Clone Selection

Last updated: 2025-01-20

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Knit directory: Genomic-Selection-for-Drought-Tolerance-Using-Genome-Wide-SNPs-in-Casava/

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Configurations and packages

To perform the analyses, we will need the following packages:

library(tidyverse)
library(kableExtra)
library(ggthemes)
library(metan)
library(psych)
library(viridis)
library(ggpubr)

Data

results <- readRDS("output/results_cv_G_BLUP.RDS") %>%
  mutate(method = "G-BLUP") %>%
  bind_rows(
    readRDS("output/results_cv_RR_BLUP.RDS") %>%
      mutate(method = "RR-BLUP"),
    readRDS("output/results_cv_RKHS.RDS") %>%
      mutate(method = "RKHS"),
    readRDS("output/results_cv_BayesA.RDS") %>%
      mutate(method = "Bayes A"),
    readRDS("output/results_cv_BayesB.RDS") %>%
      mutate(method = "Bayes B"),
    readRDS("output/results_cv_RF.RDS") %>%
      mutate(method = "RF"),
    readRDS("output/results_cv_GEBVS_DOM.RDS") %>%
      mutate(method = "G-BLUP-DOM")
  )

traits <- unique(results$Trait)

Results

Plot boxplot from Accuracy

Figure 2 Boxplot of predictive ability

results %>%
  ggplot(aes(x = method, y = Ac, fill = method)) +
  geom_boxplot() +
  facet_wrap(~ Trait, ncol = 6) +
  expand_limits(y = 0) +
  labs(y = "Accuracy", x = "", fill = "Method") +
  scale_fill_gdocs() +
  theme(
    text = element_text(size = 25),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    legend.position = "top",
    legend.title = element_blank(),
    legend.box = "horizontal",
    panel.spacing = unit(1, "lines"),
    strip.background = element_blank(),
    panel.background = element_blank(),
    plot.background = element_blank(),
    legend.background = element_blank(),
    legend.box.background = element_blank(),
    legend.key = element_blank()
  ) +
  guides(fill = guide_legend(
    nrow = 1,
    byrow = TRUE,
    keywidth = 1.5,
    keyheight = 1,
    title.position = "top"
  ))

Table to Figure 2 Boxplot of predictive ability

# Calcular médias e desvio padrão de Ac por Trait e método
results_Ac <- results %>%
  group_by(Trait, method) %>%
  summarise(
    Ac_mean = round(mean(Ac) * 100, 2),
    Ac_sd = round(sd(Ac) * 100, 2),
    .groups = "drop"  # Remove agrupamento após summarise
  ) %>%
  select(Trait, method, Ac_mean) %>%
  pivot_wider(names_from = method, values_from = Ac_mean)

# Exibir resultados em tabela com kable
results_Ac %>%
  kbl(escape = FALSE, align = "c") %>%
  kable_classic(
    "hover",
    full_width = FALSE,
    position = "center",
    fixed_thead = TRUE
  )

Trait	Bayes A	Bayes B	G-BLUP	G-BLUP-DOM	RF	RKHS	RR-BLUP
DMC	21.64	20.94	22.00	22.00	15.48	19.00	24.46
FRY	32.22	31.94	32.66	32.10	31.40	32.80	31.16
HI	24.26	23.90	24.06	23.08	25.54	26.40	24.78
N_Roots	25.28	24.94	25.42	26.02	25.78	24.84	24.62
Nstem.Plant	27.08	26.28	27.22	26.40	20.82	26.00	26.84
Plant.Height	29.58	29.72	29.32	29.56	28.86	30.32	30.30
Root.Di	31.60	31.28	31.80	31.50	28.24	31.70	29.48
Root.Le	36.94	36.54	36.72	36.24	34.46	37.14	36.16
ShY	31.76	32.02	32.10	32.16	31.82	32.88	32.40
StC	21.80	21.16	22.12	22.12	15.56	19.08	20.12
StY	30.44	30.04	31.04	29.86	28.18	30.90	31.70
Stem.D	34.58	34.64	34.68	35.34	34.12	35.12	34.02

# Calcular médias e desvio padrão de MSPE por Trait e método
results_MSPE <- results %>%
  group_by(Trait, method) %>%
  summarise(
    MSPE_mean = round(mean(MSPE) * 100, 2),
    MSPE_sd = round(sd(MSPE) * 100, 2),
    .groups = "drop"  # Remove agrupamento após summarise
  ) %>%
  select(Trait, method, MSPE_mean) %>%
  pivot_wider(names_from = method, values_from = MSPE_mean)

# Exibir resultados em tabela com kable
results_MSPE %>%
  kbl(escape = FALSE, align = "c") %>%
  kable_classic(
    "hover",
    full_width = FALSE,
    position = "center",
    fixed_thead = TRUE
  )

Trait	Bayes A	Bayes B	G-BLUP	G-BLUP-DOM	RF	RKHS	RR-BLUP
DMC	589.82	593.86	585.32	585.32	628.74	611.50	576.84
FRY	115.86	116.34	115.26	116.12	117.38	119.72	116.66
HI	1195.86	1199.12	1189.96	1198.26	1212.24	1176.24	1186.60
N_Roots	86.08	86.14	85.74	85.48	87.68	93.12	88.26
Nstem.Plant	2.76	2.80	2.76	2.78	2.98	3.30	2.88
Plant.Height	0.74	0.74	0.74	0.74	0.78	0.80	0.78
Root.Di	332.88	333.62	332.10	332.84	348.66	339.12	338.06
Root.Le	180.68	181.42	180.78	181.48	187.28	198.94	199.96
ShY	1259.58	1257.72	1253.94	1254.40	1276.28	1324.08	1261.50
StC	586.66	589.94	582.36	582.36	625.52	608.10	591.46
StY	7.08	7.10	7.02	7.10	7.24	7.24	7.02
Stem.D	0.78	0.76	0.76	0.76	0.80	0.84	0.80

Clone Selection

First let’s add the phenotypic means to the BLUPS and GEBVS

# Carregar dados
media_pheno <- read.csv("output/mean_pheno.csv")

BLUP <- readRDS("data/pheno.rds") %>%
  pivot_longer(cols = -ID_Clone, names_to = "Trait", values_to = "BLUP")

