Last updated: 2021-01-03
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Knit directory: PredictOutbredCrossVar/
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Modified: workflowr_log.R
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| html | 22e6c87 | wolfemd | 2021-01-03 | Build site. |
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| Rmd | a1950f7 | wolfemd | 2020-07-29 | Directional dominance fully implemented, except in prediction of untested crosses. Presentation to NGC Leader’s call included. |
| Rmd | c0b292a | wolfemd | 2020-06-23 | Most analyses coded and complete. Moved all wrapper functions to code/*.R scripts. |
https://www.biorxiv.org/content/10.1101/794339v1.full
ftp://ftp.cassavabase.org/manuscripts/Chan_et_al_2019/
The directory has since been compressed for storage to jj332_cas/ac2278/
Eventually the data will be here:
ftp://ftp.cassavabase.org/manuscripts/Chan_et_al_2019/shapeit2duohmm_mlhaplotypes/
and also: ftp://ftp.cassavabase.org/manuscripts/Wolfe_et_al_2020
For now, they need files are at /jj332_cas/marnin/shapeit2duohmm_mlhaplotypes/
library(tidyverse); library(magrittr); library(furrr); library(data.table)
library(furrr); options(mc.cores=18); plan(multiprocess)
path2haps<-"/workdir/marnin/shapeit2duohmm_mlhaplotypes"
haps<-tibble(Chr=1:18) %>%
dplyr::mutate(Haps=future_map(Chr,~fread(paste0(path2haps,"/chr",
stringr::str_pad(.,width = 3,side = 'left',pad = 0),
"_0.30.haps"),
stringsAsFactors = F,header = F,sep = " ") %>%
as.data.frame))
sampleids<-tibble(Chr=1:18) %>%
dplyr::mutate(Samples=future_map(Chr,# read in SAMPLE file
~fread(paste0(path2haps,"/chr",
stringr::str_pad(.,width = 3,side = 'left',pad = 0),
"_0.30.sample"),
stringsAsFactors = F,header = T,sep = " ") %>%
slice(-1))) # ignore the first (non-header) row of the SAMPLE file.
SampleIDs<-sampleids$Samples[[1]]$ID_2 %>%
tibble(ID=.,SampleIndex=1:length(.), Haplo="_HapA") %>%
bind_rows(.,dplyr::mutate(.,Haplo="_HapB")) %>%
arrange(SampleIndex) %>%
dplyr::mutate(SampleID=paste0(ID,Haplo)) %$% SampleID
options(future.globals.maxSize= 15000*1024^2)
haps %<>%
dplyr::mutate(Haps=future_map(Haps,function(Haps){
#Haps<-haps$Haps[[1]]
colnames(Haps)<-c("Chr","HAP_ID","Pos","REF","ALT",SampleIDs)
Haps %<>%
dplyr::mutate(HAP_ID=paste0(HAP_ID,"_",REF,"_",ALT)) %>%
column_to_rownames(var = "HAP_ID") %>%
select(-Chr,-Pos,-REF,-ALT)
Haps %<>% t(.) %>% as.matrix(.)
return(Haps)}))
haps<-reduce(haps$Haps,cbind)
saveRDS(haps,file=here::here("data","haps_awc.rds"))
sampleids$Samples[[1]]$ID_2 %>%
grep("TMS13|TMS14|TMS15|2013_",.,value=T,invert=T) %>%
length # 168 parents from the GeneticGain / C0 population To ensure consistency in allele counting, create dosage from haps.
library(tidyverse); library(magrittr);
haps<-readRDS(file=here::here("data","haps_awc.rds"))
dosages<-haps %>%
as.data.frame(.) %>%
rownames_to_column(var = "GID") %>%
separate(GID,c("SampleID","Haplo"),"_Hap",remove = T) %>%
select(-Haplo) %>%
group_by(SampleID) %>%
summarise_all(~sum(.)) %>%
ungroup() %>%
column_to_rownames(var = "SampleID") %>%
as.matrix
saveRDS(dosages,file=here::here("data","dosages_awc.rds"))ftp://ftp.cassavabase.org/manuscripts/Chan_et_al_2019/alphapeel.vped
/PredictOutbredCrossVar/data/
library(tidyverse); library(magrittr);
awcped<-read.table(here::here("data","alphapeel.vped"),stringsAsFactors = F, header = F) %>%
rename(FullSampleName=V1,
sireID=V2,
damID=V3) %>%
# Remove families with one or both parents unknown
filter(sireID!=0,damID!=0)
awcped %>%
count(sireID,damID) %$% summary(n)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.000 2.000 4.000 6.924 10.000 72.000
awcped %>%
count(sireID,damID) %>% dim # 462 families
saveRDS(awcped,file=here::here("data","ped_awc.rds"))Select traits and data to be analyzed.
