Last updated: 2020-12-04
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Knit directory: IITA_2020GS/
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| Rmd | 97778e7 | wolfemd | 2020-09-21 | Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs: |
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| Rmd | 7ea8b80 | wolfemd | 2020-09-17 | All steps including genomic predicting (excluding cross-validation), |
Current Step:
5-fold cross-validation. Replicate 5-times.
3 genomic models:
The data for the next step can be found on the cassavabase FTP server here.
Can be loaded directly to R from FTP.
NOTICE: You need enough RAM and a stable network connection. I do the next steps, including cross-validation on a server with plenty of RAM and a good, stable network connection, rather than on my personal computer (a laptop with 16 GB RAM).
The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
# activate multithread OpenBLAS for fast compute of SigmaM (genotypic var-covar matrix)
export OMP_NUM_THREADS=56library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
snps <- readRDS(file = url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
"DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds")))
blups <- readRDS(file = here::here("output", "iita_blupsForModelTraining.rds"))
blups %<>% select(Trait, modelOutput) %>% unnest(modelOutput) %>% select(Trait, BLUPs) %>%
unnest(BLUPs)
table(unique(blups$GID) %in% rownames(snps))
# FALSE TRUE 36837 7638
# keep only samples that are either geno+pheno or genotyped with TMS18 in the
# name.
iitaSamples2keep <- union(unique(blups$GID) %>% .[. %in% rownames(snps)], rownames(snps) %>%
grep("TMS18", ., value = T))
length(iitaSamples2keep) # [1] 9061
# subset BLUPs and snps
blups %<>% filter(GID %in% iitaSamples2keep) %>% nest(TrainingData = -Trait)
snps <- snps[iitaSamples2keep, ]
## MAF>1% filter
snps %<>% maf_filter(., 0.01)
dim(snps) # [1] 9061 68029Going to use my own kinship function.
Make the kinships.
Below e.g. A*A makes a matrix that approximates additive-by-additive epistasis relationships.
A <- kinship(snps, type = "add")
D <- kinship(snps, type = "dom")
AA <- A * A
AD <- A * D
DD <- D * D
saveRDS(snps, file = here::here("output", "DosageMatrix_IITA_2020Sep16.rds"))
saveRDS(A, file = here::here("output", "Kinship_A_IITA_2020Sep16.rds"))
saveRDS(D, file = here::here("output", "Kinship_D_IITA_2020Sep16.rds"))
saveRDS(AA, file = here::here("output", "Kinship_AA_IITA_2020Sep16.rds"))
saveRDS(AD, file = here::here("output", "Kinship_AD_IITA_2020Sep16.rds"))
saveRDS(DD, file = here::here("output", "Kinship_DD_IITA2020Sep16.rds"))
# rm(snps); gc()NOTICE: The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56library(tidyverse)
library(magrittr)
# BLUPs from the 3 stage procedure (representing stage 2 of 3) using the 2020
# standard procedure
blups_3stage_lmer <- readRDS(here::here("output", "iita_blupsForModelTraining.rds"))
# BLUPs from the 2 stage procedure (stage 1 of 2) using the 2019 procedure
blups_2stage_asreml <- readRDS(file = here::here("output", "iita_blupsForModelTraining_twostage_asreml.rds"))
# merge two sets of BLUPs
blups <- blups_2stage_asreml %>% dplyr::select(Trait, blups) %>% rename(blups2stage = blups) %>%
left_join(blups_3stage_lmer %>% dplyr::select(Trait, modelOutput) %>% unnest(modelOutput) %>%
dplyr::select(Trait, BLUPs) %>% rename(blups3stage = BLUPs))
rm(blups_2stage_asreml, blups_3stage_lmer)
# read additive kinship matrix
A <- readRDS(file = here::here("output", "Kinship_A_IITA_2020Sep16.rds"))Next, set-up a common set of train-test cross-validation folds to compare the two sets of BLUPs with.
