GWAS Visualization in R
Keith Mitchel, UC Davis Bioinformatics Core
7/7/2021
We will need to get the tdt results for the data
For a comparison, a sample dataset is provided in order to show what all chromosomes would look like. We will create some visualization with this as well.
head(gwasResults)## SNP CHR BP P
## 1 rs1 1 1 0.9148060
## 2 rs2 1 2 0.9370754
## 3 rs3 1 3 0.2861395
## 4 rs4 1 4 0.8304476
## 5 rs5 1 5 0.6417455
## 6 rs6 1 6 0.5190959Lets read in the TDT results - The adjusted file is a bit smaller here since the NA values cannot be adjusted and are ommitted. So lets merge and only keep values that exist in the adjusted file, but lets add the frequency or MAF
tdtfrq <- read.csv("data_analysis/tdtfrq.csv")
head(tdtfrq)## CHR SNP A1 A2 MAF NCHROBS
## 1 21 5034209 A G 0.003571 280
## 2 21 5034244 T C 0.007042 284
## 3 21 5063575 T C 0.003497 286
## 4 21 5063806 T C 0.003448 290
## 5 21 5063842 A G 0.010420 288
## 6 21 5089937 C T 0.017240 290tdtadj <- read.csv("data_analysis/tdtadj.csv", row.names=NULL)
head(tdtadj)## CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY
## 1 21 7946755 3.738e-05 0.005365 1 1 0.9714 0.9714 0.3734 1
## 2 21 7946763 3.738e-05 0.005365 1 1 0.9714 0.9714 0.3734 1
## 3 21 7946728 6.334e-05 0.006911 1 1 0.9976 0.9976 0.3734 1
## 4 21 7946741 6.334e-05 0.006911 1 1 0.9976 0.9976 0.3734 1
## 5 21 7946744 6.334e-05 0.006911 1 1 0.9976 0.9976 0.3734 1
## 6 21 26266437 8.770e-05 0.008081 1 1 0.9998 0.9998 0.3734 1Lets merge our frequences with the TDT results.
cleantdt <- merge(tdtfrq , tdtadj, by=c("CHR","SNP"))
head(cleantdt)## CHR SNP A1 A2 MAF NCHROBS UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD
## 1 21 10000132 AT A 0.003448 290 0.3173 0.4995 1 1 1 1
## 2 21 10000176 AT A 0.003448 290 0.3173 0.4995 1 1 1 1
## 3 21 10000297 A G 0.006897 290 0.3173 0.4995 1 1 1 1
## 4 21 10000415 G T 0.003448 290 1.0000 1.0000 1 1 1 1
## 5 21 10000434 A G 0.003448 290 0.3173 0.4995 1 1 1 1
## 6 21 10000714 A G 0.489600 288 0.7194 0.8083 1 1 1 1
## FDR_BH FDR_BY
## 1 0.3734 1
## 2 0.3734 1
## 3 0.3734 1
## 4 1.0000 1
## 5 0.3734 1
## 6 0.7983 1Lets read in the annotation information
anno <- read.csv('data_analysis/query.output.genome_summary.csv')
head(anno)## Chr Start End Ref Alt Func.refGene Gene.refGene
