%
#missing in at least 10% of the samples
gm_allqc2 <- drop_markers(gm_samqc, bad_markers$marker)
gm_allqc <- drop_nullmarkers(gm_allqc2)
gm_allqcObject of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 34537
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2643 2629 1857 1890 1774 1941 1672 1627 1878 1176 1871 1300 1549 1578 1257 935
17 18 19 X
501 913 1014 4532 save(gm_allqc, file = "data/gm_allqc_5.batches.RData")
sessionInfo()R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] fst_0.9.2 knitr_1.33 kableExtra_1.1.0 mclust_5.4.6
[5] ggrepel_0.8.2 ggplot2_3.3.5 qtlcharts_0.11-6 qtl2_0.22
[9] broman_0.70-4 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.27
[5] utf8_1.2.1 R6_2.5.0 backports_1.2.1 RSQLite_2.2.7
[9] evaluate_0.14 httr_1.4.1 highr_0.9 pillar_1.6.1
[13] rlang_1.0.2 rstudioapi_0.13 data.table_1.14.0 blob_1.2.1
[17] rmarkdown_2.1 qtl_1.46-2 webshot_0.5.2 readr_1.3.1
[21] stringr_1.4.0 bit_4.0.4 munsell_0.5.0 compiler_3.6.2
[25] httpuv_1.5.2 xfun_0.24 pkgconfig_2.0.3 htmltools_0.5.1.1
[29] tidyselect_1.1.2 tibble_3.1.2 fansi_0.5.0 viridisLite_0.4.0
[33] crayon_1.4.1 dplyr_1.0.8 withr_2.4.2 later_1.0.0
[37] grid_3.6.2 gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.1
[41] git2r_0.26.1 magrittr_2.0.1 scales_1.1.1 cli_3.0.0
[45] stringi_1.7.2 cachem_1.0.5 fs_1.4.1 promises_1.1.0
[49] xml2_1.3.1 ellipsis_0.3.2 generics_0.0.2 vctrs_0.3.8
[53] tools_3.6.2 bit64_4.0.5 glue_1.4.2 purrr_0.3.4
[57] hms_0.5.3 parallel_3.6.2 fastmap_1.1.0 yaml_2.2.1
[61] colorspace_2.0-2 rvest_0.3.5 memoise_2.0.0