Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms

Thomas R Wood,Nathan Owens

doi:10.12688/f1000research.21797.1

Abstract

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.

Highlights

Due to decreasing costs and a move towards “personalized medicine”, the use of direct-to-consumer (DTC) genetic analyses and third party interpretation services is increasing1
Selection of representative single nucleotide polymorphisms (SNPs) To provide illustrative examples, individual studies and meta-analyses of per allele effects for common SNPs most strongly-associated with risk of type 2 diabetes (Melatonin Receptor 1B, MTNR1B rs10830963), obesity (Fat mass and obesity-associated protein, fat mass and obesity-associated (FTO) rs9939609), and altered methylation and nutrient handling resulting in elevated homocysteine levels (Methylenetetrahydrofolate Reductase, MTHFR rs1801131 and rs1801133) were identified from a commonly-used thirdparty SNP analysis tool (FoundMyFitness Genetic Report) output, as well as the online SNP wiki SNPedia.com4–6
When the descriptive data were not included in the publication, as was the case for genetic risk scores associated with obesity and fasting blood glucose4,5, they were estimated from published graphs by extracting images and determining the number of pixels in each column and error bar relative to the scale bars on the axes

Summary

Introduction

Due to decreasing costs and a move towards “personalized medicine”, the use of direct-to-consumer (DTC) genetic analyses and third party interpretation services is increasing. While the academic genetic literature has clearly shown that using SNPs, including polygenic risk scores (PRSs), to determine disease risk or to personalize clinical interventions is not currently possible or evidence-based, the trend for companies giving genetic-based advice on athletic ability or dietary recommendations is increasing. While the academic genetic literature has clearly shown that using SNPs, including polygenic risk scores (PRSs), to determine disease risk or to personalize clinical interventions is not currently possible or evidence-based, the trend for companies giving genetic-based advice on athletic ability or dietary recommendations is increasing2 These risk predictions or recommendations are generally based on population average outcomes, with the heterogeneity of a given phenotype or disease risk infrequently reported. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to Invited Reviewers version 1

Methods

Results

Conclusion