Last updated: 2022-08-26

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Rmd 982e545 sabrina-mi 2022-08-23 add predictability, heritability figures
html 982e545 sabrina-mi 2022-08-23 add predictability, heritability figures

Previously, we computed heritability (h2) estimates for rat genes using GEMMA. And our PredictDB pipeline computes predictive performance (R2) for each gene when training the EN model. Now we compare it to heritability and predictability estimates for human genes generated by Wheeler Lab.

The sqlite database genarch.db contains all results from Wheeler Lab’s multi-tissue analysis of GTEx data. We queried R2 and h2 values and stored them in human_h2.csv.

suppressMessages(library(tidyverse))
library(RSQLite)
"%&%" = function(a,b) paste(a,b,sep="")
dir = "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/"

We first read in heritability estimes for humans and rats, and output their summary.

rat_h2 <- suppressMessages(read_tsv(dir %&% "GEMMA/Ac/Ac_PVE_estimates.txt", col_names = FALSE)) %>% dplyr::rename(rat_herit = X2)

human_h2_file = dir %&% "data/genarch.db"
sqlite.driver <- dbDriver("SQLite")
conn <- dbConnect(RSQLite::SQLite(), human_h2_file)
human_h2 <- dbGetQuery(conn, "select gene, ensid, en_r2 ,h2,h2_ci from results where tissue = 'DGN-WB'")
Warning in result_fetch(res@ptr, n = n): Column `en_r2`: mixed type, first seen
values of type real, coercing other values of type string
dbDisconnect(conn)
#remove ensembl version
human_h2$ensid <- sapply(strsplit(human_h2$ensid, "\\."), `[`, 1)
print("Distribution of Rat Gene Heritability")
[1] "Distribution of Rat Gene Heritability"
summary(rat_h2$rat_herit)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.02234 0.04492 0.06520 0.09822 0.11071 0.79924      58 
print("Distribution of Human Gene Heritability")
[1] "Distribution of Human Gene Heritability"
summary(human_h2$h2)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000001 0.014847 0.057980 0.126364 0.173627 0.933770

Use the rat-human gene dictionary to join the two tables, and calculate the correlation of heritability for rats and humans.

orth.rats <- read_tsv(dir %&% "data/expression/ortholog_genes_rats_humans.tsv")

── Column specification ────────────────────────────────────────────────────────
cols(
  ensembl_gene_id = col_character(),
  external_gene_name = col_character(),
  rnorvegicus_homolog_ensembl_gene = col_character(),
  rnorvegicus_homolog_associated_gene_name = col_character()
)
rat_h2 <- rat_h2 %>% mutate(ensid = orth.rats[match(rat_h2$X1, orth.rats$rnorvegicus_homolog_ensembl_gene), 1]$ensembl_gene_id)

all_h2 <- inner_join(rat_h2, human_h2, by = "ensid")

cor.test(all_h2$rat_herit, all_h2$h2)

    Pearson's product-moment correlation

data:  all_h2$rat_herit and all_h2$h2
t = 6.9336, df = 10531, p-value = 4.344e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.04837588 0.08639778
sample estimates:
      cor 
0.0674113

Repeat for predictability.

file <- dir %&% "Results/sql/Ac_output_db.db"
conn <- dbConnect(RSQLite::SQLite(), file)
rat_r2 <- dbGetQuery(conn, 'select * from extra')
dbDisconnect(conn)

rat_r2 <- rat_r2 %>% mutate(ensid = orth.rats[match(rat_r2$gene, orth.rats$rnorvegicus_homolog_ensembl_gene), 1]$ensembl_gene_id)
all_r2 <- inner_join(rat_r2, human_h2, by = "ensid")
cor.test(all_r2$R2, all_r2$en_r2)

    Pearson's product-moment correlation

data:  all_r2$R2 and all_r2$en_r2
t = 4.5828, df = 6068, p-value = 4.681e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.03362123 0.08376302
sample estimates:
       cor 
0.05872917 
Ac_h2 <- rat_h2 %>% dplyr::rename(gene.x = X1) %>% mutate(rat_ci = paste(round(rat_h2$X3, digits = 5), round(rat_h2$X4, digits = 5), sep = "-")) %>% dplyr::select(c(gene.x, rat_herit, rat_ci))

lg_df <- inner_join(all_r2, Ac_h2, by = "gene.x")

lg_df <- lg_df %>% dplyr::select(c(ensid, gene.x, genename, R2, en_r2,rat_herit,h2,  rat_ci, h2_ci))
colnames(lg_df) = c("ensembl_id", "rat_ensembl_id", "genename", "rat_r2", "human_r2", "rat_h2", "human_h2", "rat_h2_ci", "human_h2_ci")

Here we plot rat heritability against human heritability.