## 1 21 5034209 5034209 G A intronic ICOSLG
## 2 21 5034244 5034244 C T intronic ICOSLG
## 3 21 5063575 5063575 C T intergenic ICOSLG;LINC01678
## 4 21 5063806 5063806 C T intergenic ICOSLG;LINC01678
## 5 21 5063842 5063842 G A intergenic ICOSLG;LINC01678
## 6 21 5089937 5089937 T C intergenic ICOSLG;LINC01678
## GeneDetail.refGene ExonicFunc.refGene AAChange.refGene X1000G_ALL
## 1
## 2
## 3 dist=22907;dist=38485
## 4 dist=23138;dist=38254
## 5 dist=23174;dist=38218
## 6 dist=49269;dist=12123
## X1000G_AFR X1000G_AMR X1000G_EAS X1000G_EUR X1000G_SAS ExAC_Freq ExAC_AFR
## 1
## 2
## 3
## 4
## 5
## 6
## ExAC_AMR ExAC_EAS ExAC_FIN ExAC_NFE ExAC_OTH ExAC_SAS ESP6500si_ALL
## 1
## 2
## 3
## 4
## 5
## 6
## ESP6500si_AA ESP6500si_EA CG46 NCI60 dbSNP COSMIC_ID COSMIC_DIS ClinVar_SIG
## 1
## 2
## 3
## 4
## 5
## 6
## ClinVar_DIS ClinVar_ID ClinVar_DB ClinVar_DBID GWAS_DIS GWAS_OR GWAS_BETA
## 1
## 2
## 3
## 4
## 5
## 6
## GWAS_PUBMED GWAS_SNP GWAS_P SIFT_score SIFT_converted_rankscore SIFT_pred
## 1
## 2
## 3
## 4
## 5
## 6
## Polyphen2_HDIV_score Polyphen2_HDIV_rankscore Polyphen2_HDIV_pred
## 1
## 2
## 3
## 4
## 5
## 6
## Polyphen2_HVAR_score Polyphen2_HVAR_rankscore Polyphen2_HVAR_pred LRT_score
## 1
## 2
## 3
## 4
## 5
## 6
## LRT_converted_rankscore LRT_pred MutationTaster_score
## 1
## 2
## 3
## 4
## 5
## 6
## MutationTaster_converted_rankscore MutationTaster_pred MutationAssessor_score
## 1
## 2
## 3
## 4
## 5
## 6
## MutationAssessor_score_rankscore MutationAssessor_pred FATHMM_score
## 1
## 2
## 3
## 4
## 5
## 6
## FATHMM_converted_rankscore FATHMM_pred PROVEAN_score
## 1
## 2
## 3
## 4
## 5
## 6
## PROVEAN_converted_rankscore PROVEAN_pred VEST3_score VEST3_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## MetaSVM_score MetaSVM_rankscore MetaSVM_pred MetaLR_score MetaLR_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## MetaLR_pred M.CAP_score M.CAP_rankscore M.CAP_pred CADD_raw
## 1
## 2
## 3
## 4
## 5
## 6
## CADD_raw_rankscore CADD_phred DANN_score DANN_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## fathmm.MKL_coding_score fathmm.MKL_coding_rankscore fathmm.MKL_coding_pred
## 1
## 2
## 3
## 4
## 5
## 6
## Eigen_coding_or_noncoding Eigen.raw Eigen.PC.raw GenoCanyon_score
## 1
## 2
## 3
## 4
## 5
## 6
## GenoCanyon_score_rankscore integrated_fitCons_score
## 1
## 2
## 3
## 4
## 5
## 6
## integrated_fitCons_score_rankscore integrated_confidence_value GERP.._RS
## 1
## 2
## 3
## 4
## 5
## 6
## GERP.._RS_rankscore phyloP100way_vertebrate phyloP100way_vertebrate_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## phyloP20way_mammalian phyloP20way_mammalian_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## phastCons100way_vertebrate phastCons100way_vertebrate_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## phastCons20way_mammalian phastCons20way_mammalian_rankscore
