LAUSR

Background: While the academic genetic literature has clearly shown that common genetic single nucleotide polymorphisms (SNPs), and even large polygenic SNP risk scores, cannot reliably be used to determine risk of disease or to personalize interventions, a significant industry of companies providing SNP-based recommendations still exists. Healthcare practitioners must therefore be able to navigate between the promise and reality of these tools, including being able to interpret the literature that is associated with a given risk or suggested intervention. One significant hurdle to this process is the fact that 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 or their patients might truly be affected. Methods: We generated synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using simple statistical theory and full graphical representation of the generated data, we developed a method by which anybody can better understand phenotypic heterogeneity in a population, as well as the degree to which common SNPs truly drive disease risk. Results: Individual risk SNPs had a <10% likelihood of effecting the associated phenotype (bodyweight, fasting glucose, or homocysteine levels). Example polygenic risk scores including the SNPs most associated with obesity and type 2 diabetes only explained 2% and 5% of the final phenotype, respectively. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to support the pursuit of lifestyle-based interventions that are likely to be beneficial regardless of genetics.