Keywords
Genetics, single nucleotide polymorphisms, risk, obesity, methylation, blood glucose
Genetics, single nucleotide polymorphisms, risk, obesity, methylation, blood glucose
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. Though whole genome sequencing is also increasing in popularity, most DTC products involve the analysis of common single nucleotide polymorphism (SNPs). These SNPs are then reported, either by the testing company or a third party tool that analyses the data, with specific disease risks based on published population data such as that from genome-wide association studies (GWAS). 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. In fact, most GWAS studies tend to only report descriptive data (e.g. mean and standard error) for a given phenotype [such as body mass index (BMI) or fasting blood glucose] within a risk genotype. By only comparing or providing group averages based on genotype, the consumer is likely to overestimate the disease risk associated with a given SNP. Presenting only simplified descriptive data, either graphically or numerically, for a given genotype gives the impression that each SNP has consistent penetrance with respect to the phenotype in question, which is known to not be the case3. Therefore, the interpretation of disease risk based on SNPs by those not involved in the original studies and without access to the original data is almost impossible.
More important than the mean population effects of a given SNP or combination of SNPs that influence a common phenotype is the likelihood of a physiologically-relevant effect in a given individual. This includes the likelihood that there is no overall effect of genotype, particularly compared to common environmental factors that drive chronic disease risk in high income countries such as diet, sleep, and exercise. In order to allow for healthcare practitioners or self-interested parties to better understand the likelihood of a given phenotype being altered by a specific genotype, we developed a method by which synthetic datasets could be generated and analyzed. This is largely possible due to the fact that the effects of SNPs on measurable phenotypes are generally considered to follow a normal distribution, with the number of alleles or weighted genetic scores being linearly associated with the target phenotype. The method outlined is not intended to be a systematic approach to the literature of SNPs and their association with disease risk, but is instead intended to give healthcare providers a simple tool with which to better understand the literature and answer the questions of their patients. Using this approach, the significant heterogeneity of population data can be better understood, particularly with respect to how a given individual may or may not display phenotypic changes based on the presence of common genotypes.
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, 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 third-party SNP analysis tool (FoundMyFitness Genetic Report) output, as well as the online SNP wiki SNPedia.com4–6. Due to the significant effect of ethnicity on SNP disease penetrance, example population data that were likely to most closely match the Anglo-Scandinavian background of the first author were used in individual examples, including data from deCODE (Iceland) and the Northern Finland Birth Cohort (NFBC), which were included in large multi-population GWAS studies4,5. According to recently-published methods suggested by Pontzer et al., published hunter gatherer data for fasting glucose were used to provide an estimate of the effect of the Western environment on fasting glucose and diabetes risk compared to a published genetic risk score7.
Published per allele or per genetic risk score means were used to construct synthetic datasets for a given phenotype. All publications assumed data were normally distributed and that per allele/genetic risk score effects were linear. If data were expressed as mean with standard error (SE) or 95% confidence interval (CI), the standard deviation (SD) was calculated using the number (N) of participants in each group, where SD=SE*√N and SD=√N*(width of 95% CI)/3.92. 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. In all cases, enough data was included in the manuscript body to confirm that at least one of the estimated values was correctly determined using this method (such as total number of participants, or mean values in the highest or lowest genetic risk groups). For each genotype and gene, 1,000 synthetic individuals were randomly generated to re-create a normally-distributed dataset with the same mean and SD characteristics as those in the associated publication. Numbers were generated using Python 3.7 and the NumPy (1.17.0) and Pandas (0.25.0) libraries. The necessary code is available on GitHub (https://github.com/root-causing-health/SNPGaussianDistGenerator). Visual inspection of the data (Prism version 8, GraphPad Software, San Diego, CA) confirmed that they were normally distributed.