# Inserir o método em cada data frame dentro de results$result
results$result <- map2(results$result, results$method, ~mutate(.x, method = .y))

# Combinar todos os data frames em um único data frame
GEBV_BLUP <- bind_rows(results$result) %>%
  group_by(ID_Clone, Trait, method) %>%
  summarise(GEBV = mean(GEBV), .groups = "drop") %>%
  pivot_wider(names_from = method, values_from = GEBV) %>%
  full_join(BLUP, by = c("ID_Clone", "Trait"))

# Adicionar as médias fenotípicas aos valores numéricos e combinar com GEBV
GEBV_BLUP <- GEBV_BLUP %>%
  rowwise() %>%
  mutate(across(where(is.numeric), ~ . + media_pheno[[Trait]])) %>%
  ungroup()

# Visualizar os primeiros dados
GEBV_BLUP %>%
  head() %>%
  kbl(escape = FALSE, align = "c") %>%
  kable_classic("hover", full_width = FALSE, position = "center", fixed_thead = TRUE)

ID_Clone	Trait	Bayes A	Bayes B	G-BLUP	G-BLUP-DOM	RF	RKHS	RR-BLUP	BLUP
Alagoana363.250437472	FRY	5.147048	5.136213	5.188144	5.255897	5.275877	5.432111	5.225135	4.980623
Alagoana363.250437472	HI	24.607030	24.568547	24.633235	24.672048	24.723454	24.444491	25.165928	24.557231
Alagoana363.250437472	N_Roots	4.154301	4.178224	4.225263	4.243897	4.193898	4.443810	4.426670	3.958834
Alagoana363.250437472	Plant.Height	1.161012	1.161261	1.160419	1.165587	1.165375	1.187893	1.175966	1.170988
Alagoana363.250437472	Root.Di	29.609365	29.596120	29.557053	29.568693	30.003957	29.749311	29.896532	30.209694
Alagoana363.250437472	Root.Le	23.563618	23.558956	23.535616	23.534217	23.494775	23.850965	23.866019	23.341948

Now let’s group the BLUPs data with the GEBVs and GETGVs data and add a Weights column for each increase or decrease characteristic.

selection_parents <- GEBV_BLUP %>% 
  rename(GEBV = `G-BLUP`, GETGV = `G-BLUP-DOM`) |>
  mutate(Weights = ifelse(
    Trait %in% c(
      "N_Roots",
      "FRY",
      "ShY",
      "DMC" ,
      "StY",
      "Plant.Height",
      "HI" ,
      "StC" ,
      "Root.Le" ,
      "Root.Di",
      "Stem.D" ,
      "Nstem.Plant"
    ),
    "acrescimo",
    "descrescimo"
  ))

calcular_pesos <- function(data, var){
  data  %>%
  select(ID_Clone, Trait, var) %>%
  pivot_wider(names_from = Trait, values_from = var) %>%
  mutate(
    N_Roots = 15 * N_Roots,
    FRY = 20 * FRY,
    ShY = 10 * ShY,
    DMC = 15 * DMC,
    StY = 10 * StY,
    Plant.Height = 5 * Plant.Height,
    HI = 10 * HI,
    StC = 10 * StC,
    Root.Le = 5 * Root.Le,
    Root.Di = 5 * Root.Di,
    Stem.D = 5 * Stem.D,
    Nstem.Plant = 5 * Nstem.Plant
  ) %>%
  mutate(pesos =
           rowSums(.[2:13], na.rm = TRUE))
}

pesos_BLUP <- calcular_pesos(selection_parents, "BLUP")
pesos_GEBV <- calcular_pesos(selection_parents, "GEBV")
pesos_GETGV <- calcular_pesos(selection_parents, "GETGV")

Individual selection to each trait

results_kappa <- data.frame()
SI <- c(10, 15, 20, 25, 30)

for (j in traits) {
  for (i in SI) {
    sel_parents <- selection_parents |>
      filter(Trait == j)  %>%
      na.omit() |>
      droplevels() %>%
      data.frame()
    
    Clones_GEBV_sel <- pesos_GEBV %>%
      right_join(sel_parents) %>%
      droplevels() %>% 
      arrange(desc(pesos)) %>%
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      droplevels()
    
    Clones_GETGV_sel <- pesos_GETGV %>%
      right_join(sel_parents) %>%
      droplevels() %>% 
      arrange(desc(pesos)) %>%
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      droplevels()
    
    Clones_BLUPS_sel <- pesos_BLUP %>%
      right_join(sel_parents) %>%
      droplevels() %>% 
      arrange(desc(pesos)) %>%
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      droplevels()
    
    Clones_sel_BLUPS <- sel_parents %>%
      arrange(ifelse(Weights == "acrescimo", desc(BLUP), BLUP)) |>
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      #filter(ID_Clone %in% Clones_BLUPS_sel$ID_Clone) %>%
      droplevels()
    
    X0_BLUPS <- selection_parents |>
      filter(Trait == j) |>
      select(BLUP) |>
      summarise(X0 = mean(BLUP, na.rm = T))
    
    XS_BLUPS <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Clones_BLUPS_sel$ID_Clone) |>
      select(BLUP) |>
      summarise(X0 = mean(BLUP, na.rm = T))
    
    Clones_sel_GEBVS <- sel_parents %>%
      arrange(ifelse(Weights == "acrescimo", desc(GEBV), GEBV)) |>
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      #filter(ID_Clone %in% Clones_GEBV_sel$ID_Clone) %>%
      droplevels()
    
    all(Clones_sel_BLUPS$ID_Clone %in% Clones_sel_GEBVS$ID_Clone)
    
    X0_GEBV <- selection_parents |>
      filter(Trait == j) |>
      select(GEBV) |>
      summarise(X0 = mean(GEBV, na.rm = T))
    
    XS_GEBV <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Clones_GEBV_sel$ID_Clone) |>
      select(GEBV) |>
      summarise(X0 = mean(GEBV, na.rm = T))
    
