Last updated: 2021-06-08

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Knit directory: local_adaptation_sequence/

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File	Version	Author	Date	Message
Rmd	77e94f5	Troy Rowan	2021-06-08	Updated with 850K envGWAS analysis
Rmd	63f88e5	Troy Rowan	2021-05-13	Added envGWAS preliminary analysis

source("code/GCTA_functions.R")
source("code/annotation_functions.R")

Simmental

850K envGWAS

Manhattan plots with both p and q values

sim_manhattans = 
  unique(sim_envgwas$variable) %>%
    purrr::map(~plot_grid(ggmanhattan2(filter(sim_envgwas, variable == .x & p < 0.1), pcol = p, value = p) + ggtitle(.x),
                          ggmanhattan2(filter(sim_envgwas, variable == .x & p < 0.1), pcol = q, value = q),
                          nrow = 2))

envGWAS Runs

AridPrairie

sim_manhattans[[1]]

CornBelt

sim_manhattans[[2]]

Desert

sim_manhattans[[3]]

dewpoint

sim_manhattans[[4]]

elev

sim_manhattans[[5]]

FescueBelt

sim_manhattans[[6]]

Foothills

sim_manhattans[[7]]

ForestedMountains

sim_manhattans[[8]]

HighPlains

sim_manhattans[[9]]

latitude

sim_manhattans[[10]]

longitude

sim_manhattans[[11]]

maxtemp

sim_manhattans[[12]]

maxvap

sim_manhattans[[13]]

meantemp

sim_manhattans[[14]]

mintemp

sim_manhattans[[15]]

minvap

sim_manhattans[[16]]

precip

sim_manhattans[[17]]

Southeast

sim_manhattans[[18]]

UpperMidwest

sim_manhattans[[19]]

# cat('# # Simmental 850K envGWAS {.tabset}   \n')
# invisible(
#   sim_envgwas %>% 
#       dplyr::group_split(variable) %>% 
#       purrr::imap(.,~{
#         # create tabset for each group 
#         cat('### Tab',.y,'   \n')
#         cat('\n')
#         #p <- ggmanhattan2(filter(.x, p < 0.1), sigsnps = sim_multisig)
#         p <- filter(.x, p < 0.1) %>% ggplot(aes(CHR, BP))+geom_point()
#         cat(as.character(htmltools::tagList(p)))
#       })
# )

Number of significant SNPs in each analysis at 850K level

Number of SNPs in each analysis that reaches genome-wide significance at 1) Bonferroni 2) p < 1e-5 3) q < 0.1

sim_envgwas %>% 
  group_by(variable) %>% 
  summarize(bonfCount = sum(p < 6e-8),
            pCount = sum(p < 1e-5),
            qCount = sum(q < 0.1))

# A tibble: 19 x 4
   variable          bonfCount pCount qCount
   <chr>                 <int>  <int>  <int>
 1 AridPrairie               0     29     11
 2 CornBelt                  0     16      3
 3 Desert                   26    158    334
 4 dewpoint                  1      8      3
 5 elev                      1     14      3
 6 FescueBelt                0     11      0
 7 Foothills                 4     43     16
 8 ForestedMountains         1     13      3
 9 HighPlains                0     16      6
10 latitude                  1     10      1
11 longitude                 1     23      5
12 maxtemp                   0     10      2
13 maxvap                    0     11      0
14 meantemp                  1      8      2
15 mintemp                   1      9      2
16 minvap                    1     27      1
17 precip                    5     14      7
18 Southeast                 0     15      0
19 UpperMidwest              0     10      0

Number of SNPs that are shared across zones/variables

filter(sim_envgwas, p < 1e-5) %>%
  count(SNP, sort = TRUE)%>%
  filter(n>1) %>%
  kbl() %>%
  kable_styling()

SNP	n
25:40171577:C:T	10
23:11653102:T:G	6
18:58490610:T:G	5
22:24449349:A:G	5
28:37890881:G:A	5
28:37891750:A:C	5
16:42569828:C:T	4
17:72089995:A:G	4
6:45760951:A:G	4
19:59908502:C:T	3
22:46620047:A:G	3
28:37892541:G:A	3
4:84863571:A:G	3
1:102180787:T:C	2
1:116110811:T:C	2
1:153515980:C:T	2
15:17921054:T:C	2
2:72751717:T:C	2
2:97342131:G:A	2
20:8095171:T:C	2
21:11109237:C:T	2
23:1747454:A:C	2
5:21848046:C:T	2
5:83441154:A:C	2

Red Angus

850K envGWAS

ran_envgwas =
  # list.files("output/200910_RAN/gwas") %>%
  #   map(
  #     ~read_gwas2(paste0("output/200910_RAN/gwas/", .x)) %>%
  #       mutate(variable = .x)) %>%
  #   reduce(bind_rows) %>%
  # mutate(variable = str_replace(variable, pattern = "200910_RAN.", ""),
  #        variable = str_replace(variable, pattern = ".850K.mlma.gz", ""),
  #        variable = str_replace(variable, pattern = "_noLSF", ""))