require(sommer); require(rsample)
nrepeats<-5; nfolds<-5
blups %<>%
# operate on GID common to both sets of BLUPs
mutate(cvfolds=map2(blups2stage,blups3stage,
# GIDs in both sets of BLUPs
~tibble(GID=unique(intersect(.x$GID,.y$GID)) %>%
# and genotyped (in kinship)
.[. %in% rownames(A)])))
# for each trait, set-up nrepeats of nfold CVs
blups %<>%
mutate(cvfolds=map(cvfolds,~tibble(repeats=1:nrepeats,
splits=rerun(nrepeats,vfold_cv(., v = nfolds))) %>%
unnest(splits))) %>%
unnest(cvfolds)
blups %<>%
mutate(blups2stage=map(blups2stage,as.tibble)) %>%
pivot_longer(cols = c(blups2stage,blups3stage),
names_to = "VersionOfBLUPs",
values_to = "TrainTestData")
# decided to only do CV for 3 traits ("quick" test)
blups %<>%
filter(Trait %in% c("MCMDS","DM","logFYLD"))
gc()
blups %>% head# function to predict train and test sets, calc accuracy
fitModel <- possibly(function(splits, modelType, TrainTestData, grms, gid = "GID") {
# test arguments for function ---------------------- splits<-blups$splits[[1]]
# TrainTestData<-blups$TrainTestData[[1]] modelType<-'A' grms=list(A=A) gid='GID'
# ----------------------
starttime <- proc.time()[3]
# Set-up training set
trainingdata <- TrainTestData %>% filter(GID %in% training(splits)$GID)
# Subset kinship matrices
traintestgids <- union(trainingdata[[gid]], testing(splits)[[gid]])
A1 <- grms[["A"]][traintestgids, traintestgids]
trainingdata[[paste0(gid, "a")]] <- factor(trainingdata[[gid]], levels = rownames(A1))
if (modelType %in% c("AD", "ADE")) {
D1 <- grms[["D"]][traintestgids, traintestgids]
trainingdata[[paste0(gid, "d")]] <- factor(trainingdata[[gid]], levels = rownames(D1))
if (modelType == "ADE") {
AD1 <- grms[["AD"]][traintestgids, traintestgids]
diag(AD1) <- diag(AD1) + 1e-06
trainingdata[[paste0(gid, "ad")]] <- factor(trainingdata[[gid]], levels = rownames(AD1))
}
}
# Set-up random model statements
randFormula <- paste0("~vs(", gid, "a,Gu=A1)")
if (modelType %in% c("AD", "ADE")) {
randFormula <- paste0(randFormula, "+vs(", gid, "d,Gu=D1)")
if (modelType == "ADE") {
randFormula <- paste0(randFormula, "+vs(", gid, "ad,Gu=AD1)")
}
}
# Fit genomic prediction model
fit <- mmer(fixed = drgBLUP ~ 1, random = as.formula(randFormula), weights = WT,
data = trainingdata)
# Gather the BLUPs
gblups <- tibble(GID = as.character(names(fit$U[[paste0("u:", gid, "a")]]$drgBLUP)),
GEBV = as.numeric(fit$U[[paste0("u:", gid, "a")]]$drgBLUP))
if (modelType %in% c("AD", "ADE")) {
gblups %<>% mutate(GEDD = as.numeric(fit$U[[paste0("u:", gid, "d")]]$drgBLUP))
if (modelType == "ADE") {
gblups %<>% mutate(GEEDad = as.numeric(fit$U[[paste0("u:", gid, "ad")]]$drgBLUP))
}
}
# Calc GETGVs Note that for modelType=='A', GEBV==GETGV
gblups %<>% mutate(GETGV = rowSums(.[, grepl("GE", colnames(.))]))
# Test set validation data
validationData <- TrainTestData %>% dplyr::select(gid, BLUP) %>% filter(GID %in%
testing(splits)[[gid]])
# Measure accuracy in test set cor(GEBV,BLUP) cor(GETGV,BLUP)
accuracy <- gblups %>% mutate(GETGV = rowSums(.[, grepl("GE", colnames(.))])) %>%
filter(GID %in% testing(splits)[[gid]]) %>% left_join(validationData) %>%
summarize(accGEBV = cor(GEBV, BLUP, use = "complete.obs"), accGETGV = cor(GETGV,
BLUP, use = "complete.obs"))
computeTime <- proc.time()[3] - starttime
accuracy %<>% mutate(computeTime = computeTime)
return(accuracy)
}, otherwise = NA)## Run models across all train-test splits Parallelize
ncores <- 5
require(furrr)
plan(multiprocess)
options(mc.cores = ncores)
options(future.globals.maxSize = 1500 * 1024^2)
## three chunks, three servers for speed
### cbsulm15
cv_1 <- blups %>% slice(1:50) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "A", grms = list(A = A)),
.progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_1 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelA_chunk1.rds"))
### cbsulm16
cv_2 <- blups %>% slice(51:100) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "A", grms = list(A = A)),
.progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_2 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelA_chunk2.rds"))
### cbsulm17
cv_3 <- blups %>% slice(101:150) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "A", grms = list(A = A)),
.progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_3 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelA_chunk3.rds"))
# ~3 hrsSame R session as Model A… exact train-test folds not otherwise stored.
# read dominance and add-by-dom epistasis kinship matrices
D <- readRDS(file = here::here("output", "Kinship_D_IITA_2020Sep16.rds"))
AD <- readRDS(file = here::here("output", "Kinship_AD_IITA_2020Sep16.rds"))
## Run models across all train-test splits Parallelize
ncores <- 5
require(furrr)
plan(multiprocess)
options(mc.cores = ncores)
options(future.globals.maxSize = 3000 * 1024^2)
## three chunks, three servers for speed
### cbsulm15
cv_1 <- blups %>% slice(1:50) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "ADE", grms = list(A = A,
D = D, AD = AD)), .progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_1 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelADE_chunk1.rds"))
### cbsulm16
cv_2 <- blups %>% slice(51:100) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "ADE", grms = list(A = A,
D = D, AD = AD)), .progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_2 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelADE_chunk2.rds"))
### cbsulm17
cv_3 <- blups %>% slice(101:150) %>% mutate(accuracy = future_map2(splits, TrainTestData,
~fitModel(splits = .x, TrainTestData = .y, modelType = "ADE", grms = list(A = A,
D = D, AD = AD)), .progress = FALSE)) %>% unnest(accuracy)
saveRDS(cv_3 %>% dplyr::select(-TrainTestData), here::here("output", "cvresults_ModelADE_chunk3.rds"))
# 12:35pm
sessionInfo()