    Clones_sel_GETGV <- sel_parents %>%
      arrange(ifelse(Weights == "acrescimo", desc(GETGV), GETGV)) |>
      slice(1:(nlevels(ID_Clone) * (i / 100))) |>
      #filter(ID_Clone %in% Clones_GETGV_sel$ID_Clone) %>%
      droplevels()
    
    X0_GETGV <- selection_parents |>
      filter(Trait == j) |>
      select(GETGV) |>
      summarise(X0 = mean(GETGV, na.rm = T))
    
    XS_GETGV <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Clones_GETGV_sel$ID_Clone) |>
      select(GETGV) |>
      summarise(X0 = mean(GETGV, na.rm = T))
    
    Comb_sel_GEBV_BLUP <-
      Clones_sel_GEBVS |>
      #slice(1:(nlevels(ID_Clone)/2)) |>
      full_join(Clones_sel_BLUPS) |>
      #slice(1:(nlevels(ID_Clone)/2))) %>%
      resca(BLUP, GEBV, GETGV, new_min = 0, new_max = 1) %>%
      mutate(BLUP_GEBV = (BLUP_res + GEBV_res) / 2) |>
      arrange(desc(BLUP_GEBV)) %>%
      slice(1:nrow(Clones_sel_BLUPS)) |>
      droplevels()
    
    XS_comb_GEBV_BLUP <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Comb_sel_GEBV_BLUP$ID_Clone) |>
      select(GEBV) |>
      summarise(X0 = mean(GEBV, na.rm = T))
    
    Comb_sel_GETGV_BLUP <-
      Clones_sel_GETGV |>
      #slice(1:(nlevels(ID_Clone)/2)) |>
      full_join(Clones_sel_BLUPS) |>
      #slice(1:(nlevels(ID_Clone)/2))) %>%
      resca(BLUP, GEBV, GETGV, new_min = 0, new_max = 1) %>%
      mutate(BLUP_GETGV = (BLUP_res + GETGV_res) / 2) |>
      arrange(desc(BLUP_GETGV)) %>%
      slice(1:nrow(Clones_sel_GEBVS)) |>
      droplevels()
    
    XS_comb_GETGV_BLUP <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Comb_sel_GETGV_BLUP$ID_Clone) |>
      select(GETGV) |>
      summarise(X0 = mean(GETGV, na.rm = T))
    
    Comb_sel_GETGV_GEBV <-
      Clones_sel_GETGV |>
      #slice(1:(nlevels(ID_Clone)/2)) |>
      full_join(Clones_sel_GEBVS) |>
      #slice(1:(nlevels(ID_Clone)/2))) %>%
      resca(BLUP, GEBV, GETGV, new_min = 0, new_max = 1) %>%
      mutate(GETGV_GEBV = (GETGV_res + GEBV_res) / 2) |>
      arrange(desc(GETGV_GEBV)) %>%
      slice(1:nrow(Clones_sel_BLUPS)) |>
      droplevels()
    
    XS_comb_GETGV_GEBV <- selection_parents |>
      filter(Trait == j &
               ID_Clone %in% Comb_sel_GETGV_GEBV$ID_Clone) |>
      select(GETGV) |>
      summarise(X0 = mean(GETGV, na.rm = T))
    
    Clones_sel <- BLUP |>
      select(ID_Clone) |>
      mutate(
        BLUPS_sel = ifelse(ID_Clone %in% Clones_sel_BLUPS$ID_Clone,1, 0),
        GEBVS_sel = ifelse(ID_Clone %in% Clones_sel_GEBVS$ID_Clone, 1, 0),
        GETGV_sel = ifelse(ID_Clone %in% Clones_sel_GETGV$ID_Clone, 1, 0),
        Comb_sel_GEBV_BLUP = ifelse(ID_Clone %in% Comb_sel_GEBV_BLUP$ID_Clone, 1, 0),
        Comb_sel_GETGV_BLUP = ifelse(ID_Clone %in% Comb_sel_GETGV_BLUP$ID_Clone, 1, 0),
        Comb_sel_GETGV_GEBV = ifelse(ID_Clone %in% Comb_sel_GETGV_GEBV$ID_Clone, 1, 0),
      )
    
    kappa_GEBV_BLUP =  cohen.kappa(cbind(Clones_sel$BLUPS_sel, Clones_sel$GEBVS_sel))[["kappa"]]
    
    kappa_GETGV_BLUP =  cohen.kappa(cbind(Clones_sel$BLUPS_sel, Clones_sel$GETGV_sel))[["kappa"]]
    
    kappa_GETGV_GEBV =  cohen.kappa(cbind(Clones_sel$GEBVS_sel, Clones_sel$GETGV_sel))[["kappa"]]
    
    kappa_sel_GEBV_BLUP_BLUP =  cohen.kappa(cbind(Clones_sel$BLUPS_sel, Clones_sel$Comb_sel_GEBV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_BLUP_BLUP =  cohen.kappa(cbind(Clones_sel$BLUPS_sel, Clones_sel$Comb_sel_GETGV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_GEBV_BLUP =  cohen.kappa(cbind(Clones_sel$BLUPS_sel, Clones_sel$Comb_sel_GETGV_GEBV))[["kappa"]]
    
    kappa_sel_GEBV_BLUP_GEBV =  cohen.kappa(cbind(Clones_sel$GEBVS_sel, Clones_sel$Comb_sel_GEBV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_BLUP_GEBV =  cohen.kappa(cbind(Clones_sel$GEBVS_sel, Clones_sel$Comb_sel_GETGV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_GEBV_GEBV =  cohen.kappa(cbind(Clones_sel$GEBVS_sel, Clones_sel$Comb_sel_GETGV_GEBV))[["kappa"]]
    
    kappa_sel_GEBV_BLUP_GETGV =  cohen.kappa(cbind(Clones_sel$GETGV_sel, Clones_sel$Comb_sel_GEBV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_BLUP_GETGV =  cohen.kappa(cbind(Clones_sel$GETGV_sel, Clones_sel$Comb_sel_GETGV_BLUP))[["kappa"]]
    
    kappa_sel_GETGV_GEBV_GETGV =  cohen.kappa(cbind(Clones_sel$GETGV_sel, Clones_sel$Comb_sel_GETGV_GEBV))[["kappa"]]
    
