QTL Analysis - binary [PBS Myocarditis Yes vs PBS Myocarditis No]

Data Information
Loading Data
Genome-wide scan
LOD peaks
- Centimorgan (cM)
- Megabase (MB)
QTL effects
R/qtl
- scanone

Last updated: 2023-01-11

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031790_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC20622785_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS037125_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043775_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/percent_missing_id_4.batches_myo.RData
    Untracked:  data/percent_missing_marker_4.batches_myo.RData
    Untracked:  data/pheno_4.batches_myo.csv
    Untracked:  data/physical_map_4.batches_myo.csv
    Untracked:  data/physical_map_4.batches_myo_BC217.csv
    Untracked:  data/qc_info_bad_sample_4.batches_myo.RData
    Untracked:  data/sample_geno_AHB_4.batches_myo.csv
    Untracked:  data/sample_geno_AHB_4.batches_myo_BC217.csv
    Untracked:  data/sample_geno_bc_4.batches_myo.csv
    Untracked:  data/sample_geno_bc_4.batches_myo_BC217.csv
    Untracked:  data/sample_geno_raw_4.batches_myo_BC217.csv
    Untracked:  data/serreze_probs_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo_mis.rds
    Untracked:  data/summary.cg_4.batches_myo.RData

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    Modified:   analysis/_site.yml

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Data Information

Myocarditis Status: Yes, No
Murine MHC KO Status: Homo
Drug Treatment(s): PBS
Clinical Phenotype(s): Sick, EOI
Covariates (Myocarditis Score, sex)

Loading Data

We will load the data and subset indivials out that are in the groups of interest.

load("data/gm_allqc_4.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm

Object of class cross2 (crosstype "bc")

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

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

No. chromosomes                 20
Total markers                32610

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

#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")

##extracting animals with ici and pbs group status
#miceinfo <- covars[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
#table(miceinfo$group)
#mice.ids <- rownames(miceinfo)

#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)

#gm$covar$pbs.myo.yes_vs_pbs.myo.no <- ifelse(gm$covar$group == "PBS", 0, 1)
#gm.full <- gm

covars <- read_csv("data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.csv")
#removing any missing info
#covars <- subset(covars, covars$pbs.myo.yes_vs_pbs.myo.no!='')
nrow(covars)

[1] 58

table(covars$"Myocarditis Status")


 NO YES 
 13  45

table(covars$"Murine MHC KO Status")


HOM 
 58

table(covars$"Drug Treatment")


PBS 
 58

table(covars$"clinical pheno")


 EOI SICK 
  39   19

#keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm

Object of class cross2 (crosstype "bc")

Total individuals               58
No. genotyped individuals       58
No. phenotyped individuals      58
No. with both geno & pheno      58

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

No. chromosomes                 20
Total markers                32610

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

table(gm$covar$"Myocarditis Status")


 NO YES 
 13  45

table(gm$covar$"Murine MHC KO Status")


HOM 
 58

table(gm$covar$"Drug Treatment")


PBS 
 58

table(gm$covar$"clinical pheno")


 EOI SICK 
  39   19

#pr.qc.ids <- pr
#for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

##removing problmetic marker

#gm <- drop_markers(gm, "UNCHS013106")

##dropping monomorphic markers within the dataset

g <- do.call("cbind", gm$geno)

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",]

count <- rowSums(gf_mar <=0.05)
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 <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)

nrow(low_freq)

[1] 5857

low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
names(low_freq_bad)[1] <- c("marker")

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),]

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	4068
gfmar_mar_1	4068
gfmar_mar_5	5857
gfmar_mar_10	6750
gfmar_mar_15	6826
gfmar_mar_25	6854
gfmar_mar_50	31288
total_snps	32610

gm_qc <- drop_markers(gm, low_freq_bad$marker)
gm_qc <- drop_nullmarkers(gm_qc)

gm_qc

Object of class cross2 (crosstype "bc")

Total individuals               58
No. genotyped individuals       58
No. phenotyped individuals      58
No. with both geno & pheno      58

