Mapa Genético
Costa, W. G.
2025-03-25
Last updated: 2025-03-25
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Knit directory:
Importance-of-markers-for-QTL-detection-by-machine-learning-methods/
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Importance of markers for QTL detection by machine learning methods
Creating Genetic Map Chart
Libraries
Genetic map
Primeiro vamos definir nossos SNPs de interesse para as variáveis.
Essas informações foram pré-definidas e podem ser encontrados no arquivo
control_genetic. Vamos carregar o
arquivo snpsOfInterest.RData“ que comtemas infomações dos
snps considerados QTLs por variavel.
Agora vamos definir os nomes das colunas para nosso
snpsOfInterest e criar um objeto locus com as
informações de ininício e témino de cada grupo de ligação.
locus <-
data.frame(
c(
1,
401,
402,
802,
803,
1203,
1204,
1604,
1605,
2005,
2006,
2406,
2407,
2807,
2808,
3208,
3209,
3609,
3610,
4010
)
)
colnames(locus) <- c("marker")Para facilitar a visualização criei um gráfico genético das
características map_plot. Para isso criei o
map com o número de marcadores e tamanho de cada grupo de
liagação.
map <-
data.frame(rbind(
cbind(seq(1, 401, 1), rep("LG 1", 401), seq(0, 200, 0.5)),
cbind(seq(402, 802, 1), rep("LG 2", 401), seq(0, 200, 0.5)),
cbind(seq(803, 1203, 1), rep("LG 3", 401), seq(0, 200, 0.5)),
cbind(seq(1204, 1604, 1), rep("LG 4", 401), seq(0, 200, 0.5)),
cbind(seq(1605, 2005, 1), rep("LG 5", 401), seq(0, 200, 0.5)),
cbind(seq(2006, 2406, 1), rep("LG 6", 401), seq(0, 200, 0.5)),
cbind(seq(2407, 2807, 1), rep("LG 7", 401), seq(0, 200, 0.5)),
cbind(seq(2808, 3208, 1), rep("LG 8", 401), seq(0, 200, 0.5)),
cbind(seq(3209, 3609, 1), rep("LG 9", 401), seq(0, 200, 0.5)),
cbind(seq(3610, 4010, 1), rep("LG 10", 401), seq(0, 200, 0.5))
))
colnames(map) <- c("marker", "LG", "Size")
map <- map %>%
mutate(
marker = as.numeric(marker),
Size = as.numeric(Size),
LG = factor(
LG,
levels = c(
"LG 1",
"LG 2",
"LG 3",
"LG 4",
"LG 5",
"LG 6",
"LG 7",
"LG 8",
"LG 9",
"LG 10"
)
)
)Para dividir a figura e mostrar todos os maps genômicos das
características, dividi o snpsOfInterest e o
map para cada característica e inclui os SNPs de interesse
no map para cada característica.
snpsOfInterest1 <- snpsOfInterest %>%
filter(variable == 1)
snpsOfInterest2 <- snpsOfInterest %>%
filter(variable == 2)
snpsOfInterest3 <- snpsOfInterest %>%
filter(variable == 3)
snpsOfInterest4 <- snpsOfInterest %>%
filter(variable == 4)
snpsOfInterest5 <- snpsOfInterest %>%
filter(variable == 5)
map1 <- map %>%
mutate(
is_highlight = ifelse(marker %in% snpsOfInterest1$marker, "yes", "no"),
is_locus = ifelse(marker %in% locus$marker, "yes", "no")
)
map2 <- map %>%
mutate(
is_highlight = ifelse(marker %in% snpsOfInterest2$marker, "yes", "no"),
is_locus = ifelse(marker %in% locus$marker, "yes", "no")
)
map3 <- map %>%
mutate(
is_highlight = ifelse(marker %in% snpsOfInterest3$marker, "yes", "no"),
is_locus = ifelse(marker %in% locus$marker, "yes", "no")
)
map4 <- map %>%
mutate(
is_highlight = ifelse(marker %in% snpsOfInterest4$marker, "yes", "no"),
is_locus = ifelse(marker %in% locus$marker, "yes", "no")
)
map5 <- map %>%
mutate(
is_highlight = ifelse(marker %in% snpsOfInterest5$marker, "yes", "no"),
is_locus = ifelse(marker %in% locus$marker, "yes", "no")
)Agora cirei o gráfico de cada característica e depois agrupei eles em
apenas uma imagem maps.