## 1
## 2
## 3
## 4
## 5
## 6
## SiPhy_29way_logOdds SiPhy_29way_logOdds_rankscore Interpro_domain
## 1
## 2
## 3
## 4
## 5
## 6
## GTEx_V6_gene GTEx_V6_tissue gnomAD_exome_ALL gnomAD_exome_AFR
## 1
## 2
## 3
## 4
## 5
## 6
## gnomAD_exome_AMR gnomAD_exome_ASJ gnomAD_exome_EAS gnomAD_exome_FIN
## 1
## 2
## 3
## 4
## 5
## 6
## gnomAD_exome_NFE gnomAD_exome_OTH gnomAD_exome_SAS gnomAD_genome_ALL
## 1
## 2
## 3
## 4
## 5
## 6
## gnomAD_genome_AFR gnomAD_genome_AMR gnomAD_genome_ASJ gnomAD_genome_EAS
## 1
## 2
## 3
## 4
## 5
## 6
## gnomAD_genome_FIN gnomAD_genome_NFE gnomAD_genome_OTH Otherinfo Otherinfo.1
## 1 hom .
## 2 hom .
## 3 hom .
## 4 hom .
## 5 hom .
## 6 hom .There seem to be some NA values produced by plink for TDT.. why might this be?
final <- merge(cleantdt, anno, by.x=c("CHR","SNP"), by.y=c("Chr", "Start"))
head(final)## CHR SNP A1 A2 MAF NCHROBS UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD
## 1 21 10000132 AT A 0.003448 290 0.3173 0.4995 1 1 1 1
## 2 21 10000176 AT A 0.003448 290 0.3173 0.4995 1 1 1 1
## 3 21 10000297 A G 0.006897 290 0.3173 0.4995 1 1 1 1
## 4 21 10000415 G T 0.003448 290 1.0000 1.0000 1 1 1 1
## 5 21 10000434 A G 0.003448 290 0.3173 0.4995 1 1 1 1
## 6 21 10000714 A G 0.489600 288 0.7194 0.8083 1 1 1 1
## FDR_BH FDR_BY End Ref Alt Func.refGene Gene.refGene
## 1 0.3734 1 10000132 - T intergenic LINC01667;BAGE
## 2 0.3734 1 10000176 - T intergenic LINC01667;BAGE
## 3 0.3734 1 10000297 G A intergenic LINC01667;BAGE
## 4 1.0000 1 10000415 T G intergenic LINC01667;BAGE
## 5 0.3734 1 10000434 G A intergenic LINC01667;BAGE
## 6 0.7983 1 10000714 G A intergenic LINC01667;BAGE
## GeneDetail.refGene ExonicFunc.refGene AAChange.refGene X1000G_ALL
## 1 dist=179071;dist=413388 .
## 2 dist=179115;dist=413344 .
## 3 dist=179236;dist=413223 0.0018
## 4 dist=179354;dist=413105
## 5 dist=179373;dist=413086 .
## 6 dist=179653;dist=412806 .
## X1000G_AFR X1000G_AMR X1000G_EAS X1000G_EUR X1000G_SAS ExAC_Freq ExAC_AFR
## 1 . . . . . . .
## 2 . . . . . . .
## 3 0.0008 0.0014 . 0.003 0.0041 . .
## 4
## 5 . . . . . . .
## 6 . . . . . . .
## ExAC_AMR ExAC_EAS ExAC_FIN ExAC_NFE ExAC_OTH ExAC_SAS ESP6500si_ALL
## 1 . . . . . . .
## 2 . . . . . . .
## 3 . . . . . . .
## 4
## 5 . . . . . . .
## 6 . . . . . . .
## ESP6500si_AA ESP6500si_EA CG46 NCI60 dbSNP COSMIC_ID COSMIC_DIS
## 1 . . . . . . .
## 2 . . . . . . .
## 3 . . . . rs531431278 . .
## 4
## 5 . . . . . . .
## 6 . . . . rs4109702 . .
## ClinVar_SIG ClinVar_DIS ClinVar_ID ClinVar_DB ClinVar_DBID GWAS_DIS GWAS_OR
## 1 . . . . . . .
## 2 . . . . . . .
## 3 . . . . . . .
## 4
## 5 . . . . . . .
## 6 . . . . . . .
## GWAS_BETA GWAS_PUBMED GWAS_SNP GWAS_P SIFT_score SIFT_converted_rankscore
## 1 . . . . . .
## 2 . . . . . .
## 3 . . . . . .
## 4
## 5 . . . . . .
## 6 . . . . . .
## SIFT_pred Polyphen2_HDIV_score Polyphen2_HDIV_rankscore Polyphen2_HDIV_pred
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## Polyphen2_HVAR_score Polyphen2_HVAR_rankscore Polyphen2_HVAR_pred LRT_score
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## LRT_converted_rankscore LRT_pred MutationTaster_score
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## MutationTaster_converted_rankscore MutationTaster_pred MutationAssessor_score
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## MutationAssessor_score_rankscore MutationAssessor_pred FATHMM_score
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## FATHMM_converted_rankscore FATHMM_pred PROVEAN_score
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## PROVEAN_converted_rankscore PROVEAN_pred VEST3_score VEST3_rankscore
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## MetaSVM_score MetaSVM_rankscore MetaSVM_pred MetaLR_score MetaLR_rankscore