    coef_kappa <- data.frame(
      Trait = j,
      SI = i,
      X0 = media_pheno[[j]],
      X0_GEBV = X0_GEBV[[1]],
      X0_GETGV = X0_GETGV[[1]],
      X0_BLUPS = X0_BLUPS[[1]],
      XS_BLUPS = XS_BLUPS[[1]],
      XS_GEBV = XS_GEBV[[1]],
      XS_GETGV = XS_GETGV[[1]],
      XS_GEBV_BLUP = XS_comb_GEBV_BLUP[[1]],
      XS_GETGV_BLUP = XS_comb_GETGV_BLUP[[1]],
      XS_GETGV_GEBV = XS_comb_GETGV_GEBV[[1]],
      kappa_GEBV_BLUP = kappa_GEBV_BLUP,
      kappa_GETGV_BLUP = kappa_GETGV_BLUP,
      kappa_GETGV_GEBV = kappa_GETGV_GEBV,
      kappa_sel_GEBV_BLUP_BLUP = kappa_sel_GEBV_BLUP_BLUP,
      kappa_sel_GETGV_BLUP_BLUP = kappa_sel_GETGV_BLUP_BLUP,
      kappa_sel_GETGV_GEBV_BLUP = kappa_sel_GETGV_GEBV_BLUP,
      kappa_sel_GEBV_BLUP_GEBV = kappa_sel_GEBV_BLUP_GEBV,
      kappa_sel_GETGV_BLUP_GEBV = kappa_sel_GETGV_BLUP_GEBV,
      kappa_sel_GETGV_GEBV_GEBV = kappa_sel_GETGV_GEBV_GEBV,
      kappa_sel_GEBV_BLUP_GETGV = kappa_sel_GEBV_BLUP_GETGV,
      kappa_sel_GETGV_BLUP_GETGV = kappa_sel_GETGV_BLUP_GETGV,
      kappa_sel_GETGV_GEBV_GETGV = kappa_sel_GETGV_GEBV_GETGV
    )
    
    results_kappa <- rbind(results_kappa, coef_kappa)
  }
}

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RKHS, RR.BLUP, BLUP, Weights)`
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Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`
Joining with `by = join_by(ID_Clone, Trait, Bayes.A, Bayes.B, GEBV, GETGV, RF,
RKHS, RR.BLUP, BLUP, Weights)`

Figure 3 Cohen’s Kappa of coincidence

results_kappa |>
  pivot_longer(names_to = "Comparation",
               values_to = "Kappa",
               cols = 13:18) %>%
  ggplot(aes(x = Trait, y = SI, fill = Kappa)) +
  geom_tile() +
  facet_wrap(Comparation ~ .,
             labeller = as_labeller(
               c(
                 kappa_GEBV_BLUP = "GEBV x BLUP",
                 kappa_GETGV_BLUP = "GETGV x BLUP",
                 kappa_GETGV_GEBV = "GEBV x GETGV",
                 kappa_sel_GEBV_BLUP_BLUP = "GEBV_BLUP x BLUP",
                 kappa_sel_GETGV_BLUP_BLUP = "GETGV_BLUP x BLUP",
                 kappa_sel_GETGV_GEBV_BLUP = "GETGV_GEBV x BLUP"
               )
             ), ncol = 2) +
  scale_fill_viridis(discrete = FALSE, limits = c(-0.07, 1)) +
  labs(x = "" , y = "Selection Index", fill = "Kappa") +
  theme(
    text = element_text(size = 20),
    legend.key.width = unit(1.5, 'cm'),
    legend.box = "horizontal",
    legend.position = "top",
    legend.background =  element_blank(),
    strip.background = element_blank(),
    panel.background = element_blank(),
    plot.background = element_blank(),
    axis.text.x = element_text(
      angle = 45,
      hjust = 1,
      vjust = 1
    )
  )

Figure 4: Selection gains

teste <-  results_kappa %>%
  select(Trait, SI, X0, XS_GEBV, XS_GETGV)

p <- list()

for (i in levels(factor(teste$Trait))) {
  breaks <- teste %>%
    filter(Trait == i) %>%
    droplevels() %>%
    group_by(Trait) %>%
    summarise(
      min_X0 = min(X0),
      max_XS = max(c(XS_GEBV, XS_GETGV)),
      mean_X0_XS = mean(c(min_X0, max_XS))
    ) %>%
    round_cols()
  
  p[[i]] <- teste %>%
    filter(Trait == i) %>%
    droplevels() %>%
    ggplot(aes(y = SI,
               x = start)) +
    geom_segment(
      aes(
        x = X0,
        xend = XS_GEBV,
        y = SI,
        yend = SI
      ),
      linewidth = 1,
      color = "gray80"
    ) +
    geom_point(
      data = teste %>%
        filter(Trait == i) %>%
        droplevels() %>%
        pivot_longer(
          names_to = "measure",
          values_to = "value",
          cols = c("X0", "XS_GEBV", "XS_GETGV")
        ),
      aes(y = SI,
          x = value,
          color = measure),
      size = 4,
      alpha = 0.75
    )  +
    scale_x_continuous(
      limits = ~ c(min(.x), max(.x)),
      breaks = c(breaks$min_X0, breaks$mean_X0_XS, breaks$max_XS),
      expand = expansion(mult = ifelse(i == "Plant.Height" , 0.25, 0.15))
    ) +
    scale_color_gdocs() +
    labs(x = i)  +
    theme(
      text = element_text(size = 15),
      legend.text = element_text(size = 15),
      legend.box = "horizontal",
      legend.direction = "horizontal",
      legend.position = "top",
      panel.spacing = unit(2, "lines"),
      legend.title = element_blank(),
      panel.background = element_blank(),
      panel.border = element_blank(),
      plot.background = element_blank(),
      legend.background = element_blank(),
      legend.box.background = element_blank(),
      legend.key = element_blank()
    )
}

annotate_figure(
  ggarrange(
    plotlist = p,
    nrow = 3,
    ncol = 4,
    common.legend = TRUE),
  left = text_grob("Selection Index", rot = 90, size = 20)
)

