Last updated: 2019-06-12

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library(plyr)
library(tidyverse)
library(knitr)
library(data.table)
library(ggpmisc)
library("clusterProfiler")
library("org.Hs.eg.db")
library(gridExtra)
# library(ggpubr)

Here the dataset is eQTLs called from a model with the following pre-testing filters/transformations/checks:

  • cis-window=250kB
  • MAF>10%
  • genes tested must have >6 reads in 80% of samples
  • lmm with genetic relatedness matrix produced by gemma
  • Gene expression is standardized and normalized
  • 10PCs added as covariates
  • FDR estimated by Storey’s qvalue
  • Pvalues well calibrated under a permutated null

First, Read in the data…

eQTLs <- read.table(gzfile("../data/PastAnalysesDataToKeep/20190521_eQTLs_250kB_10MAF.txt.gz"), header=T)
kable(head(eQTLs))
snps gene beta statistic pvalue FDR qvalue
ID.1.126459696.ACCCTAGTAAG.A ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06
ID.1.126465687.TTGT.A ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06
ID.1.126465750.TG.CT ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06
ID.1.126465756.T.C ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06
ID.1.126465766.C.A ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06
ID.1.126465774.G.A ENSPTRG00000001061 3.570039 12.50828 0 3.5e-06 3.5e-06 
# List of chimp tested genes
ChimpTestedGenes <- rownames(read.table('../output/ExpressionMatrix.un-normalized.txt.gz', header=T, check.names=FALSE, row.names = 1))

ChimpToHumanGeneMap <- read.table("../data/Biomart_export.Hsap.Ptro.orthologs.txt.gz", header=T, sep='\t', stringsAsFactors = F)
kable(head(ChimpToHumanGeneMap))
Gene.stable.ID Transcript.stable.ID Chimpanzee.gene.stable.ID Chimpanzee.gene.name Chimpanzee.protein.or.transcript.stable.ID Chimpanzee.homology.type X.id..target.Chimpanzee.gene.identical.to.query.gene X.id..query.gene.identical.to.target.Chimpanzee.gene dN.with.Chimpanzee dS.with.Chimpanzee Chimpanzee.orthology.confidence..0.low..1.high.
ENSG00000198888 ENST00000361390 ENSPTRG00000042641 MT-ND1 ENSPTRP00000061407 ortholog_one2one 94.6541 94.6541 0.0267 0.5455 1
ENSG00000198763 ENST00000361453 ENSPTRG00000042626 MT-ND2 ENSPTRP00000061406 ortholog_one2one 96.2536 96.2536 0.0185 0.7225 1
ENSG00000210127 ENST00000387392 ENSPTRG00000042642 MT-TA ENSPTRT00000076396 ortholog_one2one 100.0000 100.0000 NA NA NA
ENSG00000198804 ENST00000361624 ENSPTRG00000042657 MT-CO1 ENSPTRP00000061408 ortholog_one2one 98.8304 98.8304 0.0065 0.5486 1
ENSG00000198712 ENST00000361739 ENSPTRG00000042660 MT-CO2 ENSPTRP00000061402 ortholog_one2one 97.7974 97.7974 0.0106 0.5943 1
ENSG00000228253 ENST00000361851 ENSPTRG00000042653 MT-ATP8 ENSPTRP00000061400 ortholog_one2one 94.1176 94.1176 0.0325 0.3331 1 
# Of this ortholog list, how many genes are one2one
table(ChimpToHumanGeneMap$Chimpanzee.homology.type)

ortholog_many2many  ortholog_one2many   ortholog_one2one 
              2278              19917             140351 
OneToOneMap <- ChimpToHumanGeneMap %>%
  filter(Chimpanzee.homology.type=="ortholog_one2one") %>%
  distinct(Chimpanzee.gene.stable.ID, .keep_all = TRUE)

# Read gtex heart egene list
# Only consider those that were tested in both species and are one2one orthologs
GtexHeartEgenes <- read.table("../data/Heart_Left_Ventricle.v7.egenes.txt.gz", header=T, sep='\t', stringsAsFactors = F) %>%
  mutate(gene_id_stable = gsub(".\\d+$","",gene_id)) %>%
  filter(gene_id_stable %in% OneToOneMap$Gene.stable.ID) %>%
  mutate(chimp_id = plyr::mapvalues(gene_id_stable, OneToOneMap$Gene.stable.ID,  OneToOneMap$Chimpanzee.gene.stable.ID, warn_missing = F)) %>%
  filter(chimp_id %in% ChimpTestedGenes)

