Last updated: 2020-09-17
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Knit directory: IITA_2020GS/
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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.