Introduction

An analysis by Alice B. Popejoy and Stephanie M. Fullerton indicates that some populations are still being left behind on the road to precision medicine. GWAS have been the preferred tool for discovering the genetic factors involved in common diseases. A 2009 analysis revealed that 96% of participants in genome-wide association studies (GWAS) were of European descent. This prompted data collection again in 2016 to see if a broader range of populations were included in the GWAS to ensure there is no bias when it comes to genomic medicine. Sample descriptions were analyzed and included in the GWAS Catalog with an approach similar to that used in 2009. Considerable heterogeneity was found in the samples.

Create data

euro_asian_other2009 <- c(96, 3, 1)
labels1 <- c("European\nancestry\n", "\nAsian\nancestry", "\n\nOther\nnon-European\nancestry")


euro_asian_other2016 <- c(81, 14, 5)
labels2 <- c("European\nancestry\n", "\nAsian\nancestry", "\n\nOther\nnon-European\nancestry")

Pie graphs

# set up par()
par(mfrow = c(1,2), mar = c(1,1,3,5))

#pie graphs 1
pie(x = euro_asian_other2009, labels = labels1, main = "2009", init.angle = -82, radius = 1, col = c(2,5,9))

# pie graph 2
pie(x = euro_asian_other2016, labels = labels2, main = "2016", init.angle = -56, radius = 1, col = c(2,5,9))

Bar graphs

# data
dat2016 <- c(14, 3,1,0.54,0.28,0.08,0.05)
dat2016_rev <- rev(dat2016)
barplotdata2016 <- matrix(c(dat2016_rev))

# labels
labels_x <- rev(c("Asian","African","Mixed", "Hispanic &\nLatin American",
                        "Pacific Islander","Arab & Middle East","Native peoples"))

par(mfrow = c(1,1))

barplot(barplotdata2016,
        width = 0.01, 
        xlim = c(0,0.1),
         axes = F,
        col = c(1,2,3,4,5,6,7),
        legend.text = labels_x)