Results
Marnin Wolfe
2020-Dec-03
Last updated: 2020-12-03
Checks: 7 0
Knit directory: IITA_2020GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3cd0f44 | wolfemd | 2020-12-03 | Refresh BLUPs and GBLUPs with trials harvested so far. Include |
| html | b9bb6f8 | wolfemd | 2020-12-03 | Build site. |
| Rmd | 9718666 | wolfemd | 2020-12-03 | Refresh BLUPs and GBLUPs with trials harvested so far. Include |
| html | c97b21b | wolfemd | 2020-11-27 | Build site. |
| Rmd | 1f8cd99 | wolfemd | 2020-11-27 | Added plots of genetic gain for 4 traits. Initial analysis of GEBV vs. |
| html | d72a9ed | wolfemd | 2020-09-21 | Build site. |
| html | 9194239 | wolfemd | 2020-09-21 | Build site. |
| Rmd | 97778e7 | wolfemd | 2020-09-21 | Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs: |
Cross-validation accuracy
Conducted 5-fold x 5-reps of cross-validation (here). Three traits only, MCMDS, logFYLD, DM.
library(tidyverse)
library(magrittr)
cvresults <- readRDS(here::here("output", "cvresults_ModelA_chunk1.rds")) %>% bind_rows(readRDS(here::here("output",
"cvresults_ModelA_chunk2.rds"))) %>% bind_rows(readRDS(here::here("output", "cvresults_ModelA_chunk3.rds"))) %>%
mutate(Model = "A") %>% bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk1.rds")) %>%
bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk2.rds"))) %>%
bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk3.rds"))) %>%
mutate(Model = "ADE"))cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGEBV, Model) %>% ggplot(.,
aes(x = Model, y = accGEBV, fill = VersionOfBLUPs)) + geom_boxplot() + theme_bw() +
facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + labs(title = "GEBV: Compare 3-stage and 2-stage prediction pipelines")
cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGETGV, Model) %>% ggplot(.,
aes(x = Model, y = accGETGV, fill = VersionOfBLUPs)) + geom_boxplot() + theme_bw() +
facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + labs(title = "GETGV: Compare 3-stage and 2-stage prediction pipelines")
cvresults %>% select(Trait, Model, repeats, id, VersionOfBLUPs, accGEBV) %>% spread(VersionOfBLUPs,
accGEBV) %>% mutate(diffAcc = blups2stage - blups3stage) %>% ggplot(., aes(x = Model,
y = diffAcc, fill = Trait)) + geom_hline(yintercept = 0, color = "darkred") +
geom_boxplot() + theme_bw() + facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() +
labs(y = "Accuracy Difference (2-stage minus 3-stage)", title = "GEBV")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
cvresults %>% select(Trait, Model, repeats, id, VersionOfBLUPs, accGETGV) %>% spread(VersionOfBLUPs,
accGETGV) %>% mutate(diffAcc = blups2stage - blups3stage) %>% ggplot(., aes(x = Model,
y = diffAcc, fill = Trait)) + geom_hline(yintercept = 0, color = "darkred") +
geom_boxplot() + theme_bw() + facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() +
labs(y = "Accuracy Difference (2-stage minus 3-stage)", title = "GETGV")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
cvresults %>% filter(VersionOfBLUPs == "blups2stage") %>% select(Trait, repeats,
id, VersionOfBLUPs, accGETGV, Model) %>% ggplot(., aes(x = Trait, y = accGETGV,
fill = Model)) + geom_boxplot(color = "gray60", notch = T) + theme_bw() + facet_wrap(~Trait,
scales = "free") + scale_fill_viridis_d() + labs(title = "Compare accuracy: models A vs. ADE")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
Genetic Gain
September 2020
library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"),
stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")
iita_gebvs %>% select(GID, GeneticGroup, any_of(traits)) %>% pivot_longer(cols = any_of(traits),
names_to = "Trait", values_to = "GEBV") %>% 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", color = "gray60",
size = 1.25) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), color = "gray60",
size = 1.25) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + theme_bw() +
geom_hline(yintercept = 0, size = 1.15, color = "black") + theme(axis.text.x = element_text(face = "bold",
angle = 0, size = 12), axis.title.y = element_text(face = "bold", size = 14),
legend.position = "none", strip.background.x = element_blank(), strip.text = element_text(face = "bold",
size = 14)) + scale_fill_viridis_d() + labs(x = NULL, y = "Mean GEBVs")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
Rate of gain
List of trials from 2020 to Prasad and Ismail… should I download fresh data?
