Arivale Launches Polygenic Profiles for Pioneers

By Andrew Magis, PhD, Senior Bioinformatics Scientist and Matthew Conomos, PhD, Statistical Geneticist

Arivale has released new genetic insights in the form of polygenic profiles on your dashboard. This is the first of many new insights we will share with you to leverage the whole genome sequencing data we collect. You will see a lot more of these profiles throughout 2017! The purpose of this blog is to explain how polygenic profiles expand your genetic reporting, and give you additional insights to support your wellness journey.

What is a polygenic profile?

A common question we hear from our Pioneers is, “Do I have the gene that causes obesity?” Or, “Do I have the gene that affects my cholesterol?”

The implication of this question is that there is a single gene involved. While there are several thousand diseases for which variations in a single gene are the primary causal factors (these are generally known as Mendelian disorders), these tend to be rare. For example, cystic fibrosis is caused by mutations in a single gene, CFTR, and has an incidence of about 1 in 13,500 people worldwide1.

In contrast, many traits, such as height, Body Mass Index (BMI), or cholesterol levels, and complex disorders, such as diabetes, heart disease, inflammatory bowel disease, or schizophrenia, are influenced by hundreds, or even thousands, of common genetic variants. In the case of complex disorders, having these variants does not necessarily mean that you have or will get these disorders. Everyone has lots of these variants throughout their genome!

So, what is your polygenic profile? The term “polygenic” simply means “many genes.”  Your polygenic profile is the sum of the effects of all the common genetic variants implicated for a quantitative trait or complex disorder that are observed within you. These common variants typically have smaller effects than rare variants, but the sum of these effects can be quite significant. See Figure 1 for an illustration of the relationship between how rare a variant is (known as the allele frequency) and its effect on disease risk.

For example, scientists have identified several hundred variants that affect BMI and are common in the human population. No one has all these variants, but everyone has some subset of them. And, most likely, no other person has the exact same subset as you. Furthermore, each of these variants contributes a slightly different effect. Some common variants might slightly raise your BMI, while others might lower it. In this manner, virtually everyone has a unique set of variants contributing to their unique polygenic profile for BMI.




Figure 1: The relationship between allele frequency (how rare a variant is) and effect size (how influential a variant is) is negative: the rarer the variant, generally the more significant its effect. The term “GWA” refers to Genome-Wide Association studies. Figure reproduced from (Manolio et al., 2009).

How do we calculate polygenic profiles?

The first polygenic profiles you will see on your dashboard are for BMI and waist circumference. These profiles are based on data published by the Genetic Investigation of ANthropometric Traits (GIANT) consortium. The GIANT consortium is an international collaboration of scientists that have analyzed hundreds of thousands of people, looking for genetic variation related to traits such as height, waist circumference, and BMI2-4. These types of studies are known as genome-wide association studies, or GWAS, because they look for variations throughout the entire genome to identify associations with a trait or disease.

Now, before we move on, a disclaimer: the scientific community recognizes that there are very few diseases or traits for which the genetic impact is completely understood. Scientists at Arivale are using what we believe is the best available research to create our polygenic profiles, but there are certainly additional genetic factors that are not accounted for by our profiles:

  • There could be more common variants with individual effect sizes so small that scientists haven’t (yet) detected them, but that cumulatively add up to a significant effect. The studies that we currently have are the largest to date2-4 (hundreds of thousands of people), but it might take millions or tens of millions of people to detect these variants.
  • Another unknown factor is the effect of rare variants. Certain rare variants are known to greatly increase the risk of high BMI, but there might be many others that we haven’t discovered because existing studies haven’t included the right people. It isn’t entirely understood just how great a role these rare variants play in risk for complex disorders, for one simple reason: they are so rare that they are hard to find! A few hundred thousand people sounds like a lot, but it’s still a very small percentage of the seven billion people in the human population. Only by sequencing larger and larger numbers of individuals can scientists identify and quantify the effects of these rare variants, and this is an area of active research.
  • Finally, scientists are only beginning to understand the ways in which all these variants interact with one another. This one is a bit complicated, but we don’t completely understand how to take a set of variants within an individual and combine them together in a way that completely incorporates the complex biological interactions that are taking place. For example, one genetic variant may affect your BMI, but only if you also have another particular genetic variant.  This means that the models we build are probably too simplistic and less predictive than they would be if we understood these interaction effects.

