QTL Analysis - binary [ICI vs EOI]

workflowr

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• Checks
• Past versions

Last updated: 2022-04-09

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220210)

The command set.seed(20220210) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: absolute

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute	relative
/Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow	.

Repository version: 2d6f369

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 2d6f369. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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    Untracked:  data/summary.cg_4.batches_bc.RData
    Untracked:  data/summary.cg_5.batches.RData
    Untracked:  output/Percent_missing_genotype_data_5.batches.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_5.batches.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf
    Untracked:  output/genotype_error_marker_5.batches.pdf
    Untracked:  output/genotype_frequency_marker_5.batches.pdf

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.

---

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---

Loading Data

We will load the data and subset indivials out that are in the groups of interest. We will create a binary phenotype from this (EOI ==0, ICI == 1).

load("data/gm_allqc_5.batches_mis.RData")

#gm_allqc
gm=gm_allqc
gm

Object 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                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

#pr <- readRDS("data/serreze_probs_allqc_5.batches_mis.rds")
#pr <- readRDS("data/serreze_probs.rds")

##extracting animals with ici and eoi group status
miceinfo <- gm$covar[gm$covar$group == "EOI" | gm$covar$group == "ICI",]
table(miceinfo$group)

EOI ICI 
164 104 

mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm

Object of class cross2 (crosstype "bc")

Total individuals               268
No. genotyped individuals       268
No. phenotyped individuals      268
No. with both geno & pheno      268

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

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

#bin_pheno <- NULL
#bin_pheno$EOI <- ifelse(gm$covar$group == "EOI", 1, 0)
#bin_pheno$ICI <- ifelse(gm$covar$group == "ICI", 1, 0)
#bin_pheno <- as.data.frame(bin_pheno)
#rownames(bin_pheno) <- rownames(gm$covar)

#dim(pr.qc[[1]])

gm$covar$ICI.vs.EOI <- ifelse(gm$covar$group == "EOI", 0, 1)
gm.full <- gm

##removing problmetic marker

gm <- drop_markers(gm, "UNCHS013106")

markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.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)

gm

Object of class cross2 (crosstype "bc")

Total individuals               268
No. genotyped individuals       268
No. phenotyped individuals      268
No. with both geno & pheno      268

No. phenotypes                    1
No. covariates                    7
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131355

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

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, data = covars)[,-1]

#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)

#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI.vs.EOI"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$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	3.655744	3.725056
0.05	3.600513	3.562065
0.1	3.531302	3.348922

The figures below show QTL maps for each phenotype

out <- scan1(pr.qc, gm$covar["ICI.vs.EOI"], Xcovar=Xcovar, model="binary")

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 <- 14 # 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 pbs  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)$X, peakdrop=3, drop=1.5)
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]