## 1 . . . . .
## 2 . . . . .
## 3 . . . . .
## 4
## 5 . . . . .
## 6 . . . . .
## MetaLR_pred M.CAP_score M.CAP_rankscore M.CAP_pred CADD_raw
## 1 . . . . .
## 2 . . . . .
## 3 . . . . .
## 4
## 5 . . . . .
## 6 . . . . .
## CADD_raw_rankscore CADD_phred DANN_score DANN_rankscore
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## fathmm.MKL_coding_score fathmm.MKL_coding_rankscore fathmm.MKL_coding_pred
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## Eigen_coding_or_noncoding Eigen.raw Eigen.PC.raw GenoCanyon_score
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## GenoCanyon_score_rankscore integrated_fitCons_score
## 1 . .
## 2 . .
## 3 . .
## 4
## 5 . .
## 6 . .
## integrated_fitCons_score_rankscore integrated_confidence_value GERP.._RS
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## GERP.._RS_rankscore phyloP100way_vertebrate phyloP100way_vertebrate_rankscore
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## phyloP20way_mammalian phyloP20way_mammalian_rankscore
## 1 . .
## 2 . .
## 3 . .
## 4
## 5 . .
## 6 . .
## phastCons100way_vertebrate phastCons100way_vertebrate_rankscore
## 1 . .
## 2 . .
## 3 . .
## 4
## 5 . .
## 6 . .
## phastCons20way_mammalian phastCons20way_mammalian_rankscore
## 1 . .
## 2 . .
## 3 . .
## 4
## 5 . .
## 6 . .
## SiPhy_29way_logOdds SiPhy_29way_logOdds_rankscore Interpro_domain
## 1 . . .
## 2 . . .
## 3 . . .
## 4
## 5 . . .
## 6 . . .
## GTEx_V6_gene GTEx_V6_tissue gnomAD_exome_ALL gnomAD_exome_AFR
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## gnomAD_exome_AMR gnomAD_exome_ASJ gnomAD_exome_EAS gnomAD_exome_FIN
## 1 . . . .
## 2 . . . .
## 3 . . . .
## 4
## 5 . . . .
## 6 . . . .
## gnomAD_exome_NFE gnomAD_exome_OTH gnomAD_exome_SAS gnomAD_genome_ALL
## 1 . . . 0.0020
## 2 . . . 3.229e-05
## 3 . . . 0.0014
## 4
## 5 . . . 0.0013
## 6 . . . 0.5038
## gnomAD_genome_AFR gnomAD_genome_AMR gnomAD_genome_ASJ gnomAD_genome_EAS
## 1 0.0074 0 0 0
## 2 0 0 0 0.0006
## 3 0.0003 0.0024 0 0
## 4
## 5 0.0002 0 0 0
## 6 0.5532 0.4973 0.4661 0.4938
## gnomAD_genome_FIN gnomAD_genome_NFE gnomAD_genome_OTH Otherinfo Otherinfo.1
## 1 0 0 0 unknown .
## 2 0 0 0 unknown .
## 3 0.0029 0.0017 0.0031 hom .
## 4 hom .
## 5 0.0023 0.0019 0.0020 hom .
## 6 0.4870 0.4786 0.4857 hom .What are the top dbSNP ids for the UNADJ p values
annotate <- head(final %>% arrange(UNADJ), 20)$dbSNP
annotate <- annotate[annotate != '' & annotate !='.']Make the Manhattan plot on the final dataset Make the Manhattan plot on the gwasResults dataset
manhattan(final, chr="CHR", bp="SNP", snp="dbSNP", p="UNADJ" )
manhattan(gwasResults)
A list of SNP of interest is provided with the library: Let’s highlight them, with a bit of customization on the plot By default -log base 10 is used to transform our p-values. Lets see why
manhattan(final, chr="CHR", bp="SNP", snp="dbSNP", p="UNADJ", highlight = annotate, logp=F)
manhattan(final, chr="CHR", bp="SNP", snp="dbSNP", p="UNADJ", highlight = annotate, logp=T)
manhattan(final, chr="CHR", bp="SNP", snp="dbSNP", p="UNADJ", annotatePval = 0.5)
manhattan(gwasResults, annotatePval = 0.05)
We have tons of other metadata, what else would be interesting to explore?
circular version with CMplot
The CMplot library by Lilin Yin is a good choice if you want to make a circular version of your manhattanplot. I believe than doing a circular version makes sense: it gives less space to all the non significant SNPs that do not interest us, and gives more space for the significant association. Moreover, the CMplot makes their realization straightforward.
library("CMplot")## Much appreciate for using CMplot.## Full description, Bug report, Suggestion and the latest codes:## https://github.com/YinLiLin/CMplotCMplot(gwasResults, plot.type="c", r=1.6, cir.legend=TRUE,
outward=TRUE, cir.legend.col="black", cir.chr.h=.1 ,chr.den.col="orange", file="jpg",
memo="", dpi=300, chr.labels=seq(1,22))## Circular-Manhattan Plotting P.
