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Introduction

This script estimates BLUPs (Best Linear Unbiased Predictions) for key agronomic and quality traits in cassava across different breeding cycles (C0, C1, C2).
The goals are:
- Fit mixed models by trial and across trials
- Estimate heritability (H²) and BLUPs for each clone
- Compare genetic progress across cycles (C0 → C1 → C2)
- Visualize results with regression plots and boxplots
- Compute a Selection Index (SI) integrating multiple traits

Step 1: Load Packages

library(data.table)   # Efficient data handling
library(metan)        # Mixed models and BLUPs
library(tidyverse)    # Data wrangling and visualization
library(foreach)      # Parallel loops
library(doParallel)   # Parallel backend
library(ggthemes)     # Plot themes
library(ggpubr)       # Publication-ready plots
library(ggpmisc)      # Regression annotations
library(tidytext)     # Text manipulation

Comment:
These packages provide the statistical and visualization tools needed for BLUP estimation and interpretation.

Step 2: Load Data

phenosPod <- readxl::read_excel("data/Dados_podridao_2018_2021.xlsx", na = "NA")

dbdata <- readRDS(here::here("data", "phenotypes_cleaned.rds")) %>%
  left_join(phenosPod, by = "observationUnitName") %>%
  filter(!is.na(studyYear))

Interpretation:
We merge the cleaned phenotypic dataset with pod rot data (Podridao). Trials without year information are excluded.

Step 3: Define Effects and Traits

efeitos <- c("germplasmName","yearInLoc","blockInTrial","studyYear",
             "trialInLocYr","studyName","Podridao","Pop","entryType")

traits <- c("DMCg", "DRY", "PA", "StC", "FRW", "FSW")

Interpretation:
- Effects: identifiers for genotype, environment, design, and population.
- Traits: dry matter, yield, plant architecture, starch, root and shoot weights.

Step 4: Data Filtering

We keep only relevant trials, remove those with high pod rot, and retain the dominant population per trial.

trialInLocYr_Pop_top <- dbdata %>%
  group_by(trialInLocYr, Pop) %>%
  tally() %>%
  filter(!is.na(Pop)) %>%
  top_n(1, n) %>%
  filter(str_detect(trialInLocYr, "UYT|AYT|PYT")) %>%
  filter(!(str_detect(trialInLocYr, "PYT") & Pop == "C1")) %>%
  rename(Population = Pop)

podridao <- dbdata %>%
  group_by(trialInLocYr) %>%
  summarise_if(is.numeric, ~ mean(.x, na.rm = TRUE)) %>%
  filter(Podridao >= 0.5)

dbdata <- dbdata %>%
  select(all_of(efeitos), all_of(traits)) %>%
  filter(studyYear >= 2019 & !(trialInLocYr %in% podridao$trialInLocYr)) %>%
  full_join(trialInLocYr_Pop_top)

Interpretation:
- Trials with >50% pod rot are excluded.
- Only multi-environment trials (UYT, AYT, PYT) are kept.
- Ensures data quality and comparability across cycles.

Step 5: Define Checks

checks <- c("BRS-NovoHorizonte","BGM-2017","BRS-Mulatinha","BGM-2095","BGM-2151")

Interpretation:
These are standard check varieties used for benchmarking genetic gain.

Step 6: BLUP Estimation by Trial

We define a helper function to extract heritability (H²), BLUPs, and predicted means from each model.

analise_metan <- function(model, trait, trial) {
  H2 <- get_model_data(model, "genpar") %>%
    filter(Parameters == "H2") %>%
    pull(trait)
  
  BLUPS <- get_model_data(model, "ranef")$GEN
  
  Predicted_values <- predict(model) %>%
    group_by(GEN) %>%
    summarise(across(where(is.numeric), mean)) %>%
    pull(trait)
  
  tibble(trialInLocYr = trial, trait = trait, H2 = H2,
         germplasmName = BLUPS[[1]], BLUPS = BLUPS[[2]],
         Predicted = Predicted_values)
}

We then run models in parallel for efficiency.

num_cores <- detectCores()
registerDoParallel(cores = num_cores)

BLUPS <- foreach(trait = traits, .combine = bind_rows, .packages  = c("tidyverse", "metan")) %dopar% {
  DRG <- list()
  for (l in unique(dbdata$trialInLocYr)) {
    data <- dbdata %>% filter(trialInLocYr == l) %>% droplevels()
    if (!is.na(mean(data[[trait]], na.rm = TRUE))) {
      model <- gamem(data, gen = germplasmName, rep = blockInTrial, resp = sym(trait))
      DRG <- append(DRG, list(analise_metan(model, trait, l)))
    }
  }
  bind_rows(DRG)
}
registerDoSEQ()

Interpretation:
This step fits mixed models per trial and extracts BLUPs and H² for each trait.