#if(nrow(peaks) < 50){
rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.EOI	1	10.9350	3.952959	6.087	80.869	ICR010
ICI.vs.EOI	2	103.8650	9.991889	103.842	103.872	UNC4609660
ICI.vs.EOI	3	2.2110	3.934932	2.120	2.229	UNC4673471
ICI.vs.EOI	3	2.2980	4.670847	2.249	2.307	UNC4681411
ICI.vs.EOI	3	4.9960	6.214670	4.948	5.039	UNC4793677
ICI.vs.EOI	3	5.1490	4.085019	5.131	5.180	UNC4799475
ICI.vs.EOI	3	5.2690	4.852938	5.256	5.277	UNCHS008129
ICI.vs.EOI	3	5.4190	6.382669	5.278	5.480	UNC4809586
ICI.vs.EOI	3	5.5620	6.316256	5.541	5.576	UNC4813186
ICI.vs.EOI	3	5.8540	4.264341	5.822	5.896	JAX00104971
ICI.vs.EOI	3	6.0990	7.025615	6.039	6.109	UNC4820379
ICI.vs.EOI	3	6.7510	6.948542	6.569	7.168	UNCJPD001170
ICI.vs.EOI	3	7.5370	7.095025	7.517	7.547	B6_rs31740628
ICI.vs.EOI	3	7.5940	3.939544	7.592	7.595	UNCHS008173
ICI.vs.EOI	3	7.7150	6.776514	7.672	7.743	JAX00105203
ICI.vs.EOI	3	8.7050	7.116656	8.511	8.983	ICR1269
ICI.vs.EOI	3	9.2130	6.908519	9.086	9.418	UNC4875653
ICI.vs.EOI	3	9.6150	6.776497	9.490	9.799	UNCHS008193
ICI.vs.EOI	3	10.0020	7.005251	9.955	10.163	UNCHS008209
ICI.vs.EOI	3	10.3710	7.180532	10.309	10.436	UNCHS008215
ICI.vs.EOI	3	10.5080	7.187361	10.492	10.519	UNCHS008219
ICI.vs.EOI	3	10.5680	5.536605	10.541	10.573	JAX00517218
ICI.vs.EOI	3	10.7300	7.531659	10.606	10.745	UNCHS008231
ICI.vs.EOI	3	10.7550	7.118922	10.750	10.759	UNCHS008235
ICI.vs.EOI	3	10.7820	7.530266	10.761	10.783	UNC4909830
ICI.vs.EOI	3	10.8180	7.784012	10.812	10.829	UNC4912288
ICI.vs.EOI	3	10.8600	8.377882	10.842	10.872	UNC4914130
ICI.vs.EOI	3	10.9840	7.070732	10.982	10.988	UNCHS008253
ICI.vs.EOI	3	10.9890	9.102704	10.988	11.009	UNCHS008256
ICI.vs.EOI	3	11.0280	8.845301	11.019	11.059	UNCHS008259
ICI.vs.EOI	3	11.2560	8.295718	11.251	11.291	UNC4923398
ICI.vs.EOI	3	11.3890	8.024002	11.367	11.398	JAX00517674
ICI.vs.EOI	3	11.7700	8.641860	11.581	11.800	UNCHS008271
ICI.vs.EOI	3	12.2900	8.353227	12.256	12.337	UNCHS008286
ICI.vs.EOI	3	12.5310	9.294376	12.353	12.576	JAX00517906
ICI.vs.EOI	3	12.5820	9.319949	12.581	12.585	UNC4945186
ICI.vs.EOI	3	12.6000	8.641863	12.587	12.657	UNC4947492
ICI.vs.EOI	3	13.5040	8.574218	13.138	13.531	UNCJPD001198
ICI.vs.EOI	3	13.9100	8.642374	13.623	13.954	UNC4957859
ICI.vs.EOI	3	14.2710	8.377779	14.269	14.276	UNCHS008311
ICI.vs.EOI	3	14.3080	8.604534	14.288	14.311	JAX00518418
ICI.vs.EOI	3	14.3310	8.047391	14.330	14.370	UNCHS008317
ICI.vs.EOI	3	14.3820	8.377810	14.373	14.386	UNC4964062
ICI.vs.EOI	3	15.5320	8.641860	15.516	15.558	UNC4979914
ICI.vs.EOI	3	15.5680	8.424382	15.566	15.595	JAX00518684
ICI.vs.EOI	3	15.6740	8.398103	15.656	15.687	UNC4985064
ICI.vs.EOI	3	15.8330	9.096014	15.751	15.849	UNC4990111
ICI.vs.EOI	3	15.8770	9.110889	15.857	15.882	UNC4991565
ICI.vs.EOI	3	16.0130	8.428051	15.960	16.043	JAX00518911r
ICI.vs.EOI	3	16.1840	8.641860	16.090	16.201	UNC5001723
ICI.vs.EOI	3	16.3340	6.906936	16.296	16.340	UNCHS008372
ICI.vs.EOI	3	16.4140	11.782053	16.361	16.429	UNC5008656
ICI.vs.EOI	3	16.5810	8.449367	16.514	16.675	UNCHS008379
ICI.vs.EOI	3	17.3390	9.183915	17.338	17.393	UNCHS008410
ICI.vs.EOI	3	17.4570	9.666550	17.450	17.466	UNC5042361
ICI.vs.EOI	3	17.4830	10.525957	17.482	18.126	UNCHS008415
ICI.vs.EOI	3	18.2220	10.494279	18.210	18.293	UNCHS008431
ICI.vs.EOI	3	18.3440	9.951958	18.294	18.346	UNC5055259
ICI.vs.EOI	3	18.5320	11.132723	18.355	18.533	JAX00520122
ICI.vs.EOI	3	18.6530	10.242226	18.595	18.693	UNC5076935
ICI.vs.EOI	3	18.7350	10.537780	18.709	18.750	UNC5082757
ICI.vs.EOI	3	20.6440	8.607019	20.613	20.677	UNC5173293
ICI.vs.EOI	3	20.7180	3.691418	20.710	20.720	JAX00106957
ICI.vs.EOI	3	20.7750	8.668345	20.774	20.777	UNC5182782
ICI.vs.EOI	3	20.9190	9.650080	20.849	21.500	UNC5193970
ICI.vs.EOI	3	21.5790	6.491448	21.549	21.583	UNCHS008609
ICI.vs.EOI	3	21.7760	9.564407	21.682	22.131	UNC5240484
ICI.vs.EOI	3	22.7680	9.002360	22.751	22.891	UNC5256922
ICI.vs.EOI	3	23.1240	8.902703	23.056	23.163	UNCHS008649
ICI.vs.EOI	3	23.2830	9.002326	23.269	23.284	UNCHS008656
ICI.vs.EOI	3	23.4370	8.902703	23.393	23.458	UNC5266664
ICI.vs.EOI	3	23.5590	8.902703	23.494	23.666	UNC5267813
ICI.vs.EOI	3	24.4200	8.813526	23.771	24.421	UNC5274554
ICI.vs.EOI	3	24.7470	8.658099	24.620	24.827	UNC5277061
ICI.vs.EOI	3	25.0960	8.730579	24.828	25.112	UNC5282259
ICI.vs.EOI	3	25.3760	8.680426	25.347	25.399	UNC5287977
ICI.vs.EOI	3	25.7150	8.741048	25.502	26.583	UNC5301460
ICI.vs.EOI	3	26.6250	8.427738	26.594	26.732	UNCHS008723
ICI.vs.EOI	3	26.7500	8.134807	26.742	26.751	UNCJPD001276
ICI.vs.EOI	3	26.8140	8.134886	26.751	26.944	UNC5321559
ICI.vs.EOI	3	27.0130	8.565537	26.995	27.331	UNC5322455
ICI.vs.EOI	3	27.3960	8.290597	27.363	27.708	UNC5328596
ICI.vs.EOI	3	28.5610	8.134648	28.547	28.612	UNCHS008802
ICI.vs.EOI	3	28.6430	4.114904	28.629	28.649	UNC5350384
ICI.vs.EOI	3	28.7285	8.406835	28.667	28.784	UNCHS008803
ICI.vs.EOI	3	28.8880	6.419277	28.882	28.898	UNCHS008808
ICI.vs.EOI	3	28.9220	8.380769	28.911	28.944	UNC5354724
ICI.vs.EOI	3	29.0020	8.483933	28.984	29.020	UNCHS008815
ICI.vs.EOI	3	29.0380	8.592291	29.027	29.075	JAX00107930
ICI.vs.EOI	3	29.1270	7.717547	29.092	29.143	JAX00107980
ICI.vs.EOI	3	29.2380	7.717769	29.148	29.264	UNCHS008834
ICI.vs.EOI	3	29.3060	5.769562	29.304	29.322	UNCHS008838
ICI.vs.EOI	3	29.5270	8.110488	29.329	29.572	JAX00108068
ICI.vs.EOI	3	29.6190	6.378957	29.602	29.739	UNC5375208
ICI.vs.EOI	3	29.8590	7.370876	29.812	29.869	JAX00108114
ICI.vs.EOI	3	29.9130	4.202102	29.906	29.917	UNC5384072
ICI.vs.EOI	3	29.9360	8.008115	29.926	29.979	JAX00524717
ICI.vs.EOI	3	30.0130	6.176556	30.003	30.018	JAX00524828
ICI.vs.EOI	3	30.0650	6.924416	30.033	30.091	UNC5400314
ICI.vs.EOI	3	30.2120	7.313553	30.211	30.232	UNCHS008914
ICI.vs.EOI	3	30.2410	7.953266	30.236	30.345	JAX00525380
ICI.vs.EOI	3	30.5690	7.313610	30.464	30.740	UNCHS008928
ICI.vs.EOI	3	30.7620	6.857062	30.760	30.768	UNC5433945
ICI.vs.EOI	3	30.8140	7.248166	30.789	30.842	UNC5435874
ICI.vs.EOI	3	31.0600	7.313998	30.861	31.155	UNCHS008948
ICI.vs.EOI	3	31.2460	7.867810	31.216	31.305	JAX00525709
ICI.vs.EOI	3	31.4900	7.985129	31.475	31.749	UNCHS008959
