Last updated: 2025-07-29
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Importance-of-markers-for-QTL-detection-by-machine-learning-methods/
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Nesse script, vamos realizar uma análise de associação genômica ampla (GWAS) utilizando o modelo linear generalizado (GLM) com o pacote GAPIT. O objetivo é identificar marcadores genéticos associados a características fenotípicas específicas.
#Vamos carregar os pacotes necessários para a análise. Se o pacote GAPIT não
#estiver instalado, você pode instalá-lo usando o comando abaixo (descomente a
#linha de instalação):
#install.packages("devtools")
#library(devtools)
#devtools::install_github("jiabowang/GAPIT", force=TRUE)
library(GAPIT)
library(tidyverse)Os dados utilizados neste exemplo são simulados pelo software GENES e
foram gerados para fins de análise. Eles incluem informações
fenotípicas, genotípicas e um mapa genético. Para executar este script,
certifique-se de que os arquivos necessários estejam disponíveis no
diretório data/:
# Carregando os dados fenotípicos, genotípicos e o mapa genético
pheno <- read.table("data/FEN.txt")[, c(1:5, 9:13)]
head(pheno) V1 V2 V3 V4 V5 V9 V10 V11
1 105.4142 105.1269 104.8504 104.5909 104.7855 104.4782 105.1641 104.5857
2 99.4438 101.3238 102.9940 104.0350 103.8966 100.5017 103.5687 103.0709
3 97.7980 102.0987 105.3410 104.1339 104.2567 96.5674 102.5507 103.5267
4 101.2721 104.0863 105.0626 103.6401 104.2028 99.9238 102.3068 103.8091
5 104.9151 104.0448 103.1747 103.7256 104.9352 103.9866 102.9234 103.8994
6 100.9011 101.6252 102.8092 103.1760 103.8143 101.4402 101.3351 102.7348
V12 V13
1 104.7281 104.8005
2 103.6733 104.0019
3 103.8272 104.2087
4 103.2741 104.1401
5 103.8341 104.0395
6 102.9343 103.2702# Ajustando os nomes das colunas para garantir que a coluna ID seja a primeira
geno <- read.table("data/GEN.txt") + 1
geno[1:5,1:5] V1 V2 V3 V4 V5
1 1 1 1 1 1
2 1 1 1 1 1
3 1 1 1 1 1
4 0 0 0 0 0
5 1 1 1 1 1# Carregando o mapa genético
GM <- readRDS("data/map.rds")
GM$GL <- as.numeric(GM$GL)
GM$marker <- as.character(GM$marker)
head(GM) marker GL Tamanho
1 1 1 0.0
2 2 1 0.5
3 3 1 1.0
4 4 1 1.5
5 5 1 2.0
6 6 1 2.5Para garantir que os dados estejam no formato correto para a análise, precisamos ajustar as colunas de ID e marcador nos data frames de genótipo e mapa genético. A coluna ID deve ser a primeira no data frame de genótipos, e a coluna marker deve ser a primeira no data frame do mapa genético.
Vamos ajustar o modelo GLM para cada um dos fenótipos disponíveis no conjunto de dados. O loop abaixo itera sobre os fenótipos e executa a análise GWAS para cada um deles, salvando os resultados em arquivos separados.
O pacote GAPIT é utilizado para realizar a análise de associação, especificando o modelo GLM e os dados genotípicos e fenotípicos. O modelo GLM é adequado para dados fenotípicos contínuos e pode ser utilizado para identificar associações entre marcadores genéticos e características fenotípicas
# Definindo os índices dos genótipos e fenótipos
traits <- 1:ncol(pheno)
# Ajuste da GWAS e coleta dos resultados
for (j in 1:10) {
# Atualizando Y para garantir que a coluna ID seja a primeira
Y <- pheno[j]
Y$ID <- rownames(pheno)
Y <- Y[, c("ID", names(Y)[names(Y) != "ID"])]
modelo <- GAPIT(
Y = Y,
GD = GD,
GM = GM,
model = "GLM",
file.output = T,
file.path = "output/"
)
}Após a execução do modelo GLM, os resultados são salvos em arquivos CSV. Vamos ler esses arquivos, adicionar informações sobre o método e a variável analisada, e consolidar os resultados em um único data frame.
results_gwas <-
list.files(path = "output/",
pattern = "GAPIT.Association.GWAS_Results",
full.names = TRUE)
results_gwas <- map(results_gwas, function(i) {
gwas <- read.csv(i, header = T, sep = ",")
gwas$variable <- str_split_i(i, "[.]", -2)
gwas$method <- "GLM"
gwas
})
results_gwas <- bind_rows(results_gwas)
save(results_gwas, file = "output/results_gwas.rda")Após a execução do código acima, você terá um data frame
results_gwas contendo os resultados da análise GWAS para
cada fenótipo, com as colunas adicionais variable e
method indicando a variável analisada e o método
utilizado.
O próximo passo é analisar esses resultados para verificar quais métodos apresentaram melhor desempenho na identificação de marcadores associados aos fenótipos estudados. Isso será feita no próximo script, onde compararemos os resultados do GLM com os resultados obtidos por métodos de machine learning GWAS.html.
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
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] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.1.0 readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
[9] ggplot2_3.5.2 tidyverse_2.0.0 GAPIT_3.5.0
loaded via a namespace (and not attached):
[1] sass_0.4.10 generics_0.1.4 stringi_1.8.7 hms_1.1.3
[5] digest_0.6.37 magrittr_2.0.3 timechange_0.3.0 evaluate_1.0.4
[9] grid_4.4.1 RColorBrewer_1.1-3 fastmap_1.2.0 rprojroot_2.0.4
[13] workflowr_1.7.1 jsonlite_2.0.0 whisker_0.4.1 promises_1.3.3
[17] scales_1.4.0 jquerylib_0.1.4 cli_3.6.5 rlang_1.1.6
[21] withr_3.0.2 cachem_1.1.0 yaml_2.3.10 tools_4.4.1
[25] tzdb_0.5.0 httpuv_1.6.16 vctrs_0.6.5 R6_2.6.1
[29] lifecycle_1.0.4 git2r_0.36.2 fs_1.6.6 pkgconfig_2.0.3
[33] pillar_1.11.0 bslib_0.9.0 later_1.4.2 gtable_0.3.6
[37] glue_1.8.0 Rcpp_1.1.0 xfun_0.52 tidyselect_1.2.1
[41] rstudioapi_0.17.1 knitr_1.50 farver_2.1.2 htmltools_0.5.8.1
[45] rmarkdown_2.29 compiler_4.4.1