Step 6.1: Adicionar variável Pop

Classificamos os clones em suas respectivas populações (C0, C1, C2), com base no nome ou código de tecido.

C0 <- fread("data/TP-BR-18-EMBRAPA-Nextgen.csv",
            sep = ";",
            na.strings = "") %>%
      filter(!tissue_id %in% read.table("data/exclud_c0.csv")[[1]])

BLUPS1 <- BLUPS %>%
  mutate(Pop = as.factor(
    case_when(
      germplasmName %like% "BR-20GS-" |
      germplasmName %like% "BR-19GS-" ~ "C2",
      germplasmName %like% "BR-18GS-"  ~ "C1",
      germplasmName %in% C0$tissue_id  ~ "C0"
    )
  )) %>%
  mutate(entryType = ifelse(germplasmName %in% checks, "check", "test"))

#save(BLUPS1, file = "output/BLUPS_metan.RData")

Interpretation: - Each clone is assigned to its breeding cycle. - This classification is crucial for comparing genetic gains across cycles.

Interpretação: Essa classificação permite comparar o desempenho genético entre ciclos de seleção.

C0: população base (fundadores)

C1: clones selecionados em 2018

C2: clones selecionados em 2019–2020

Step 7: Regression Across Cycles

We test whether predicted means increase across populations (C0 → C1 → C2).

resultado <- NULL
for (i in traits) {
  test <- BLUPS1 %>%
    filter(trait == i & !is.na(Pop) & H2 >= 0.5) %>%
    aov(formula = Predicted ~ as.numeric(Pop))
  pvalue <- round(anova(test)[[1, 5]], 4)
  resultado <- c(resultado, paste0(i,": ",round(coef(test)[[2]], 4),
                                   " (p", ifelse(pvalue < 0.001,"<0.001",paste0("=",pvalue)),")"))
}
names(resultado) <- traits

We then plot regression lines:

BLUPS1 %>%
  filter(!is.na(Pop) & H2 >= 0.5) %>%
  ggplot(aes(x = as.numeric(Pop), y = Predicted)) +
  geom_point() +
  stat_poly_line() +
  facet_wrap(~ trait, ncol = 3, scales = "free_y", labeller = as_labeller(resultado)) +
  theme_bw(base_size = 15) +
  scale_x_continuous(breaks = 1:3, labels = c("C0","C1","C2")) +
  labs(x = "Population", y = "")

Interpretation:
Regression slopes indicate genetic gain per cycle. Significant positive slopes confirm progress.

Step 8: Boxplots by Cycle

BLUPS1 %>%
  full_join(trialInLocYr_Pop_top) %>%
  filter(!is.na(Population) & H2 >= 0.5) %>%
  mutate(entryType = ifelse(germplasmName %in% checks, "check", "test")) %>%
  ggboxplot(x = "Population", y = "Predicted", fill = "entryType") +
  facet_wrap(trait ~ ., scales = "free_y", strip.position = "left") +
  theme_bw(base_size = 15) +
  labs(x = "Population", y = "", fill = "Entry Type")

Interpretation:
Boxplots show the distribution of predicted values per cycle, contrasting checks vs tests. This highlights the superiority of selected clones over checks.

Step 9: Joint Analysis

Até aqui, rodamos modelos por ensaio. Agora, fazemos uma análise conjunta (multi‑environment trial analysis), que permite estimar BLUPs considerando todos os ambientes (locais/anos) simultaneamente. Isso aumenta a precisão e permite avaliar a estabilidade dos clones.

Função auxiliar

analise_metan_joint <- function(model, trait) {
  H2 <- get_model_data(model, "genpar") %>%
    filter(Parameters == "Heritability") %>%
    pull(trait)
  
  BLUPS <- get_model_data(model, "ranef")$GEN
  
  Predicted_values <- predict(model) %>%
    group_by(GEN) %>%
    summarise(across(where(is.numeric), mean)) %>%
    pull(trait)
  
  tibble(
    trait = trait,
    H2 = H2,
    germplasmName = BLUPS[[1]],
    BLUPS = BLUPS[[2]],
    Predicted = Predicted_values
  )
}

Comentário:
Essa função retorna, para cada característica:
- H² (heritabilidade) → precisão da seleção.
- BLUPs → valores ajustados por clone.
- Predicted → médias preditas considerando todos os ambientes.

