GPSM
Troy Rowan
2020-09-20
Last updated: 2020-10-12
Checks: 7 0
Knit directory: local_adaptation_sequence/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 50ddc20 | Troy Rowan | 2020-10-12 | Updated GPSM with GREML estimates and manhattan plots |
| Rmd | f01e8ba | Troy Rowan | 2020-09-29 | RMarkdown updates that haven’t been pushed by workflowr |
simmental = read_csv("output/200907_SIM/phenotypes/200907_SIM.info.csv")
redangus = read_csv("output/200910_RAN/phenotypes/200910_RAN.info.csv")Core SNP edits for BOLT-LMM These core “GRM” SNPs are used to control for structure in the population if we need to use BOLT-LMM
Red Angus GPSM Analysis
REML variance component estimates
Raw Age
Raw Age (n= 46,454):
| Source | Variance | SE |
|---|---|---|
| V(G) | 5.244 | 0.128 |
| V(e) | 4.789 | 0.040 |
| V(p) | 10.03 | 0.118 |
| V(G)/Vp | 0.523 | 0.007 |
Square Root
Square Root Transformed Age (n= 46,454):
| Source | Variance | SE |
|---|---|---|
| V(G) | 0.242 | 0.005 |
| V(e) | 0.151 | 0.001 |
| V(p) | 0.393 | 0.005 |
| V(G)/Vp | 0.616 | 0.006 |
Cube Root
Cube Root Transformed Age (n= 46,454):
| Source | Variance | SE |
|---|---|---|
| V(G) | 0.065 | 0.001 |
| V(e) | 0.037 | 0.000 |
| V(p) | 0.101 | 0.001 |
| V(G)/Vp | 0.637 | 0.006 |
Box-Cox
Box-Cox Transformed Age (n= 46,454):
| Source | Variance | SE |
|---|---|---|
| V(G) | 0.115 | 0.002 |
| V(e) | 0.060 | 0.001 |
| V(p) | 0.175 | 0.002 |
| V(G)/Vp | 0.657 | 0.006 |
Log
Log Transformed Age (n= 46,454):
| Source | Variance | SE |
|---|---|---|
| V(G) | 0.222 | 0.005 |
| V(e) | 0.115 | 0.001 |
| V(p) | 0.337 | 0.005 |
| V(G)/Vp | 0.657 | 0.006 |
Individual Residuals and Breeding Values
These are REML estimates of individual’s breeding values and residuals from GCTA GREML analysis
Age
plot_grid(
read_blp("output/200910_RAN/greml/200910_RAN.age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = Residual))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("Raw Age GPSM\nResiduals")+
theme_cowplot(),
read_blp("output/200910_RAN/greml/200910_RAN.age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = BV))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("\nBreeding Values")+
theme_cowplot())
Square Root
plot_grid(
read_blp("output/200910_RAN/greml/200910_RAN.sqrt_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = Residual))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("Square Root Transformed Age \nGPSM Residuals")+
theme_cowplot(),
read_blp("output/200910_RAN/greml/200910_RAN.sqrt_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = BV))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("\nBreeding Values")+
theme_cowplot())
Cube Root
plot_grid(
read_blp("output/200910_RAN/greml/200910_RAN.cbrt_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = Residual))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("Cube Root Transformed Age \nGPSM Residuals")+
theme_cowplot(),
read_blp("output/200910_RAN/greml/200910_RAN.cbrt_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = BV))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("\nGPSM Breeding Values")+
theme_cowplot())
Box-Cox
plot_grid(
read_blp("output/200910_RAN/greml/200910_RAN.bc_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = Residual))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("Box-Cox Transformed Age \nGPSM Residuals")+
theme_cowplot(),
read_blp("output/200910_RAN/greml/200910_RAN.bc_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = BV))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("\nGPSM Residuals")+
theme_cowplot())
Log
plot_grid(read_blp("output/200910_RAN/greml/200910_RAN.log_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = Residual))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("Log Transformed Age \nGPSM Residuals")+
theme_cowplot(),
read_blp("output/200910_RAN/greml/200910_RAN.log_age.850K.indi.blp") %>%
left_join(redangus %>%
select(international_id, age)) %>%
ggplot(aes(sample = BV))+
stat_qq()+
stat_qq_line(color = "red")+
ggtitle("\nGPSM Breeding Values")+
theme_cowplot())
n(SigSNPs)
The number of significant SNPs in each analysis at various significance level cutoffs for both p and q values
| p<1e-5 | p<1e7.55e-7 | q<0.1 | q<0.05 | |
|---|---|---|---|---|
| Raw | 315 | 214 | 509 | 398 |
| Sqrt | 453 | 333 | 729 | 559 |
| Cbrt | 471 | 357 | 817 | 596 |
| BoxCox | 540 | 390 | 907 | 754 |
| Log | 513 | 377 | 822 | 715 |
GPSM GWAS Manhattan Plots for Transformed Age
Raw Age
(Significance threshold - Bonferroni)
plot_grid(
ggmanhattan2(ran_gpsm_age,
prune = 0.1,
sig_threshold_p = 7.546167e-07),
ggmanhattan2(ran_gpsm_age,
prune = 0.1,
sig_threshold_p = 7.546167e-07)+
ylim(c(0,15)),
nrow = 2)
#Saving significant SNPs for highlighting in other plots:
raw_age_sigsnps =
ran_gpsm_age %>%
filter(p < 7.546167e-07) %>% .$SNPSquare Root
Square root transformed age as phenotype (Significance threshold - Bonferroni)
Green points indicate novel SNPs in this transformed analysis (at Bonferroni significance levels) that weren’t identified in the GPSM analysis of raw age.