#write_csv(ran_envgwas, "output/200910_RAN/gwas/200910_RAN.AllGWAS.850K.mlma.gz")
  read_csv("output/200910_RAN/gwas/200910_RAN.AllGWAS.850K.mlma.gz",
           col_types = cols(SNP = col_character(), chrbp = col_character())) %>%
  filter(CHR < 30)

ran_multisig =
  filter(ran_envgwas, p < 1e-5) %>%
  count(SNP, sort = TRUE)%>%
  filter(n>1) %>%
  .$SNP

ran_manhattans = 
  unique(ran_envgwas$variable) %>%
    purrr::map(~ggmanhattan2(filter(ran_envgwas, variable == .x & p < 0.1),pcol = p, sigsnps = ran_multisig)+
                 ggtitle(.x))

envGWAS Runs

AridPrairie

ran_manhattans[[1]]

CornBelt

ran_manhattans[[2]]

Desert

ran_manhattans[[3]]

dewpoint

ran_manhattans[[4]]

elev

ran_manhattans[[5]]

FescueBelt

ran_manhattans[[6]]

Foothills

ran_manhattans[[7]]

ForestedMountains

ran_manhattans[[8]]

HighPlains

ran_manhattans[[9]]

latitude

ran_manhattans[[10]]

longitude

ran_manhattans[[11]]

maxtemp

ran_manhattans[[12]]

maxvap

ran_manhattans[[13]]

meantemp

ran_manhattans[[14]]

mintemp

ran_manhattans[[15]]

minvap

ran_manhattans[[16]]

precip

ran_manhattans[[17]]

Southeast

ran_manhattans[[18]]

UpperMidwest

ran_manhattans[[19]]

Annotating Genes

Skipping for now

Number of significant SNPs in each analysis at 850K level

Number of SNPs in each analysis that reaches genome-wide significance at 1) Bonferroni 2) p < 1e-5 3) q < 0.1

sim_envgwas %>% 
  group_by(variable) %>% 
  summarize(bonfCount = sum(p < 6e-8),
            pCount = sum(p < 1e-5),
            qCount = sum(q < 0.1))

# A tibble: 19 x 4
   variable          bonfCount pCount qCount
   <chr>                 <int>  <int>  <int>
 1 AridPrairie               0     29     11
 2 CornBelt                  0     16      3
 3 Desert                   26    158    334
 4 dewpoint                  1      8      3
 5 elev                      1     14      3
 6 FescueBelt                0     11      0
 7 Foothills                 4     43     16
 8 ForestedMountains         1     13      3
 9 HighPlains                0     16      6
10 latitude                  1     10      1
11 longitude                 1     23      5
12 maxtemp                   0     10      2
13 maxvap                    0     11      0
14 meantemp                  1      8      2
15 mintemp                   1      9      2
16 minvap                    1     27      1
17 precip                    5     14      7
18 Southeast                 0     15      0
19 UpperMidwest              0     10      0

Number of SNPs that are shared across zones/variables

At p < 1e-5 threshold

filter(ran_envgwas, p < 1e-5) %>%
  count(SNP, sort = TRUE)%>%
  filter(n>1) %>%
  kbl() %>%
  kable_styling()

SNP	n
5:56294803:C:T	8
6:89632622:C:T	8
11:66022330:T:C	5
16:31189754:C:T	5
26:22147551:A:G	5
5:55940985:C:A	4
5:99932542:A:C	4
15:63936462:G:A	3
23:1760296:G:A	3
23:1762985:C:T	3
23:1764305:C:T	3
23:1765645:C:T	3
23:1768070:C:T	3
23:1773893:A:G	3
23:1780836:T:C	3
4:15143313:T:C	3
5:29337648:C:T	3
5:99970391:T:C	3
13:11805638:G:A	2
15:63923159:C:T	2
15:63930524:T:C	2
18:1421130:A:G	2
18:55917363:G:A	2
18:55918169:C:G	2
18:55923037:C:T	2
2:76462949:C:A	2
23:1741368:T:C	2
23:1742961:G:A	2
23:1744435:G:A	2
23:1745568:C:T	2
23:1747454:A:C	2
23:1750999:G:T	2
23:1753411:C:A	2
23:1755721:T:C	2
29:44876613:A:G	2
4:114194176:G:A	2
4:117086624:G:A	2
5:34082134:C:T	2
5:83684035:C:T	2
6:83637243:A:G	2

sessionInfo()