ICI.vs.EOI	3	31.8050	7.983110	31.764	31.884	UNC5454839
ICI.vs.EOI	3	32.0190	7.059955	31.982	32.065	UNCHS008973
ICI.vs.EOI	3	32.2020	7.368413	32.199	32.206	UNCHS008987
ICI.vs.EOI	3	32.2210	7.018397	32.219	32.230	UNC5466669
ICI.vs.EOI	3	32.2360	7.030305	32.230	32.281	UNCJPD001315
ICI.vs.EOI	3	32.4170	6.971143	32.311	32.445	UNCHS009001
ICI.vs.EOI	3	32.5570	6.812330	32.554	32.558	ICR5325
ICI.vs.EOI	3	32.6780	5.727694	32.649	32.679	UNCHS009025
ICI.vs.EOI	3	32.7640	6.394648	32.734	32.830	UNC5517972
ICI.vs.EOI	3	32.9050	6.812372	32.842	32.981	UNCHS009034
ICI.vs.EOI	3	33.0970	7.112913	33.025	33.140	UNC5522257
ICI.vs.EOI	3	33.1950	7.607611	33.185	33.215	UNC5523265
ICI.vs.EOI	3	33.6310	7.150500	33.627	33.654	UNC5532424
ICI.vs.EOI	3	33.7230	6.954565	33.717	33.741	ICR5348
ICI.vs.EOI	3	33.7680	7.352037	33.750	33.792	UNC5553882
ICI.vs.EOI	3	34.3180	7.007820	34.307	34.319	UNCHS009087
ICI.vs.EOI	3	34.3500	3.959036	34.338	34.364	UNC5572051
ICI.vs.EOI	3	34.5870	6.954499	34.559	34.693	JAX00527291
ICI.vs.EOI	3	34.8310	6.721575	34.829	34.836	UNC5589260
ICI.vs.EOI	3	34.8360	6.045898	34.836	34.838	UNCHS009109
ICI.vs.EOI	3	34.8710	7.002081	34.865	34.927	UNC5595555
ICI.vs.EOI	3	34.9860	6.812372	34.934	35.012	JAX00527587
ICI.vs.EOI	3	35.1050	4.242291	35.103	35.114	UNCHS009142
ICI.vs.EOI	3	35.4300	6.777501	35.404	35.444	UNC5623239
ICI.vs.EOI	3	35.8910	6.197989	35.864	35.910	UNC5639830
ICI.vs.EOI	3	36.0680	6.551574	36.023	36.099	UNC5645844
ICI.vs.EOI	3	36.5320	5.626030	36.521	36.614	UNCJPD001355
ICI.vs.EOI	3	37.3720	5.714554	37.152	37.374	UNCHS009207
ICI.vs.EOI	3	37.3780	5.614589	37.374	37.404	JAX00109750
ICI.vs.EOI	3	37.4220	5.156350	37.413	37.459	UNCHS009216
ICI.vs.EOI	3	37.5540	4.964222	37.472	37.570	UNC5680234
ICI.vs.EOI	3	37.6100	4.872108	37.590	37.628	UNC5682314
ICI.vs.EOI	3	38.2330	5.111981	38.002	38.340	UNCHS009306
ICI.vs.EOI	3	38.3730	4.853965	38.361	38.387	UNCHS009316
ICI.vs.EOI	3	38.7840	5.297275	38.545	38.800	UNC5725356
ICI.vs.EOI	3	38.8160	4.683760	38.812	38.830	UNC5727351
ICI.vs.EOI	3	38.8310	5.172901	38.830	38.842	UNCJPD001373
ICI.vs.EOI	3	39.0420	4.993001	39.023	39.048	UNC5734420
ICI.vs.EOI	3	39.1720	4.739243	39.142	39.179	JAX00529707
ICI.vs.EOI	3	39.1820	5.188506	39.179	39.188	UNCHS009364
ICI.vs.EOI	3	39.1930	4.819732	39.191	39.195	UNC5743257
ICI.vs.EOI	3	39.2320	4.964300	39.218	39.259	UNCHS009377
ICI.vs.EOI	3	39.9410	5.042723	39.921	39.971	UNCHS009382
ICI.vs.EOI	3	40.0650	5.042701	40.048	40.073	UNC5751567
ICI.vs.EOI	3	40.0980	5.042701	40.096	40.101	UNC5752623
ICI.vs.EOI	3	40.1110	5.306063	40.103	40.118	UNCJPD001382
ICI.vs.EOI	3	40.1450	5.042701	40.133	40.154	UNCHS009387
ICI.vs.EOI	3	40.1590	5.046659	40.158	40.173	UNCHS009395
ICI.vs.EOI	3	40.1900	4.142900	40.184	40.192	UNCHS009413
ICI.vs.EOI	3	40.3200	4.597869	40.258	40.330	JAX00189245
ICI.vs.EOI	3	40.6750	4.273579	40.668	40.703	UNCHS009428
ICI.vs.EOI	3	40.7110	4.430406	40.706	40.728	UNC5776974
ICI.vs.EOI	3	40.7520	4.628122	40.737	41.276	UNC5778977
ICI.vs.EOI	3	41.3790	4.322683	41.328	41.381	UNCHS009463
ICI.vs.EOI	3	41.6610	4.301659	41.534	41.909	UNC5788507
ICI.vs.EOI	3	41.9370	4.129711	41.923	41.951	UNC5792468
ICI.vs.EOI	3	41.9790	3.954858	41.965	41.983	UNC5794598
ICI.vs.EOI	3	41.9940	4.129711	41.992	42.000	UNC5795561
ICI.vs.EOI	3	42.0200	4.322668	42.004	42.028	UNCHS009503
ICI.vs.EOI	3	42.1990	4.628429	42.070	42.212	UNC5802298
ICI.vs.EOI	3	42.3200	4.322642	42.240	42.337	UNC5806628
ICI.vs.EOI	3	42.6240	4.364393	42.620	42.835	UNCHS009532
ICI.vs.EOI	3	42.8400	4.234562	42.835	42.928	UNC5817478
ICI.vs.EOI	3	43.0440	4.414075	42.928	43.206	UNCHS009541
ICI.vs.EOI	3	43.2650	4.259340	43.214	43.535	JAX00531601
ICI.vs.EOI	3	43.5710	4.220240	43.536	43.703	UNC5836999
ICI.vs.EOI	3	43.7925	4.129711	43.749	43.816	UNC5841501
ICI.vs.EOI	3	43.8600	3.918333	43.841	43.932	UNCHS009560
ICI.vs.EOI	3	43.9770	4.612331	43.948	44.075	UNC5847927
ICI.vs.EOI	3	44.1100	3.758892	44.078	44.231	UNCHS009574
ICI.vs.EOI	3	44.4380	3.791272	44.266	48.538	JAX00532122
ICI.vs.EOI	3	48.8190	4.047880	48.563	60.740	UNC5966417
ICI.vs.EOI	4	7.9560	5.843992	7.931	7.998	UNC6851205
ICI.vs.EOI	4	68.0110	3.712733	49.776	68.040	UNCHS012976
ICI.vs.EOI	4	68.7810	3.622412	68.514	69.696	UNC8299912
ICI.vs.EOI	4	73.6280	3.935620	71.758	74.446	UNCHS013130
ICI.vs.EOI	4	74.7790	3.694491	74.464	74.996	UNCHS013139
ICI.vs.EOI	4	75.3340	4.198831	74.996	75.500	UNC8381981
ICI.vs.EOI	4	79.7090	3.628621	75.562	79.859	UNC8436434
ICI.vs.EOI	4	80.2850	3.749518	79.904	83.729	UNC8439633
ICI.vs.EOI	4	87.0020	11.622030	86.879	87.009	UNC8530763
ICI.vs.EOI	5	50.8140	12.883836	50.735	50.822	JAX00590770
ICI.vs.EOI	5	61.7970	13.674294	61.775	61.816	UNC10044126
ICI.vs.EOI	7	8.2260	4.491276	8.151	8.233	ICR1830
ICI.vs.EOI	7	34.1100	6.570169	34.109	34.111	cr31snv87
ICI.vs.EOI	7	43.9120	3.833067	43.907	43.916	UNC13170794
ICI.vs.EOI	8	24.4420	11.558219	24.422	24.443	JAX00667121
ICI.vs.EOI	8	53.0710	4.021354	53.066	53.072	UNC15430711
ICI.vs.EOI	8	59.4610	8.568802	59.450	59.475	UNC15524531
ICI.vs.EOI	9	1.1440	11.322257	1.132	1.145	UNCHS024593
ICI.vs.EOI	9	68.5130	10.832022	68.499	68.551	UNC17203329
ICI.vs.EOI	10	29.1720	6.602722	29.145	29.185	UNCJPD009532
ICI.vs.EOI	10	37.2790	3.771203	34.785	37.869	UNCJPD004316
ICI.vs.EOI	10	38.4480	3.828690	37.998	38.502	ICR4295
ICI.vs.EOI	10	68.8270	10.165790	68.825	68.830	UNC18856953
ICI.vs.EOI	11	8.8780	3.988671	8.869	8.899	UNC19155926
ICI.vs.EOI	11	46.8110	9.767372	46.810	46.812	UNC19970181
ICI.vs.EOI	11	67.4560	4.020110	67.442	67.468	UNCJPD004792
ICI.vs.EOI	12	62.5350	10.934540	62.518	62.589	ICR4497
ICI.vs.EOI	13	54.3290	7.321163	54.287	54.345	UNCHS036964
ICI.vs.EOI	14	30.1560	9.102806	30.149	30.161	UNC24056202
ICI.vs.EOI	17	18.4330	8.471812	18.432	18.433	UNCrs47191360
ICI.vs.EOI	17	19.1500	11.490916	19.149	19.153	UNCHS044241
ICI.vs.EOI	18	2.9370	9.715960	2.912	2.938	UNCHS045344
ICI.vs.EOI	19	8.8030	11.030926	8.785	8.832	UNCJPD007087
ICI.vs.EOI	X	3.7930	7.222338	3.779	3.805	UNC30627130
ICI.vs.EOI	X	8.9700	11.423860	8.930	9.020	UNCHS048313

