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John Attia, MD, PhD, discusses how to use an article about genetic association.
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John Attia, MD, PhD, discusses how to use an article about genetic association.
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Segment:0 .
>> Welcome to the latest issue of JAMAevidence. I'm Gordon Guyatt from McMaster University and I'm going to be interviewing John Attia, who led a users' guide to genetic association studies, one of the important users' guides in a particularly innovative field. He's a professor in Newcastle and he has been active in the field, and so well-qualified for this particular users' guide.
John, welcome. >> Thank you. >> Can I start off by asking you what were we trying to achieve with this particular users' guide? >> So I think the readers probably have seen over the last few years more and more genetic association studies being published. Initially, they were just candidate gene associations where people had some hypotheses about which genes should be involved in a particular disease outcome, and so people would look at variants in that particular gene in relation to disease outcome.
And then, more recently, these genome-wide association studies where, basically, microarrays or a slide that's the size of a normal microscope slide where you can genotype millions of genetic variants in one go on a person. So these genome-wide association studies look at millions of genetic variants in relation to disease. And people have been wondering how to read those and what are the criteria for validity in those studies and, particularly, how they apply to clinical practice.
And I think that's how -- what we've tried to focus on in those users' guides. >> And the way I remember it, the initial users' guides were more specific studies rather than the genome-wide associations. Is that right? >> Yes. So that's how the field went as well. Before the human genome project, people basically looked at specific genetic variants in genes that they already knew were associated with disease, whereas these genome-wide association studies are more agnostic and probably are a better approach because, in fact, we have a very poor handle on what biological pathways play a role in which diseases.
So by screening the entire genome, new hits have come up that have shed light on which biological pathways are involved in disease. >> So by casting a wide net, we pick up things that we weren't even suspecting. Do I understand it correctly? >> That's exactly right. Yes, pathways that we didn't suspect were part of a disease process are now showing up in these genome-wide association studies.
>> So people see results of the genome-wide association studies. What insights can they hope to gain from our users' guides? >> So at the moment, the users' guide is more to help people interpret the results. And the results, at this point, I think, are still very much of interest to research clinicians. What we are finding is that the odds ratios associated with a genetic variant and a disease, at least most complex diseases like diabetes, obesity, asthma, those sorts of things, are still very, very tiny.
So the odds ratios are, you know, 1.05 to 1.2, 1.4, that sort of range. So they are not powerful enough yet to use by themselves in prognosis of disease or diagnosis. At this point, they seem to be most useful in terms of shedding light on which pathways are involved and helping basic scientists go back and look at the biology and the mechanism of disease. There are a few complex diseases like diabetes, for example, where the genetic variants do seem to have a much larger effect and, in those settings, potentially we can start using the genes as prognostic factors for the risk of developing disease.
And what they do there is use tens to hundreds, sometimes even thousands, of genes together to make up a polygenic risk score. >> So as I recall it from the first time we tried this, polygenic risk scores did not even appear, at least, not appear prominently, in the guide. Is that right? >> Yes, that's right. They are getting a bit better, but it's still a bit disappointing. What we find is that, if you look at examples at the AUC for predicting the development of diabetes or the development of a stroke, the normal risk factors will give you an AUC of 0.7, 0.75, something like that.
And even when you add tens or hundreds of genes to that risk score, the AUC still goes up only, you know, 0.01 or 0.02. So it's been disappointing in that way. But where the benefit has come is in highlighting new pathways to target with new therapeutics. So just as an example, there's been GWA studies of high cholesterol. And one of the hits there was PCSK9, which is a new protein, it's a hepatic receptor, and that wasn't a target previously.
And between the time that it was identified in a GWAS and the time that a monoclonal antibody was developed and tested in an RCT and published in New England Journal, it was, I think, six years' time. So that's amazingly quick compared to what drug development used to take. >> So, just to make sure that everybody listening is clear, "GWA" is genome-wide association. Is that right? >> Yes, that's right.
>> And you mentioned "AUC." Not everybody listening might be clear on AUC. What is AUC and does our users' guide tell people about that? >> So I think we do have a users' guide telling people about the ROC curve, receiver operator characteristic curve, which is basically when you have a test that has a continuous measure and you're not sure where to set the threshold for calling a positive or a negative test. And basically, what the ROC score does is plot sensitivity versus one minus specificity at the various thresholds that are possible.
And, obviously, a perfect test, a perfect threshold would have a sensitivity of 100% and a specificity of 100%. And so the closer we come to that, the better that test is. But the area under that ROC curve is a reflection of the predictive ability of the test. So usually, an AUC of 0.5 means you're no better than the flip of a coin at predicting whether somebody is going to have disease. And an AUC of 1 is the perfect test where you're 100% accurate.