library(tidyverse); library(magrittr);
blups<-readRDS(url("https://raw.github.com/wolfemd/IITA_2019GS/master/data/iita_blupsForCrossVal_72619.rds"))
trials<-readRDS(url("https://raw.github.com/wolfemd/IITA_2019GS/master/data/IITA_ExptDesignsDetected_72619.rds"))
haps<-readRDS(here::here("data","haps_awc.rds"))
germnames<-trials %>%
unnest_legacy(TrialData) %>%
select(FullSampleName,germplasmName) %>%
distinct %>%
mutate(GID=ifelse(is.na(FullSampleName),germplasmName,FullSampleName))
#blups %>% select(Trait,Dataset,blups) %>% spread(Dataset,blups)
blups %<>%
filter(Dataset=="2013toPresent",
Trait %in% c("DM","logFYLD","MCMDS","TCHART"))
blups %<>%
select(Trait,blups,varcomp) %>%
mutate(blups=map(blups,~inner_join(.,germnames) %>%
filter(germplasmName %in% gsub("_HapA|_HapB","",rownames(haps)))))
saveRDS(blups,file=here::here("data","blups_forawcdata.rds"))library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>%
select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
unnest(blups) %>%
select(Trait,germplasmName,drgBLUP) %>%
spread(Trait,drgBLUP)
indices<-blups %>%
summarize_if(is.numeric,sd, na.rm=T) %>%
pivot_longer(cols = everything(), names_to = "Trait", values_to = "blupSD") %>%
left_join(tibble(Trait=c("DM","logFYLD","MCMDS","TCHART"),
stdSI_unscaled=c(5, 10, -10, -5),
biofortSI_unscaled=c(10, 5, -5,10))) %>%
mutate(stdSI=stdSI_unscaled/blupSD,
biofortSI=biofortSI_unscaled/blupSD)
indices %>% mutate_if(is.numeric,~round(.,2))
saveRDS(indices,file=here::here("data","selection_index_weights_4traits.rds"))https://www.biorxiv.org/content/10.1101/794339v1.full
ftp://ftp.cassavabase.org/manuscripts/Chan_et_al_2019/
# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56library(tidyverse); library(magrittr); library(predCrossVar)
genmap<-tibble(Chr=1:18) %>%
dplyr::mutate(geneticMap=map(Chr,~readRDS(paste0("/workdir/marnin/ac2278_shapeit2_maxna/chr",
stringr::str_pad(.,width = 3,side = 'left',pad = 0),
"_AWC.rds"))))
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.);
dim(snps)
genmap %<>%
dplyr::mutate(geneticMap=map(geneticMap,as.data.frame)) %>%
unnest(geneticMap) %>%
dplyr::mutate(SNP_ID=paste0(Chr,"_",pos)) %>%
rename(cM=V2) %>%
filter(SNP_ID %in% gsub("_C|_G|_T|_A","",colnames(snps))) %>%
left_join(tibble(SNP_ID=gsub("_C|_G|_T|_A","",colnames(snps)),
IDwithREF=colnames(snps)))
saveRDS(genmap,file=here::here("data","genmap_awc_May2020.rds"))Construct a matrix of recombination frequencies at loci for all study loci. Pre-compute 1-2c to save time predicting cross variance.
m<-genmap$cM;
names(m)<-genmap$IDwithREF
library(predCrossVar)
recombFreqMat<-1-(2*genmap2recombfreq(m,nChr = 18))
saveRDS(recombFreqMat,file=here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds")
sessionInfo()