#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 <- 14
    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 pbs  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')

  }
}

if(nrow(peaks) < 50){

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 too many peaks (",nrow(peaks)," 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 too many peaks (214 peaks) that have a LOD that reaches suggestive (p<0.05) level of 3.60051275492842 [autosomes]/3.5620647834095 [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)$X, peakdrop=3, drop=1.5)
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]

#if(nrow(peaks_mba) < 50){

#peaks_mbl <- list()
##corresponding info in Mb
#for(i in 1:nrow(peaks)){
#  #lodindex <- peaks$lodindex[i]
#  phenotype <- peaks$phenotype[i]
#  chr <- as.character(peaks$chr[i])
#  lod <- peaks$lod[i]
#  mark <- peaks$marker[i]
#  pos <- mapdf[mapdf$marker==mark,]$pmapdf
#  ci_lo <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_lo[i] & mapdfnd$chr == peaks$chr[i])]
#  ci_hi <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_hi[i] & mapdfnd$chr == peaks$chr[i])]
#  peaks_mb=as.data.frame(cbind(phenotype, chr, pos, lod, ci_lo, ci_hi, mark))
#  names(peaks_mb)[7] <- c("marker")
#  peaks_mbl[[i]] <- peaks_mb
#}
#peaks_mba2 <- do.call(rbind, peaks_mbl)
#peaks_mba2 <- as.data.frame(peaks_mba)
#peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")] <- sapply(peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")], as.numeric)

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)
  )

