• Phenotypic data - Cassavabase
    • 1. Download of the phenotypic dataset from Cassavabase wizard tool
    • 2. Phenotypic data Information
    • Next Steps

Last updated: 2021-10-11

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Phenotypic data - Cassavabase

Initialy we are going to download phenotypic data from Cassavabase. In this case we are going to download the data from traits related to plant Architecture, as the following table:


Trait Cassavabase code Description
plant architecture visual rating 1-5 CO_334:0000099 Plant architecture on a 1-5 scale with 1 = excellent, 2 good, 3 = fair, 4 = bad, and 5 = very bad
plant architecture visual rating 1-5 at month 8 COMP:0000119
flowering ability visual assessment 0-3 CO_334:0000233 Presence of flowers (0=none; 1=little; 2=intermediate; 3=many).
flower visual rating 0&1 CO_334:0000111 Visual rating of flowers (50%) in plant with 0 = absent and 1 = present.
initial plant vigor assessment 1-5 CO_334:0000220 Visual assessment of plant vigor during establishment (1=very little vigor, and 5 = very vigorous). as being evaluated by CIAT.
initial vigor assessment 1-7 CO_334:0000009 Visual assessment of plant vigor during establishment scored one month after planting. 3 = Not vigorous, 5 = Medium vigor, 7 = highly vigorous.
number of forks counting CO_334:0000146 Number of branches (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) at every branching level.
number of forks on branching level 1 counting CO_334:0000522 Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the first branching level.
number of forks on branching level 2 counting CO_334:0000523 Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the second branching level.
number of forks on branching level 3 counting CO_334:0000524 Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the third branching level.
number of nodes at branching level 1 counting CO_334:0000352 Number of nodes at the first branching level.
number of nodes at branching level 2 counting CO_334:0000363 Number of nodes at the second branching level.
number of nodes at branching level 3 counting CO_334:0000368 Number of nodes at the third branching level.
first apical branch height measurement in cm CO_334:0000106 Height of first apical branch (ground level to point of first Apical branch, 9 months after planting) in cm
plant height measurement in cm CO_334:0000018 Vertical height of plants from the ground to top of the canopy measured in centimeter (cm).
plant height measurement in cm at month 12 COMP:0000181
plant height with leaf in cm CO_334:0000123 Portion of the stem with leaves measured as the distance in centimeter from the point of attachment of the oldest leaf to the youngest leaf (apical leaf portion).
plant height without leaf CO_334:0000125 Portion of stem with no leaf measured in centimeter (cm) by deducting plant height with leaf from plant height.
plant height without leaf at month 12 COMP:0000182
stalk length evaluation CO_334:0000227 Visual assessment of the average length of the stalks (1=short; 2=intermediate; 3=long)
stem diameter measurement in cm CO_334:0000257 Measurement of stem diameter taken on the middle of the plant in centimeter (cm) using the vernier caliper.
stem diameter measurement in cm at month 5 COMP:0000129
stem diameter measurement in cm at month 6 COMP:0000130


1. Download of the phenotypic dataset from Cassavabase wizard tool

Use the following filters:

  1. Select the trait list TraitsLucianoGS
  2. Select the following Trial Types:
    • Clonal Evaluation;
    • Preliminary Yield Trial;
    • Advanced Yield Trial;
    • Uniform Yield Trial;
    • Regional Trials;
    • phenotyping_trial.
  3. Select Years from 2010 to 2021.
  4. Select all the trials available at cassavabase after the filter.

2. Phenotypic data Information

library(tidyverse); library(reactable)
PhenoData <- readRDS("data/phenotypePAGP.RDS")