## Plots are stored in: /Users/keithmitchell/Desktop/Repositories/2021-July-Genome-Wide-Association-Studies
Explore the metadata:
- MAF
p <- ggplot(final, aes(x=MAF, fill=as.factor(CHR))) + geom_histogram( color="#e9ecef", binwidth = 0.01, alpha=0.6, position = 'identity') +
scale_fill_manual(values=c("blue", "green")) +
labs(fill="")
p
- population MAF
p <- ggplot(final, aes(x=as.numeric(X1000G_ALL), fill=as.factor(CHR))) + geom_histogram( color="#e9ecef", binwidth = 0.03, alpha=0.6, position = 'identity') +
scale_fill_manual(values=c("blue", "green")) +
labs(fill="")
p## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion## Warning: Removed 93654 rows containing non-finite values (stat_bin).
final$Delta <- as.numeric(final$X1000G_ALL) - as.numeric(final$MAF)## Warning: NAs introduced by coercion- delta of population and samples
p <- ggplot(final, aes(x=Delta, fill=as.factor(CHR))) + geom_histogram( color="#e9ecef", binwidth = 0.03, alpha=0.4, position = 'identity') +
scale_fill_manual(values=c("blue", "green")) +
labs(fill="")
p## Warning: Removed 93654 rows containing non-finite values (stat_bin).
Isolating just exonic regions:
final_exonic <- final %>% filter(Func.refGene == "exonic")