Supplementary Table 3 - Cohen’s Kappa of coincidence

results_kappa |>
  select(1,2, starts_with("kappa")) %>% 
  kbl(escape = F, align = 'c') |>
  kable_classic("hover", full_width = F, position = "center", fixed_thead = T)

Trait	SI	kappa_GEBV_BLUP	kappa_GETGV_BLUP	kappa_GETGV_GEBV	kappa_sel_GEBV_BLUP_BLUP	kappa_sel_GETGV_BLUP_BLUP	kappa_sel_GETGV_GEBV_BLUP	kappa_sel_GEBV_BLUP_GEBV	kappa_sel_GETGV_BLUP_GEBV	kappa_sel_GETGV_GEBV_GEBV	kappa_sel_GEBV_BLUP_GETGV	kappa_sel_GETGV_BLUP_GETGV	kappa_sel_GETGV_GEBV_GETGV
N_Roots	10	0.0593333	0.0593333	0.8063333	0.5850000	0.6956667	0.0870000	0.4743333	0.3360000	0.9170000	0.3913333	0.3636667	0.8893333
N_Roots	15	0.0453052	0.0647887	0.8051643	0.6298122	0.6103286	0.0842723	0.4154930	0.3960094	0.8830986	0.3765258	0.4544601	0.9025822
N_Roots	20	0.0796185	0.0642788	0.8005840	0.5858283	0.5704887	0.0642788	0.4937902	0.4784505	0.9233015	0.4017521	0.4937902	0.8772825
N_Roots	25	0.0709151	0.1101722	0.8560573	0.5681718	0.5812575	0.0840008	0.5027433	0.4634862	0.9345715	0.5027433	0.5289147	0.9214858
N_Roots	30	0.0784000	0.1133974	0.8250126	0.5566987	0.5800304	0.0784000	0.5217012	0.4750379	0.9066734	0.5450329	0.5333671	0.9183392
FRY	10	0.0593333	0.0870000	0.8893333	0.5850000	0.7233333	0.0870000	0.4743333	0.3360000	0.9446667	0.5020000	0.3636667	0.9446667
FRY	15	0.2312679	0.1736130	0.8270353	0.6348523	0.6925072	0.2120496	0.5964157	0.5195425	0.9231268	0.5387608	0.4811059	0.9039085
FRY	20	0.2176758	0.2023361	0.8772825	0.6011680	0.6471871	0.2330155	0.6165077	0.5704887	0.9693206	0.6011680	0.5551490	0.9079619
FRY	25	0.1886864	0.1756007	0.8691430	0.5943432	0.6466860	0.1886864	0.5943432	0.5420004	0.9345715	0.5681718	0.5289147	0.9345715
FRY	30	0.2801992	0.2453701	0.8490740	0.6400996	0.6865384	0.2685895	0.6400996	0.5820511	0.9419515	0.6052705	0.5588318	0.9071225
ShY	10	-0.0284335	-0.0013695	0.8376158	0.5940394	0.5940394	-0.0284335	0.3775271	0.3504630	0.9458719	0.2963349	0.4045911	0.8917438
ShY	15	0.0198666	0.0967398	0.8078170	0.5579791	0.5579791	0.0198666	0.4618876	0.4234510	0.9039085	0.4811059	0.5387608	0.9039085
ShY	20	0.1337069	0.1793013	0.8784150	0.5136600	0.4984619	0.1641031	0.6200469	0.6352450	0.9088113	0.6352450	0.6808394	0.9696038
ShY	25	0.2251680	0.2380819	0.8579475	0.5480147	0.5092731	0.2380819	0.6771533	0.7029811	0.9483445	0.6771533	0.7288088	0.9096029
ShY	30	0.2951609	0.2258325	0.8844526	0.5378104	0.5262557	0.2489420	0.7573505	0.7342410	0.9537810	0.6880221	0.6995768	0.9306716
DMC	10	0.0238575	0.0238575	1.0000000	0.5467910	0.5467910	0.0238575	0.4770665	0.4770665	1.0000000	0.4770665	0.4770665	1.0000000
DMC	15	0.0953812	0.0953812	1.0000000	0.5110168	0.5110168	0.0953812	0.5843643	0.5843643	1.0000000	0.5843643	0.5843643	1.0000000
DMC	20	0.1467148	0.1467148	1.0000000	0.5259527	0.5259527	0.1467148	0.6207621	0.6207621	1.0000000	0.6207621	0.6207621	1.0000000
DMC	25	0.1389380	0.1389380	1.0000000	0.5375778	0.5375778	0.1389380	0.6013602	0.6013602	1.0000000	0.6013602	0.6013602	1.0000000
DMC	30	0.1130702	0.1130702	1.0000000	0.5288185	0.5288185	0.1130702	0.5842517	0.5842517	1.0000000	0.5842517	0.5842517	1.0000000
StY	10	-0.0060606	-0.0060606	0.8562771	0.6047619	0.5688312	-0.0060606	0.3891775	0.4251082	0.9281385	0.3891775	0.4251082	0.9281385
StY	15	0.1198303	0.1198303	0.9022034	0.6088135	0.6332626	0.1442795	0.5110168	0.4865677	0.9266525	0.5110168	0.4865677	0.9755508
StY	20	0.1928313	0.1736130	0.8654719	0.6156340	0.6348523	0.1543947	0.5771974	0.5579791	0.9423451	0.5579791	0.5387608	0.9231268
StY	25	0.2824483	0.2346115	0.9043264	0.7129793	0.7448705	0.2665027	0.5694690	0.5375778	0.9681088	0.5216322	0.4897410	0.9202720
StY	30	0.2737919	0.2737919	0.9022412	0.6927581	0.7067237	0.3017230	0.5810338	0.5670682	0.9581034	0.5810338	0.5670682	0.9441378
Plant.Height	10	0.1610147	0.1610147	0.9188079	0.5399113	0.5940394	0.1610147	0.6211034	0.5399113	0.9729360	0.5669753	0.5669753	0.9458719
Plant.Height	15	0.2120496	0.1736130	0.8654719	0.5195425	0.5964157	0.2120496	0.6925072	0.5579791	0.9615634	0.6156340	0.5771974	0.9039085
Plant.Height	20	0.2552919	0.2400938	0.8784150	0.5744525	0.6352450	0.2552919	0.6808394	0.6200469	0.9240094	0.6504431	0.6048488	0.9544056