So what do these polygenic profiles actually mean for you?

As you know, for a complex trait like BMI or waist circumference, there are a lot of factors other than genetics that are important. Scientists tend to lump these factors together into the term environment, meaning anything that is not genetics. Environment can mean your diet, activity, medications, or other behavioral variables. For a trait like BMI or waist circumference, these environmental effects may play a significantly larger role than genetics in determining your actual value! Nevertheless, we can demonstrate that genetics are still very important. Figure 2 plots actual BMI vs. the polygenic profile for BMI in all our current Pioneers. The published studies that we used demonstrated that the genetic variants that affect BMI in males and females are somewhat different, therefore we calculate your profile specifically for your biological sex.



Figure 2: Actual BMI vs. BMI polygenic profile in females (left) and males (right). The bold line is the average BMI, and the shaded band represents the 95% confidence interval of the average. A wider shaded band means the average value is more uncertain at that polygenic profile value. The color on the background represents the reference ranges for BMI.

Figure 2 has a lot of information, so we’ll walk you through it. First, the background colors (green, yellow, red) are the reference ranges for BMI, just as you see on your Arivale dashboard. A BMI below 18.5 (not shown) is considered ‘underweight.’ A BMI between 18.5 and 25 is considered ‘healthy,’ a BMI between 25 and 30 is considered ‘overweight,’ and a BMI 30 or above is considered ‘obese.’ The x-axis represents the polygenic profile that Arivale has calculated, and the y-axis represents actual BMI values. The bold black line shows the average BMI of our pioneers at each value of the polygenic profile. Finally, the shaded gray band represents the confidence we have in that average value – a wider shaded band means the average BMI for that polygenic profile value is more uncertain.

Summing up the figures in a concise manner: based on our Pioneer data, a higher polygenic profile is correlated with a higher BMI on average. Females with the lowest polygenic profiles that we observe have an average BMI of about 23.7 (normal), while females with the highest polygenic profiles that we observe have an average BMI of about 29.8 (overweight, but nearly obese). Males with the lowest polygenic profiles that we observe have an average BMI of about 26.5 (overweight), while males with the highest polygenic profiles that we observe have an average BMI of about 33 (obese). This is a striking difference in BMI that is likely attributable to genetics!

How does your behavior interact with these polygenic profiles?

Your behavior plays an important role in ultimately determining your BMI or waist circumference. There is a lot of variability around the averages shown in Figure 2 (not shown in the figure) which is attributable to the environmental and behavioral effects we mentioned above, as well as potential unmeasured genetic effects. But the profiles we’ve discussed so far are calculated without consideration of behavior or its interaction with your genetics.

Scientists have known for a long time that genetics and environment are largely inseparable. You exist in an environment, and it is as much a part of your development as the genes you inherit from your parents. Nearly all genetic effects are dependent on your environment — some genes are more active in certain environments (or with certain behaviors), while some genes are less active. The polygenic profiles described above do not take this into account.

Arivale scientists have curated four additional polygenic profiles that are not based on genome-wide association studies, but instead are focused on gene-environment (GxE) interactions5-18. These profiles are specifically tailored to how certain behaviors, such as consumption of fat, consumption of saturated fat, consumption of carbohydrates, and sedentary lifestyle, interact with your genetics to influence your weight. These GxE polygenic profiles are not as predictive of your BMI as the GWAS polygenic profiles, but, together with your blood and lifestyle data, they can yield actionable recommendations tailored to you.  For example, using your GxE polygenic profiles along with your lab results, lifestyle, and family history, your Arivale Coach can help tailor your personalized nutrition plan.

How will this information be used by your Coach?

