Raw data

BLUPs

Prediction accuracy

Table of mean accuracies
Boxplot of accuracies

Last updated: 2020-12-23

Checks: 7 0

Knit directory: TARI_2020GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/05-Results.Rmd) and HTML (docs/05-Results.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	6a50ab2	wolfemd	2020-12-23	Tweak one heading…
html	abaf52a	wolfemd	2020-12-23	Build site.
Rmd	fae176a	wolfemd	2020-12-23	Publish the first set of analyses and files for TARI 2020 GS.

Raw data

Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data. See here for details..

library(tidyverse)
library(magrittr)
rawdata <- readRDS(file = here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
rawdata %>% summarise(Nplots = nrow(.), across(c(locationName, studyYear, studyName, 
    TrialType, GID), ~length(unique(.)), .names = "N_{.col}")) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

Nplots <int>	N_locationName <int>	N_studyYear <int>	N_studyName <int>	N_TrialType <int>	N_GID <int>
12718	15	5	138	7	3773

So ~3700 unique clone names in the phenotype data, across >12K plots. This is not the same number of clones as are expected to be genotyped-and-phenotyped.

Break down the plots based on the trial design and TrialType (really a grouping of the population that is breeding program specific), captured by two logical variables, CompleteBlocks and IncompleteBlocks.

rawdata %>% count(TrialType, CompleteBlocks, IncompleteBlocks) %>% spread(TrialType, 
    n) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

CompleteBlocks <lgl>	IncompleteBlocks <lgl>	AYT <int>	CET <int>	CrossingBlock <int>	PYT <int>	RegionalTrial <int>	UYT <int>	VarietyTrial <int>
FALSE	FALSE	NA	644	NA	NA	NA	NA	NA
FALSE	TRUE	NA	2361	NA	NA	319	NA	NA
TRUE	FALSE	1370	NA	392	967	162	885	390
TRUE	TRUE	965	1114	NA	1645	885	619	NA

Next, look at breakdown of plots by TrialType (rows) and locations (columns):

rawdata %>% count(locationName, TrialType) %>% spread(locationName, n) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

TrialType <chr>	Bunda <int>	Bwanga <int>	Chambezi <int>	Chato <int>	Gairo <int>	Hombolo <int>	ILONGA <int>	Kakonko <int>	Kasulu <int>	Kibaha <int>	Kisesa-Magu <int>	Maruku <int>	Mkuranga <int>	Ukerewe <int>
AYT	258	114	1178	81	NA	NA	NA	NA	60	NA	NA	NA	128	258
CET	NA	NA	2553	NA	NA	NA	NA	NA	NA	452	NA	NA	NA	NA
CrossingBlock	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	288	NA	NA
PYT	154	NA	1306	NA	NA	NA	NA	NA	130	NA	160	NA	NA	24
RegionalTrial	90	90	319	90	NA	NA	72	NA	90	90	NA	180	NA	90
UYT	219	171	295	NA	81	285	NA	NA	NA	NA	NA	NA	NA	222
VarietyTrial	93	24	42	NA	NA	NA	NA	24	NA	NA	NA	18	NA	72

traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
rawdata %>% select(locationName, studyYear, studyName, TrialType, any_of(traits)) %>% 
    pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>% 
    ggplot(., aes(x = Value, fill = Trait)) + geom_histogram() + facet_wrap(~Trait, 
    scales = "free") + theme_bw() + scale_fill_viridis_d() + labs(title = "Distribution of Raw Phenotypic Values")

Version	Author	Date
abaf52a	wolfemd	2020-12-23

How many genotyped-and-phenotyped clones?

rawdata %>% select(locationName, studyYear, studyName, TrialType, germplasmName, 
    FullSampleName, GID, any_of(traits)) %>% pivot_longer(cols = any_of(traits), 
    values_to = "Value", names_to = "Trait") %>% filter(!is.na(Value), !is.na(FullSampleName)) %>% 
    distinct(germplasmName, FullSampleName, GID) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

germplasmName <chr>	FullSampleName <chr>	GID <chr>
ALBERT	ALBERT:CA8RLANXX:7:526312	ALBERT:CA8RLANXX:7:526312
CH05_203	CH05_203:250442976	CH05_203:250442976
EYOPE	EYOPE:250442952	EYOPE:250442952
F10_30_R2	F10_30_R2:250442916	F10_30_R2:250442916
KALAWE	KALAWE:250442951	KALAWE:250442951
TMEB1786	KIBANDAMENO:250254266	KIBANDAMENO:250254266
KIZIMBANI	KIZIMBANI:250304603	KIZIMBANI:250304603
MKOMBOZI	MKOMBOZI:CA8RLANXX:7:526255	MKOMBOZI:CA8RLANXX:7:526255
MKURANGA1	MKURANGA1:CA8RLANXX:2:525804	MKURANGA1:CA8RLANXX:2:525804
NASE14	NASE14:250329604	NASE14:250329604