Plant.Height	25	0.2768235	0.2509958	0.8966891	0.5738424	0.5867563	0.2509958	0.7029811	0.6900672	0.9612584	0.6642395	0.6642395	0.9354307
Plant.Height	30	0.3067157	0.2720515	0.8728979	0.6302484	0.6186936	0.2720515	0.6764673	0.6649126	0.9306716	0.6302484	0.6533578	0.9422263
HI	10	-0.0513333	-0.0236667	0.8063333	0.4466667	0.4466667	-0.0236667	0.5020000	0.4743333	0.8893333	0.4743333	0.5296667	0.9170000
HI	15	0.0775215	0.0583032	0.8654719	0.5003242	0.5003242	0.0583032	0.5771974	0.5579791	0.9231268	0.5387608	0.5579791	0.9423451
HI	20	0.1256376	0.1409773	0.9233015	0.5091299	0.5244696	0.1256376	0.6165077	0.5858283	0.9693206	0.6318474	0.6165077	0.9539809
HI	25	0.1363436	0.1363436	0.8953144	0.5289147	0.5420004	0.1363436	0.6074289	0.5812575	0.9476572	0.5943432	0.5943432	0.9476572
HI	30	0.1524926	0.1989313	0.9071225	0.5588318	0.5472221	0.1641023	0.5936608	0.5936608	0.9535612	0.6168802	0.6517093	0.9535612
StC	10	0.0238575	0.0238575	1.0000000	0.5119288	0.5119288	0.0238575	0.5119288	0.5119288	1.0000000	0.5119288	0.5119288	1.0000000
StC	15	0.0709320	0.0709320	1.0000000	0.4865677	0.4865677	0.0709320	0.5843643	0.5843643	1.0000000	0.5843643	0.5843643	1.0000000
StC	20	0.1467148	0.1467148	1.0000000	0.5259527	0.5259527	0.1467148	0.6207621	0.6207621	1.0000000	0.6207621	0.6207621	1.0000000
StC	25	0.1389380	0.1389380	1.0000000	0.5375778	0.5375778	0.1389380	0.6013602	0.6013602	1.0000000	0.6013602	0.6013602	1.0000000
StC	30	0.1269285	0.1269285	1.0000000	0.5426768	0.5426768	0.1269285	0.5842517	0.5842517	1.0000000	0.5842517	0.5842517	1.0000000
Root.Le	10	0.1423333	0.1423333	0.8616667	0.5573333	0.5850000	0.1146667	0.5850000	0.4743333	0.9170000	0.5296667	0.5573333	0.9446667
Root.Le	15	0.2791080	0.2401408	0.8441315	0.5323944	0.5323944	0.2596244	0.7467136	0.7077465	0.9415493	0.7077465	0.7077465	0.9025822
Root.Le	20	0.3250536	0.3403933	0.8466031	0.5244696	0.5704887	0.3250536	0.8005840	0.7238856	0.9386412	0.7852443	0.7699046	0.9079619
Root.Le	25	0.3326291	0.3326291	0.8822287	0.5420004	0.5812575	0.3326291	0.7906287	0.7513716	0.9476572	0.7644573	0.7513716	0.9345715
Root.Le	30	0.3583797	0.3117164	0.8600101	0.5916962	0.6266937	0.3467139	0.7666835	0.6850228	0.9533367	0.6966886	0.6850228	0.8950076
Root.Di	10	0.1146667	0.1146667	1.0000000	0.5296667	0.5296667	0.1146667	0.5850000	0.5850000	1.0000000	0.5850000	0.5850000	1.0000000
Root.Di	15	0.1427230	0.1037559	0.9610329	0.5518779	0.5518779	0.1232394	0.5908451	0.5908451	0.9805164	0.5518779	0.5518779	0.9805164
Root.Di	20	0.2330155	0.2483551	0.9539809	0.5398093	0.5551490	0.2330155	0.6932062	0.6625268	0.9846603	0.6932062	0.6932062	0.9693206
Root.Di	25	0.2672006	0.2802863	0.9607429	0.5943432	0.5812575	0.2802863	0.6728574	0.6859431	0.9738286	0.6859431	0.6990288	0.9869143
Root.Di	30	0.2650531	0.2650531	0.9766684	0.5916962	0.5800304	0.2650531	0.6733569	0.6850228	1.0000000	0.6733569	0.6850228	0.9766684
Stem.D	10	0.1610147	0.0798226	0.7564236	0.6481675	0.5669753	0.1339507	0.5128473	0.5399113	0.8646798	0.3775271	0.5128473	0.8917438
Stem.D	15	0.2036005	0.1656767	0.8483049	0.6207621	0.6018002	0.2036005	0.5828383	0.5828383	0.9241524	0.5069908	0.5638765	0.9241524
Stem.D	20	0.2704900	0.2248956	0.8024244	0.6200469	0.6352450	0.2704900	0.6504431	0.6200469	0.9240094	0.5744525	0.5896506	0.8784150
Stem.D	25	0.3026512	0.2897374	0.8450336	0.7029811	0.7029811	0.3026512	0.5996702	0.5996702	0.9096029	0.5738424	0.5867563	0.9354307
Stem.D	30	0.2984425	0.2984425	0.8159849	0.7814821	0.7469793	0.3099435	0.5169604	0.5054595	0.9079925	0.5054595	0.5514633	0.9079925
Nstem.Plant	10	0.0238575	0.0238575	1.0000000	0.4422043	0.4770665	0.0238575	0.5816532	0.5467910	1.0000000	0.5816532	0.5467910	1.0000000
Nstem.Plant	15	0.1362165	0.1122225	0.9520120	0.4721323	0.4961263	0.1362165	0.6640842	0.6400902	0.9760060	0.6400902	0.6160962	0.9760060
Nstem.Plant	20	0.1391595	0.1391595	0.8877165	0.5508658	0.5695797	0.1391595	0.5882937	0.5695797	0.9251443	0.5695797	0.5695797	0.9625722
Nstem.Plant	25	0.2495479	0.2339135	0.9687312	0.5778707	0.6247740	0.2495479	0.6716772	0.6247740	1.0000000	0.6404084	0.6091395	0.9687312
Nstem.Plant	30	0.2901316	0.2628289	0.9590461	0.5495066	0.5768092	0.2764803	0.7406250	0.7133224	0.9863487	0.7133224	0.6860197	0.9726974