map_plot1 <- ggplot(map1, aes(x = LG, y = Size)) +
geom_segment(aes(
yend = 200,
y = 0,
x = LG,
xend = LG
),
color = "skyblue",
size = 1) +
geom_point(
data = subset(map1, is_locus == "yes"),
color = "skyblue",
size = 0.5
) +
geom_point(
data = subset(map1, is_highlight == "yes"),
color = "Orange",
size = 0.5
) +
geom_text_repel(
data = subset(map1, is_highlight == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
geom_text_repel(
data = subset(map1, is_locus == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
scale_x_discrete(expand = expansion(mult = c(0.15, 0.05))) +
scale_y_continuous(expand = expansion(mult = c(0.03, 0.05))) +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 4),
axis.ticks = element_blank()
) +
labs(y = "", x = "")
map_plot2 <- ggplot(map2, aes(x = LG, y = Size)) +
geom_segment(aes(
yend = 200,
y = 0,
x = LG,
xend = LG
),
color = "skyblue",
size = 1) +
geom_point(
data = subset(map2, is_locus == "yes"),
color = "skyblue",
size = 0.5
) +
geom_point(
data = subset(map2, is_highlight == "yes"),
color = "Orange",
size = 0.5
) +
geom_text_repel(
data = subset(map2, is_highlight == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
geom_text_repel(
data = subset(map2, is_locus == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
scale_x_discrete(expand = expansion(mult = c(0.15, 0.05))) +
scale_y_continuous(expand = expansion(mult = c(0.03, 0.05))) +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 4),
axis.ticks = element_blank()
) +
labs(y = "", x = "")
map_plot3 <- ggplot(map3, aes(x = LG, y = Size)) +
geom_segment(aes(
yend = 200,
y = 0,
x = LG,
xend = LG
),
color = "skyblue",
size = 1) +
geom_point(
data = subset(map3, is_locus == "yes"),
color = "skyblue",
size = 0.5
) +
geom_point(
data = subset(map3, is_highlight == "yes"),
color = "Orange",
size = 0.5
) +
geom_text_repel(
data = subset(map3, is_highlight == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
geom_text_repel(
data = subset(map3, is_locus == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
scale_x_discrete(expand = expansion(mult = c(0.15, 0.05))) +
scale_y_continuous(expand = expansion(mult = c(0.03, 0.05))) +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 4),
axis.ticks = element_blank()
) +
labs(y = "", x = "")
map_plot4 <- ggplot(map4, aes(x = LG, y = Size)) +
geom_segment(aes(
yend = 200,
y = 0,
x = LG,
xend = LG
),
color = "skyblue",
size = 1) +
geom_point(
data = subset(map4, is_locus == "yes"),
color = "skyblue",
size = 0.5
) +
geom_point(
data = subset(map4, is_highlight == "yes"),
color = "Orange",
size = 0.5
) +
geom_text_repel(
data = subset(map4, is_highlight == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
geom_text_repel(
data = subset(map4, is_locus == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
scale_x_discrete(expand = expansion(mult = c(0.15, 0.05))) +
scale_y_continuous(expand = expansion(mult = c(0.03, 0.05))) +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 4),
