The increasing prevalence of DTC genetic analyses is resulting in more and more healthcare providers being asked to interpret SNP-based disease risk by their patients, or attempting to incorporate these analyses into personalised treatment approaches. Here we demonstrate that, by using simple statistical theory and synthetic datasets generated based on published population phenotypic data from well-characterised SNPs, the likelihood of any given genotype resulting in a meaningful difference in phenotype is relatively small. For individual common SNPs determined to have large effect sizes, such as FTO rs9939609 on BMI and MTNR1B rs10830963 on fasting glucose, even those with two alleles have a less than 10% chance of displaying a difference in phenotype due to significant population variability. Additionally, baseline disease risks suggest that the vast majority of health outcomes associated with common SNPs are dominated by the environment.

The best-characterised SNP associated with risk of overweight and obesity is FTO rs9939609, with an average per A allele increase in BMI of 0.3 kg/m^2.7 However, an average population effect is less useful to an individual than the likelihood that they are going to be affected in the first place. For a single FTO A allele, this likelihood is around 3%, increasing to 7% in individuals with two A alleles, with 0.4% of overall BMI explained by FTO genotype. Though it may be the SNP most well associated with increases in BMI, the vast majority of individuals are unlikely to have their BMI meaningfully affected by their FTO SNPs. Importantly, even this negligible effect of FTO on BMI is largely dominated by the environment, with recent analyses suggesting that FTO rs9939609 genotype was not associated with BMI in those born before 1942.¹³ Similarly, analyses of both FTO rs9939609 SNPs and composite obesity genetic risk scores suggest that those who partake in regular movement or exercise (∼1h of moderate-vigorous physical activity per day) have similar BMIs regardless of genetics.^14,15 In the well-characterised EPIC Norfolk cohort, the risk of being overweight was above 50% regardless of a genetic score consisting of the eight SNPs most tightly-associated with BMI, again suggesting a significant environmental component. Considering that current Centers for Disease Control and Prevention data suggests that 39.8% of the adult population in the United States is obese,¹⁶ the degree to which common genetic SNPs contribute to BMI may be statistically significant but borderline physiologically irrelevant compared to the impact of the environment.

Similar results to those seen with genetic obesity risk were found when analysing genetic risk of elevated fasting blood glucose and type 2 diabetes. Of the SNPs associated with increased fasting blood glucose, MTNR1B SNP rs10830963 (C:G) has one of the largest effect sizes, with each G copy associated with around a 1.3 mg/dl increase in fasting blood glucose.⁹ In our analysis, only 5.6% of individuals with a single G copy would be expected to experience an increase in fasting blood sugar relative to those with the CC genotype, increasing to 8.2% in homozygotes. Using the genetic risk score developed by Dupuis et al. is more predictive, with more than a doubling of risk of prediabetes in those with the highest genetic frisk score compared to those with the lowest genetic risk. However, linear regression analysis suggested that only around 5% of fasting blood glucose is determined by genetic risk. This is just very similar to the proportion of explained variance that Dupuis et al. state in their original manuscript,³ which provides some support for the use of synthetic datasets when variance and absolute numbers are not provided in the published literature. More importantly, however, it the way in which this information is placed into the context of the consumer using DTC genetic analysis to assess disease risk. For instance, the variance in fasting blood glucose (∼5%) attributed to the loci included in the genetic risk score is smaller than the variance in reproducibility of commonly-used hand held at home glucometers used to monitor blood glucose in individuals with diabetes. Any effect of genetic risk is also largely a reflection of a slight amplification of the risk associated with the Western environment. Compared to hunter gatherer populations,^5,10,11 fasting glucose is around 25-30 mg/dl higher even in the lowest genetic risk group, and the risk of prediabetes is 200-400 times higher. Indeed, in a recent analysis of the Bolivian Tsimane, prevalence of type 2 diabetes was 0%,¹⁷ on top of which any increase in genetic risk would be essentially meaningless. Therefore, the presence of any prediabetes appears to simply be a reflection of disease risk in the US as a whole, where more than 80% are thought to have suboptimal metabolic health, including more than 50% with fasting glucose >100 mg/dl.¹⁸ Based on multiple lines of evidence, close to 100% of the disease risk associated with elevated fasting blood glucose in the Western world can be attributed to the modern environment.