There are 423 so far.

rawdata %>% select(locationName, studyYear, studyName, TrialType, germplasmName, 
    FullSampleName, GID, any_of(traits)) %>% # pivot_longer(cols = any_of(traits), values_to = 'Value', names_to = 'Trait')
# %>% filter(!is.na(Value)) %>%
distinct(germplasmName, FullSampleName, GID) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

germplasmName <chr>	FullSampleName <chr>	GID <chr>
ALBERT	ALBERT:CA8RLANXX:7:526312	ALBERT:CA8RLANXX:7:526312
CH05_203	CH05_203:250442976	CH05_203:250442976
EYOPE	EYOPE:250442952	EYOPE:250442952
F10_30_R2	F10_30_R2:250442916	F10_30_R2:250442916
KALAWE	KALAWE:250442951	KALAWE:250442951
TMEB1786	KIBANDAMENO:250254266	KIBANDAMENO:250254266
KIPUSA	NA	KIPUSA
kiroba	NA	kiroba
KIZIMBANI	KIZIMBANI:250304603	KIZIMBANI:250304603
LML_2008_363	NA	LML_2008_363

Table of germplasmName-DNA-sample-name matches are here: output/germplasmName_to_DNAname_matches_TARI_2020Dec22.csv.

List of DNA-sample-names are here:

RefPanel (containing TARI TP): output/rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv
New samples (DCAs20-5629): output/rownames_DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.csv

BLUPs

These are the BLUPs combining data for each clone across trials/locations without genomic information, used as input for genomic prediction downstream.

library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))

blups %>% left_join(nestDesignsDetectedByTraits(dbdata, traits) %>% mutate(Nplots = map_dbl(MultiTrialTraitData, 
    nrow)) %>% select(Trait, Nplots)) %>% mutate(Nclones = map_dbl(blups, ~nrow(.)), 
    NoutliersRemoved = map2_dbl(outliers1, outliers2, ~length(.x) + length(.y))) %>% 
    # relocate(c(Nclones,NoutliersRemoved),.after = Trait) %>%
# select(-blups,-varcomp,-outliers1,-outliers2) %>%
select(Trait, Nplots, Nclones, NoutliersRemoved, Vg, Ve, H2) %>% mutate(across(is.numeric, 
    ~round(., 4))) %>% arrange(desc(H2)) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

Trait <chr>	Nplots <dbl>	Nclones <dbl>	NoutliersRemoved <dbl>	Vg <dbl>	Ve <dbl>	H2 <dbl>
MCMDS	5883	1505	171	0.3908	0.1305	0.7497
PLTHT	902	268	12	1205.7089	446.2521	0.7299
logTOPYLD	3676	1302	47	0.2234	0.2588	0.4632
MCBSDS	6004	1510	151	0.0628	0.0798	0.4404
HI	1840	865	18	0.0053	0.0119	0.3073
logRTNO	3615	1291	44	0.0628	0.1687	0.2712
CBSDRS	4174	1300	171	0.0192	0.0558	0.2564
CGMS1	2636	468	16	0.0544	0.2464	0.1809
DM	2708	557	35	1.7293	10.4056	0.1425
logFYLD	1845	862	11	0.0765	0.4749	0.1388
CGMS2	2612	468	5	0.0576	0.3838	0.1305
logDYLD	386	137	3	0.0315	0.3100	0.0922

blups %>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = drgBLUP, fill = Trait)) + 
    geom_histogram() + facet_wrap(~Trait, scales = "free") + theme_bw() + scale_fill_viridis_d() + 
    labs(title = "Distribution of de-regressed BLUP Values")

Version	Author	Date
abaf52a	wolfemd	2020-12-23

blups %>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = REL, fill = Trait)) + 
    geom_histogram() + facet_wrap(~Trait, scales = "free") + theme_bw() + scale_fill_viridis_d() + 
    labs(title = "Distribution of BLUP Reliabilities")

Version	Author	Date
abaf52a	wolfemd	2020-12-23

Prediction accuracy

Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
- Compare prediction accuracy with vs. without IITA’s training data to augment.