plot_grid(
ggmanhattan2(ran_gpsm_sqrtage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_sqrtage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP),
ggmanhattan2(ran_gpsm_sqrtage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_sqrtage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP)+
ylim(c(0,15)),
nrow = 2)
Cube Root
Cube Root Transformed Age Manahattan Plots (Significance threshold - Bonferroni)
Green points indicate novel SNPs in this transformed analysis (at Bonferroni significance levels) that weren’t identified in the GPSM analysis of raw age.
plot_grid(
ggmanhattan2(ran_gpsm_cbrtage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_cbrtage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP),
ggmanhattan2(ran_gpsm_cbrtage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_cbrtage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP)+
ylim(c(0,15)),
nrow = 2)
Box-Cox
Box-Cox Transformed Age Manahattan Plots (Significance threshold - Bonferroni)
Green points indicate novel SNPs in this transformed analysis (at Bonferroni significance levels) that weren’t identified in the GPSM analysis of raw age.
plot_grid(
ggmanhattan2(ran_gpsm_bcage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_bcage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP),
ggmanhattan2(ran_gpsm_bcage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_bcage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP)+
ylim(c(0,15)),
nrow = 2)
Log
Log Transformed Age Manahattan Plots (Significance threshold - Bonferroni)
Green points indicate novel SNPs in this transformed analysis (at Bonferroni significance levels) that weren’t identified in the GPSM analysis of raw age.
plot_grid(
ggmanhattan2(ran_gpsm_logage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_logage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP),
ggmanhattan2(ran_gpsm_logage,
prune = 0.1,
sig_threshold_p = 7.546167e-07,
sigsnps = filter(ran_gpsm_logage,
p < 7.546e-7 & !SNP %in% raw_age_sigsnps) %>%
.$SNP)+
ylim(c(0,15)),
nrow = 2)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] viridis_0.5.1 viridisLite_0.3.0 cowplot_1.1.0 GALLO_0.99.0
[5] qvalue_2.20.0 pedigree_1.4 reshape_0.8.8 HaploSim_1.8.4
[9] Matrix_1.2-18 lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.2 readr_1.3.1 tidyr_1.1.2 tibble_3.0.3
[17] tidyverse_1.3.0 here_0.1 ggcorrplot_0.1.3 corrr_0.4.2
[21] factoextra_1.0.7 ggplot2_3.3.2 purrr_0.3.4 ggthemes_4.2.0
[25] maps_3.3.0 knitr_1.30 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 doParallel_1.0.15 RColorBrewer_1.1-2
[4] httr_1.4.2 rprojroot_1.3-2 dynamicTreeCut_1.63-1
[7] tools_4.0.2 backports_1.1.10 R6_2.4.1
[10] DBI_1.1.0 colorspace_1.4-1 withr_2.3.0
[13] gridExtra_2.3 tidyselect_1.1.0 compiler_4.0.2
[16] git2r_0.27.1 cli_2.0.2 rvest_0.3.6
[19] xml2_1.3.2 labeling_0.3 scales_1.1.1
[22] digest_0.6.25 rmarkdown_2.3 pkgconfig_2.0.3
[25] htmltools_0.5.0 dbplyr_1.4.4 rlang_0.4.7
[28] GlobalOptions_0.1.2 readxl_1.3.1 rstudioapi_0.11
[31] farver_2.0.3 shape_1.4.5 generics_0.0.2
[34] jsonlite_1.7.1 magrittr_1.5 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] plyr_1.8.6 grid_4.0.2 blob_1.2.1
[46] parallel_4.0.2 promises_1.1.1 ggrepel_0.8.2
[49] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
[52] splines_4.0.2 circlize_0.4.10 hms_0.5.3
[55] pillar_1.4.6 reshape2_1.4.4 codetools_0.2-16
[58] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[61] unbalhaar_2.0 data.table_1.13.0 modelr_0.1.8
[64] vctrs_0.3.4 httpuv_1.5.4 foreach_1.5.0
[67] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[70] xfun_0.17 broom_0.7.0 later_1.1.0.1
[73] iterators_1.0.12 ellipsis_0.3.1