And to be clinically useful, an AUC has to be around 0.75, 0.8, to start making a difference clinically. >> So from what you've said, AUC is one of the ways of judging the value of the additional predictive power of a genetic test. Is that right? >> Yes. That's right. >> And from what you've said, sometimes it doesn't work out that well. >> [Laughing] Most of the time, it doesn't work out that well.
We find that these polygenic risk scores only add a tiny bit to the AUC. >> Well, so let me be somewhat challengingly cynical and say what this users' guide is about is to tell clinicians they shouldn't worry too much about genetic association studies because seldom do they show anything useful for clinicians. They may be useful to investigators but, clinicians, don't worry about it.
Is that too cynical a stance? >> Um, it might be a bit too wide a net to cast. There are particular circumstances where the genetic associations are particularly strong. And one area is pharmacogenomics. So there have been adverse drug reactions. I'm trying to remember the one you get with antibiotics Flucloxacillin -- Stevens-Johnson. >> Right. >> I think that's the one.
So people have done genome-wide association studies of that one or liver toxicity with some of the antibiotics. And they do find some incredibly strong genetic hits that predict whether somebody will have that kind of reaction. And the odds ratios there are anywhere between 8 and 20. So those are particularly strong genetic effects that are worth worrying about, and those are strong enough that genotyping people can make a difference clinically.
So before you use -- I mean, there's some examples with chemotherapy agents where you have severe bone marrow toxicity if you have a particular genotype. And so, in those kinds of setting, it is already coming into clinical practice. So don't ignore the field completely. >> Right. >> There are some practical applications. >> And the users' guide highlights the distinction between when it's practical and when it is not. >> Yes.
>> So if you were trying to sell, say, to a clinician, "Yes, you should read these users' guide, tell me the sales pitch." >> [Laughing] What would I say? Well, I guess the whole way medicine is moving towards precision medicine, genetics will be a large part of that. And understanding what is meant by a SNP, an allele, a polygenic risk score, I think all of this will become part of the vocabulary, the common vocabulary, of medicine in the next ten years.
So I'd encourage people to at least start becoming familiar with the terms and starting to look at the studies for that reason. I think my personal skepticism is that the proponents of precision medicine focus mainly on the genetics and, of course, you get your genes at birth. And if you look at nothing else, it basically says that the rest of your life can be predicted from what you're given at birth. And I don't think that's reasonable. I think the advances will come when we combine the genetics with the environmental factors because it doesn't make sense to me to try and predict disease risk without taking into account some environmental factors.
>> So there have been lots of guides for using genetic studies. They -- our users' guide is far from the only one. Is there anything that makes our users' guides for genetic studies special? >> [Laughing] I'd like to think it's the clarity and the distillation of what is absolutely essential for validity in these studies. >> And you've also said that part of the reason people might read them is just to become familiar with the basic terms.
Anything special about our users' guides in that way? >> Yes. So the big thing I think we've done differently is that we've published the users' guide to genetic literature in three parts. And the first part is devoted entirely to clarifying terminology that people will see and it's basically a refresher or a primer on genetics. And that, people might find quite useful. >> Yeah. I'm going to push you a little farther in that because, if I remember correctly, at the end of what we did initially, you said there's nothing like this out there.
This is -- this is different. Do you still feel that way and, if so, can you elaborate? >> [Laughing] I think this is still the only one that I know about that is specifically geared to clinicians and that starts from the assumption of no knowledge in genetics. And we take people through the basic concepts, the biology of genetics, the terms, how to read the study, and what practical implications there might be for clinical practice. I still have not seen a series do that whole spectrum in the same way we have.
>> Yeah, I think that's very important. So, John, I've asked you a bunch of questions to describe our users' guides and you've done some very interesting perspectives in the field in general. Anything else you want to add or comment or reflect on? >> I guess the next big area that I think will potentially impact medicine, but it might still be a few years away, is the whole field of epigenomics, the idea that our environment, what we eat, whether we smoke, whether we drink, affects the epigenome, which is the methylation or the acetylation of our DNA.
And that almost, like, leaves a blueprint of our environmental history on our DNA. That might be one way in which the genes and the environment are brought together to allow better prognosis in medicine. But I think that is still many years away. >> So do you foresee a major revision of this users' guide to deal with those sort of issues? >> I think it's still a few years away but, yes, I'd be happy to collaborate with you again on an updated version that includes epigenomics.
But I think we're still a few years away from that. >> Okay. So Part IV of the user's guides remain as a vision for the future. >> Yes, that's right. >> Thank you, John. That was just great. It was great that you showed us what the users' guide does and showed us the limitations of the genetic association studies and their potential of the future. And how, in particular, the users' guide can introduce clinicians to this whole area of genetic association studies.