Just because a Pioneer has a high polygenic profile doesn’t mean they have a high BMI. Similarly, just because a Pioneer has a low polygenic profile doesn’t mean they have a low BMI. These profiles only tell you about your genetic predisposition. This is where your Arivale Coach comes in!  We want to give you as much information about your genetic baseline as we can, so you know what opportunities you have.  Using this information, your Coach will recommend an Action Plan designed to help you reach your target BMI. By combining the GWAS polygenic profiles with the GxE polygenic profiles, your Coach is able to give you your genetic baseline as well as tailored actionable recommendations. Visit your dashboard to see how you compare to the Arivale population, and make a plan to discuss this on your next coaching call. We are eager to hear your feedback and suggestion on how we can make this information more useful for you!

What’s next for polygenic profiles?

The polygenic profiles we posted on your dashboard are just the beginning! Scientists are always researching additional genetic variants that influence different traits. As we continue to identify and curate these insights, we will calculate more polygenic profiles unique to you that estimate the impact of your genetics on more of your traits. The next profiles you can expect to see are for lipids (LDL cholesterol, HDL cholesterol, Total cholesterol, and Triglycerides) in the first half of 2017.


  1. Rath, A. & Wakap, S. N. Prevalence and incidence of rare diseases: Bibliographic data. Orphanet Report Series, Rare Diseases collection (2016).
  2. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
  3. Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).
  4. Winkler, T. W. et al. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLoS Genet. 11, e1005378 (2015).
  5. Corella, D. et al. APOA5 gene variation modulates the effects of dietary fat intake on body mass index and obesity risk in the Framingham Heart Study. J. Mol. Med. 85, 119–128 (2007).
  6. Sánchez-Moreno, C. et al. APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population. J. Nutr. 141, 380–385 (2011).
  7. Sonestedt, E. et al. Fat and carbohydrate intake modify the association between genetic variation in the FTO genotype and obesity. Am. J. Clin. Nutr. 90, 1418–1425 (2009).
  8. Grau, K. et al. TCF7L2 rs7903146-macronutrient interaction in obese individuals’ responses to a 10-wk randomized hypoenergetic diet. Am. J. Clin. Nutr. 91, 472–479 (2010).
  9. Joffe, Y. T. et al. Tumor necrosis factor-alpha gene -308 G/A polymorphism modulates the relationship between dietary fat intake, serum lipids, and obesity risk in black South African women. J. Nutr. 140, 901–907 (2010).
  10. Corella, D. et al. APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations. Arch. Intern. Med. 169, 1897–1906 (2009).
  11. Corella, D. et al. Association between the APOA2 promoter polymorphism and body weight in Mediterranean and Asian populations: replication of a gene-saturated fat interaction. Int J Obes (Lond) 35, 666–675 (2011).
  12. Smith, C. E. et al. Apolipoprotein A2 Polymorphism Interacts with Intakes of Dairy Foods to Influence Body Weight in 2 U.S. Populations. Journal of Nutrition 143, 1865–1871 (2013).
  13. Phillips, C. M. et al. High dietary saturated fat intake accentuates obesity risk associated with the fat mass and obesity-associated gene in adults. J. Nutr. 142, 824–831 (2012).
  14. Smith, C. E. et al. Perilipin polymorphism interacts with dietary carbohydrates to modulate anthropometric traits in hispanics of Caribbean origin. J. Nutr. 138, 1852–1858 (2008).
  15. Li, S. et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. 7, e1000332 (2010).
  16. Ahmad, S. et al. Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry. PLoS Genet. 9, e1003607 (2013).
  17. Corbalán, M. S., Marti, A., Forga, L., Martínez-González, M. A. & Martínez, J. A. The 27Glu polymorphism of the beta2-adrenergic receptor gene interacts with physical activity influencing obesity risk among female subjects. Clin. Genet. 61, 305–307 (2002).
  18. Marti, A., Corbalán, M. S., Martínez-González, M. A. & Martínez, J. A. TRP64ARG polymorphism of the beta3-adrenergic receptor gene and obesity risk: effect modification by a sedentary lifestyle. Diabetes, Obesity and Metabolism 4, 428–430 (2002).

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