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

No. chromosomes                 20
Total markers                26753

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2349 2281 1627 1684 1541 1728 1461 1437 1666  994 1644 1127 1358 1417 1046  755 
  17   18   19    X 
 585  760  899  394

## dropping disproportionate markers
dismark <- read.csv("data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv")
nrow(dismark)

[1] 26753

names(dismark)[1] <- c("marker")
dismark <- dismark[!dismark$Include,]
nrow(dismark)

[1] 21621

gm_qc_dis <- drop_markers(gm_qc, dismark$marker)
gm_qc_dis <- drop_nullmarkers(gm_qc_dis)

gm = gm_qc_dis
gm

Object of class cross2 (crosstype "bc")

Total individuals              58
No. genotyped individuals      58
No. phenotyped individuals     58
No. with both geno & pheno     58

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

No. chromosomes                20
Total markers                5132

No. markers by chr:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19   X 
592 361 319 103 199 296 166 235 461 172 423 304 231  96 170 252   7 195 370 180

markers <- marker_names(gm)
gmapdf <- read.csv("data/genetic_map_4.batches_myo_BC217.csv")
pmapdf <- read.csv("data/physical_map_4.batches_myo_BC217.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]

pr.qc <- calc_genoprob(gm)

colnames(covars) <- gsub(" ", ".", colnames(covars))

Genome-wide scan

For each of the phenotype analyzed, permutations were used for each model to obtain genome-wide LOD significance threshold for p < 0.01, p < 0.05, p < 0.10, respectively, separately for X and automsomes (A).

The table shows the estimated significance thresholds from permutation test.

We also looked at the kinship to see how correlated each sample is. Kinship values between pairs of samples range between 0 (no relationship) and 1.0 (completely identical). The darker the colour the more indentical the pairs are.

#Xcovar <- get_x_covar(gm)
addcovar = model.matrix(~sex+Histology.Score, data = covars)[,-1]
covars$pbs.myo.yes_vs_pbs.myo.no= as.numeric(covars$pbs.myo.yes_vs_pbs.myo.no)

kinship <- calc_kinship(pr.qc)
heatmap(kinship)

operm <- scan1perm(pr.qc, covars["pbs.myo.yes_vs_pbs.myo.no"], model="binary", addcovar=addcovar, n_perm=1000, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)

rownames(summary_table) <- NULL

summary_table[c(3,1:2)] %>%
  kable(escape = F,align = c("ccc")) %>%
  kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)

significance.level	autosomes	X
0.01	0	0
0.05	0	0
0.1	0	0

The figures below show QTL maps for each phenotype

#out <- scan1(pr.qc, covars["pbs.myo.yes_vs_pbs.myo.no"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, covars["pbs.myo.yes_vs_pbs.myo.no"], model="binary",addcovar=addcovar)

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))


plot_lod<-function(out,map){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

    ##par(mar=c(5.1, 6.1, 1.1, 1.1))
    #ymx <- 11 # overall maximum LOD score
    #plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
    #add_threshold(map,  summary(operm, alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    ##for (j in 1: dim(summary_table)[1]){
    ##  abline(h=summary_table[j, i],col="red")
    ##  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    ##}
    ##dev.off()
  }
}

plot_lod(out,gm$gmap)

LOD peaks

The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.

Centimorgan (cM)

peaks <- find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]

rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

#plot only peak chromosomes

plot_lod_chr<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    
    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

  }
}


for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
  #plot_lod_chr(out,gm$gmap, peaks$chr[i])
  plot_lod_chr(out,gm$gmap, i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

Megabase (MB)

print("peaks in MB positions")

[1] “peaks in MB positions”

peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]


rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

plot_lod_chr_mb<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')


  }
}

for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
  #plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
  plot_lod_chr_mb(out,gm$pmap,i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("code/cc_variants.sqlite")
query_genes <- create_gene_query_func("code/mouse_genes_mgi.sqlite")

if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){


  g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
  #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
  #par(mar=c(4.1, 4.1, 1.5, 0.6))
  plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
  title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
  ##dev.off()

  chr = peaks$chr[i]

