Last updated: 2022-08-23

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Identify Significant Genes

library(readr)
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.6     ✔ dplyr   1.0.2
✔ tibble  3.0.4     ✔ stringr 1.4.0
✔ tidyr   1.1.2     ✔ forcats 0.5.0
✔ purrr   0.3.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
tis="Ac"
"%&%" = function(a,b) paste(a,b,sep="")
dir="/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/"

Read in association results for each phenotype, and combine in one dataframe.

library(stringr)
filelist <- list.files(dir %&% "Results/PrediXcan/metabolic_traits", pattern = "Ac__association_", full.names = TRUE)
full_df <- data.frame()

for(file in filelist) {
  assoc_file <- read_tsv(file, col_names = TRUE)
  # extract phenotype from regex matching in file name
  pheno_id <- str_match(file, "association_(.*?).txt")[,2]
  tempo <- cbind(assoc_file, metabolic_trait=pheno_id) %>% select(-c(status))
  full_df <- rbind(full_df, tempo)
} 

── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)


── Column specification ────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  effect = col_double(),
  se = col_double(),
  zscore = col_double(),
  pvalue = col_double(),
  n_samples = col_double(),
  status = col_logical()
)

We first filter for all significant genes for any of the 10 phenotypes. The last two lines of the chunk count the number of traits each gene is significantly associated with and the number of significant genes for each trait.

#full_df <- read_tsv("/Users/natashasanthanam/Github/rat-genomic-analysis/data/rat_metabolic_traits_best_Ac_full_assocs.txt", col_names = TRUE)

tempo_df <- full_df %>% filter(pvalue <  9.279881e-06)

#428 sig genes
tempo_df %>% group_by(gene) %>% summarise(n = n())
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 429 x 2
   gene                   n
   <chr>              <int>
 1 ENSRNOG00000000245     1
 2 ENSRNOG00000000246     1
 3 ENSRNOG00000000571     1
 4 ENSRNOG00000000763     1
 5 ENSRNOG00000000775     1
 6 ENSRNOG00000000778     1
 7 ENSRNOG00000000867     1
 8 ENSRNOG00000000891     3
 9 ENSRNOG00000000902     2
10 ENSRNOG00000000974     2
# … with 419 more rows
#all 11 traits
tempo_df %>% group_by(metabolic_trait) %>% summarise(n = n())
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 10 x 2
   metabolic_trait            n
   <chr>                  <int>
 1 bmi_bodylength_w_tail     30
 2 bmi_bodylength_wo_tail    19
 3 bodylength_w_tail        101
 4 bodylength_wo_tail        33
 5 bodyweight                82
 6 epifat                    46
 7 fasting_glucose           12
 8 parafat                   26
 9 retrofat                 185
10 tail_length               53

For each trait, identify the tissue each gene is most significantly associated with and the sign of effect. We interpret the sign for the most significant association as the direction of effect on a given trait.

suppressMessages(library(Rfast))
pheno <- read_csv(dir %&% "data/expression/processed_obesity_rat_Palmer_phenotypes.csv", col_names=TRUE)
n_pheno = ncol(pheno) - 1
for(i in 2:n_pheno) {
  trait <- colnames(pheno)[i]
  filelist <- list.files(dir %&% "Results/prediXcan/metabolic_traits", pattern = "association_" %&% trait %&% ".txt", full.names = TRUE)
  tempo <- data.frame(gene= as.character())
  for(file in filelist) {
    tis <- str_match(file, "Results/prediXcan/metabolic_traits/(.*?)__association")[,2]
    df <- suppressMessages(read_tsv(file)) %>% select(c(gene, effect, pvalue))
    new_eff <- paste("effect", tis, sep = "_")
    new_pval <- paste("pvalue", tis, sep = "_")
    colnames(df)[2] <- new_eff
    colnames(df)[3] <- new_pval 
    