cat("Table 2. Number of trial per Institute in Cassavabase with Cassava Plant Shape Traits.", "\n")
Table 2. Number of trial per Institute in Cassavabase with Cassava Plant Shape Traits. 
PhenoData %>% group_by(programName, studyYear) %>% summarise(n_trials = unique(studyName)) %>%
   count(paste(programName,studyYear)) %>% select(programName, studyYear, n) %>%
   reactable(defaultPageSize = 20)
programName
studyYear
n
CIAT
2010
62
CIAT
2011
58
CIAT
2012
99
CIAT
2013
58
CIAT
2014
74
CIAT
2015
58
CIAT
2016
9
CIAT
2017
42
CIAT
2018
34
CIAT
2019
69
CIAT
2020
29
CIAT
2021
1
Embrapa
2019
2
IITA
2010
58
IITA
2011
41
IITA
2012
65
IITA
2013
84
IITA
2014
74
IITA
2015
100
IITA
2016
116
1–20 of 45 rows
cat("Table 3. Plot number of the trials available in Cassavabase with Cassava Plant Shape traits.", "\n")
Table 3. Plot number of the trials available in Cassavabase with Cassava Plant Shape traits. 
PhenoData %>% group_by(programName, studyYear) %>% count(studyName) %>% 
   reactable(defaultPageSize = 50)
programName
studyYear
studyName
n
CIAT
2010
201011CQCOB_stom
1148
CIAT
2010
201012CQCOB_stom
131
CIAT
2010
201013CQEPR_pita
299
CIAT
2010
201014CQEPR_pita
298
CIAT
2010
201015CQEPR_pita
300
CIAT
2010
201016CQEAR_stom
215
CIAT
2010
201017CQEAR_pita
216
CIAT
2010
201018CQEAR_pita
90
CIAT
2010
201019CQEAR_stom
90
CIAT
2010
201020CQEAR_saba
90
CIAT
2010
201021CQEAR_pita
90
CIAT
2010
201022CQEAR_stom
90
CIAT
2010
201024CQEAR_stom
146
CIAT
2010
201025CQPRC_pita
105
CIAT
2010
201026CQPRC_polo
104
CIAT
2010
201027CQPRC_stom
101
CIAT
2010
201028CQPRC_saba
105
CIAT
2010
201029CQPRC_pita
108
CIAT
2010
201030CQPRC_pita
108
CIAT
2010
201031CQPRC_stom
106
CIAT
2010
201032CQPRC_stom
108
CIAT
2010
201033CQPRC_stom
176
CIAT
2010
201036DMEAR_stom
48
CIAT
2010
201037CQEAR_stom
90
CIAT
2010
201045CQCOB_ciat
954
CIAT
2010
201046CQEPR_ciat
192
CIAT
2010
201047CQEPR_ciat
192
CIAT
2010
201048CQEPR_ciat
192
CIAT
2010
201049CQEAR_ciat
192
CIAT
2010
201050CQEAR_ciat
144
CIAT
2010
201060CQCOB_plop
676
CIAT
2010
201061CQEPR_plop
252
CIAT
2010
201062CQEPR_plop
255
CIAT
2010
201063CQEPR_plop
249
CIAT
2010
201064CQEAR_plop
120
CIAT
2010
201065CQEAR_plop
108
CIAT
2010
201066CQPRC_plop
60
CIAT
2010
201067CQPRC_plop
90
CIAT
2010
201070CQPRC_plop
40
CIAT
2010
201071CQPRC_plop
60
CIAT
2010
GY201012
131
CIAT
2010
GY201013
298
CIAT
2010
GY201014
298
CIAT
2010
GY201015
300
CIAT
2010
GY201016
215
CIAT
2010
GY201017
216
CIAT
2010
GY201019
90
CIAT
2010
GY201020
90
CIAT
2010
GY201021
90
CIAT
2010
GY201022
90
1–50 of 1649 rows
...

Next Steps

  • Download Phenotypic dataset;
  • select trials by reability;
  • estimate the BLUPS, and genetic correlations between the traits;
  • Create a list with the clone names;
  • Download the Genotypic dataset from the clones phenotyped;
  • Perform the genomic prediction single-trait with G-BLUP Add, Add + Dom genetic models. - 50 replicates - 100 clones per fold.

home


sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=Portuguese_Brazil.1252  LC_CTYPE=Portuguese_Brazil.1252   
[3] LC_MONETARY=Portuguese_Brazil.1252 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reactable_0.2.3 forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7    
 [5] purrr_0.3.4     readr_2.0.1     tidyr_1.1.3     tibble_3.1.4   
 [9] ggplot2_3.3.5   tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        lubridate_1.7.10  assertthat_0.2.1  rprojroot_2.0.2  
 [5] digest_0.6.28     utf8_1.2.2        reactR_0.4.4      R6_2.5.1         
 [9] cellranger_1.1.0  backports_1.2.1   reprex_2.0.1      evaluate_0.14    
[13] httr_1.4.2        pillar_1.6.2      rlang_0.4.11      readxl_1.3.1     
[17] rstudioapi_0.13   whisker_0.4       jquerylib_0.1.4   rmarkdown_2.11   
[21] htmlwidgets_1.5.4 munsell_0.5.0     broom_0.7.9       compiler_4.1.1   
[25] httpuv_1.6.3      modelr_0.1.8      xfun_0.26         pkgconfig_2.0.3  
[29] htmltools_0.5.2   tidyselect_1.1.1  workflowr_1.6.2   fansi_0.5.0      
[33] crayon_1.4.1      tzdb_0.1.2        dbplyr_2.1.1      withr_2.4.2      
[37] later_1.3.0       grid_4.1.1        jsonlite_1.7.2    gtable_0.3.0     
[41] lifecycle_1.0.0   DBI_1.1.1         git2r_0.28.0      magrittr_2.0.1   
[45] scales_1.1.1      cli_3.0.1         stringi_1.7.4     fs_1.5.0         
[49] promises_1.2.0.1  xml2_1.3.2        bslib_0.3.0       ellipsis_0.3.2   
[53] generics_0.1.0    vctrs_0.3.8       tools_4.1.1       glue_1.4.2       
[57] hms_1.1.0         fastmap_1.1.0     yaml_2.2.1        colorspace_2.0-2 
[61] rvest_1.0.1       knitr_1.34        haven_2.4.3       sass_0.4.0