The concept of methylation capacity and its association with long-term health has recently gained a lot of interest in the alternative health community and popular press. As a result, DTC testing of common SNPs in the MTHFR and other related genes is being used to estimate an individual’s capacity to (re)generate methylfolate in order to guide disease risk or nutrient supplementation. One potential biomarker of methyl cycle function, including MTHFR activity, is homocysteine, which is associated with and increased risk of cardiovascular disease, dementia, and all-cause mortality when elevated.^19-21 Though there are multiple pathways for the metabolism of homocysteine, one is dependent on methylfolate, and homocysteine levels are often used as a proxy for the status of the folate cycle.²² Importantly, SNPs resulting in decreased in vitro MTHFR function are common. The “wild type” genotype 677CC/1298AA associated with 100% MTHFR function is only found in around 15% of the population,²³ which makes some degree of reduced MTHFR function a more representative “normal” state. In addition to this, the degree of MTHFR function appears to be only loosely associated with homocysteine levels. For instance, only 1% of homocysteine was accounted for by the five rs1801131 (A1298C) and rs1801133 (C677T) combinations that encompass 47.7-100% mean MTHFR activity. This suggests significant redundancy in the system that is unlikely to be able to inform any interventions based solely on genotype. Additionally, homocysteine levels are more likely to be determined by factors not associated with direct enzyme function, as those with the 1298AC/677CC genotype have higher MTHFR activity than 1298AA/677CT (83.2% versus 66.8% relative enzyme function), but also had higher mean homocysteine levels (13.6 µmol/L versus 12.8 µmol/L).¹² The non-linearity of the association between MTHFR and homocysteine levels is typified by the 1298AA/677TT genotype, who have around 75% loss of enzyme function and 50% higher mean homocysteine levels but, importantly, display a high degree of variability and values that do not appear to be normally distributed. Therefore, any specific recommendations to this group must be based in phenotypic measurements, including individual homocysteine levels and nutrient status. Indeed, though MTHFR is associated with the folate cycle, ensuring adequate B6 and B12 may be at least as important with respect to homocysteine levels.²⁴ Homocysteine in 677TT carriers can also be significantly reduced with a small amount of supplemental riboflavin.²⁵ This again suggests that phenotypic measurements and ensuring adequate environmental/nutrient status has a much greater impact than does knowledge of genotype. However, it must be cautioned that, as yet, reducing homocysteine with nutritional supplements has not yet been shown to result robustly improve health outcomes, though there may be a small reduction in stroke risk.²⁶

This study does have some limitations. The approach used relies on the use of both simulated and statistically-ideal normal distributions based on published descriptive data rather than the data itself. However, where the methods could be tested against known data, such as the degree to which the glucose risk score explains glucose variability, the results were very similar to the original analyses. Importantly, if this approach fails to accurately recreate datasets similar to those in the published literature, then it is likely that those datasets were not normally-distributed and the original analyses were therefore inappropriate. This is probably the case for homocysteine levels in individuals with the MTHFR 1298AA/677TT genotype based on the widely-cited study by van der Put et al..¹² Though all the SNPs analysed here have low penetrance, they were specifically chosen because they are well-characterized in multiple populations and commonly included in third party DTC analyses of consumer genetic data. Though we have only highlighted a few SNPs, the techniques applied here could be used by any practitioner or interested individual to better understand their disease or outcome risk based on common genetic SNPs.

Even though there is inherent error in our approach, it is clear that using population means to determine genetic risk and make recommendations based on genetics, as is very common in the DTC market, is likely to be highly-flawed due to inherent phenotypic variability. This is includes variability in risk based on common factors such as socioeconomic status and ethnicity. For instance, FTO genotypes are associated with increased BMI in Caucasians, but not in those of African origin.⁷ For the risk of both obesity and prediabetes or type 2 diabetes, particularly, the effect of the environment (diet, exercise, nutrient status) is likely to dominate the phenotype such that knowing about an individual’s SNPs associated with risk will have little benefit. A focus on genetic risk may indeed be detrimental due to the fact that i) thinking that you have a risk SNP can have an effect on physiology regardless of whether you have that SNP,²⁷ ii) the majority of people have average genetic risk for a given phenotype, iii) DTC genetics testing still includes significant variability and error,²⁸ iv) there is little to no evidence that specific interventions for a given common SNP have any effect on health outcomes, v) communicating genetic risk does not appear to alter health behaviours,²⁹ and vi) though statistically significant, the final effect of most SNPs on phenotype could often be considered physiologically irrelevant. These risks have generally been acknowledged by the scientific community performing genetic research, but the over-interpretation of risk by third-parties relying on published population averages remains a significant worry, likely due to misinterpretation of the nature of the data.