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Rmd eac8419 Troy Rowan 2020-10-27 811K Simmental runs completed
Rmd 9bf9aea Troy Rowan 2020-10-26 Updates to Simmental and RAN analysis 
source("code/GCTA_functions.R")
source("code/annotation_functions.R")
simmental = read_csv("output/200907_SIM/phenotypes/200907_SIM.info.csv")

Simmental GPSM Analysis

REML variance component estimates

Purebred Simmental GPSM Analysis

REML variance component estimates

Phenotype n h^2 SE
Full Age 78787 0.619 0.005
Full Log Age 78787 0.600 0.005
Young Age 73811 0.540 0.005
Old Age 4976 0.436 0.021
SimAngus (AN) Age 11429 0.665 0.011
SimAngus (SIM) Age 46136 0.642 0.006
Majority SIM Age 31225 0.558 0.008
Majority SIM Log Age 31225 0.561 0.008
Purebred Age 13379 0.555 0.011
Purebred Log Age 13379 0.560 0.011
Purebred Young Age 11148 0.497 0.013
Purebred Old Age 2231 0.462 0.030

Individual Residuals and Breeding Values

These are REML estimates of individual’s breeding values and residuals from GCTA GREML analysis

Full Age

All animals with SIM > 0.05

n = 78,787

plot_grid(
  read_blp("output/200907_SIM/greml/200907_SIM.full_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                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/200907_SIM/greml/200907_SIM.full_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nBreeding Values")+
    theme_cowplot())

Full Log Age

All animals with SIM > 0.05

Log-transformed age as dependent variable

n = 78,787

plot_grid(
  read_blp("output/200907_SIM/greml/200907_SIM.full_log_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                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/200907_SIM/greml/200907_SIM.full_log_age.850K.indi.blp") %>% 
  left_join(simmental %>% 
              select(international_id, age)) %>% 
  ggplot(aes(sample = BV))+
  stat_qq()+
  stat_qq_line(color = "red")+
  ggtitle("\nBreeding Values")+
  theme_cowplot())

Young Age

Animals Born since 2008 with SIM > 0.05

n = 73,811

plot_grid(
  read_blp("output/200907_SIM/greml/200907_SIM.full_young_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Young Age \nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.full_young_age.850K.indi.blp") %>% 
  left_join(simmental %>% 
              select(international_id, age)) %>% 
  ggplot(aes(sample = BV))+
  stat_qq()+
  stat_qq_line(color = "red")+
  ggtitle("\nGPSM Breeding Values")+
  theme_cowplot())

Old Age

Animals Born prior to 2008 with SIM > 0.05

n = 4,976

plot_grid(
  read_blp("output/200907_SIM/greml/200907_SIM.pb_old_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Old Animals\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.pb_old_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Residuals")+
    theme_cowplot())

SimAngus (Angus) Age

Animals with SIM < 0.30 and ANG > 0.50

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.simangus3050.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("SimAngus (>50% AN)\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.simangus3050.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

SimAngus Age

Animals with SIM > 0.20 and SIM < 0.70

n = 11,429

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.simangus2070.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("SimAngus\nRaw Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.simangus2070.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Majority Simmental Age

Animals with SIM > 0.70

n = 31,225

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.sim70_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Majority Simmental Animals\nRaw Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.sim70_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Majority Simmental Log Age

Animals with SIM > 0.70

n = 31,225

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.sim70_log_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Majority Simmental Animals\nLog Transformed Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.sim70_log_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Purebred Age

Animals with SIM = 1.0

n = 13,379

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.pb_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Purebred Simmental\nRaw Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.pb_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Purebred Log Age

Animals with SIM = 1.0

n = 13,379

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.pb_log_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Purebred Simmental\nLog Transformed Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.pb_log_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Purebred Young Age

Animals with SIM = 1.0 born since 2008

n = 11,148

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.pb_young_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Purebred Simmental (Post-2007)\nRaw Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.pb_young_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

Purebred Old Age

Animals with SIM = 1, born before 2008

n = 2,231

plot_grid(read_blp("output/200907_SIM/greml/200907_SIM.pb_old_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = Residual))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("Purebred Simmental (Pre 2008)\nRaw Age\nGPSM Residuals")+
    theme_cowplot(),
  read_blp("output/200907_SIM/greml/200907_SIM.pb_old_age.850K.indi.blp") %>% 
    left_join(simmental %>% 
                select(international_id, age)) %>% 
    ggplot(aes(sample = BV))+
    stat_qq()+
    stat_qq_line(color = "red")+
    ggtitle("\nGPSM Breeding Values")+
    theme_cowplot())

n(SigSNPs)

GWAS results for the 685,120 SNPs with MAF > 0.01 in our imputed dataset.