    tempo <- full_join(tempo, df,  by = "gene")
  }
  # returns most significant tissue for each gene
  most_sig = rowMins(as.matrix(tempo[,c(3,5,7,9,11)]))
  Ac <- tempo[most_sig == 1, c(1,2)] %>% rename(effect = effect_Ac )
  Il <- tempo[most_sig == 2, c(1,4)] %>% rename(effect = effect_Il )
  Lh <- tempo[most_sig == 3, c(1,6)] %>% rename(effect = effect_Lh )
  Pl <- tempo[most_sig == 4, c(1,8)] %>% rename(effect = effect_Pl )
  Vo <- tempo[most_sig == 5, c(1,10)] %>% rename(effect = effect_Vo )
  
  df <- rbind(Ac, Il, Lh, Pl, Vo) 
  df <- df %>% mutate(sign = sign(effect))
  write_tsv(df, dir %&% "Results/prediXcan/metabolic_traits/most_sig_zscores/" %&% trait %&% "_avg_zscore.txt", col_names = FALSE)
}

Plot Association Results

The orth.rats file gives a dictionary between human genes and the corresponding gene in rats, queried from the Biomart database.

In the following chunk, we use it to annotate our dataframe with gene symbols. Then we replace metabolic trait names and add a column marking all the significant genes.

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()
)
full_df <- full_df %>% filter(metabolic_trait == "bmi_bodylength_w_tail" | metabolic_trait ==  "bodylength_w_tail"| metabolic_trait == "bodyweight" | metabolic_trait == "fasting_glucose" | metabolic_trait == "epifat" |  metabolic_trait == "retrofat" | metabolic_trait == "parafat")

full_df <- full_df %>% mutate(gene_name = orth.rats[match(full_df$gene, orth.rats$rnorvegicus_homolog_ensembl_gene),4]$rnorvegicus_homolog_associated_gene_name, .before = effect)

full_df$metabolic_trait[full_df$metabolic_trait == "bmi_bodylength_w_tail" ] <- "Body Mass Index (BMI) with tail"
full_df$metabolic_trait[full_df$metabolic_trait == "bodylength_w_tail" ] <- "Body length including tail"
full_df$metabolic_trait[full_df$metabolic_trait == "bodyweight" ] <- "Body weight"
full_df$metabolic_trait[full_df$metabolic_trait == "fasting_glucose" ] <- "Fasting Glucose"
full_df$metabolic_trait[full_df$metabolic_trait == "epifat" ] <- "Epididymal fat"
full_df$metabolic_trait[full_df$metabolic_trait == "retrofat" ] <- "Retroperitoneal fat"
full_df$metabolic_trait[full_df$metabolic_trait == "parafat" ] <- "Parametrial fat"

full_df <- full_df %>% mutate(bf_sig = ifelse(full_df$pvalue <= 9.279881e-06, "Yes", "No"))

We add loci information and separate results for each phenotype.

gene_annot <- readRDS(dir %&% "data/gene_annotation.RDS") %>% select(c("chr", "gene_id", "start", "end")) %>% rename(gene = gene_id)

tempo_manhatt <- inner_join(gene_annot, full_df, by = "gene")
tempo_manhatt$chr <- as.numeric(tempo_manhatt$chr)

bmi_manhat <- tempo_manhatt %>% filter(metabolic_trait == "Body Mass Index (BMI) with tail") 
bmi_manhat <- bmi_manhat %>% mutate(gene_name = orth.rats[match(bmi_manhat$gene, orth.rats$rnorvegicus_homolog_ensembl_gene), 4]$rnorvegicus_homolog_associated_gene_name)

height_manhat <- tempo_manhatt %>% filter(metabolic_trait == "Body length including tail") 
height_manhat <- height_manhat %>% mutate(gene_name = orth.rats[match(height_manhat$gene, orth.rats$rnorvegicus_homolog_ensembl_gene), 4]$rnorvegicus_homolog_associated_gene_name)