head(final_exonic)## CHR SNP A1 A2 MAF NCHROBS UNADJ GC
## 1 21 10413729 A G 0.493100 290 0.92870 0.9518
## 2 21 10413748 C T 0.003448 290 0.31730 0.4995
## 3 21 10413753 C T 0.482800 290 0.85630 0.9027
## 4 21 10414915 ATTCATCTTCTGAGGTTTCG AGTTTCG 0.010490 286 0.08326 0.2422
## 5 21 10454231 G C 0.486200 290 0.78840 0.8562
## 6 21 10541120 T C 0.003448 290 0.31730 0.4995
## BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY End Ref
## 1 1 1 1 1 1.0000 1 10413729 G
## 2 1 1 1 1 0.3734 1 10413748 T
## 3 1 1 1 1 0.9395 1 10413753 T
## 4 1 1 1 1 0.3734 1 10414921 AGTTTCG
## 5 1 1 1 1 0.8696 1 10454231 C
## 6 1 1 1 1 0.3734 1 10541120 C
## Alt Func.refGene Gene.refGene GeneDetail.refGene
## 1 A exonic BAGE;BAGE4;BAGE5
## 2 C exonic BAGE2;BAGE3
## 3 C exonic BAGE2;BAGE3
## 4 ATTCATCTTCTGAGGTTTCG exonic BAGE4
## 5 G exonic BAGE2;BAGE3
## 6 T exonic TPTE
## ExonicFunc.refGene
## 1 nonsynonymous SNV
## 2 synonymous SNV
## 3 nonsynonymous SNV
## 4 synonymous SNV
## 5 nonsynonymous SNV
## 6 nonsynonymous SNV
## AAChange.refGene
## 1 BAGE:NM_001187:exon1:c.G10A:p.G4R,BAGE4:NM_181704:exon1:c.G10A:p.G4R,BAGE5:NM_182484:exon1:c.G10A:p.G4R
## 2 BAGE3:NM_182481:exon1:c.T9C:p.A3A,BAGE2:NM_182482:exon1:c.T9C:p.A3A
## 3 BAGE3:NM_182481:exon1:c.T14C:p.V5A,BAGE2:NM_182482:exon1:c.T14C:p.V5A
## 4 BAGE4:NM_181704:exon2:c.A120A:p.X40X
## 5 BAGE3:NM_182481:exon3:c.C214G:p.P72A,BAGE2:NM_182482:exon3:c.C214G:p.P72A
## 6 TPTE:NM_199259:exon5:c.C20T:p.P7L,TPTE:NM_199260:exon5:c.C20T:p.P7L,TPTE:NM_199261:exon5:c.C20T:p.P7L
## X1000G_ALL X1000G_AFR X1000G_AMR X1000G_EAS X1000G_EUR X1000G_SAS ExAC_Freq
## 1
## 2
## 3
## 4
## 5
## 6 . . . . . . .
## ExAC_AFR ExAC_AMR ExAC_EAS ExAC_FIN ExAC_NFE ExAC_OTH ExAC_SAS ESP6500si_ALL
## 1
## 2
## 3
## 4
## 5
## 6 . . . . . . . .
## ESP6500si_AA ESP6500si_EA CG46 NCI60 dbSNP COSMIC_ID COSMIC_DIS ClinVar_SIG
## 1
## 2
## 3
## 4
## 5
## 6 . . . . . . . .
## ClinVar_DIS ClinVar_ID ClinVar_DB ClinVar_DBID GWAS_DIS GWAS_OR GWAS_BETA
## 1
## 2
## 3
## 4
## 5
## 6 . . . . . . .
## GWAS_PUBMED GWAS_SNP GWAS_P SIFT_score SIFT_converted_rankscore SIFT_pred
## 1
## 2
## 3
## 4
## 5
## 6 . . . . . .
## Polyphen2_HDIV_score Polyphen2_HDIV_rankscore Polyphen2_HDIV_pred
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## Polyphen2_HVAR_score Polyphen2_HVAR_rankscore Polyphen2_HVAR_pred LRT_score
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## LRT_converted_rankscore LRT_pred MutationTaster_score
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## MutationTaster_converted_rankscore MutationTaster_pred MutationAssessor_score
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## MutationAssessor_score_rankscore MutationAssessor_pred FATHMM_score
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## FATHMM_converted_rankscore FATHMM_pred PROVEAN_score
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## PROVEAN_converted_rankscore PROVEAN_pred VEST3_score VEST3_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## MetaSVM_score MetaSVM_rankscore MetaSVM_pred MetaLR_score MetaLR_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 . . . . .
## MetaLR_pred M.CAP_score M.CAP_rankscore M.CAP_pred CADD_raw
## 1
## 2
## 3
## 4
## 5
## 6 . 0.000 0.002 T .
## CADD_raw_rankscore CADD_phred DANN_score DANN_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## fathmm.MKL_coding_score fathmm.MKL_coding_rankscore fathmm.MKL_coding_pred
## 1
## 2
## 3
## 4
## 5
## 6 . . .
## Eigen_coding_or_noncoding Eigen.raw Eigen.PC.raw GenoCanyon_score
## 1
## 2
## 3
## 4
## 5
## 6 c -1.123 -1.317 0.001
## GenoCanyon_score_rankscore integrated_fitCons_score
## 1
## 2
## 3
## 4
## 5
## 6 0.079 0.487
## integrated_fitCons_score_rankscore integrated_confidence_value GERP.._RS
## 1
## 2
## 3
## 4
## 5
## 6 0.133 0 -1.46
## GERP.._RS_rankscore phyloP100way_vertebrate phyloP100way_vertebrate_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 0.083 -0.518 0.063
## phyloP20way_mammalian phyloP20way_mammalian_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 -0.418 0.060
## phastCons100way_vertebrate phastCons100way_vertebrate_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 0.000 0.063
## phastCons20way_mammalian phastCons20way_mammalian_rankscore
## 1
## 2
## 3
## 4
## 5
## 6 0.000 0.016
## SiPhy_29way_logOdds SiPhy_29way_logOdds_rankscore Interpro_domain
## 1
## 2
## 3
## 4
## 5
## 6 1.678 0.026 .
## GTEx_V6_gene GTEx_V6_tissue gnomAD_exome_ALL gnomAD_exome_AFR
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## gnomAD_exome_AMR gnomAD_exome_ASJ gnomAD_exome_EAS gnomAD_exome_FIN
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## gnomAD_exome_NFE gnomAD_exome_OTH gnomAD_exome_SAS gnomAD_genome_ALL
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## gnomAD_genome_AFR gnomAD_genome_AMR gnomAD_genome_ASJ gnomAD_genome_EAS
## 1
## 2
## 3
## 4
## 5
## 6 . . . .
## gnomAD_genome_FIN gnomAD_genome_NFE gnomAD_genome_OTH Otherinfo Otherinfo.1
## 1 het .
## 2 hom .
## 3 het .
## 4 unknown .
## 5 het .
## 6 . . . hom .
## Delta
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA- which gene has the most exonic regions
which.max(table(final_exonic$Gene.refGene))## CELSR1
## 88