axis.ticks = element_blank()
) +
labs(y = "", x = "")
map_plot5 <- ggplot(map5, aes(x = LG, y = Size)) +
geom_segment(aes(
yend = 200,
y = 0,
x = LG,
xend = LG
),
color = "skyblue",
size = 1) +
geom_point(
data = subset(map5, is_locus == "yes"),
color = "skyblue",
size = 0.5
) +
geom_point(
data = subset(map5, is_highlight == "yes"),
color = "Orange",
size = 0.5
) +
geom_text_repel(
data = subset(map5, is_highlight == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
geom_text_repel(
data = subset(map5, is_locus == "yes"),
aes(label = marker),
size = 1.5,
max.overlaps = Inf,
min.segment.length = 0,
force = 0,
nudge_x = -0.55,
nudge_y = -1.5,
direction = "x",
hjust = 0.5,
segment.curvature = -1e-20,
segment.angle = 45,
segment.size = 0.1
) +
scale_x_discrete(expand = expansion(mult = c(0.15, 0.05))) +
scale_y_continuous(expand = expansion(mult = c(0.03, 0.05))) +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 4),
axis.ticks = element_blank()
) +
labs(y = "", x = "")
maps <- ggdraw() +
draw_plot(
map_plot1,
x = 0.05,
y = .5,
width = .3,
height = .5
) +
draw_plot(
map_plot2,
x = .4,
y = .5,
width = .3,
height = .5
) +
draw_plot(
map_plot3,
x = .75,
y = .5,
width = .3,
height = .5
) +
draw_plot(
map_plot4,
x = 0.25,
y = 0,
width = 0.3,
height = 0.5
) +
draw_plot(
map_plot5,
x = 0.65,
y = 0,
width = 0.3,
height = 0.5
) +
draw_plot_label(
label = c("A", "B", "C", "D", "E"),
size = 15,
x = c(0, 0.35, 0.7, 0.2, 0.6),
y = c(1, 1, 1, 0.5, 0.5)
)
print(maps)
R version 4.4.3 (2025-02-28 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] tidytext_0.4.2 cowplot_1.1.3 ggpubr_0.6.0 ggrepel_0.9.6
[5] ggthemes_5.1.0 metan_1.19.0 data.table_1.17.0 lubridate_1.9.4
[9] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[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 janeaustenr_1.0.0 tweenr_2.0.3
[7] mathjaxr_1.6-0 promises_1.3.2 digest_0.6.37
[10] timechange_0.3.0 lifecycle_1.0.4 tokenizers_0.3.0
[13] magrittr_2.0.3 compiler_4.4.3 rlang_1.1.5
[16] sass_0.4.9 tools_4.4.3 yaml_2.3.10
[19] knitr_1.49 ggsignif_0.6.4 labeling_0.4.3
[22] plyr_1.8.9 RColorBrewer_1.1-3 abind_1.4-8
[25] workflowr_1.7.1 withr_3.0.2 numDeriv_2016.8-1.1
[28] grid_4.4.3 polyclip_1.10-7 git2r_0.35.0
[31] colorspace_2.1-1 scales_1.3.0 MASS_7.3-64
[34] cli_3.6.4 rmarkdown_2.29 reformulas_0.4.0
[37] generics_0.1.3 rstudioapi_0.17.1 tzdb_0.4.0
[40] minqa_1.2.8 cachem_1.1.0 ggforce_0.4.2
[43] splines_4.4.3 vctrs_0.6.5 boot_1.3-31
[46] Matrix_1.7-2 jsonlite_1.9.1 carData_3.0-5
[49] car_3.1-3 hms_1.1.3 patchwork_1.3.0
[52] rstatix_0.7.2 Formula_1.2-5 jquerylib_0.1.4
[55] glue_1.8.0 nloptr_2.2.1 ggstats_0.9.0
[58] stringi_1.8.4 gtable_0.3.6 later_1.4.1
[61] lme4_1.1-36 lmerTest_3.1-3 munsell_0.5.1
[64] pillar_1.10.1 htmltools_0.5.8.1 R6_2.6.1
[67] Rdpack_2.6.3 rprojroot_2.0.4 evaluate_1.0.3
[70] lattice_0.22-6 SnowballC_0.7.1 rbibutils_2.3
[73] backports_1.5.0 broom_1.0.7 httpuv_1.6.15
[76] bslib_0.9.0 Rcpp_1.0.14 nlme_3.1-167
[79] xfun_0.51 fs_1.6.5 pkgconfig_2.0.3