Note: I’m not sure where to find the Human_phenomeXcan_all_traits.txt file, but I assume it is a dictionary for human genes significantly associated to a set of phenotypes and their homologous rat genes. Until I find out how this file was queried, we skip this step and continue to the Manhattan plots. In any case, we have all the data we need to generate the plots, we only reference the human genes to label some of the significant genes in the figure.

human_height_genes <- read_tsv("/Users/natashasanthanam/Downloads/Human_phenomeXcan_all_traits.txt", col_names = TRUE)
human_height_genes <- human_height_genes %>% mutate(rat_gene = orth.rats[match(human_height_genes$gene_name, orth.rats$external_gene_name), 4]$rnorvegicus_homolog_associated_gene_name) %>% filter(pvalue_Height <= 0.01)


human_bmi_genes <- read_tsv("/Users/natashasanthanam/Downloads/Human_phenomeXcan_all_traits.txt", col_names = TRUE) 
colnames(human_bmi_genes)[2] = "pvalue_BMI"
human_bmi_genes <- human_bmi_genes %>% mutate(rat_gene = orth.rats[match(human_bmi_genes$gene_name, orth.rats$external_gene_name), 4]$rnorvegicus_homolog_associated_gene_name) %>% filter(pvalue_BMI <= 0.01 )

Generate Manhattan plots for BMI and height. If you wanted to add labels for human genes, you can uncomment the geom_label_repel line.

library(ggrepel)
data_cum <- bmi_manhat %>% 
  group_by(chr) %>% 
  summarise(max_bp = as.numeric(max(start))) %>% 
  mutate(bp_add = lag(cumsum(max_bp), default = 0)) %>% 
  select(chr, bp_add)
`summarise()` ungrouping output (override with `.groups` argument)
gwas_data <- bmi_manhat %>% 
  inner_join(data_cum, by = "chr") %>% 
  mutate(bp_cum = start + bp_add)


axis_set <- gwas_data %>% 
  group_by(chr) %>% 
  summarize(center = mean(bp_cum))
`summarise()` ungrouping output (override with `.groups` argument)
ylim <- gwas_data %>% 
  filter(pvalue == min(pvalue)) %>% 
  mutate(ylim = abs(floor(log10(pvalue))) + 2) %>% 
  pull(ylim)

sig <-  0.05/(5388)

bmi_manhplot <- ggplot(gwas_data, aes(x = bp_cum, y = -log10(pvalue), color = as_factor(chr), size = -log10(pvalue))) +
  geom_hline(yintercept = -log10(sig), color = "grey40", linetype = "dashed") + 
  geom_hline(yintercept = -log10(0.0001), color = "red", linetype = "dashed") + 
  geom_point(alpha = 0.75, shape = ifelse((gwas_data$zscore >= 4.863456), 17, ifelse(gwas_data$zscore <= -4.863456, 25, 19)), fill = "dodgerblue4") +
#  geom_label_repel(aes(label=ifelse((pvalue <=  sig & gene_name %in% human_bmi_genes$rat_gene), gene_name, "")), size = 6) + 
  ylim(c(0,8)) + 
  scale_x_continuous(label = axis_set$chr, breaks = axis_set$center) +
  scale_color_manual(values = rep(c("dodgerblue4", "midnightblue"), unique(length(axis_set$chr)))) +
  scale_size_continuous(range = c(0.5,3)) +
  labs(x = NULL, 
       y = expression(-log[10](italic(p)))) + 
  theme_minimal() +
  theme( 
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    axis.text.x = element_text(angle = 90, size = 12), 
    axis.text.y = element_text( size = 12,  vjust = 0), 
    axis.title = element_text(size = 20))

bmi_manhplot
Warning: Removed 2 rows containing missing values (geom_point).

