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A genotype by trait biplot can help understand the relationships among traits (breeding objectives) and help identify traits that are positively or negatively associated, traits that are redundantly measured, and traits that can be used in indirect selection for another trait. It also helps to visualize the trait profiles (strength and weakness) of genotypes, which is important for parent as well as variety selection. The code How to compute GT Biplots in R ? library(metan) attach(data_ge2) View(data_ge2) mod =gtb(data_ge2, GEN, resp = contains("E")) Basic Biplot plot(mod, type = 1, col.gen = 'blue', col.env = 'red', size.text.gen = 4) #The Average Tester Coordination view for genotype-trait plot(mod, type = 2, col.gen = 'blue', col.env = 'red', size.text.gen = 3,repel=TRUE) Which-won-where plot(mod, type = 3, col.gen = 'blue', col.env = 'red', size.text.gen = 4) Discriminativeness vs. representativeness. plot(mod, type = 4, col.gen = 'blue', col.env = 'red', size.text.gen = 4) Examine trait plot(mod, type = 5, col.gen = 'blue', col.env = 'red', size.text.gen = 4)# not worked plot(mod,sel_env = "ENV") #Ranking traits plot(mod, type = 6, col.gen = 'blue', col.env = 'red', size.text.gen = 4) Examine a genotype plot(mod, type = 7, col.gen = 'blue', col.env = 'red', size.text.gen = 4)# not worked plot(mod,sel_gen = "GEN") #Ranking genotypes plot(mod, type = 8, col.gen = 'blue', col.env = 'red', size.text.gen = 4) #Compare two genotypes plot(mod, type = 9, col.gen = 'blue', col.env = 'red', size.text.gen = 4)# not worked plot(mod,sel_gen1 = "H1",sel_gen2 = "H10") Relationship among traits plot(mod, type = 10, col.gen = 'blue', col.env = 'red', size.text.gen = 4)