ChimpToHuman.ID <- function(Chimp.ID){
  #function to convert chimp ensembl to human ensembl gene ids
  return(
    plyr::mapvalues(Chimp.ID, OneToOneMap$Chimpanzee.gene.stable.ID, OneToOneMap$Gene.stable.ID, warn_missing = F)
  )}

Now compare with GTEx by making 2x2 contigency table (eGene/not-eGene in Chimp/human). The odds ratio from this table is symetrical.

HumanFDR <- 0.1
ChimpFDR <- 0.1

#Get chimp eQTLs
Chimp_eQTLs <- eQTLs %>%
  filter(qvalue<ChimpFDR)

# Count chimp eGenes
length(unique(Chimp_eQTLs$gene))
[1] 336
# Count human eGenes
length(GtexHeartEgenes %>% filter(qval< HumanFDR) %>% pull(chimp_id))
[1] 5410
# Count number genes tested in both species (already filtered for 1to1 orthologs)
length(GtexHeartEgenes$gene_id_stable)
[1] 11586

The number of human eGenes is huge (about half of all tested genes) and GTEx over-powered compared to chimp. With huge power, everything is an eGene and the eGene classification becomes devoid of meaningful information. So I will play with different ways to classify human eGenes.

#Change FDR thresholds or take top N eGenes by qvalue
HumanTopN <- 600
HumanFDR <- 0.1
ChimpFDR <- 0.1

# Filter human eGenes by qval threshold
HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% pull(chimp_id)

# Filter human eGenes by topN qval
HumanSigGenes <- GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% pull(chimp_id)

# Filter human eGeness by qval threshold then topN betas 
# HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% top_n(1000, abs(slope)) %>% pull(chimp_id)

HumanNonSigGenes <- GtexHeartEgenes %>%
  filter(!chimp_id %in% HumanSigGenes) %>%
  pull(chimp_id)

ChimpSigGenes <- GtexHeartEgenes %>%
  filter(chimp_id %in% Chimp_eQTLs$gene) %>%
  pull(chimp_id)

ChimpNonSigGenes <- GtexHeartEgenes %>%
  filter(! chimp_id %in% Chimp_eQTLs$gene) %>%
  pull(chimp_id)



ContigencyTable <- matrix( c( length(intersect(ChimpSigGenes,HumanSigGenes)),
                              length(intersect(HumanSigGenes,ChimpNonSigGenes)),
                              length(intersect(ChimpSigGenes,HumanNonSigGenes)),
                              length(intersect(ChimpNonSigGenes,HumanNonSigGenes))), 
                           nrow = 2)

rownames(ContigencyTable) <- c("Chimp eGene", "Not Chimp eGene")
colnames(ContigencyTable) <- c("Human eGene", "Not human eGene")

#what is qval threshold for human eGene classification in this contigency table
print(GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% top_n(1, qval) %>% pull(qval))
[1] 5.83223e-12
#Contigency table of one to one orthologs tested in both chimps and humans of whether significant in humans, or chimps, or both, or neither
ContigencyTable
                Human eGene Not human eGene
Chimp eGene              28             252
Not Chimp eGene         572           10734
#One-sided Fisher test for greater overlap than expected by chance
fisher.test(ContigencyTable, alternative="greater")

    Fisher's Exact Test for Count Data

data:  ContigencyTable
p-value = 0.0006395
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
 1.444504      Inf
sample estimates:
odds ratio 
  2.084996

The above contingency table and one-sided fisher test indicated a greater-than-chance overlap between the sets of eGenes in chimp and human.