Supplementary Table 4 - Diferential selection

diferencial_selection <- results_kappa %>%
  mutate(DS_GEBV = ((XS_GEBV - X0) / X0)*100,
         DS_GETGV = ((XS_GETGV - X0) / X0)*100) %>%
  select(1:3, XS_GEBV, XS_GETGV, DS_GEBV, DS_GETGV)  

diferencial_selection %>%
  kbl(escape = F, align = 'c') |>
  kable_classic(
    "hover",
    full_width = F,
    position = "center",
    fixed_thead = T
  )

Trait	SI	X0	XS_GEBV	XS_GETGV	DS_GEBV	DS_GETGV
N_Roots	10	4.292942	4.541419	4.557449	5.7880281	6.1614259
N_Roots	15	4.292942	4.503344	4.519741	4.9010983	5.2830616
N_Roots	20	4.292942	4.481755	4.497237	4.3982166	4.7588500
N_Roots	25	4.292942	4.464347	4.475947	3.9927102	4.2629092
N_Roots	30	4.292942	4.453953	4.468989	3.7505913	4.1008390
FRY	10	4.946156	5.621869	5.626471	13.6613850	13.7544159
FRY	15	4.946156	5.535921	5.513274	11.9236965	11.4658381
FRY	20	4.946156	5.456486	5.463552	10.3176999	10.4605750
FRY	25	4.946156	5.396319	5.394556	9.1012692	9.0656263
FRY	30	4.946156	5.344933	5.344045	8.0623704	8.0444023
ShY	10	14.227608	15.614549	15.668526	9.7482357	10.1276180
ShY	15	14.227608	15.396293	15.425476	8.2142027	8.4193181
ShY	20	14.227608	15.253308	15.221572	7.2092241	6.9861649
ShY	25	14.227608	15.193934	15.244274	6.7919073	7.1457241
ShY	30	14.227608	15.144660	15.202695	6.4455773	6.8534854
DMC	10	29.058253	29.629875	29.603745	1.9671586	1.8772381
DMC	15	29.058253	29.547617	29.581090	1.6840798	1.7992711
DMC	20	29.058253	29.460600	29.505979	1.3846225	1.5407891
DMC	25	29.058253	29.425355	29.434808	1.2633324	1.2958617
DMC	30	29.058253	29.388151	29.411355	1.1352988	1.2151511
StY	10	1.516377	1.679046	1.683829	10.7274852	11.0428979
StY	15	1.516377	1.646614	1.647351	8.5886986	8.6372579
StY	20	1.516377	1.636301	1.630397	7.9085461	7.5192118
StY	25	1.516377	1.623207	1.621700	7.0450695	6.9457224
StY	30	1.516377	1.615213	1.614606	6.5178949	6.4778388
Plant.Height	10	1.191888	1.218483	1.214519	2.2313010	1.8987484
Plant.Height	15	1.191888	1.210971	1.209804	1.6010244	1.5031639
Plant.Height	20	1.191888	1.206531	1.205358	1.2285295	1.1300874
Plant.Height	25	1.191888	1.204783	1.204905	1.0818354	1.0921086
Plant.Height	30	1.191888	1.203777	1.205768	0.9974479	1.1644728
HI	10	24.555931	25.967289	25.864255	5.7475261	5.3279358
HI	15	24.555931	25.748140	25.698310	4.8550771	4.6521506
HI	20	24.555931	25.607577	25.631202	4.2826563	4.3788664
HI	25	24.555931	25.451107	25.410918	3.6454574	3.4817930
HI	30	24.555931	25.306057	25.271414	3.0547635	2.9136891
StC	10	24.419548	25.003698	24.973808	2.3921413	2.2697371
StC	15	24.419548	24.917941	24.950143	2.0409587	2.1728304
StC	20	24.419548	24.829042	24.874099	1.6769121	1.8614220
StC	25	24.419548	24.793605	24.800732	1.5317909	1.5609773
StC	30	24.419548	24.755365	24.777394	1.3751977	1.4654074
Root.Le	10	23.214730	23.875648	23.829303	2.8469756	2.6473407
Root.Le	15	23.214730	23.755371	23.707679	2.3288690	2.1234319
Root.Le	20	23.214730	23.697609	23.642656	2.0800555	1.8433369
Root.Le	25	23.214730	23.633618	23.606921	1.8044044	1.6894068
Root.Le	30	23.214730	23.582253	23.606713	1.5831471	1.6885089
Root.Di	10	28.878541	29.679103	29.651571	2.7721691	2.6768318
Root.Di	15	28.878541	29.580579	29.578739	2.4310051	2.4246312
Root.Di	20	28.878541	29.506113	29.506605	2.1731426	2.1748497
Root.Di	25	28.878541	29.445294	29.426660	1.9625430	1.8980161
Root.Di	30	28.878541	29.371944	29.353524	1.7085465	1.6447608
Stem.D	10	2.112489	2.136110	2.135334	1.1181604	1.0814120
Stem.D	15	2.112489	2.132438	2.132330	0.9443425	0.9391971
Stem.D	20	2.112489	2.131034	2.128834	0.8778750	0.7737288
Stem.D	25	2.112489	2.129767	2.129634	0.8178633	0.8115949
Stem.D	30	2.112489	2.129346	2.129527	0.7979657	0.8065366
Nstem.Plant	10	2.130936	2.155840	2.159164	1.1686756	1.3246821
Nstem.Plant	15	2.130936	2.153327	2.155611	1.0507461	1.1579252
Nstem.Plant	20	2.130936	2.150895	2.151224	0.9366498	0.9520742
Nstem.Plant	25	2.130936	2.151740	2.150396	0.9762647	0.9132139
Nstem.Plant	30	2.130936	2.151629	2.149755	0.9710932	0.8831386