phenotype	chr	pos	lod	ci_lo	ci_hi	marker
ICI.vs.EOI	1	27.306300	3.952959	19.332368	174.037187	ICR010
ICI.vs.EOI	2	181.936880	9.991889	181.896228	181.949841	UNC4609660
ICI.vs.EOI	3	8.754874	3.934932	7.873811	8.914368	UNC4673471
ICI.vs.EOI	3	9.347212	4.670847	9.090464	9.390672	UNC4681411
ICI.vs.EOI	3	18.058158	6.214670	17.896125	18.192696	UNC4793677
ICI.vs.EOI	3	18.529609	4.085019	18.475212	18.600171	UNC4799475
ICI.vs.EOI	3	18.797105	4.852938	18.746841	18.823728	UNCHS008129
ICI.vs.EOI	3	19.239884	6.382669	18.829830	19.344397	UNC4809586
ICI.vs.EOI	3	19.486223	6.316256	19.450945	19.509623	UNC4813186
ICI.vs.EOI	3	19.746152	4.264341	19.729641	19.767484	JAX00104971
ICI.vs.EOI	3	19.960171	7.025615	19.860068	19.976141	UNC4820379
ICI.vs.EOI	3	20.880840	6.948542	20.534245	21.256005	UNCJPD001170
ICI.vs.EOI	3	21.938137	7.095025	21.882051	21.966653	B6_rs31740628
ICI.vs.EOI	3	22.128100	3.939544	22.116264	22.135790	UNCHS008173
ICI.vs.EOI	3	22.827171	6.776514	22.578694	22.989141	JAX00105203
ICI.vs.EOI	3	23.385771	7.116656	23.348076	23.442997	ICR1269
ICI.vs.EOI	3	23.795038	6.908519	23.601025	24.108394	UNC4875653
ICI.vs.EOI	3	24.409399	6.776497	24.217431	24.689830	UNCHS008193
ICI.vs.EOI	3	24.999463	7.005251	24.927357	25.245612	UNCHS008209
ICI.vs.EOI	3	25.563143	7.180532	25.468891	25.662030	UNCHS008215
ICI.vs.EOI	3	25.826157	7.187361	25.768904	25.863246	UNCHS008219
ICI.vs.EOI	3	26.036604	5.536605	25.942432	26.053831	JAX00517218
ICI.vs.EOI	3	26.472706	7.531659	26.169453	26.550415	UNCHS008231
ICI.vs.EOI	3	26.619798	7.118922	26.585735	26.646783	UNCHS008235
ICI.vs.EOI	3	26.806043	7.530266	26.660777	26.812074	UNC4909830
ICI.vs.EOI	3	27.057748	7.784012	27.013258	27.108249	UNC4912288
ICI.vs.EOI	3	27.232274	8.377882	27.160976	27.281670	UNC4914130
ICI.vs.EOI	3	27.588424	7.070732	27.583922	27.596356	UNCHS008253
ICI.vs.EOI	3	27.599368	9.102704	27.596356	27.679713	UNCHS008256
ICI.vs.EOI	3	27.760448	8.845301	27.724388	27.890550	UNCHS008259
ICI.vs.EOI	3	27.960656	8.295718	27.959521	27.968104	UNC4923398
ICI.vs.EOI	3	28.018212	8.024002	28.003389	28.024244	JAX00517674
ICI.vs.EOI	3	28.275874	8.641860	28.148245	28.296129	UNCHS008271
ICI.vs.EOI	3	28.700046	8.353227	28.650846	28.765253	UNCHS008286
ICI.vs.EOI	3	29.039964	9.294376	28.788297	29.324998	JAX00517906
ICI.vs.EOI	3	29.516906	9.319949	29.476492	29.554860	UNC4945186
ICI.vs.EOI	3	29.691281	8.641863	29.574105	29.921127	UNC4947492
ICI.vs.EOI	3	30.351115	8.574218	30.246463	30.358767	UNCJPD001198
ICI.vs.EOI	3	30.467266	8.642374	30.385270	30.479998	UNC4957859
ICI.vs.EOI	3	30.580466	8.377779	30.573412	30.594168	UNCHS008311
ICI.vs.EOI	3	30.695414	8.604534	30.633711	30.705175	JAX00518418
ICI.vs.EOI	3	30.770902	8.047391	30.767156	30.892420	UNC4961954
ICI.vs.EOI	3	30.929770	8.377810	30.901472	30.944242	UNC4964062
ICI.vs.EOI	3	32.027756	8.641860	31.991600	32.086010	UNC4979914
ICI.vs.EOI	3	32.107424	8.424382	32.103130	32.170175	JAX00518684
ICI.vs.EOI	3	32.348522	8.398103	32.307341	32.376408	UNC4985064
ICI.vs.EOI	3	32.706610	9.096014	32.521820	32.743868	UNC4990111
ICI.vs.EOI	3	32.807687	9.110889	32.760715	32.818589	UNC4991565
ICI.vs.EOI	3	33.115028	8.428051	32.994011	33.182663	JAX00518911r
ICI.vs.EOI	3	33.500000	8.641860	33.288147	33.538978	UNC5001723
ICI.vs.EOI	3	33.839869	6.906936	33.754355	33.853051	UNCHS008372
ICI.vs.EOI	3	34.019613	11.782053	33.900624	34.055268	UNC5008656
ICI.vs.EOI	3	34.111945	8.449367	34.090757	34.185553	UNCHS008379
ICI.vs.EOI	3	35.869195	9.183915	35.866646	35.997429	UNCHS008410
ICI.vs.EOI	3	36.345726	9.666550	36.300748	36.408616	UNC5042361
ICI.vs.EOI	3	36.532077	10.525957	36.524146	36.814914	UNCHS008416
ICI.vs.EOI	3	36.905615	10.494279	36.894315	36.972349	UNCHS008431
ICI.vs.EOI	3	37.218426	9.951958	36.973387	37.230431	UNC5055259
ICI.vs.EOI	3	38.171450	11.132723	37.285826	38.177563	JAX00520122
ICI.vs.EOI	3	38.633789	10.242226	38.413275	38.783305	UNC5076935
ICI.vs.EOI	3	38.923762	10.537780	38.844788	38.958074	UNC5082757
ICI.vs.EOI	3	46.151173	8.607019	46.093688	46.254422	UNC5173293
ICI.vs.EOI	3	46.399332	3.691418	46.373593	46.407515	JAX00106957
ICI.vs.EOI	3	46.809519	8.668345	46.773347	46.882040	UNC5182782
ICI.vs.EOI	3	47.600691	9.650080	47.456615	48.786379	UNC5193970
ICI.vs.EOI	3	48.948638	6.491448	48.887661	48.956447	UNCHS008609
ICI.vs.EOI	3	50.691453	9.564407	49.483815	51.204914	UNC5240484
ICI.vs.EOI	3	51.865416	9.002360	51.849038	51.985943	UNC5256922
ICI.vs.EOI	3	52.215405	8.902703	52.148481	52.253753	UNCHS008649
ICI.vs.EOI	3	52.371789	9.002326	52.357805	52.372695	UNCHS008656
ICI.vs.EOI	3	52.523213	8.902703	52.479513	52.543277	UNC5266664
ICI.vs.EOI	3	52.595514	8.902703	52.563536	52.648179	UNC5267813
ICI.vs.EOI	3	53.040565	8.813526	52.700497	53.041383	UNC5274554
ICI.vs.EOI	3	53.222116	8.658099	53.151880	53.262718	UNC5277061
ICI.vs.EOI	3	53.524514	8.730579	53.262896	53.545666	UNC5282259
ICI.vs.EOI	3	53.889338	8.680426	53.851633	53.919023	UNC5287977
ICI.vs.EOI	3	54.819603	8.741048	54.277979	55.417042	UNC5301460
ICI.vs.EOI	3	55.632929	8.427738	55.477054	56.208134	UNCHS008723
ICI.vs.EOI	3	56.313632	8.134807	56.265747	56.315733	UNCJPD001276
ICI.vs.EOI	3	56.386738	8.134886	56.315733	56.438556	UNC5321559
ICI.vs.EOI	3	56.466293	8.565537	56.459210	56.785971	UNC5322455
ICI.vs.EOI	3	56.971362	8.290597	56.890286	57.367840	UNC5328596
ICI.vs.EOI	3	58.491809	8.134648	58.470901	58.572449	UNCHS008802
ICI.vs.EOI	3	58.621399	4.114904	58.600147	58.630781	UNC5350384
ICI.vs.EOI	3	58.756052	8.406835	58.659755	58.843810	UNCHS008803
ICI.vs.EOI	3	59.007878	6.419277	58.997877	59.023406	UNCHS008808
ICI.vs.EOI	3	59.061417	8.380769	59.042696	59.095390	UNC5354724
ICI.vs.EOI	3	59.186472	8.483933	59.157742	59.275246	UNCHS008815
ICI.vs.EOI	3	59.429352	8.592291	59.337846	59.752495	JAX00107930
ICI.vs.EOI	3	60.135901	7.717547	59.874552	60.253406	JAX00524417
ICI.vs.EOI	3	60.706694	7.717769	60.289169	60.801872	UNCHS008834
ICI.vs.EOI	3	60.953219	5.769562	60.947585	61.010137	UNCHS008838
ICI.vs.EOI	3	61.292774	8.110488	61.035391	61.341755	JAX00108068
ICI.vs.EOI	3	61.393825	6.378957	61.375303	61.526126	UNC5375208
ICI.vs.EOI	3	61.927634	7.370876	61.607716	61.995796	JAX00108114
ICI.vs.EOI	3	62.298941	4.202102	62.255007	62.331050	UNC5384072
ICI.vs.EOI	3	62.461653	8.008115	62.391679	62.798722	JAX00524717
ICI.vs.EOI	3	63.163762	6.176556	63.058661	63.209659	JAX00524828
ICI.vs.EOI	3	63.712556	6.924416	63.378904	64.019276	UNCHS008880
ICI.vs.EOI	3	65.604405	7.313553	65.592606	65.881828	UNCHS008914
ICI.vs.EOI	3	66.014995	7.953266	65.938315	66.196891	UNC5422316
ICI.vs.EOI	3	66.477015	7.313610	66.346136	66.731642	UNCHS008928
ICI.vs.EOI	3	66.949250	6.857062	66.930995	67.011279	UNC5433945
ICI.vs.EOI	3	67.245596	7.248166	67.212787	67.281610	UNC5435874
ICI.vs.EOI	3	67.565981	7.313998	67.306307	67.690028	UNCHS008948
ICI.vs.EOI	3	67.807743	7.867810	67.769242	67.884984	JAX00525709
ICI.vs.EOI	3	68.125680	7.985129	68.106246	68.463689	UNCHS008959