Each synthetic dataset was graphically represented using a violin plot to show the full distribution of the data. Percent chance of a null effect from a risk allele was calculated by determining the percent overlap of the normal distribution of the wild type phenotype with that of a risk genotype using statistics.NormalDist in Python 3.8 Beta. The percent likelihood of the phenotype in a risk allele group being at or below the mean value of the “wild type” was also calculated, and linear regression analysis was performed to determine the percent contribution of risk alleles to a given phenotype. Again, to provide graphical and statistical examples, similar analyses were performed using published multi-SNP PRSs for type 2 diabetes and obesity4,5. As of the time of writing, perhaps the largest and most comprehensive published PRS for obesity (Khera et al., 2.1 million common SNPs in >300,000 individuals) could not be analyzed using this method, as only the mean phenotype in each decile of genetic risk is presented, with no error metric in either the text or figures8.
To encourage attempts to perform similar analyses, a number of free online tools can be used that do not require significant technical skills. After calculating mean and SD as described above, free gaussian random number generators such as from Random.org can be used to generate synthetic datasets. Though the Box-Muller transform used by this tool is unlikely to produce a truly normal distribution9, this is also unlikely to meaningfully affect the outcome. Similar online tools can be used to determine the likelihood of being at, above, or below, a given point in a normal distribution to determine null effects of a given SNP or risk score (http://onlinestatbook.com/2/calculators/normal_dist.html). Finally, free online graphing software can be used to visually represent the datasets for visual examination of variability and overlap (e.g. Plotly), and perform linear regression analyses (e.g. GraphPad).
Published meta-analyses suggest an increase in BMI of 0.3 kg/m2 per FTO rs9939609 A allele10. From this meta-analysis, data from the NFBC at 31 years of age (n=4,435) were used as a graphical example (Figure 1A)11. Mean (SD) BMI across the three genotypes was 24.12 (3.87) kg/m2, 24.43 (3.94) kg/m2, and 24.82 (3.95) kg/m2 for TT, AT, and AA respectively. In this population the risk of being overweight (BMI >25 kg/m2) was 41%, 44%, and 48%, resulting in an absolute 7% increase in risk in the TT genotype. BMI at or below the TT genotype was 47% in those with the AT genotype, and 43% in those with the TT genotype. The likelihood of null effect (percent overlap in BMI distribution of those with AT and AA genotypes compared to TT) was 96.8% and 92.8%, respectively. Therefore, only 3.2% of AT and 7.2% of AA genotypes would be expected to display any increase in BMI due to FTO genotype relative to TT. Linear regression found a significant association between number of A copies and BMI (p=0.001, R2=0.0035), suggesting that only around 0.4% of the variability in BMI is determined by FTO genotype (Figure 1B).
Willer et al. established a BMI genetic score using eight validated SNPs associated with BMI, weighted to effect size (with FTO rs9939609 given the largest weighting)5. This score was applied to the European Prospective Investigation of Cancer (EPIC) Norfolk cohort, where the top 1.2% of people (risk score >12) had an average BMI of 1.46 kg/m2 greater than those in the bottom 1.4% (risk score <4). However, the majority of participants had risk scores in the middle of the range (6–10), with large variability across the whole range of scores (Figure 2A). In the highest genetic risk groups (genetic scores of 11, 12, and >12), the likelihood of null effect was at least 80% (Table 1). The likelihood of null effect in the most common genetic score (score of 8, 18.4% of participants) was 88.1%. This suggests that regardless of an individual’s genetic score, there is less than a 20% chance that they will display any increase in BMI due to their score relative to those 1.4% of individuals with the lowest genetic risk. Across the entire range of scores, linear regression found a significant association between risk score and BMI (p<0.001, R2=0.018), suggesting that only around 2% of BMI is determined by the eight SNPs most significantly associated with BMI (Figure 2B).