GPSM GWAS Manhattan Plots for Transformed Age

Full Age

All animals with SIM > 0.05

n = 78,787

ggmanhattan2(full_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(full_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Full Log Age

All animals with SIM > 0.05

Log-transformed age as dependent variable

n = 78,787

ggmanhattan2(full_log_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(full_log_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Young Age

Animals Born since 2008 with SIM > 0.05

n = 73,811

ggmanhattan2(young_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(young_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Old Age

Animals Born prior to 2008 with SIM > 0.05

n = 4,976

ggmanhattan2(old_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(old_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

SimAngus (Angus) Age

Animals with SIM < 0.30 and ANG > 0.50

ggmanhattan2(simangus_an_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(simangus_an_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

SimAngus Age

Animals with SIM > 0.20 and SIM < 0.70

n = 11,429

ggmanhattan2(simangus_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(simangus_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Majority Simmental Age

Animals with SIM > 0.70

n = 31,225

ggmanhattan2(maj_sim_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(maj_sim_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Majority Simmental Log Age

Animals with SIM > 0.70

n = 31,225

ggmanhattan2(maj_sim_log_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(maj_sim_log_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Purebred Age

Animals with SIM = 1.0

n = 13,379

ggmanhattan2(pb_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(pb_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Purebred Log Age

Animals with SIM = 1.0

n = 13,379

ggmanhattan2(pb_log_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(pb_log_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Purebred Young Age

Animals with SIM = 1.0 born since 2008

n = 11,148

ggmanhattan2(pb_young_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(pb_young_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)

Purebred Old Age

Animals with SIM = 1, born before 2008

n = 2,231

ggmanhattan2(pb_old_age,
               prune = 0.01, 
               sig_threshold_p = 7.298e-08)

ggmanhattan2(pb_old_age,
             value = q,
             prune = 0.9,
             sig_threshold_q = 0.1)


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 DT_0.15           gprofiler2_0.2.0 
 [5] cowplot_1.1.0     GALLO_0.99.0      qvalue_2.20.0     pedigree_1.4     
 [9] reshape_0.8.8     HaploSim_1.8.4    Matrix_1.2-18     lubridate_1.7.9  
[13] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       readr_1.3.1      
[17] tidyr_1.1.2       tibble_3.0.3      tidyverse_1.3.0   here_0.1         
[21] ggcorrplot_0.1.3  corrr_0.4.2       factoextra_1.0.7  ggplot2_3.3.2    
[25] purrr_0.3.4       ggthemes_4.2.0    maps_3.3.0        knitr_1.30       
[29] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1            ellipsis_0.3.1             
 [3] dynamicTreeCut_1.63-1       rprojroot_1.3-2            
 [5] circlize_0.4.10             XVector_0.28.0             
 [7] GenomicRanges_1.40.0        GlobalOptions_0.1.2        
 [9] fs_1.5.0                    rstudioapi_0.11            
[11] farver_2.0.3                ggrepel_0.8.2              
[13] fansi_0.4.1                 xml2_1.3.2                 
[15] codetools_0.2-16            splines_4.0.2              
[17] doParallel_1.0.15           jsonlite_1.7.1             
[19] Rsamtools_2.4.0             broom_0.7.0                
[21] dbplyr_1.4.4                compiler_4.0.2             
[23] httr_1.4.2                  backports_1.1.10           
[25] assertthat_0.2.1            lazyeval_0.2.2             
[27] cli_2.0.2                   later_1.1.0.1              
[29] htmltools_0.5.0             tools_4.0.2                
[31] gtable_0.3.0                glue_1.4.2                 
[33] GenomeInfoDbData_1.2.3      reshape2_1.4.4             
[35] Rcpp_1.0.5                  Biobase_2.48.0             
[37] cellranger_1.1.0            vctrs_0.3.4                
[39] Biostrings_2.56.0           rtracklayer_1.48.0         
[41] crosstalk_1.1.0.1           iterators_1.0.12           
[43] xfun_0.17                   rvest_0.3.6                
[45] lifecycle_0.2.0             XML_3.99-0.5               
[47] zlibbioc_1.34.0             scales_1.1.1               
[49] hms_0.5.3                   promises_1.1.1             
[51] SummarizedExperiment_1.18.2 parallel_4.0.2             
[53] RColorBrewer_1.1-2          yaml_2.2.1                 
[55] gridExtra_2.3               stringi_1.5.3              
[57] unbalhaar_2.0               S4Vectors_0.26.1           
[59] foreach_1.5.0               BiocGenerics_0.34.0        
[61] BiocParallel_1.22.0         shape_1.4.5                
[63] GenomeInfoDb_1.24.2         matrixStats_0.56.0         
[65] rlang_0.4.7                 pkgconfig_2.0.3            
[67] bitops_1.0-6                evaluate_0.14              
[69] lattice_0.20-41             labeling_0.3               
[71] GenomicAlignments_1.24.0    htmlwidgets_1.5.1          
[73] tidyselect_1.1.0            plyr_1.8.6                 
[75] magrittr_1.5                R6_2.4.1                   
[77] IRanges_2.22.2              generics_0.0.2             
[79] DelayedArray_0.14.1         DBI_1.1.0                  
[81] pillar_1.4.6                haven_2.3.1                
[83] whisker_0.4                 withr_2.3.0                
[85] RCurl_1.98-1.2              modelr_0.1.8               
[87] crayon_1.3.4                plotly_4.9.2.1             
[89] rmarkdown_2.3               grid_4.0.2                 
[91] readxl_1.3.1                data.table_1.13.0          
[93] blob_1.2.1                  git2r_0.27.1               
[95] reprex_0.3.0                digest_0.6.25              
[97] httpuv_1.5.4                stats4_4.0.2               
[99] munsell_0.5.0