#Chimp eGenes vs non chimp eGenes
ToPlot <- GtexHeartEgenes %>%
  mutate(group = case_when(
        chimp_id %in% ChimpSigGenes ~ "chimp.eGene",
        !chimp_id %in% ChimpSigGenes ~ "not.chimp.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee)
Chimp.dNdS.plot <- ggplot(ToPlot, aes(color=group,x=dN.dS)) +
  stat_ecdf(geom = "step") +
  ylab("Cumulative frequency") +
  xlab("dN/dS") +
  scale_x_continuous(trans='log10', limits=c(0.01,10)) +
  annotate("text", x = 1, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, dN.dS ~ group, alternative="greater")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())
Chimp.identity.plot <- ggplot(ToPlot, aes(color=group, x=100-X.id..query.gene.identical.to.target.Chimpanzee.gene)) +
  stat_ecdf(geom = "step") +
  scale_x_continuous(trans='log1p', limits=c(0,100), expand=expand_scale()) +
  ylab("Cumulative frequency") +
  xlab("Percent non-identitical amino acid between chimp and human") +
  annotate("text", x = 10, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, X.id..query.gene.identical.to.target.Chimpanzee.gene ~ group, alternative="less")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())

#Human eGenes vs non human eGenes
ToPlot <- GtexHeartEgenes %>%
  mutate(group = case_when(
        chimp_id %in% HumanSigGenes ~ "human.eGene",
        !chimp_id %in% HumanSigGenes ~ "not.human.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee)
Human.dNdS.plot <- ggplot(ToPlot, aes(color=group,x=dN.dS)) +
  stat_ecdf(geom = "step") +
  ylab("Cumulative frequency") +
  xlab("dN/dS") +
  scale_x_continuous(trans='log10', limits=c(0.01,10)) +
  annotate("text", x = 1, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, dN.dS ~ group, alternative="greater")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())
Human.identity.plot <- ggplot(ToPlot, aes(color=group, x=100-X.id..query.gene.identical.to.target.Chimpanzee.gene)) +
  stat_ecdf(geom = "step") +
  scale_x_continuous(trans='log1p', limits=c(0,100), expand=expand_scale()) +
  ylab("Cumulative frequency") +
  xlab("Percent non-identitical amino acid between chimp and human") +
  annotate("text", x = 10, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, X.id..query.gene.identical.to.target.Chimpanzee.gene ~ group, alternative="less")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())

#Shared eGenes vs human-specific eGenes
ToPlot <- GtexHeartEgenes %>%
  mutate(group = case_when(
        chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
        chimp_id %in% setdiff(HumanSigGenes, ChimpSigGenes) ~ "human.specific.eGene")) %>%
  filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(HumanSigGenes, ChimpSigGenes)))%>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee)
Shared.human.dNdS.plot <- ggplot(ToPlot, aes(color=group,x=dN.dS)) +
  stat_ecdf(geom = "step") +
  ylab("Cumulative frequency") +
  xlab("dN/dS") +
  scale_x_continuous(trans='log10', limits=c(0.01,10)) +
  annotate("text", x = 1, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, dN.dS ~ group, alternative="less")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())
Shared.human.identity.plot <- ggplot(ToPlot, aes(color=group, x=100-X.id..query.gene.identical.to.target.Chimpanzee.gene)) +
  stat_ecdf(geom = "step") +
  scale_x_continuous(trans='log1p', limits=c(0,100), expand=expand_scale()) +
  ylab("Cumulative frequency") +
  xlab("Percent non-identitical amino acid between chimp and human") +
  annotate("text", x = 10, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, X.id..query.gene.identical.to.target.Chimpanzee.gene ~ group, alternative="greater")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())

#Shared eGenes vs chimp-specific eGenes
ToPlot <- GtexHeartEgenes %>%
  left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee) %>%
mutate(group = case_when(
        chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
        chimp_id %in% setdiff(ChimpSigGenes, HumanSigGenes) ~ "chimp.specific.eGene")) %>%
dplyr::filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(ChimpSigGenes, HumanSigGenes)))
Shared.chimp.dNdS.plot <- ggplot(ToPlot, aes(color=group,x=dN.dS)) +
  stat_ecdf(geom = "step") +
  ylab("Cumulative frequency") +
  xlab("dN/dS") +
  scale_x_continuous(trans='log10', limits=c(0.01,10)) +
  annotate("text", x = 1, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, dN.dS ~ group, alternative="less")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())
Shared.chimp.identity.plot <- ggplot(ToPlot, aes(color=group, x=100-X.id..query.gene.identical.to.target.Chimpanzee.gene+0.001)) +
  stat_ecdf(geom = "step") +
  scale_x_continuous(trans='log1p', limits=c(0,100), expand=expand_scale()) +
  ylab("Cumulative frequency") +
  xlab("Percent non-identitical amino acid between chimp and human") +
  annotate("text", x = 10, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, X.id..query.gene.identical.to.target.Chimpanzee.gene ~ group, alternative="greater")$p.value, 2) )) +
  theme_bw() +
  theme(legend.position = c(.80, .2), legend.title=element_blank())

Chimp.dNdS.plot
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 2951 rows containing non-finite values (stat_ecdf).