Of the common SNPs associated with increased blood sugar, 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 glucose12. Data from the deCODE cohort (n=6,240) were used as a graphical example (Figure 3A)12. Mean (SD) fasting blood glucose across the three genotypes was 95.2 (12.8) mg/dl, 97.0 (12.8) mg/dl, and 97.9 (12.8) mg/dl for CC, CG, and GG respectively. The likelihood of null effect was 94.4% in those with the CG genotype, and 91.6% in those with the GG genotype. Linear regression found a significant association between number of G copies and fasting blood glucose (p<0.001, R2=0.01), with around 1% of the variability in blood glucose being determined by MTNR1B rs10830963 genotype (Figure 3B).
Similar to the approach of Willer et al., Dupuis et al. published a genetic risk score for elevated fasting blood glucose and risk of type 2 diabetes4, including MTNR1B and 15 other loci. This score was applied to the Framingham cohort, where the top 3.1% of people (risk score >22) had an average fasting blood glucose ~6 mg/dl greater than those in the bottom 4.2% (risk score <13). Similar to the obesity risk score, significant heterogeneity in blood glucose levels was seen across the range of scores (Figure 4A). The likelihood of null effect in the most common genetic score (score of 18, 14.3% of participants) was 84.5% (Table 2). In those with the highest genetic risk score (scores 21, 22, and >22), the risk of prediabetic level blood glucose (>100mg/dl) was double that of those in the lowest risk group. However, even in these groups the likelihood of a given genetic score being associated with blood sugar outside of the distribution of those in the lowest risk group was only 25.5–27.7%, suggesting that fewer than 30% of people with the highest genetic risk of prediabetes experience that risk as a disease phenotype. Across the entire range of scores, linear regression found a significant association between risk score and fasting glucose (p<0.001, R2=0.049), suggesting that around 5% of fasting glucose is determined by the 16 SNPs most significantly associated with type 2 diabetes risk (Figure 4B). By comparison to the Framingham cohort, where mean (SD) fasting blood glucose was 92.5 (8.7) mg/dl in the lowest genetic risk group, free living hunter gathers from Tukisenta and Kitava reportedly have fasting blood glucose of around 75 (8) and 65 (14) mg/dl, respectively (Figure 4C)13,14. Based on these data, the Tukisentans would have a 98.6% likelihood of having a blood sugar below the mean of those in the Framingham cohort with the lowest genetic risk score, with a 97.5% likelihood in the Kitavans, and normal distributions that display only 19.5% and 27.3% overlap with the lowest risk Framingham group. This translates to a 0.09% and 0.05% risk of prediabetic fasting blood glucose, respectively. Therefore, even in the lowest risk genetic group in the Framingham cohort, the relative risk of prediabetic fasting blood sugar levels (19.4%) is around 200–400 times higher than in hunter gatherer populations.
Two common polymorphisms in the MTHFR gene, which alter in vitro enzyme activity and are associated with reduced capacity to produce 5-methyltetrahydrofolate, are frequently discussed in the popular and alternative health fields with regard to the methyl cycle and associated changes in detoxification, cellular repair, and detoxification pathways. In 1998, van der Put et al. described in vitro MTHFR activity of the most common combinations of alleles at rs1801131 and rs1801133, as well as homocysteine levels in the same participants6. In the most common genotypes, excluding 1298AA/677TT, which account for around 88% of the population on average, MTHFR function across five genotypes varies from 100% to 47.7% (Table 3). However, even in those with 47.7% function (1298AC/677CT) there is an 82.1% chance of null effect compared to 1298AA/677CC “wild type” with 100% function (Table 3). Across these common mutations, MTHFR function only explains around 1% of the variability in homocysteine levels (p<0.001, R2=0.01; Figure 5A). The addition of 1298AA/677TT, which has around 12% prevalence in the population and is associated with a 75.2% loss of MTHFR function, increases the explanation of variance to 7% (Figure 5B); however, the synthetic dataset included 6.9% negative values due to the large SD in this population. This suggests significant heterogeneity of homocysteine in those with the 677TT/1298AA genotype, which is not normally distributed. Indeed, though the percent chance of non-significant difference in homocysteine levels compared to 1298AA/677CC was only 35% in those with 1298AA/677TT, this includes a large proportion of the distribution in homocysteine levels that would be below that of the “wild type” due to the very large SD in the 1298AA/677TT group; 31.3% would be predicted to have homocysteine levels below the mean of 1298AA/677CC.
