# code to create phenotypes.rda goes here
# These phenotypes were merged and then carefully checked for errors and
# outliers in the `pvdiv-phenotypes` git repository.
# Thereafter, specific phenotypes were published as part of the switchgrass
# genome paper - seven environmental phenotypes, overwinter survival at three
# northern sites, and biomass in 2019 at the three core common gardens.
# Additionally, we include GWAS_CT, the number of times that genotype was
# clonally propagated and planted at these common gardens.
library(tidyverse)
phe_ex <- readRDS("~/Github/pvdiv-phenotypes/data/pre_replant/Phenotypes_all_pre_2019_replant.rds")
exampleGWAS <- phe_ex %>%
filter(!(PLANT_ID %in% c("AP13", "UNK"))) %>%
group_by(PLANT_ID, PLOT_GL) %>%
tally() %>% tally(name = "GWAS_CT")
envGWAS <- readRDS("~/Github/pvdiv-fitness-2019/data/Seven_climate_gwas.rds")
fitnessGWAS <- readRDS("~/Github/pvdiv-fitness-2019/data/Phenotypes_fitness_linear.rds") %>%
dplyr::select(PLANT_ID, FRAC_SRV_THREE, CLMB_BIOMASS, KBSM_BIOMASS, PKLE_BIOMASS)
pvdiv_phenotypes <- exampleGWAS %>%
full_join(envGWAS) %>%
full_join(fitnessGWAS)
usethis::use_data(pvdiv_phenotypes, compress = "gzip", overwrite = TRUE)
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