In this episode of Functional Medicine Research, I interview Dr. Tommy Wood on the question of whether commercial genetic testing for SNPs is helpful or harmful. This is a topic I have wanted to cover in detail for a long time so when I read Dr. Wood's paper and listened to him speak, I knew his expertise would be invaluable to this conversation. It is important that practitioners and patients know the truth about the current state of genetic testing and whether or not it is scientifically valid or invalid.
Dr. Wood has done the necessary research to outline all of the reasons why genetic testing is not a valuable tool in practice and he presents compelling data that it can be more harmful than helpful. As stated in the interview, this is an area that I have never bought into because the science simply doesn't support genetic testing or interventions to address SNPs in clincial practice. I think you'll find this interiew invaluable to your understanding of genetic testing.
If you have any published papers to refute any of this information, myself and Dr. Wood would love to read these papers.
Below is a transcript of the interview if genetic testing is helpful or harmful:
Dr. Hedberg: Well, welcome, everyone, to "Functional Medicine Research." I'm Dr. Hedberg and very excited today to have Dr. Tommy Wood on the show. We're gonna be talking about genetics and genetic testing. And Dr. Wood is a research assistant professor of pediatrics in the University of Washington, Division of Neonatology. Most of his academic work is focused on developing therapies for brain injury in newborn infants but also includes adult neurodegenerative and metabolic diseases, as well as nutritional approaches to sports performance. Tommy received an undergraduate degree in biochemistry from the University of Cambridge before obtaining his medical degree from the University of Oxford. After working as a doctor in central London, he moved to Norway for his PhD, and then to the University of Washington as a postdoc.
So, in addition to his academic training, he's coached athletes and dozens of sports, weekend warriors to Olympians and world champions. He's the outgoing President of the Physicians for Ancestral Health Society, a Director of the British Society of Lifestyle Medicine, and sits on the Scientific Advisory Board of Hinson performance, which includes researching performance optimization strategies for Formula One drivers. Tommy's current research interests include the physiological and metabolic responses to brain injury and their long-term effects on brain health, as well as developing easily accessible methods with which to track human health performance and longevity. So, Dr. Wood, welcome to the show.
Dr. Wood: Thanks so much for having me. I'm excited to be here.
Dr. Hedberg: Great. So, before we got on, we were just talking about a lot of the big issues in functional medicine include, unscientific and unvalidated testing and therapies and things like that. And so that's why I was really looking forward to this because genetics is something that I've never really got on board with as far as testing and treating patients. So, why don't we lay some bedrock for the listeners? And if you could just let us know, what is the current academic position by scientists on commercial genetic testing for SNPs and the interventions that some practitioners are using?
Dr. Wood: That's a great question. And having spent a lot of time sort of straddling both traditional allopathic medicine, traditional academic research, and then also functional medicine, particularly with athletes, but also with various clients with chronic health conditions, there's this real tension between the two in terms of, you know, what's done and the evidence that supports it. And I think that's where some of these genetics stuff comes into play. And when I started really looking into this, you see very rapidly that academic geneticists who have been, you know, studying these things for, you know, probably two decades now, at least, since the beginning of the Human Genome Project. The current state of the science would say that direct to consumer tests, single nucleotide polymorphisms are essentially useless in terms of their ability to either predict disease risk or say response to a personalized nutrition or supplement regimen based on SNPs because most, A, the disease risk is usually very small if there is an increase in risk associated with the SNP, and then the vast majority, you know, 99.99% of suggested interventions based on SNPs just haven't been rigorously tested in any kind of clinical trial. So, if you will get to ask an academic geneticist about direct consumer tests and actionable advice based on them, they would tell you, there's basically nothing that you can do with your 23andMe, for example.
Dr. Hedberg: Right, right. I do wanna mention your paper for those interested. The title is "Using Synthetic Datasets to Bridge the Gap Between the Promise and Reality of Basing Health-Related Decisions on Common Single Nucleotide Polymorphisms." It's a great paper. It is freely available, and I'll link to that so everyone can read it. Let's just talk a little bit about how these things are actually studied. So, when we're looking at design risk in genetics. What are the problems with the methodology in these studies from your point of view?
Dr. Wood: So, the main... Well, there's two ways that you might look at the heritability of a certain phenotype. So, say obesity or an increase in BMI is perhaps one of the ones that was first looked at. And you can do twin and family studies which tried to sort of isolate the effect of genetics like what is passed down from parents to children, the difficult to control for the effects of a shared environment. But sort of looking at population in general, you might do something called a genome-wide association study where you look at hopefully hundreds of thousands of people and then maybe millions of potential SNPs and you look at which particular SNPs might then be associated with a given phenotype. So, say an increased risk of obesity or an elevated average BMI. And then from there, you sort of can dig in a bit further and try and quantify the effect.
There is a problem with doing that because you're looking at more SNPs than you have people to study. And for statistical reasons, this basically means that you're likely to overestimate the effect size of a given SNP. So, that's one aspect, but then when you try and quantify the effect and then report it back, so you might want to tell somebody that there's a given effect size of say, an FTO SNP, so the Fat and Obesity Associated protein. There is one SNP in the FTO gene that's probably the single SNP that's most associated with an increase in BMI or risk of obesity. And they might say, on average, if you have one copy, your BMI is 0.3 higher, which is about 2 pounds in body weight for an average person. And then it's double that if you have two copies of this. So, you can have either one, two or zero copies of any given SNP.
The problem there is that those are based on singular averages. So, you take the mean, you know, you add up all the BMIs and everybody with a given genotype and you divide that by the number of people and then you do the same for those with a SNP. And then just on average, you might see a slight increase. The real problem comes from the fact that there is so much variability, which is lost when you try and describe that average increase. So, the thing that I did in the paper was using basic statistical theory. If somebody tells you the mean and standard deviation of, say, BMI for a given SNP, you can then reconstruct a full data set that follows a Gaussian or normal distribution, which it should. And then you can look at, say, how much does the full distribution of BMI is in those with a higher risk genotype overlap with those with a lower risk genotype.
And you can do that for any single genotype. You could do it with polygenic risk scores as, you know, multiple SNPs affect the same disease or disease risk. And what you start to see or what we saw is that for most single SNPs that people are talking about in terms of disease risk, there is more than 90% overlap between the high risk and the low-risk genotypes. So, that means that if you have, say, two copies of the higher risk FTO SNP, you still...90% of the people with that genotype would have a BMI that is perfectly in keeping with a low-risk genotype. So, then only 10% of people might have a BMI that is associated with an increased risk due to genotype. And so that's a very different way of talking about risk. So, if somebody just talks about the average effects, they might say, "Oh, you're going to be on average four pounds heavier." Whereas in reality, there are probably less than 10% of people who will see any effect on their weight because of their genotype. And when you look at the effect of the genotype on BMI in total, FTO genotype explains things about 0.2% of the variability in BMI. It's so tiny that is basically inconsequential.
Dr. Hedberg: Yeah, that was so interesting about your paper. I mean, some of these numbers for some of the SNPs that you mentioned, you know, the overall, the actual impact on the individual was sometimes less than 1%, you know, 0.4%, 0.09 to 0.05%. And that alone is not factoring in all of the factors in an individual's life on top of that. I mean, we don't know if they were breastfed or, you know, fed formula or if they had a lot of antibiotics the first three years of life. We don't know they're necessarily, you know, smoking, exercise, adverse childhood experiences. I mean, there's so many factors that could come into play in these numbers. Do you agree with that or do you think that there's been a good job of taking into account all of those confounding factors.
Dr. Wood: So, in the studies,