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 personalized treatment approaches. Here we demonstrate that, by using simple statistical theory and synthetic datasets generated based on published population phenotypic data from well-characterized 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-characterized SNP associated with risk of overweight and obesity is FTO rs9939609, with an average per A allele increase in BMI of 0.3 kg/m210. 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 194216. 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 genetics17,18. In the well-characterized 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, 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. For instance, using a small PRS for obesity by Willer et al.5, our analysis suggested that around 2% of BMI determined by the eight SNPs most significantly associated with BMI. Khera et al. developed a 141-SNP PRS for obesity (that could not be analyzed here due to lack of reporting of group error/SD statistics), and even then this only explained 13.3% of phenotypic variability in bodyweight8.
Similar results to those seen with genetic obesity risk were found when analyzing 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 glucose12. 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 manuscript4, 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’s 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 populations7,13,14, 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%19, 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/dl20. 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 elevated21–23. 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. 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 population15, 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)6. 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 levels24. Homocysteine in 677TT carriers can also be significantly reduced with a small amount of supplemental riboflavin25. 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 risk26.
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.6. Though all the SNPs analyzed 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. Importantly, the methods described here are not intended to include a systematic exploration of the true association between common genetic polymorphisms and disease risk, but instead provide a tool that any individual can use to better understand genetic-based risk in the context of the heterogeneity of the population. Our analysis and suggestions also do not preclude the potential future utility and application of genetics in disease risk stratification when used in combination with clinical risk factors2. For instance, those with the highest genetic risk for cardiovascular disease appear to be most likely to benefit from lipid-modifying therapies2. Khera et al. also examined 50 risk SNPs for coronary disease in over 50,000 individuals, and found that those at the greatest genetic risk received the greatest risk reduction as a result of a healthy lifestyle27. However, it is also worth mentioning that the same study showed that all groups benefited from the presence of healthy lifestyle factors regardless of genetic risk, again suggesting that an individual’s environment is the common factor driving the majority of baseline disease risk.
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 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 origin10. 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 SNP28, ii) the majority of people have average genetic risk for a given phenotype, iii) DTC genetics testing still includes significant variability and error29, 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 behaviors30, 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 research2, 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.
Using simple statistical techniques, either with Python code or freely-available online tools, we have outlined a method by which healthcare providers and third-party genetic analysis tools can more accurately analyze genetic disease risk. Importantly, it is worth noting that the widely-characterized and cited SNPs for obesity, type 2 diabetes, and methylation status appear to have negligible overall effects on phenotype compared to the dominant effect of the environment.
Code used to generate synthetic datasets: https://github.com/root-causing-health/SNPGaussianDistGenerator
Archived code and synthetic dataset as at time of publication: https://doi.org/10.5281/zenodo.358343931
License: MIT
Views | Downloads | |
---|---|---|
F1000Research | - | - |
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Genetic epidemiology; Psychiatric genetics
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
References
1. Dias R, Torkamani A: Artificial intelligence in clinical and genomic diagnostics. Genome Medicine. 2019; 11 (1). Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Health Informatics, Clinical Bioinformatics, Genomic Medicine, GWAS, Molecular Diagnostics
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | ||
---|---|---|
1 | 2 | |
Version 1 30 Dec 19 |
read | read |
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Already registered? Sign in
The email address should be the one you originally registered with F1000.
You registered with F1000 via Google, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Google account password, please click here.
You registered with F1000 via Facebook, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Facebook account password, please click here.
If your email address is registered with us, we will email you instructions to reset your password.
If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance.
Comments on this article Comments (0)