R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] viridis_0.5.1     viridisLite_0.3.0 kableExtra_1.3.4  UpSetR_1.4.0     
 [5] DT_0.17           gprofiler2_0.2.0  cowplot_1.1.1     GALLO_1.1        
 [9] qvalue_2.22.0     pedigree_1.4      reshape_0.8.8     HaploSim_1.8.4   
[13] Matrix_1.3-2      lubridate_1.7.10  forcats_0.5.1     stringr_1.4.0    
[17] dplyr_1.0.5       readr_1.4.0       tidyr_1.1.3       tibble_3.1.0     
[21] tidyverse_1.3.0   here_1.0.1        ggcorrplot_0.1.3  corrr_0.4.3      
[25] factoextra_1.0.7  ggplot2_3.3.3     purrr_0.3.4       ggthemes_4.2.4   
[29] maps_3.3.0        knitr_1.31        workflowr_1.6.2  

loaded via a namespace (and not attached):
  [1] colorspace_2.0-0            ellipsis_0.3.1             
  [3] rprojroot_2.0.2             circlize_0.4.12            
  [5] XVector_0.30.0              GenomicRanges_1.42.0       
  [7] GlobalOptions_0.1.2         fs_1.5.0                   
  [9] rstudioapi_0.13             farver_2.1.0               
 [11] ggrepel_0.9.1               fansi_0.4.2                
 [13] xml2_1.3.2                  codetools_0.2-18           
 [15] splines_4.0.4               doParallel_1.0.16          
 [17] jsonlite_1.7.2              Rsamtools_2.6.0            
 [19] broom_0.7.5                 dbplyr_2.1.0               
 [21] compiler_4.0.4              httr_1.4.2                 
 [23] backports_1.2.1             assertthat_0.2.1           
 [25] lazyeval_0.2.2              cli_2.3.1                  
 [27] later_1.1.0.1               htmltools_0.5.1.1          
 [29] tools_4.0.4                 GenomeInfoDbData_1.2.4     
 [31] gtable_0.3.0                glue_1.4.2                 
 [33] reshape2_1.4.4              Rcpp_1.0.6                 
 [35] Biobase_2.50.0              cellranger_1.1.0           
 [37] Biostrings_2.58.0           vctrs_0.3.6                
 [39] svglite_2.0.0               rtracklayer_1.50.0         
 [41] iterators_1.0.13            xfun_0.22                  
 [43] rvest_1.0.0                 lifecycle_1.0.0            
 [45] XML_3.99-0.6                zlibbioc_1.36.0            
 [47] scales_1.1.1                MatrixGenerics_1.2.1       
 [49] hms_1.0.0                   promises_1.2.0.1           
 [51] SummarizedExperiment_1.20.0 parallel_4.0.4             
 [53] RColorBrewer_1.1-2          yaml_2.2.1                 
 [55] gridExtra_2.3               stringi_1.5.3              
 [57] highr_0.8                   unbalhaar_2.0              
 [59] S4Vectors_0.28.1            foreach_1.5.1              
 [61] BiocGenerics_0.36.1         BiocParallel_1.24.1        
 [63] shape_1.4.5                 GenomeInfoDb_1.26.7        
 [65] matrixStats_0.58.0          rlang_0.4.10               
 [67] pkgconfig_2.0.3             systemfonts_1.0.1          
 [69] bitops_1.0-6                evaluate_0.14              
 [71] lattice_0.20-41             labeling_0.4.2             
 [73] GenomicAlignments_1.26.0    htmlwidgets_1.5.3          
 [75] tidyselect_1.1.0            plyr_1.8.6                 
 [77] magrittr_2.0.1              R6_2.5.0                   
 [79] IRanges_2.24.1              generics_0.1.0             
 [81] DelayedArray_0.16.3         DBI_1.1.1                  
 [83] pillar_1.5.1                haven_2.3.1                
 [85] whisker_0.4                 withr_2.4.1                
 [87] RCurl_1.98-1.3              modelr_0.1.8               
 [89] crayon_1.4.1                utf8_1.2.1                 
 [91] plotly_4.9.3                rmarkdown_2.7              
 [93] grid_4.0.4                  readxl_1.3.1               
 [95] data.table_1.14.0           git2r_0.28.0               
 [97] reprex_1.0.0                digest_0.6.27              
 [99] webshot_0.5.2               httpuv_1.5.5               
[101] stats4_4.0.4                munsell_0.5.0

envGWAS_850K

Troy Rowan

2021-03-22

Simmental

850K envGWAS

envGWAS Runs

AridPrairie

CornBelt

Desert

dewpoint

elev

FescueBelt

Foothills

ForestedMountains

HighPlains

latitude

longitude

maxtemp

maxvap

meantemp

mintemp

minvap

precip

Southeast

UpperMidwest

Number of significant SNPs in each analysis at 850K level

Number of SNPs that are shared across zones/variables

Red Angus

850K envGWAS

envGWAS Runs

AridPrairie

CornBelt

Desert

dewpoint

elev

FescueBelt

Foothills

ForestedMountains

HighPlains

latitude

longitude

maxtemp

maxvap

meantemp

mintemp

minvap

precip

Southeast

UpperMidwest

Annotating Genes

Number of significant SNPs in each analysis at 850K level

Number of SNPs that are shared across zones/variables