# Plot 2
  pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
  blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]],addcovar = addcovar)
  blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]], addcovar = addcovar)

  write.csv(as.data.frame(blup_sub), paste0("data/pbs-myo-yes.vs.pbs-myo-no_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv"), quote=F)

  plot_coef(blup, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
       )

  plot_coef(blup_sub, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
       )

  #Table 1
  chr = peaks_mba$chr[i]
  start=as.numeric(peaks_mba$ci_lo[i])
  end=as.numeric(peaks_mba$ci_hi[i])

  genesgss = query_genes(chr, start, end)

  write.csv(genesgss, file=paste0("data/pbs-myo-yes.vs.pbs-myo-no_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv"), quote=F)

  rownames(genesgss) <- NULL
  genesgss$strand_old = genesgss$strand
  genesgss$strand[genesgss$strand=="+"] <- "positive"
  genesgss$strand[genesgss$strand=="-"] <- "negative"

  print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))


}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

R/qtl

scanone

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] abind_1.4-5       qtl2_0.22         reshape2_1.4.4    ggplot2_3.4.0    
 [5] tibble_3.1.8      psych_2.2.9       readxl_1.4.1      cluster_2.1.4    
 [9] dplyr_1.0.10      optparse_1.7.3    rhdf5_2.34.0      mclust_6.0.0     
[13] tidyr_1.2.1       data.table_1.14.6 knitr_1.41        kableExtra_1.3.4 
[17] workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] httr_1.4.4         sass_0.4.4         bit64_4.0.5        jsonlite_1.7.2    
 [5] viridisLite_0.4.1  bslib_0.4.2        assertthat_0.2.1   getPass_0.2-2     
 [9] highr_0.8          blob_1.2.1         cellranger_1.1.0   yaml_2.2.1        
[13] pillar_1.8.1       RSQLite_2.2.3      lattice_0.20-41    glue_1.6.2        
[17] digest_0.6.31      promises_1.1.1     rvest_1.0.3        colorspace_2.0-0  
[21] htmltools_0.5.4    httpuv_1.5.5       plyr_1.8.8         pkgconfig_2.0.3   
[25] purrr_0.3.4        scales_1.2.1       webshot_0.5.4      processx_3.8.0    
[29] svglite_2.1.0      whisker_0.4        getopt_1.20.3      later_1.1.0.1     
[33] git2r_0.28.0       generics_0.1.3     cachem_1.0.3       withr_2.5.0       
[37] cli_3.6.0          mnormt_2.1.1       magrittr_2.0.1     memoise_2.0.1     
[41] evaluate_0.19      ps_1.5.0           fs_1.5.2           fansi_0.4.2       
[45] nlme_3.1-149       xml2_1.3.3         tools_4.0.3        lifecycle_1.0.3   
[49] stringr_1.4.0      Rhdf5lib_1.12.1    munsell_0.5.0      callr_3.7.3       
[53] compiler_4.0.3     jquerylib_0.1.3    systemfonts_1.0.1  rlang_1.0.6       
[57] grid_4.0.3         rhdf5filters_1.2.1 rstudioapi_0.13    rmarkdown_2.19    
[61] gtable_0.3.0       DBI_1.1.1          R6_2.5.0           bit_4.0.4         
[65] fastmap_1.1.0      utf8_1.1.4         rprojroot_2.0.2    stringi_1.5.3     
[69] parallel_4.0.3     Rcpp_1.0.6         vctrs_0.5.1        tidyselect_1.2.0  
[73] xfun_0.36

QTL Analysis - binary [PBS Myocarditis Yes vs PBS Myocarditis No]

Belinda Cornes

2023-01-11

Data Information

Loading Data

Genome-wide scan

LOD peaks

Centimorgan (cM)

Megabase (MB)

QTL effects

R/qtl

scanone