Obtenção dos BLUPs conjuntos

num_cores <- detectCores()
registerDoParallel(cores = num_cores)

BLUPS_join <- foreach(
  trait = traits,
  .combine = bind_rows,
  .multicombine = TRUE,
  .verbose = TRUE,
  .packages = c("tidyverse", "metan")
) %dopar% {
  DRG <- list()
  
  data <- dbdata %>%
    select(1:6, trait, 16) %>%
    na.omit() %>%
    droplevels()
  
  if (!is.na(mean(data[[trait]], na.rm = TRUE))) {
    model <- gamem_met(
      data,
      env = trialInLocYr,
      gen = germplasmName,
      rep = blockInTrial,
      resp = sym(trait)
    )
    DRG <- append(DRG, list(analise_metan_joint(model, trait)))
  }
  
  bind_rows(DRG)
}

discovered package(s): 
automatically exporting the following variables from the local environment:
  analise_metan_joint, dbdata 
explicitly exporting package(s): tidyverse, metan
numValues: 6, numResults: 0, stopped: TRUE
got results for task 1
numValues: 6, numResults: 1, stopped: TRUE
returning status FALSE
got results for task 2
numValues: 6, numResults: 2, stopped: TRUE
returning status FALSE
got results for task 3
numValues: 6, numResults: 3, stopped: TRUE
returning status FALSE
got results for task 4
numValues: 6, numResults: 4, stopped: TRUE
returning status FALSE
got results for task 5
numValues: 6, numResults: 5, stopped: TRUE
returning status FALSE
got results for task 6
numValues: 6, numResults: 6, stopped: TRUE
first call to combine function
evaluating call object to combine results:
  fun(result.1, result.2, result.3, result.4, result.5, result.6)
returning status TRUE

registerDoSEQ()

Interpretação:
Aqui usamos o gamem_met para ajustar modelos mistos multiambiente. Isso permite separar efeitos de genótipo, ambiente e interação G×E, fornecendo BLUPs mais robustos.

Step 10: Adicionar variável Pop

C0 <- fread("data/TP-BR-18-EMBRAPA-Nextgen.csv",
            sep = ";",
            na.strings = "") %>%
      filter(!tissue_id %in% read.table("data/exclud_c0.csv")[[1]])

BLUPS_join1 <- BLUPS_join %>%
  mutate(Pop = as.factor(
    case_when(
      germplasmName %like% "BR-20GS-" |
      germplasmName %like% "BR-19GS-" ~ "C2",
      germplasmName %like% "BR-18GS-" |
      germplasmName %like% "BR-18-040.40" ~ "C1",
      germplasmName %in% C0$tissue_id  ~ "C0"
    )
  ))

#save(BLUPS_join1, file = "output/BLUPS_metan_join.RData")

Interpretação:
Cada clone recebe sua classificação de ciclo (C0, C1, C2). Isso é essencial para comparar ganhos genéticos entre gerações.

Excelente, Weverton 🙌. Esse trecho já está muito bem estruturado, mas podemos deixar as descrições e interpretações ainda mais claras e didáticas, para que qualquer leitor entenda não só o que foi feito, mas também por que foi feito e como interpretar os resultados.

Step 11: Selection Index (SI)

O Índice de Seleção (SI) é uma métrica composta que integra múltiplas características em uma única pontuação ponderada.
Ele reflete os objetivos do programa de melhoramento, atribuindo maior peso às características de maior relevância (ex.: rendimento e qualidade de raiz).

BLUPS_join1 <- BLUPS_join1 %>%
  select(-H2,-BLUPS) %>%
  pivot_wider(names_from = trait, values_from = Predicted) %>%
  mutate(
    SI = 5 * coalesce(DMCg, 0) +
      8 * (coalesce(PA, 0) + coalesce(FSW, 0)) +
      10 * (coalesce(StC, 0) + coalesce(FRW, 0) + coalesce(DRY, 0)),
    entryType = if_else(germplasmName %in% checks, "check", "test"),
    germplasmName = reorder(germplasmName, desc(SI))
  )

Interpretação:
- Os pesos (5, 8, 10) refletem a prioridade do programa:
- Rendimento (FRW, DRY, StC) → peso maior (10).
- Arquitetura e biomassa aérea (PA, FSW) → peso intermediário (8).
- Qualidade (DMCg) → peso menor, mas ainda relevante (5).
- O SI permite ranquear clones de forma integrada, evitando decisões baseadas em apenas uma característica.
- Clones com SI mais alto são os principais candidatos à seleção.