# Plot h2 between rats and Humans with line for null 
lg_df %>% mutate(human_shuffled_h2 = sample(human_h2, nrow(lg_df), replace=F)) %>% ggplot(aes(rat_h2, human_h2)) + geom_smooth(col = "darkgreen") + geom_smooth(aes(rat_h2, human_shuffled_h2),col="dark gray")+theme_bw()+ xlab("Rat Heritability") + ylab("Human Heritability")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Version Author Date
982e545 sabrina-mi 2022-08-23

And repeat for predictability.

# Plot R2 between rats and Humans with line for null 
lg_df %>% mutate(human_shuffled_r2 = sample(human_r2, nrow(lg_df), replace=F)) %>% ggplot(aes(rat_r2, human_r2)) + geom_smooth(col = "midnightblue") + xlab("Rat Predictability") + ylab("Human Predictability")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Version Author Date
982e545 sabrina-mi 2022-08-23

Plot correlation between predictability for every pair of tissues.

model.dir <- dir %&% "Results/sql/"
filelist <- c("Il_output_db.db", "Lh_output_db.db", "Ac_output_db.db", "Vo_output_db.db", "Pl_output_db.db")
tis.list <- c("Infralimbic Cortex", "Lateral Habenula", "Nucleus accumbens", "Orbitofrontal Cortex", "Prelimibic Cortex")

pred_tis <- data.frame(gene = as.character())
for(file in filelist) {
  filename <- model.dir %&% file
  print(filename)
  conn <- dbConnect(RSQLite::SQLite(), filename)
  i <- match(file, filelist)
  tis <- tis.list[i]
  extra <- dbGetQuery(conn, 'select * from extra') %>% select(c(gene, R2)) 
  colnames(extra)[2] = tis
  pred_tis <- full_join(pred_tis, extra, by = "gene") 
  dbDisconnect(conn)
}
[1] "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/sql/Il_output_db.db"
[1] "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/sql/Lh_output_db.db"
[1] "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/sql/Ac_output_db.db"
[1] "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/sql/Vo_output_db.db"
[1] "/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/sql/Pl_output_db.db"
rownames(pred_tis) <- pred_tis$gene
pred_tis <- pred_tis %>% select(-c(gene))


d1 <- cor(pred_tis %>% na.omit)
d1[lower.tri(d1)] <- NA
d1 <- d1 %>%
  as.data.frame %>%
  rownames_to_column(var = 'var1') %>%
  gather(var2, value, -var1) 
d1 <- d1 %>% dplyr::rename(r = value) %>% na.omit() 


ggplot(data = d1, aes(x = var1, y = var2, fill = r)) +
 geom_tile(color = "white")+
 scale_fill_gradient(low="dodgerblue", high="dodgerblue4") +
  theme_minimal() +
  theme(panel.grid = element_blank()) + ylab("") + xlab("") + theme(axis.text = element_text(size = 15), axis.text.x = element_text(angle = 90, hjust = 1)) + theme(aspect.ratio=1)

Version Author Date
982e545 sabrina-mi 2022-08-23

Repeat for analogous GTEx tissues. These EN models can be downloaded from PredictDB.

