8.18 Top GWAS SNP

One common future direction for GWAS studies is following up on the top SNP(s). Read in top_snp.vcf, a VCF of just the top SNP in the dataset, so that we can plot boxplots of the top SNP genotype stratified by phenotype:

# extract genotypes of the top SNP
top_snp <- vcfR2tidy(read.vcfR("top_snp.vcf"))
## Scanning file to determine attributes.
## File attributes:
##   meta lines: 27
##   header_line: 28
##   variant count: 1
##   column count: 185
## Meta line 27 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 1
##   Character matrix gt cols: 185
##   skip: 0
##   nrows: 1
##   row_num: 0
## Processed variant: 1
## All variants processed
## Extracting gt element GT
top_snp_gt <- top_snp$gt %>%
  drop_na()

# merge with phenotype data
gwas_data <- merge(top_snp_gt, phenotypes,
                   by.x = "Indiv", by.y = "IID")

# plot boxplots
ggplot(data = gwas_data) +
  geom_boxplot(aes(x = gt_GT_alleles,
                   y = GS451_IC50))
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).

Other potential follow-up directions include:

  • Investigating the genomic environment in the UCSC Genome Browser
  • Looking at nearby haplotype structure with LDproxy
    • Note that the genotype data we’re using come from the Yoruba population
  • Using the Geography of Genetic Variants browser to find the global allele frequencies of the variant
  • Search for SNP in a phenotype database to see if there are other associations with it