rm(list = ls())
gc()

          used (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
Ncells 1286042 68.7    3034042 162.1         NA  2202262 117.7
Vcells 2431465 18.6   10146329  77.5     102400 10142130  77.4

library(tidyverse)
library(magrittr)
cv <- readRDS(here::here("output", "cvresults_A_2020Dec21.rds")) %>% bind_rows(readRDS(here::here("output", 
    "cvresults_ADE_2020Dec21.rds"))) %>% unnest(CVresults) %>% select(-splits, -accuracy)
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
cv %<>% mutate(Trait = factor(Trait, levels = traits), modelType = factor(modelType, 
    levels = c("A", "ADE")))

Table of mean accuracies

5-fold cross-validation, replicated 10 times.

Mean accuracy and upper/lower 95% interval.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% group_by(Trait, modelType) %>% # use accGETGV. For modelA we GETGV==GEBV. For modelADE we don't want GEBV, just
# GETGV.
summarize(meanAccuracy = mean(accGETGV, na.rm = T), lower5pct = quantile(accGETGV, 
    probs = c(0.05), na.rm = T), upper5pct = quantile(accGETGV, probs = c(0.95), 
    na.rm = T)) %>% mutate(across(is.numeric, ~round(., 2))) %>% arrange(modelType, 
    desc(meanAccuracy)) %>% rmarkdown::paged_table()

ABCDEFGHIJ0123456789

Trait <fct>	modelType <fct>	meanAccuracy <dbl>	lower5pct <dbl>	upper5pct <dbl>
logRTNO	A	0.31	0.17	0.50
logTOPYLD	A	0.30	0.12	0.45
DM	A	0.29	0.13	0.42
CGMS1	A	0.27	0.11	0.41
MCBSDS	A	0.25	0.03	0.38
CGMS2	A	0.16	0.01	0.30
CBSDRS	A	0.13	-0.01	0.28
HI	A	0.12	-0.37	0.68
PLTHT	A	0.08	-0.69	0.68
logDYLD	A	-0.13	-0.72	0.34
MCMDS	A	-0.19	-0.23	-0.15
logFYLD	A	-0.38	-0.73	0.04

Boxplot of accuracies

5-fold cross-validation, replicated 10 times.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=modelType)) + 
  geom_boxplot(position = "dodge2",color='gray50',size=0.5) + 
  geom_hline(yintercept = 0, color='darkred') + 
#  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  theme_bw() + 
  theme(strip.text.x = element_text(face='bold', size=12),
        axis.text.y = element_text(face='bold', size=14, angle = 0),
        axis.text.x = element_text(face='bold', size=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + # coord_flip() + 
  labs(title="Prediction Accuracies", y="GEBV or GETGV accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

Version	Author	Date
abaf52a	wolfemd	2020-12-23

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] tidyselect_1.1.0  xfun_0.19         haven_2.3.1       colorspace_2.0-0 
 [5] vctrs_0.3.5       generics_0.1.0    viridisLite_0.3.0 htmltools_0.5.0  
 [9] yaml_2.2.1        rlang_0.4.9       later_1.1.0.1     pillar_1.4.7     
[13] withr_2.3.0       glue_1.4.2        DBI_1.1.0         dbplyr_2.0.0     
[17] modelr_0.1.8      readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0    
[21] gtable_0.3.0      cellranger_1.1.0  rvest_0.3.6       evaluate_0.14    
[25] labeling_0.4.2    knitr_1.30        ps_1.5.0          httpuv_1.5.4     
[29] fansi_0.4.1       broom_0.7.2       Rcpp_1.0.5        promises_1.1.1   
[33] backports_1.2.1   scales_1.1.1      formatR_1.7       jsonlite_1.7.2   
[37] farver_2.0.3      fs_1.5.0          hms_0.5.3         digest_0.6.27    
[41] stringi_1.5.3     rprojroot_2.0.2   grid_4.0.2        here_1.0.1       
[45] cli_2.2.0         tools_4.0.2       crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.3   ellipsis_0.3.1    xml2_1.3.2        reprex_0.3.0     
[53] lubridate_1.7.9.2 rstudioapi_0.13   assertthat_0.2.1  rmarkdown_2.6    
[57] httr_1.4.2        R6_2.5.0          git2r_0.27.1      compiler_4.0.2

Results

2020-October-15

Raw data

BLUPs

Prediction accuracy

Table of mean accuracies

Boxplot of accuracies