ICI.vs.EOI	3	68.536022	7.983110	68.483153	68.638865	UNC5454839
ICI.vs.EOI	3	68.814147	7.059955	68.766920	68.877353	UNCHS008973
ICI.vs.EOI	3	69.250770	7.368413	69.221690	69.285828	UNCHS008987
ICI.vs.EOI	3	69.433592	7.018397	69.415768	69.522341	UNC5466669
ICI.vs.EOI	3	69.582498	7.030305	69.526423	69.790268	UNCJPD001315
ICI.vs.EOI	3	70.012693	6.971143	69.840253	70.162040	UNCHS009001
ICI.vs.EOI	3	71.043331	6.812330	70.945831	71.069488	ICR5325
ICI.vs.EOI	3	72.731724	5.727694	72.568110	72.734062	UNCHS009025
ICI.vs.EOI	3	73.205013	6.394648	73.039794	73.276198	UNC5517972
ICI.vs.EOI	3	73.338566	6.812372	73.285890	73.401554	UNCHS009034
ICI.vs.EOI	3	73.498429	7.112913	73.437910	73.533777	UNC5522257
ICI.vs.EOI	3	73.579124	7.607611	73.570827	73.596217	UNC5523265
ICI.vs.EOI	3	74.163450	7.150500	74.114817	74.447311	UNC5532424
ICI.vs.EOI	3	75.295904	6.954565	75.226821	75.515168	ICR5348
ICI.vs.EOI	3	75.711287	7.352037	75.629040	75.744643	UNC5553882
ICI.vs.EOI	3	76.725942	7.007820	76.605322	76.733916	UNC5569296
ICI.vs.EOI	3	76.935588	3.959036	76.878493	76.998775	UNC5572051
ICI.vs.EOI	3	77.447996	6.954499	77.416124	77.572701	JAX00527291
ICI.vs.EOI	3	78.149252	6.721575	78.121212	78.202303	UNC5589260
ICI.vs.EOI	3	78.208171	6.045898	78.202303	78.230342	UNCJPD001342
ICI.vs.EOI	3	78.587111	7.002081	78.523889	79.109418	UNCHS009115
ICI.vs.EOI	3	79.310408	6.812372	79.133656	79.401409	JAX00527587
ICI.vs.EOI	3	79.717859	4.242291	79.713539	79.750967	UNCHS009141
ICI.vs.EOI	3	80.438201	6.777501	80.387983	80.463849	UNC5623239
ICI.vs.EOI	3	81.503929	6.197989	81.415941	81.565523	UNC5639830
ICI.vs.EOI	3	81.949482	6.551574	81.864954	82.005274	UNC5645844
ICI.vs.EOI	3	82.571467	5.626030	82.558057	82.677871	UNCJPD001355
ICI.vs.EOI	3	83.784492	5.714554	83.322014	83.788610	UNCHS009207
ICI.vs.EOI	3	83.800764	5.614589	83.788745	83.879902	JAX00109750
ICI.vs.EOI	3	83.934519	5.156350	83.909638	84.060519	UNCHS009216
ICI.vs.EOI	3	84.354270	4.964222	84.110990	84.393182	UNC5680234
ICI.vs.EOI	3	84.498035	4.872108	84.446845	84.542761	UNC5682314
ICI.vs.EOI	3	87.642525	5.111981	87.014619	87.733650	UNCHS009306
ICI.vs.EOI	3	87.755930	4.853965	87.747923	87.765515	UNCHS009316
ICI.vs.EOI	3	88.072184	5.297275	87.873111	88.172489	UNC5725356
ICI.vs.EOI	3	88.276633	4.683760	88.253213	88.366401	UNC5727351
ICI.vs.EOI	3	88.374516	5.172901	88.369914	88.447358	UNCJPD001373
ICI.vs.EOI	3	89.138096	4.993001	89.078386	89.156228	UNC5734420
ICI.vs.EOI	3	89.537778	4.739243	89.443855	89.592112	JAX00529707r
ICI.vs.EOI	3	89.618265	5.188506	89.592112	89.762446	UNCHS009364
ICI.vs.EOI	3	89.973143	4.819732	89.900265	90.041182	UNC5743257
ICI.vs.EOI	3	90.470819	4.964300	90.413379	90.576094	UNCHS009377
ICI.vs.EOI	3	90.704923	5.042723	90.690473	90.726447	UNCHS009382
ICI.vs.EOI	3	91.270560	5.042701	91.088075	91.360328	UNC5751567
ICI.vs.EOI	3	91.634601	5.042701	91.622302	91.667540	UNC5752623
ICI.vs.EOI	3	91.786423	5.306063	91.696514	91.863260	UNCJPD001382
ICI.vs.EOI	3	92.173618	5.042701	92.023843	92.410437	UNCHS009388
ICI.vs.EOI	3	92.570609	5.046659	92.544528	92.948680	UNCHS009395
ICI.vs.EOI	3	93.301012	4.142900	93.237202	93.303950	UNCHS009413
ICI.vs.EOI	3	93.516258	4.597869	93.413869	93.532376	JAX00189245
ICI.vs.EOI	3	94.525893	4.273579	94.472816	94.767678	UNCHS009428
ICI.vs.EOI	3	94.835860	4.430406	94.794358	94.982310	UNC5776974
ICI.vs.EOI	3	95.096023	4.628122	95.060197	95.662518	UNC5778977
ICI.vs.EOI	3	95.773711	4.322683	95.718481	95.775955	UNCHS009463
ICI.vs.EOI	3	96.083280	4.301659	95.941154	96.560177	UNC5788507
ICI.vs.EOI	3	96.624186	4.129711	96.586347	96.700075	UNCHS009488
ICI.vs.EOI	3	96.852349	3.954858	96.775040	96.877582	UNC5794598
ICI.vs.EOI	3	96.933980	4.129711	96.923546	96.967419	UNCHS009498
ICI.vs.EOI	3	97.077430	4.322668	96.990370	97.119562	UNCHS009503
ICI.vs.EOI	3	97.507819	4.628429	97.238954	97.534421	UNCHS009510
ICI.vs.EOI	3	97.796724	4.322642	97.593360	97.852722	UNC5806628
ICI.vs.EOI	3	98.186704	4.364393	98.183047	98.648362	UNCHS009532
ICI.vs.EOI	3	98.668175	4.234562	98.649075	98.974489	UNC5817478
ICI.vs.EOI	3	99.202095	4.414075	98.974489	99.797959	UNCHS009541
ICI.vs.EOI	3	99.911624	4.259340	99.813474	100.306554	JAX00531601
ICI.vs.EOI	3	100.357402	4.220240	100.308520	100.543098	UNC5836999
ICI.vs.EOI	3	100.724518	4.129711	100.633923	100.774572	JAX00110988
ICI.vs.EOI	3	100.866491	3.918333	100.827163	101.018631	UNCHS009560
ICI.vs.EOI	3	101.113305	4.612331	101.051929	101.319147	UNC5847927
ICI.vs.EOI	3	101.391517	3.758892	101.326014	101.646318	UNCHS009574
ICI.vs.EOI	3	101.956099	3.791272	101.719129	110.390268	JAX00532122
ICI.vs.EOI	3	111.381092	4.047880	110.719290	131.031652	UNC5966417
ICI.vs.EOI	4	20.304945	5.843992	20.287314	20.333338	UNC6851205
ICI.vs.EOI	4	135.873974	3.712733	106.711322	135.911634	UNC8291641
ICI.vs.EOI	4	136.481519	3.622412	136.267978	137.188420	UNC8299912
ICI.vs.EOI	4	141.281820	3.935620	140.593371	141.551932	UNCHS013130
ICI.vs.EOI	4	141.834023	3.694491	141.557646	141.958231	UNCHS013139
ICI.vs.EOI	4	142.152049	4.198831	141.958231	142.279078	UNC8381981
ICI.vs.EOI	4	149.528958	3.628621	142.363202	149.589090	UNC8436434
ICI.vs.EOI	4	149.780071	3.749518	149.607314	153.509778	UNC8439633
ICI.vs.EOI	4	155.695464	11.622030	155.641372	155.698101	UNC8530763
ICI.vs.EOI	5	105.291035	12.883836	105.136311	105.304706	JAX00590770
ICI.vs.EOI	5	121.347301	13.674294	121.284594	121.404128	UNC10044126
ICI.vs.EOI	7	14.516642	4.491276	14.244336	14.540129	ICR1830
ICI.vs.EOI	7	60.379063	6.570169	60.368115	60.389171	cr31snv87
ICI.vs.EOI	7	76.716159	3.833067	76.652534	76.769366	UNC13170794
ICI.vs.EOI	8	43.610135	11.558219	43.510568	43.616326	JAX00667121
ICI.vs.EOI	8	105.142670	4.021354	105.075239	105.164531	UNC15430711
ICI.vs.EOI	8	113.430254	8.568802	113.418547	113.445482	UNC15524531
ICI.vs.EOI	9	3.406661	11.322257	3.369866	3.410051	UNCHS024593
ICI.vs.EOI	9	116.323653	10.832022	116.305490	116.373801	UNC17203329
ICI.vs.EOI	10	57.446352	6.602722	57.392222	57.471562	UNCJPD009532
ICI.vs.EOI	10	72.602635	3.771203	66.556498	73.683324	UNCJPD004316
ICI.vs.EOI	10	74.736404	3.828690	73.920536	75.001613	ICR4295
ICI.vs.EOI	10	120.916126	10.165790	120.914011	120.920534	UNC18856953
ICI.vs.EOI	11	16.184217	3.988671	16.150116	16.257227	UNC19155926
ICI.vs.EOI	11	79.371544	9.767372	79.358114	79.380107	UNC19970181
ICI.vs.EOI	11	103.706406	4.020110	103.677865	103.730073	UNCJPD004792
ICI.vs.EOI	12	115.519395	10.934540	115.406843	115.874633	ICR4497
ICI.vs.EOI	13	102.204967	7.321163	102.144333	102.227537	UNCHS036964
ICI.vs.EOI	14	57.450881	9.102806	57.434906	57.460058	UNC24056202
ICI.vs.EOI	17	34.732558	8.471812	34.731841	34.732603	UNCrs47191360
ICI.vs.EOI	17	37.770490	11.490916	37.755718	37.796386	UNCHS044241
ICI.vs.EOI	18	4.868078	9.715960	4.821668	4.868823	UNCHS045344
ICI.vs.EOI	19	13.466848	11.030926	13.389238	13.596563	UNCJPD007087
ICI.vs.EOI	X	8.325334	7.222338	8.295563	8.351654	UNC30627130
ICI.vs.EOI	X	14.016673	11.423860	13.980012	14.062892	UNCHS048313