Step 12: Visualização dos Melhores Clones por Ciclo

Gráfico de Barras – GS-C1

Aqui visualizamos os 30 melhores clones da população C1 (mais checks), ranqueados pelo SI.

BLUPS_join1 %>% 
  mutate(entryType = if_else(germplasmName %in% checks, "check", "test")) %>% 
  filter(Pop == "C1" | entryType == "check") %>%
  droplevels() %>% 
  group_by(germplasmName) %>%
  arrange(desc(SI)) %>%
  ungroup() %>%
  slice(1:30) %>%
  ggplot(aes(x = germplasmName, y = SI, fill = entryType)) +
  geom_col(width = 0.75, alpha = 0.9, show.legend = F) +
  scale_fill_gdocs() +
  theme_minimal() +
  scale_x_reordered() +
  theme(text = element_text(size = 15, face = "bold"),
        axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1.1),
        legend.title = element_blank(),
        legend.position = "bottom",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.spacing.x = unit(0.2, "lines"),
        strip.background = element_blank(),
        strip.text.x = element_text(size = 12, face = "bold")) +
  labs(x = "Clone",
       y = "Selection Index",
       fill = "Entry Type",
       title = "GS-C1")

Interpretação:
- O gráfico mostra os 30 clones mais promissores da C1.
- A presença dos checks permite comparar o desempenho dos novos clones com variedades já conhecidas.
- Clones acima dos checks indicam avanço genético real.

Gráfico de Barras – GS-C2

O mesmo procedimento é feito para a população C2.

BLUPS_join1 %>% 
  mutate(entryType = if_else(germplasmName %in% checks, "check", "test")) %>%
  filter(Pop == "C2" | entryType == "check") %>%
  droplevels() %>% 
  group_by(germplasmName) %>%
  arrange(desc(SI)) %>%
  ungroup() %>%
  slice(1:30) %>%
  ggplot(aes(x = germplasmName, y = SI, fill = entryType)) +
  geom_col(width = 0.75, alpha = 0.9, show.legend = F) +
  scale_fill_gdocs() +
  theme_minimal() +
  scale_x_reordered() +
  theme(text = element_text(size = 15, face = "bold"),
        axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1.1),
        legend.title = element_blank(),
        legend.position = "bottom",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.spacing.x = unit(0.2, "lines"),
        strip.background = element_blank(),
        strip.text.x = element_text(size = 12, face = "bold")) +
  labs(x = "Clone",
       y = "Selection Index",
       fill = "Entry Type",
       title = "GS-C2")

Interpretação:
- Os 30 melhores clones da C2 apresentam, em geral, SI mais alto que os da C1.
- Isso confirma o ganho genético entre ciclos, com C2 superando C1 e os checks.
- O gráfico evidencia a eficiência da seleção genômica no programa.

Conclusão

Neste script, realizamos:
- Estimativa de BLUPs por ensaio e conjunta
- Cálculo de H² para avaliar confiabilidade
- Comparação entre ciclos (C0 → C1 → C2) via regressão e boxplots
- Construção de um Índice de Seleção (SI) para priorizar clones superiores
- Visualização dos 30 melhores clones por ciclo em gráficos de barras

Mensagem-chave:
Os resultados confirmam ganhos genéticos consistentes ao longo dos ciclos, com clones mais recentes (C2) apresentando desempenho superior em rendimento e qualidade.
O uso de BLUPs e índices de seleção fornece uma base sólida para decisões estratégicas no programa de melhoramento, acelerando o lançamento de variedades mais produtivas e adaptadas.