For further content on users' guides to the medical literature and applying evidence-based medicine to clinical practice, please go to JAMAevidence.com.
Segment:0 .
>> Welcome to the latest issue of JAMAevidence. I'm Gordon Guyatt from McMaster University and I'm going to be interviewing John Attia, who led a users' guide to genetic association studies, one of the important users' guides in a particularly innovative field. He's a professor in Newcastle and he has been active in the field, and so well-qualified for this particular users' guide.
John, welcome. >> Thank you. >> Can I start off by asking you what were we trying to achieve with this particular users' guide? >> So I think the readers probably have seen over the last few years more and more genetic association studies being published. Initially, they were just candidate gene associations where people had some hypotheses about which genes should be involved in a particular disease outcome, and so people would look at variants in that particular gene in relation to disease outcome.
And then, more recently, these genome-wide association studies where, basically, microarrays or a slide that's the size of a normal microscope slide where you can genotype millions of genetic variants in one go on a person. So these genome-wide association studies look at millions of genetic variants in relation to disease. And people have been wondering how to read those and what are the criteria for validity in those studies and, particularly, how they apply to clinical practice.
And I think that's how -- what we've tried to focus on in those users' guides. >> And the way I remember it, the initial users' guides were more specific studies rather than the genome-wide associations. Is that right? >> Yes. So that's how the field went as well. Before the human genome project, people basically looked at specific genetic variants in genes that they already knew were associated with disease, whereas these genome-wide association studies are more agnostic and probably are a better approach because, in fact, we have a very poor handle on what biological pathways play a role in which diseases.
So by screening the entire genome, new hits have come up that have shed light on which biological pathways are involved in disease. >> So by casting a wide net, we pick up things that we weren't even suspecting. Do I understand it correctly? >> That's exactly right. Yes, pathways that we didn't suspect were part of a disease process are now showing up in these genome-wide association studies.
>> So people see results of the genome-wide association studies. What insights can they hope to gain from our users' guides? >> So at the moment, the users' guide is more to help people interpret the results. And the results, at this point, I think, are still very much of interest to research clinicians. What we are finding is that the odds ratios associated with a genetic variant and a disease, at least most complex diseases like diabetes, obesity, asthma, those sorts of things, are still very, very tiny.
So the odds ratios are, you know, 1.05 to 1.2, 1.4, that sort of range. So they are not powerful enough yet to use by themselves in prognosis of disease or diagnosis. At this point, they seem to be most useful in terms of shedding light on which pathways are involved and helping basic scientists go back and look at the biology and the mechanism of disease. There are a few complex diseases like diabetes, for example, where the genetic variants do seem to have a much larger effect and, in those settings, potentially we can start using the genes as prognostic factors for the risk of developing disease.
And what they do there is use tens to hundreds, sometimes even thousands, of genes together to make up a polygenic risk score. >> So as I recall it from the first time we tried this, polygenic risk scores did not even appear, at least, not appear prominently, in the guide. Is that right? >> Yes, that's right. They are getting a bit better, but it's still a bit disappointing. What we find is that, if you look at examples at the AUC for predicting the development of diabetes or the development of a stroke, the normal risk factors will give you an AUC of 0.7, 0.75, something like that.
And even when you add tens or hundreds of genes to that risk score, the AUC still goes up only, you know, 0.01 or 0.02. So it's been disappointing in that way. But where the benefit has come is in highlighting new pathways to target with new therapeutics. So just as an example, there's been GWA studies of high cholesterol. And one of the hits there was PCSK9, which is a new protein, it's a hepatic receptor, and that wasn't a target previously.
And between the time that it was identified in a GWAS and the time that a monoclonal antibody was developed and tested in an RCT and published in New England Journal, it was, I think, six years' time. So that's amazingly quick compared to what drug development used to take. >> So, just to make sure that everybody listening is clear, "GWA" is genome-wide association. Is that right? >> Yes, that's right.
>> And you mentioned "AUC." Not everybody listening might be clear on AUC. What is AUC and does our users' guide tell people about that? >> So I think we do have a users' guide telling people about the ROC curve, receiver operator characteristic curve, which is basically when you have a test that has a continuous measure and you're not sure where to set the threshold for calling a positive or a negative test. And basically, what the ROC score does is plot sensitivity versus one minus specificity at the various thresholds that are possible.
And, obviously, a perfect test, a perfect threshold would have a sensitivity of 100% and a specificity of 100%. And so the closer we come to that, the better that test is. But the area under that ROC curve is a reflection of the predictive ability of the test. So usually, an AUC of 0.5 means you're no better than the flip of a coin at predicting whether somebody is going to have disease. And an AUC of 1 is the perfect test where you're 100% accurate.