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 <- 14
    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 pbs 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')


  }
}

if(nrow(peaks_mba) < 50){

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 too many peaks (",nrow(peaks_mba)," 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 too many peaks (214 peaks) that have a LOD that reaches suggestive (p<0.05) level of 3.60051275492842 [autosomes]/3.5620647834095 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")

if(nrow(peaks) < 50){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
  #Plot 1
  g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
  #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
  #par(mar=c(4.1, 4.1, 1.5, 0.6))
  plot_pxg(g, gm$covar[,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$ci_hi[i]))
  #coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
  #coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
  #coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
  #coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
  blup <- scan1blup(pr.qc[,chr], gm$covar[peaks$phenotype[i]])
  blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]])

  write.csv(as.data.frame(blup_sub), paste0("data/ici.vs.eoi_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)

  #plot_coef(coeff, 
  #     gm$gmap, columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM] )")
  #     )

  #plot_coef(coeff_sub, 
  #     gm$gmap, columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
  #     )


  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] )")
       )


 # Plot 3
  #c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
  #c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
  ##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
  ##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
  #plot(c2effB, gm$gmap[chr], columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
  #     )
  #plot(c2effBb, gm$gmap[chr], columns=1:2,
  #     bgcolor="gray95", legend="bottomleft", 
  #     main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
  #     )
  ##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
  ##for(t in seq(along=last_coef))
  ##  axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)


  #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/ici.vs.eoi_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)

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

  #genesgss <- 
  #table <- 
  #genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
  #kable(escape = F,align = c("ccccccccccc")) %>%
  #kable_styling("striped", full_width = T) #%>% 
  #cat #%>%
  #column_spec(1, bold=TRUE)
#
  #print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))

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

  #table
  

}
} else {
  print(paste0("There are too many peaks (",nrow(peaks)," 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 too many peaks (214 peaks) that have a LOD that reaches suggestive (p<0.05) level of 3.60051275492842 [autosomes]/3.5620647834095 [x-chromosome]”

R/qtl

scanone

gm

Object of class cross2 (crosstype "bc")

Total individuals               268
No. genotyped individuals       268
No. phenotyped individuals      268
No. with both geno & pheno      268

No. phenotypes                    1
No. covariates                    7
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131355