SNP-based heritability estimate

results_h2_GBLUP <- readRDS("output/results_cv_G_BLUP.RDS") %>%
  select(Trait, narrow_sense) %>% 
  group_by(Trait) %>%
  summarise(SNP_H2_narrow_sense = mean(narrow_sense))

results_h2_GBLUP %>%
  kbl(escape = F, align = 'c') |>
  kable_classic("hover", full_width = F, position = "center", fixed_thead = T)

Trait	SNP_H2_narrow_sense
DMC	0.3806575
FRY	0.3658975
HI	0.2960106
N_Roots	0.1836692
Nstem.Plant	0.2799462
Plant.Height	0.2965907
Root.Di	0.2868806
Root.Le	0.3153513
ShY	0.3753985
StC	0.3793279
StY	0.4226721
Stem.D	0.4469456

Table 2 Broad-sense heritability and SNP-based heritability

H2 <- read.csv("output/H2_row_col_random.csv")
pheno_mean_sd <- read.csv("output/pheno_mean_sd.csv")

Broad_SNP_h2 <- H2 %>%
  rename(Trait = trait) %>% 
  full_join(results_h2_GBLUP) %>% 
  full_join(pheno_mean_sd %>%
              rename("Trait" = "variable")) %>%
  round_cols(digits = 2) %>%
  mutate(mean = str_c(mean, " (", min, " - ", max, ")")) %>%
  select(Trait, H2_Broad, H2_narrow, SNP_H2_narrow_sense, mean, cv)

Joining with `by = join_by(Trait)`
Joining with `by = join_by(Trait)`

Broad_SNP_h2 %>%
  kbl(escape = F, align = 'c') |>
  kable_classic(
    "hover",
    full_width = F,
    position = "center",
    fixed_thead = T
  )

Trait	H2_Broad	H2_narrow	SNP_H2_narrow_sense	mean	cv
N_Roots	0.79	0.46	0.18	4.29 (0.12 - 15.67)	58.56
FRY	0.66	0.37	0.37	4.95 (0.12 - 22.2)	81.79
ShY	0.83	0.54	0.38	14.23 (0.69 - 61.17)	71.45
DMC	0.79	0.54	0.38	29.06 (11.98 - 48.34)	21.00
StY	0.51	0.27	0.42	1.52 (0.02 - 8.87)	84.01
Plant.Height	0.72	0.35	0.30	1.19 (0.36 - 3.03)	27.43
HI	0.67	0.40	0.30	24.56 (1.57 - 71.97)	48.42
StC	0.79	0.54	0.38	24.42 (7.33 - 43.69)	25.05
Root.Le	0.64	0.24	0.32	23.21 (7 - 47.33)	24.99
Root.Di	0.66	0.30	0.29	28.88 (6.12 - 63.3)	27.35
Stem.D	0.59	0.24	0.45	2.11 (1.01 - 4.37)	17.90
Nstem.Plant	0.47	0.22	0.28	2.13 (1 - 6.67)	44.53

sessionInfo()

R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.utf8    

time zone: America/Sao_Paulo
tzcode source: internal

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

other attached packages:
 [1] ggpubr_0.6.0      viridis_0.6.5     viridisLite_0.4.2 psych_2.4.12     
 [5] metan_1.19.0      ggthemes_5.1.0    kableExtra_1.4.0  lubridate_1.9.4  
 [9] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2      
[13] readr_2.1.5       tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1    
[17] tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
 [4] GGally_2.2.1        tweenr_2.0.3        mathjaxr_1.6-0     
 [7] promises_1.3.2      digest_0.6.37       timechange_0.3.0   
[10] lifecycle_1.0.4     magrittr_2.0.3      compiler_4.3.3     
[13] rlang_1.1.4         sass_0.4.9          tools_4.3.3        
[16] yaml_2.3.10         ggsignif_0.6.4      knitr_1.49         
[19] labeling_0.4.3      mnormt_2.1.1        plyr_1.8.9         
[22] xml2_1.3.6          RColorBrewer_1.1-3  abind_1.4-8        
[25] workflowr_1.7.1     withr_3.0.2         numDeriv_2016.8-1.1
[28] grid_4.3.3          polyclip_1.10-7     git2r_0.35.0       
[31] colorspace_2.1-1    scales_1.3.0        MASS_7.3-60.0.1    
[34] cli_3.6.3           rmarkdown_2.29      ragg_1.3.3         
[37] reformulas_0.4.0    generics_0.1.3      rstudioapi_0.17.1  
[40] tzdb_0.4.0          minqa_1.2.8         cachem_1.1.0       
[43] ggforce_0.4.2       splines_4.3.3       parallel_4.3.3     
[46] vctrs_0.6.5         boot_1.3-31         Matrix_1.6-1       
[49] carData_3.0-5       jsonlite_1.8.9      car_3.1-3          
[52] hms_1.1.3           patchwork_1.3.0     rstatix_0.7.2      
[55] ggrepel_0.9.6       Formula_1.2-5       systemfonts_1.1.0  
[58] jquerylib_0.1.4     glue_1.8.0          nloptr_2.1.1       
[61] ggstats_0.8.0       cowplot_1.1.3       stringi_1.8.4      
[64] gtable_0.3.6        later_1.4.1         lme4_1.1-36        
[67] lmerTest_3.1-3      munsell_0.5.1       pillar_1.10.1      
[70] htmltools_0.5.8.1   R6_2.5.1            textshaping_0.4.1  
[73] Rdpack_2.6.2        rprojroot_2.0.4     evaluate_1.0.3     
[76] lattice_0.22-6      backports_1.5.0     rbibutils_2.3      
[79] broom_1.0.7         httpuv_1.6.15       bslib_0.8.0        
[82] Rcpp_1.0.14         svglite_2.1.3       gridExtra_2.3      
[85] nlme_3.1-166        xfun_0.50           fs_1.6.5           
[88] pkgconfig_2.0.3

Weverton Gomes da Costa, Pós-Doutorando, Embrapa Mandioca e Fruticultura, wevertonufv@gmail.com ↩︎

Clone Selection

Costa, W. G.1

2025-01-20

Configurations and packages

Data

Results

Plot boxplot from Accuracy

Clone Selection

Individual selection to each trait

SNP-based heritability estimate

Costa, W. G.¹