dbdata <- readRDS(here::here("output", "IITA_CleanedTrialData.rds"))
trialsHarvested2019to2020 <- dbdata %>% filter(studyYear >= 2019) %>% group_by(studyYear,
locationName, studyName, plantingDate, harvestDate) %>% summarize(Nhav = sum(!is.na(NOHAV)))
trialsHarvested2019to2020 %>% write.csv(., file = here::here("output", "trials_uploaded_by_Nharvested_15Sep2020.csv"),
row.names = F)September 2020 GEBV
library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"),
stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")
ggcycletime <- readxl::read_xls(here::here("data", "PedigreeGeneticGainCycleTime_aafolabi_01122020.xls"))
table(ggcycletime$Accession %in% iita_gebvs$GID)
FALSE
807 Need germplasmName field from raw trial data to match GEBV and cycle time
library(tidyverse)
library(magrittr)
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected.rds"))
iita_gebvs %<>% left_join(dbdata %>% select(-MaxNOHAV) %>% unnest(TrialData) %>%
distinct(germplasmName, GID)) %>% group_by(GID) %>% slice(1) %>% ungroup()
table(ggcycletime$Accession %in% iita_gebvs$germplasmName)
FALSE TRUE
193 614 table(ggcycletime$Year_Accession)
1973 1974 1975 1976
5 5 1 2
1977 1978 1981 1982
3 2 2 4
1983 1984 1985 1987
1 4 2 2
1988 1989 1990 1991
6 3 2 20
1992 1993 1994 1995
25 20 17 37
1996 1997 1998 1999
55 43 29 33
2000 2001 2002 2003
35 74 25 34
2005 2006 2007 2008
24 21 59 39
2009 2010 2011 2012
37 22 1 4
2013 2014 2015 2016
19 19 26 10
BENIN BEN86052 CARICASS II LIBERIA CARICASS LIBERIA CIAT
1 1 1 1
COTE D'IVOIRE EastAfrica GHANA ANKRA KENYA GUZO
1 15 1 1
KIROBA EastAfrica NRCRI RWANDA CREOLINA SLARI
1 6 1 1
TOGO TOMA 326 TOGO TOMA 393 ZAMBIA Bangweulu
1 1 1 iita_gebvs %<>% left_join(., ggcycletime %>% rename(germplasmName = Accession) %>%
mutate(Year_Accession = as.numeric(Year_Accession)))iita_gebvs %<>% mutate(Year_Accession = case_when(grepl("2013_|TMS13", germplasmName) ~
2013, grepl("TMS14", germplasmName) ~ 2014, grepl("TMS15", germplasmName) ~ 2015,
grepl("TMS18", germplasmName) ~ 2018, !grepl("2013_|TMS13|TMS14|TMS15|TMS18",
germplasmName) ~ Year_Accession))iita_gebvs %>% ggplot(., aes(x = TCHART, y = BCHROMO)) + geom_hex() + theme_bw() +
facet_wrap(~GeneticGroup, nrow = 1) + theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
# iita_gebvs %>% select(germplasmName,GeneticGroup,Year_Accession,any_of(traits))
# %>% pivot_longer(cols=any_of(traits),names_to = 'Trait',values_to = 'GEBV') %>%
# ggplot(.,aes(x=Year_Accession,y=GEBV,color=GeneticGroup)) + geom_point() +
# facet_wrap(~Trait,scales='free_y', ncol=1) + theme_bw() + #
# geom_hline(yintercept = 0, size=1.15, color='black') + theme(axis.text.x =
# element_text(face = 'bold',angle = 0, size=12), axis.title.y =
# element_text(face = 'bold',size=14), #legend.position = 'none',
# strip.background.x = element_blank(), strip.text =
# element_text(face='bold',size=14)) + scale_color_viridis_d()
# labs(x=NULL,y='Mean GEBVs')iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(traits)) %>%
mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", "PreGS")) %>% pivot_longer(cols = any_of(traits),