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

#detach("package:qtl2", unload=TRUE)
#library(qtl)

cross <- qtl::read.cross("csv", file = "data/ici.vs.eoi_gm_qtl_5.batches_mis.csv",alleles=c("A","B"))

 --Read the following data:
     268  individuals
     131355  markers
     3  phenotypes
 --Cross type: bc 

cross <- qtl::jittermap(cross)

summary(cross)

    Backcross

    No. individuals:    268 

    No. phenotypes:     3 
    Percent phenotyped: 100 100 100 

    No. chromosomes:    20 
        Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
        X chr:          X 

    Total markers:      131355 
    No. markers:        9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 
                        6110 6082 5966 5346 5015 5080 4605 3562 4770 
    Percent genotyped:  99.7 
    Genotypes (%):    
          Autosomes:    AA:87.4  AB:12.6 
       X chromosome:    AA:94.2  AB:5.8  

cross.probs <- qtl::calc.genoprob(cross)

print("method == hk")

[1] "method == hk"

scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.EOI" , model="binary", method="hk")
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI.vs.EOI", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
plot(operm.hk)

print(summary(operm.hk, alpha=c(0.01,  0.05, 0.1)))

Autosome LOD thresholds (10 permutations)
     lod
1%  3.28
5%  3.19
10% 3.07

X chromosome LOD thresholds (182 permutations)
     lod
1%  3.72
5%  3.58
10% 3.39

#plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")  
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

ymx <- maxlod(out) # overall maximum LOD score
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]"))  
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

ymx <- 14
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]\n(using same scale as pbs vs. ici for easier comparison)"))
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk,  perms= operm.hk, alpha=0.1, col = 'purple')

print(as.data.frame(summary(scanone.hk, perms=operm.hk, pvalues=TRUE, format="allpeaks")))

             chr        pos       lod      pval
ICR010         1  10.935913  3.952599 0.0000000
UNCHS008007    2 103.881977  9.991845 0.0000000
UNC5008656     3  16.415323 11.782051 0.0000000
UNC8530763     4  87.009507 11.622032 0.0000000
UNC10044126    5  61.802663 13.674291 0.0000000
UNCJPD002870   6  63.095311  2.525485 0.6196652
cr31snv87      7  34.112218  6.570335 0.0000000
JAX00667121    8  24.443849 11.558219 0.0000000
UNCHS024593    9   1.144010 11.322115 0.0000000
UNC18857016   10  68.835945 10.255441 0.0000000
JAX00029420   11  46.814322  9.767372 0.0000000
UNCJPD005186  12  62.551844 11.096887 0.0000000
UNCHS036964   13  54.334007  7.321163 0.0000000
UNC24056202   14  30.158518  9.102806 0.0000000
UNC26010016   15  39.933956  2.867144 0.4166281
UNCHS041992   16  12.313908  2.940638 0.3136026
UNC27844356   17  19.150849 12.173336 0.0000000
UNCHS045344   18   2.937078  9.715960 0.0000000
UNC29919321   19   8.785439 11.031035 0.0000000
JAX00177269r   X   8.930429 11.423860 0.0000000

print("all peaks with a p-value less or equal to 0.05 (suggestive)")

[1] "all peaks with a p-value less or equal to 0.05 (suggestive)"

print(as.data.frame(summary(scanone.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE, format="allpeaks")))

             chr        pos       lod pval
ICR010         1  10.935913  3.952599    0
UNCHS008007    2 103.881977  9.991845    0
UNC5008656     3  16.415323 11.782051    0
UNC8530763     4  87.009507 11.622032    0
UNC10044126    5  61.802663 13.674291    0
cr31snv87      7  34.112218  6.570335    0
JAX00667121    8  24.443849 11.558219    0
UNCHS024593    9   1.144010 11.322115    0
UNC18857016   10  68.835945 10.255441    0
JAX00029420   11  46.814322  9.767372    0
UNCJPD005186  12  62.551844 11.096887    0
UNCHS036964   13  54.334007  7.321163    0
UNC24056202   14  30.158518  9.102806    0
UNC27844356   17  19.150849 12.173336    0
UNCHS045344   18   2.937078  9.715960    0
UNC29919321   19   8.785439 11.031035    0
JAX00177269r   X   8.930429 11.423860    0

#print("method == ehk")

#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.EOI" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.vs.EOI", n.perm = 1000, perm.Xsp = TRUE, model="binary", verbose=FALSE)
#plot(operm.ehk)
#print(summary(operm.ehk, alpha=c(0.01,  0.05, 0.1)))

#plot(scanone.ehk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")  
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.ehk,  perms= operm.ehk, alpha=0.1, col = 'purple')

#print(as.data.frame(summary(scanone.ehk)))
#print(as.data.frame(summary(scanone.ehk, perms=operm.ehk, alpha=0.05, pvalues=TRUE, format="allpeaks")))

Session information

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] abind_1.4-5       qtl2_0.22         reshape2_1.4.4    ggplot2_3.3.5    
 [5] tibble_3.1.2      psych_2.0.7       readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.8       optparse_1.6.6    rhdf5_2.28.1      mclust_5.4.6     
[13] tidyr_1.0.2       data.table_1.14.0 knitr_1.33        kableExtra_1.1.0 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.1        bit64_4.0.5       viridisLite_0.4.0 assertthat_0.2.1 
 [5] highr_0.9         blob_1.2.1        cellranger_1.1.0  yaml_2.2.1       
 [9] pillar_1.6.1      RSQLite_2.2.7     backports_1.2.1   lattice_0.20-38  
[13] glue_1.4.2        digest_0.6.27     promises_1.1.0    rvest_0.3.5      
[17] colorspace_2.0-2  htmltools_0.5.1.1 httpuv_1.5.2      plyr_1.8.6       
[21] pkgconfig_2.0.3   purrr_0.3.4       scales_1.1.1      webshot_0.5.2    
[25] qtl_1.46-2        getopt_1.20.3     later_1.0.0       git2r_0.26.1     
[29] generics_0.0.2    ellipsis_0.3.2    cachem_1.0.5      withr_2.4.2      
[33] cli_3.0.0         mnormt_1.5-7      magrittr_2.0.1    crayon_1.4.1     
[37] memoise_2.0.0     evaluate_0.14     fs_1.4.1          fansi_0.5.0      
[41] nlme_3.1-142      xml2_1.3.1        tools_3.6.2       hms_0.5.3        
[45] lifecycle_1.0.1   stringr_1.4.0     Rhdf5lib_1.6.3    munsell_0.5.0    
[49] compiler_3.6.2    rlang_1.0.2       grid_3.6.2        rstudioapi_0.13  
[53] rmarkdown_2.1     gtable_0.3.0      DBI_1.1.1         R6_2.5.0         
[57] fastmap_1.1.0     bit_4.0.4         utf8_1.2.1        rprojroot_1.3-2  
[61] readr_1.3.1       stringi_1.7.2     parallel_3.6.2    Rcpp_1.0.7       
[65] vctrs_0.3.8       tidyselect_1.1.2  xfun_0.24