Using synthetic datasets to better understand and explain health outcomes associated with common single nucleotide polymorphisms
Due to decreasing costs and a move towards \"personalised medicine\", the use of direct-to-consumer genetic analyses is increasing. Both consumers and healthcare practitioners must therefore be able to understand the true disease risks associated with common genetic single nucleotide polymorphisms (SNPs). However, most population studies of common SNPs only provide average (+/-error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves might truly be effected. Here, we describe the use of synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using both simple statistical theory and full graphical representation of the generated data, we show that single common SNPs are associated with a less than 10% likelihood of effecting final phenotype, even in homozygotes. The significant heterogeneity in the data, as well as the baseline disease risk of Western populations suggests that most disease risk is dominated by the effect of the modern environment.