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] tidytext_0.4.3    ggpmisc_0.6.2     ggpp_0.5.9        ggpubr_0.6.1     
 [5] ggthemes_5.1.0    doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2    
 [9] lubridate_1.9.4   forcats_1.0.0     stringr_1.5.2     dplyr_1.1.4      
[13] purrr_1.1.0       readr_2.1.5       tidyr_1.3.1       tibble_3.3.0     
[17] ggplot2_4.0.0     tidyverse_2.0.0   metan_1.19.0      data.table_1.17.8

loaded via a namespace (and not attached):
 [1] Rdpack_2.6.4        polynom_1.4-1       readxl_1.4.5       
 [4] rlang_1.1.6         magrittr_2.0.4      git2r_0.36.2       
 [7] compiler_4.5.1      vctrs_0.6.5         quantreg_6.1       
[10] pkgconfig_2.0.3     fastmap_1.2.0       backports_1.5.0    
[13] labeling_0.4.3      promises_1.3.3      rmarkdown_2.29     
[16] tzdb_0.5.0          nloptr_2.2.1        MatrixModels_0.5-4 
[19] xfun_0.53           cachem_1.1.0        jsonlite_2.0.0     
[22] SnowballC_0.7.1     later_1.4.4         tweenr_2.0.3       
[25] broom_1.0.10        R6_2.6.1            bslib_0.9.0        
[28] stringi_1.8.7       RColorBrewer_1.1-3  GGally_2.4.0       
[31] car_3.1-3           boot_1.3-31         cellranger_1.1.0   
[34] jquerylib_0.1.4     numDeriv_2016.8-1.1 Rcpp_1.1.0         
[37] knitr_1.50          httpuv_1.6.16       Matrix_1.7-3       
[40] splines_4.5.1       timechange_0.3.0    tidyselect_1.2.1   
[43] rstudioapi_0.17.1   abind_1.4-8         yaml_2.3.10        
[46] codetools_0.2-20    lattice_0.22-7      lmerTest_3.1-3     
[49] withr_3.0.2         S7_0.2.0            evaluate_1.0.5     
[52] survival_3.8-3      ggstats_0.11.0      polyclip_1.10-7    
[55] pillar_1.11.1       carData_3.0-5       janeaustenr_1.0.0  
[58] whisker_0.4.1       reformulas_0.4.1    generics_0.1.4     
[61] rprojroot_2.1.1     mathjaxr_1.8-0      hms_1.1.3          
[64] scales_1.4.0        minqa_1.2.8         glue_1.8.0         
[67] tools_4.5.1         tokenizers_0.3.0    lme4_1.1-37        
[70] SparseM_1.84-2      ggsignif_0.6.4      fs_1.6.6           
[73] grid_4.5.1          rbibutils_2.3       nlme_3.1-168       
[76] patchwork_1.3.2     ggforce_0.5.0       Formula_1.2-5      
[79] cli_3.6.5           workflowr_1.7.2     gtable_0.3.6       
[82] rstatix_0.7.2       sass_0.4.10         digest_0.6.37      
[85] ggrepel_0.9.6       farver_2.1.2        htmltools_0.5.8.1  
[88] lifecycle_1.0.4     here_1.0.2          MASS_7.3-65

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

Blups cycles

Costa, W. G.¹

2025-10-04

Introduction

Step 1: Load Packages

Step 2: Load Data

Step 3: Define Effects and Traits

Step 4: Data Filtering

Step 5: Define Checks

Step 6: BLUP Estimation by Trial

Step 6.1: Adicionar variável Pop

Step 7: Regression Across Cycles

Step 8: Boxplots by Cycle

Step 9: Joint Analysis

Função auxiliar

Obtenção dos BLUPs conjuntos

Step 10: Adicionar variável Pop

Step 11: Selection Index (SI)

Step 12: Visualização dos Melhores Clones por Ciclo

Gráfico de Barras – GS-C1

Gráfico de Barras – GS-C2

Conclusão

Blups cycles

Costa, W. G.1

2025-10-04

Introduction

Step 1: Load Packages

Step 2: Load Data

Step 3: Define Effects and Traits

Step 4: Data Filtering

Step 5: Define Checks

Step 6: BLUP Estimation by Trial

Step 6.1: Adicionar variável Pop

Step 7: Regression Across Cycles

Step 8: Boxplots by Cycle

Step 9: Joint Analysis

Função auxiliar

Obtenção dos BLUPs conjuntos

Step 10: Adicionar variável Pop

Step 11: Selection Index (SI)

Step 12: Visualização dos Melhores Clones por Ciclo

Gráfico de Barras – GS-C1

Gráfico de Barras – GS-C2

Conclusão

Costa, W. G.¹