model.dir <- "~/elastic_net_models/"
tis.list = c("Amygdala", "Cerebellum", "Nucleus accumbens",  "Spinal cord", "Substantia nigra")
filelist <- c("en_Brain_Amygdala.db", "en_Brain_Cerebellum.db", "en_Brain_Nucleus_accumbens_basal_ganglia.db", "en_Brain_Spinal_cord_cervical_c-1.db", "en_Brain_Substantia_nigra.db")
pred_tis <- data.frame(gene = as.character())
for(file in filelist) {
  filename <- model.dir %&% file
  conn <- dbConnect(RSQLite::SQLite(), filename)
  i <- match(file, filelist)
  tis <- tis.list[i]
  extra <- dbGetQuery(conn, 'select * from extra') %>% select(c(gene, pred.perf.R2)) 
  colnames(extra)[2] = tis
  pred_tis <- full_join(pred_tis, extra, by = "gene") 
  dbDisconnect(conn)
}
rownames(pred_tis) <- pred_tis$gene
pred_tis <- pred_tis %>% select(-c(gene))

d2 <- cor(pred_tis %>% na.omit)
d2[lower.tri(d2)] <- NA
d2 <- d2 %>%
  as.data.frame %>%
  rownames_to_column(var = 'var1') %>%
  gather(var2, value, -var1) 
d2 <- d2 %>% dplyr::rename(r = value) %>% na.omit()

ggplot(data = d2, aes(x = var1, y = var2, fill = r)) +
 geom_tile(color = "white")+
 scale_fill_gradient(low="maroon1", high="maroon4") +
  theme_minimal() +
  theme(panel.grid = element_blank()) + ylab("") + xlab("") + theme(axis.text = element_text(size = 15), axis.text.x = element_text(angle = 90, hjust = 1)) + theme(aspect.ratio=1)

Version Author Date
982e545 sabrina-mi 2022-08-23

Compare number of SNPs used in prediction model for rats and humans.

summary(rat_r2$n.snps)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    5.00    9.00   11.93   16.00   77.00 
#human NAcc models
filename <- "~/elastic_net_models/en_Brain_Nucleus_accumbens_basal_ganglia.db"
sqlite.driver <- dbDriver("SQLite")
conn <- dbConnect(RSQLite::SQLite(), filename)
human_r2 <- dbGetQuery(conn, 'select * from extra') 

summary(human_r2$n.snps.in.model)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00   13.00   24.00   31.08   42.00  226.00 

sessionInfo()
R version 4.0.3 (2020-10-10)
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] RSQLite_2.2.1   forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
 [9] ggplot2_3.3.6   tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     lattice_0.20-41  lubridate_1.7.9  assertthat_0.2.1
 [5] rprojroot_1.3-2  digest_0.6.27    R6_2.4.1         cellranger_1.1.0
 [9] backports_1.1.10 reprex_0.3.0     evaluate_0.15    highr_0.8       
[13] httr_1.4.2       pillar_1.4.6     rlang_1.0.2      readxl_1.3.1    
[17] rstudioapi_0.11  whisker_0.4      jquerylib_0.1.4  blob_1.2.1      
[21] Matrix_1.2-18    rmarkdown_2.14   labeling_0.4.2   splines_4.0.3   
[25] bit_4.0.4        munsell_0.5.0    broom_0.8.0      compiler_4.0.3  
[29] httpuv_1.5.4     modelr_0.1.8     xfun_0.31        pkgconfig_2.0.3 
[33] mgcv_1.8-33      htmltools_0.5.2  tidyselect_1.1.0 workflowr_1.6.2 
[37] crayon_1.3.4     dbplyr_1.4.4     withr_2.3.0      later_1.1.0.1   
[41] grid_4.0.3       nlme_3.1-150     jsonlite_1.7.1   gtable_0.3.0    
[45] lifecycle_0.2.0  DBI_1.1.0        git2r_0.27.1     magrittr_1.5    
[49] scales_1.1.1     cli_3.3.0        stringi_1.5.3    farver_2.0.3    
[53] fs_1.5.0         promises_1.1.1   xml2_1.3.2       bslib_0.3.1     
[57] ellipsis_0.3.2   generics_0.0.2   vctrs_0.4.1      tools_4.0.3     
[61] bit64_4.0.5      glue_1.6.2       hms_1.1.2        fastmap_1.1.0   
[65] yaml_2.2.1       colorspace_1.4-1 rvest_0.3.6      memoise_1.1.0   
[69] knitr_1.39       haven_2.3.1      sass_0.4.1