Estimate Genetic Gain
wolfemd
2020-2-13
Last updated: 2020-02-14
Checks: 7 0
Knit directory: IITA_2019GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7a9c618 | wolfemd | 2020-02-14 | Show gen. gain with box and barplots of GEBVs. |
Objective
Given a selection index and the GEBV previously obtained previously, estimate genetic gain.
library(tidyverse); library(magrittr)
gebvs<-read.csv("output/GEBV_IITA_OutliersRemovedTRUE_73119.csv", stringsAsFactors = F) %>%
select(-contains("_"))
unique(gebvs$GeneticGroup)[1] "TMS13F" "GGetc" "TMS14F" "TMS15F" "TMS18F"gebvs$GeneticGroup <-factor(gebvs$GeneticGroup,levels=c("GGetc","TMS13F","TMS14F","TMS15F","TMS18F"))
traits<-colnames(gebvs) %>% grep("GID|GeneticGroup",.,value = T, invert = T)Boxplot of GEBVs
boxplotGenGain<-function(gebvs,traits){
# Input
# traits: vector of columns in df containing gebvs
# gebvs: an input dataframe with columns containing gebvs
## gebvs$GeneticGroup: one column should be named GeneticGroup...
## and be a factor with levels ordered
## in sequence by breeding cycle
gebvs_long<-gebvs %>%
tidyr::pivot_longer(cols=traits,names_to = "Trait",values_to = "GEBV")
gebvs_long %>%
ggplot2::ggplot(.,aes(x=GeneticGroup,y=GEBV, fill=GeneticGroup)) +
geom_boxplot() +
facet_wrap(~Trait,scales='free_y') +
theme(axis.text.x = element_text(face = 'bold',angle = 90),
legend.position = 'none') }
## Barplot (Mean + SE) GEBVs
barplotGenGain<-function(gebvs,traits){
# Input
# traits: vector of columns in df containing gebvs
# gebvs: an input dataframe with columns containing gebvs
## gebvs$GeneticGroup: one column should be named GeneticGroup...
## and be a factor with levels ordered
## in sequence by breeding cycle
gebvs_long<-gebvs %>%
tidyr::pivot_longer(cols=traits,names_to = "Trait",values_to = "GEBV")
gebvs_long %>%
group_by(Trait,GeneticGroup) %>%
summarize(meanGEBV=mean(GEBV),
stdErr=sd(GEBV)/sqrt(n()),
upperSE=meanGEBV+stdErr,
lowerSE=meanGEBV-stdErr) %>%
ggplot(.,aes(x=GeneticGroup,y=meanGEBV,fill=Trait)) +
geom_bar(stat = 'identity') +
geom_linerange(aes(ymax=upperSE,
ymin=lowerSE)) +
facet_wrap(~Trait,scales='free_y') +
theme(axis.text.x = element_text(face = 'bold',angle = 90),
legend.position = 'none') }
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
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_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_1.5 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[5] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[9] ggplot2_3.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.11 haven_2.2.0
[4] lattice_0.20-38 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 htmltools_0.4.0 yaml_2.2.0
[10] rlang_0.4.1 later_1.0.0 pillar_1.4.2
[13] withr_2.1.2 glue_1.3.1 modelr_0.1.5
[16] readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[19] gtable_0.3.0 workflowr_1.5.0.9000 cellranger_1.1.0
[22] rvest_0.3.5 evaluate_0.14 labeling_0.3
[25] knitr_1.26 httpuv_1.5.2 broom_0.5.2
[28] Rcpp_1.0.3 promises_1.1.0 backports_1.1.5
[31] scales_1.1.0 jsonlite_1.6 farver_2.0.1
[34] fs_1.3.1 hms_0.5.2 digest_0.6.22
[37] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.6.1 lazyeval_0.2.2
[43] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[46] zeallot_0.1.0 xml2_1.2.2 lubridate_1.7.4
[49] assertthat_0.2.1 rmarkdown_1.17 httr_1.4.1
[52] rstudioapi_0.10 R6_2.4.1 nlme_3.1-142
[55] git2r_0.26.1 compiler_3.6.1