names_to = "Trait", values_to = "GEBV") %>% group_by(Trait, GeneticGroup, Year_Accession) %>%
summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()),
upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession,
y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(c(traits,
"TCHART", "BCHROMO"))) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013,
"GS", "PreGS")) %>% pivot_longer(cols = any_of(c(traits, "TCHART", "BCHROMO")),
names_to = "Trait", values_to = "GEBV") %>% mutate(Trait = factor(Trait, c(traits,
"TCHART", "BCHROMO"))) %>% group_by(Trait, GeneticGroup, Year_Accession) %>%
summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()),
upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession,
y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(c(traits,
"TCHART", "BCHROMO"))) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013,
"GS", "PreGS")) %>% filter(BCHROMO < 5, TCHART < 0.5) %>% pivot_longer(cols = any_of(c(traits,
"TCHART", "BCHROMO")), names_to = "Trait", values_to = "GEBV") %>% mutate(Trait = factor(Trait,
c(traits, "TCHART", "BCHROMO"))) %>% group_by(Trait, GeneticGroup, Year_Accession) %>%
summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()),
upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession,
y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
December 2020 GETGV
library(tidyverse); library(magrittr);
iita_getgvs<-read.csv(here::here("output","GETGV_IITA_ModelADE_twostage_IITA_2020Dec03.csv"), stringsAsFactors = F)
#traits<-c("DM","logFYLD","logTOPYLD","MCMDS")
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
"logDYLD", # <-- logDYLD now included.
"logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
ggcycletime<-readxl::read_xls(here::here("data","PedigreeGeneticGainCycleTime_aafolabi_01122020.xls"))
# table(ggcycletime$Accession %in% iita_getgvs$GID)
# FALSE
# 807
# Need germplasmName field from raw trial data to match GEBV and cycle time
dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2020Dec03.rds"))
iita_getgvs %<>%
left_join(dbdata %>%
select(-MaxNOHAV) %>% unnest(TrialData) %>%
distinct(germplasmName,GID)) %>%
group_by(GID) %>%
slice(1) %>%
ungroup()
rm(dbdata)
# table(ggcycletime$Accession %in% iita_getgvs$germplasmName)
# FALSE TRUE
# 193 614
# table(ggcycletime$Year_Accession)
iita_getgvs %<>%
left_join(.,ggcycletime %>%
rename(germplasmName=Accession) %>%
mutate(Year_Accession=as.numeric(Year_Accession)))
iita_getgvs %<>%
mutate(Year_Accession=case_when(grepl("2013_|TMS13",germplasmName)~2013,
grepl("TMS14",germplasmName)~2014,
grepl("TMS15",germplasmName)~2015,
grepl("TMS18",germplasmName)~2018,
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",germplasmName)~Year_Accession))
write.csv(iita_getgvs, file = here::here("output","GETGV_IITA_ModelADE_twostage_IITA_2020Dec03_withAccessionYear.csv"), row.names = F)What is yellow?