Version Author Date
3902557 sabrina-mi 2022-08-22 
data_cum <- height_manhat %>% 
  group_by(chr) %>% 
  summarise(max_bp = as.numeric(max(start))) %>% 
  mutate(bp_add = lag(cumsum(max_bp), default = 0)) %>% 
  select(chr, bp_add)
`summarise()` ungrouping output (override with `.groups` argument)
gwas_data <- height_manhat %>% 
  inner_join(data_cum, by = "chr") %>% 
  mutate(bp_cum = start + bp_add)


axis_set <- gwas_data %>% 
  group_by(chr) %>% 
  summarize(center = mean(bp_cum))
`summarise()` ungrouping output (override with `.groups` argument)
ylim <- gwas_data %>% 
  filter(pvalue == min(pvalue)) %>% 
  mutate(ylim = abs(floor(log10(pvalue))) + 2) %>% 
  pull(ylim)

sig <-   0.05/(5388)

height_manhplot <- ggplot(gwas_data, aes(x = bp_cum, y = -log10(pvalue), 
                                  color = as_factor(chr), size = -log10(pvalue))) +
  geom_hline(yintercept = -log10(sig), color = "grey40", linetype = "dashed") + 
  geom_hline(yintercept = -log10(0.0001), color = "red", linetype = "dashed") + 
  geom_point(alpha = 0.75, shape = ifelse((gwas_data$zscore >= 4.863456), 17, ifelse(gwas_data$zscore <= -4.863456, 25, 19)), fill = "dodgerblue4") +
#  geom_label_repel(aes(label=ifelse((pvalue <=  sig & gene_name %in% human_height_genes$rat_gene), gene_name, "")), size = 6) + 
  ylim(c(0,10)) + 
  scale_x_continuous(label = axis_set$chr, breaks = axis_set$center) +
  scale_color_manual(values = rep(c("dodgerblue4", "midnightblue"), unique(length(axis_set$chr)))) +
  scale_size_continuous(range = c(0.5,3)) +
  labs(x = NULL, 
       y = expression(-log[10](italic(p)))) + 
  theme_minimal() +
  theme( 
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    axis.text.x = element_text(angle = 90, size = 12), 
    axis.text.y = element_text( size = 12,  vjust = 0), 
    axis.title = element_text(size = 20))

height_manhplot
Warning: Removed 3 rows containing missing values (geom_point).

Version Author Date
3902557 sabrina-mi 2022-08-22 

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] ggrepel_0.9.1   forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.4     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.6  
 [9] tidyverse_1.3.0 readr_1.4.0    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     lubridate_1.7.9  assertthat_0.2.1 rprojroot_1.3-2 
 [5] digest_0.6.27    utf8_1.1.4       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] rmarkdown_2.14   labeling_0.4.2   munsell_0.5.0    broom_0.8.0     
[25] compiler_4.0.3   httpuv_1.5.4     modelr_0.1.8     xfun_0.31       
[29] pkgconfig_2.0.3  htmltools_0.5.2  tidyselect_1.1.0 workflowr_1.6.2 
[33] fansi_0.4.1      crayon_1.3.4     dbplyr_1.4.4     withr_2.3.0     
[37] later_1.1.0.1    grid_4.0.3       jsonlite_1.7.1   gtable_0.3.0    
[41] lifecycle_0.2.0  DBI_1.1.0        git2r_0.27.1     magrittr_1.5    
[45] scales_1.1.1     cli_3.3.0        stringi_1.5.3    farver_2.0.3    
[49] fs_1.5.0         promises_1.1.1   xml2_1.3.2       bslib_0.3.1     
[53] ellipsis_0.3.2   generics_0.0.2   vctrs_0.4.1      tools_4.0.3     
[57] glue_1.6.2       hms_1.1.2        fastmap_1.1.0    yaml_2.2.1      
[61] colorspace_1.4-1 rvest_0.3.6      knitr_1.39       haven_2.3.1     
[65] sass_0.4.1