Chimp.identity.plot

Human.dNdS.plot
Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 2951 rows containing non-finite values (stat_ecdf).

Human.identity.plot

Shared.human.dNdS.plot
Warning: Removed 122 rows containing non-finite values (stat_ecdf).

Shared.human.identity.plot

Shared.chimp.dNdS.plot
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 58 rows containing non-finite values (stat_ecdf).

Shared.chimp.identity.plot

Some general and unsurprising conclusions:

eGenes are less conserved than non eGenes, and species shared eGenes are less conserved than species specific eGenes.


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] gridExtra_2.3          org.Hs.eg.db_3.7.0     AnnotationDbi_1.44.0  
 [4] IRanges_2.16.0         S4Vectors_0.20.1       Biobase_2.42.0        
 [7] BiocGenerics_0.28.0    clusterProfiler_3.10.1 ggpmisc_0.3.1         
[10] data.table_1.12.2      knitr_1.23             forcats_0.4.0         
[13] stringr_1.4.0          dplyr_0.8.1            purrr_0.3.2           
[16] readr_1.3.1            tidyr_0.8.3            tibble_2.1.3          
[19] ggplot2_3.1.1          tidyverse_1.2.1        plyr_1.8.4            

loaded via a namespace (and not attached):
 [1] nlme_3.1-140        fs_1.3.1            enrichplot_1.2.0   
 [4] lubridate_1.7.4     bit64_0.9-7         progress_1.2.2     
 [7] RColorBrewer_1.1-2  httr_1.4.0          UpSetR_1.4.0       
[10] rprojroot_1.3-2     tools_3.5.1         backports_1.1.4    
[13] R6_2.4.0            DBI_1.0.0           lazyeval_0.2.2     
[16] colorspace_1.4-1    withr_2.1.2         prettyunits_1.0.2  
[19] tidyselect_0.2.5    bit_1.1-14          compiler_3.5.1     
[22] git2r_0.25.2        cli_1.1.0           rvest_0.3.4        
[25] xml2_1.2.0          labeling_0.3        triebeard_0.3.0    
[28] scales_1.0.0        ggridges_0.5.1      digest_0.6.19      
[31] rmarkdown_1.13      DOSE_3.8.2          pkgconfig_2.0.2    
[34] htmltools_0.3.6     highr_0.8           rlang_0.3.4        
[37] readxl_1.3.1        rstudioapi_0.10     RSQLite_2.1.1      
[40] gridGraphics_0.4-1  generics_0.0.2      farver_1.1.0       
[43] jsonlite_1.6        BiocParallel_1.16.6 GOSemSim_2.8.0     
[46] magrittr_1.5        ggplotify_0.0.3     GO.db_3.7.0        
[49] Matrix_1.2-17       Rcpp_1.0.1          munsell_0.5.0      
[52] viridis_0.5.1       stringi_1.4.3       yaml_2.2.0         
[55] ggraph_1.0.2        MASS_7.3-51.4       qvalue_2.14.1      
[58] grid_3.5.1          blob_1.1.1          ggrepel_0.8.1      
[61] DO.db_2.9           crayon_1.3.4        lattice_0.20-38    
[64] cowplot_0.9.4       haven_2.1.0         splines_3.5.1      
[67] hms_0.4.2           pillar_1.4.1        fgsea_1.8.0        
[70] igraph_1.2.4.1      reshape2_1.4.3      fastmatch_1.1-0    
[73] glue_1.3.1          evaluate_0.14       modelr_0.1.4       
[76] urltools_1.7.3      tweenr_1.0.1        cellranger_1.1.0   
[79] gtable_0.3.0        polyclip_1.10-0     assertthat_0.2.1   
[82] xfun_0.7            ggforce_0.2.2       europepmc_0.3      
[85] broom_0.5.2         viridisLite_0.3.0   rvcheck_0.1.3      
[88] memoise_1.1.0       workflowr_1.4.0