And to be clinically useful, an AUC has to be around 0.75, 0.8, to start making a difference clinically. >> So from what you've said, AUC is one of the ways of judging the value of the additional predictive power of a genetic test. Is that right? >> Yes. That's right. >> And from what you've said, sometimes it doesn't work out that well. >> [Laughing] Most of the time, it doesn't work out that well.
We find that these polygenic risk scores only add a tiny bit to the AUC. >> Well, so let me be somewhat challengingly cynical and say what this users' guide is about is to tell clinicians they shouldn't worry too much about genetic association studies because seldom do they show anything useful for clinicians. They may be useful to investigators but, clinicians, don't worry about it.
Is that too cynical a stance? >> Um, it might be a bit too wide a net to cast. There are particular circumstances where the genetic associations are particularly strong. And one area is pharmacogenomics. So there have been adverse drug reactions. I'm trying to remember the one you get with antibiotics Flucloxacillin -- Stevens-Johnson. >> Right. >> I think that's the one.
So people have done genome-wide association studies of that one or liver toxicity with some of the antibiotics. And they do find some incredibly strong genetic hits that predict whether somebody will have that kind of reaction. And the odds ratios there are anywhere between 8 and 20. So those are particularly strong genetic effects that are worth worrying about, and those are strong enough that genotyping people can make a difference clinically.
So before you use -- I mean, there's some examples with chemotherapy agents where you have severe bone marrow toxicity if you have a particular genotype. And so, in those kinds of setting, it is already coming into clinical practice. So don't ignore the field completely. >> Right. >> There are some practical applications. >> And the users' guide highlights the distinction between when it's practical and when it is not. >> Yes.
>> So if you were trying to sell, say, to a clinician, "Yes, you should read these users' guide, tell me the sales pitch." >> [Laughing] What would I say? Well, I guess the whole way medicine is moving towards precision medicine, genetics will be a large part of that. And understanding what is meant by a SNP, an allele, a polygenic risk score, I think all of this will become part of the vocabulary, the common vocabulary, of medicine in the next ten years.
So I'd encourage people to at least start becoming familiar with the terms and starting to look at the studies for that reason. I think my personal skepticism is that the proponents of precision medicine focus mainly on the genetics and, of course, you get your genes at birth. And if you look at nothing else, it basically says that the rest of your life can be predicted from what you're given at birth. And I don't think that's reasonable. I think the advances will come when we combine the genetics with the environmental factors because it doesn't make sense to me to try and predict disease risk without taking into account some environmental factors.
>> So there have been lots of guides for using genetic studies. They -- our users' guide is far from the only one. Is there anything that makes our users' guides for genetic studies special? >> [Laughing] I'd like to think it's the clarity and the distillation of what is absolutely essential for validity in these studies. >> And you've also said that part of the reason people might read them is just to become familiar with the basic terms.
Anything special about our users' guides in that way? >> Yes. So the big thing I think we've done differently is that we've published the users' guide to genetic literature in three parts. And the first part is devoted entirely to clarifying terminology that people will see and it's basically a refresher or a primer on genetics. And that, people might find quite useful. >> Yeah. I'm going to push you a little farther in that because, if I remember correctly, at the end of what we did initially, you said there's nothing like this out there.
This is -- this is different. Do you still feel that way and, if so, can you elaborate? >> [Laughing] I think this is still the only one that I know about that is specifically geared to clinicians and that starts from the assumption of no knowledge in genetics. And we take people through the basic concepts, the biology of genetics, the terms, how to read the study, and what practical implications there might be for clinical practice. I still have not seen a series do that whole spectrum in the same way we have.
>> Yeah, I think that's very important. So, John, I've asked you a bunch of questions to describe our users' guides and you've done some very interesting perspectives in the field in general. Anything else you want to add or comment or reflect on? >> I guess the next big area that I think will potentially impact medicine, but it might still be a few years away, is the whole field of epigenomics, the idea that our environment, what we eat, whether we smoke, whether we drink, affects the epigenome, which is the methylation or the acetylation of our DNA.
And that almost, like, leaves a blueprint of our environmental history on our DNA. That might be one way in which the genes and the environment are brought together to allow better prognosis in medicine. But I think that is still many years away. >> So do you foresee a major revision of this users' guide to deal with those sort of issues? >> I think it's still a few years away but, yes, I'd be happy to collaborate with you again on an updated version that includes epigenomics.
But I think we're still a few years away from that. >> Okay. So Part IV of the user's guides remain as a vision for the future. >> Yes, that's right. >> Thank you, John. That was just great. It was great that you showed us what the users' guide does and showed us the limitations of the genetic association studies and their potential of the future. And how, in particular, the users' guide can introduce clinicians to this whole area of genetic association studies.
For further content on users' guides to the medical literature and applying evidence-based medicine to clinical practice, please go to JAMAevidence.com.
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