iita_getgvs %>% ggplot(., aes(x = TCHART, y = BCHROMO)) + geom_hex() + theme_bw() +
facet_wrap(~GeneticGroup, nrow = 1) + theme(legend.position = "none") + geom_vline(xintercept = 0.5) +
geom_hline(yintercept = 5) + labs(title = "Arbitrary suggested cut-offs for `white` rooted GETGVs",
subtitle = "horiz. and vert. lines")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
Mean GETGV-by-Year
mean_getgvs <- iita_getgvs %>% select(germplasmName, GeneticGroup, Year_Accession,
any_of(traits)) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS",
"PreGS")) %>% pivot_longer(cols = any_of(traits), names_to = "Trait", values_to = "GETGV") %>%
group_by(Trait, GeneticGroup, Year_Accession) %>% summarize(meanGETGV = mean(GETGV),
Nclones = n(), stdErr = sd(GETGV)/sqrt(n()), upperSE = meanGETGV + stdErr, lowerSE = meanGETGV -
stdErr) %>% ungroup()
write.csv(mean_getgvs, file = here::here("output", "meanGETGVbyYear_IITA_2020Dec03.csv"),
row.names = F)# traits<-c('logDYLD','logFYLD','MCMDS','DM','TCHART','BCHROMO')
traits <- c("logDYLD", "logFYLD", "MCMDS", "DM", "TCHART", "BCHROMO", "PLTHT", "BRNHT1",
"BRLVLS", "HI", "logTOPYLD", "logRTNO", "LCHROMO", "ACHROMO")Plot all germplasm vs. year
mean_getgvs %>% mutate(Trait = factor(Trait, traits)) %>% ggplot(., aes(x = Year_Accession,
y = meanGETGV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 2) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
Plot “white” germplasm vs. year
mean_getgvs_whiteroots <- iita_getgvs %>% select(germplasmName, GeneticGroup, Year_Accession,
any_of(traits)) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS",
"PreGS")) %>% filter(BCHROMO <= 5, TCHART <= 0.5) %>% pivot_longer(cols = any_of(traits),
names_to = "Trait", values_to = "GETGV") %>% group_by(Trait, GeneticGroup, Year_Accession) %>%
summarize(meanGETGV = mean(GETGV), Nclones = n(), stdErr = sd(GETGV)/sqrt(n()),
upperSE = meanGETGV + stdErr, lowerSE = meanGETGV - stdErr) %>% ungroup()mean_getgvs_whiteroots %>% mutate(Trait = factor(Trait, traits)) %>% ggplot(., aes(x = Year_Accession,
y = meanGETGV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 2) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d() +
labs(title = "Mean GETGV vs. Accession Year of White-rooted Clones Only")
| Version | Author | Date |
|---|---|---|
| b9bb6f8 | wolfemd | 2020-12-03 |
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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_2.0.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lattice_0.20-41 lubridate_1.7.9.2 here_1.0.0
[5] ps_1.4.0 assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27
[9] R6_2.5.0 cellranger_1.1.0 backports_1.2.0 reprex_0.3.0
[13] evaluate_0.14 httr_1.4.2 pillar_1.4.7 rlang_0.4.9
[17] readxl_1.3.1 rstudioapi_0.13 whisker_0.4 hexbin_1.28.1
[21] Matrix_1.2-18 rmarkdown_2.5 splines_4.0.2 labeling_0.4.2
[25] munsell_0.5.0 broom_0.7.2 compiler_4.0.2 httpuv_1.5.4
[29] modelr_0.1.8 xfun_0.19 pkgconfig_2.0.3 mgcv_1.8-33
[33] htmltools_0.5.0 tidyselect_1.1.0 fansi_0.4.1 viridisLite_0.3.0
[37] crayon_1.3.4 dbplyr_2.0.0 withr_2.3.0 later_1.1.0.1
[41] grid_4.0.2 nlme_3.1-150 jsonlite_1.7.1 gtable_0.3.0
[45] lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1 formatR_1.7
[49] scales_1.1.1 cli_2.2.0 stringi_1.5.3 farver_2.0.3
[53] fs_1.5.0 promises_1.1.1 xml2_1.3.2 ellipsis_0.3.1
[57] generics_0.1.0 vctrs_0.3.5 tools_4.0.2 glue_1.4.2
[61] hms_0.5.3 yaml_2.2.1 colorspace_2.0-0 rvest_0.3.6
[65] knitr_1.30 haven_2.3.1