So, having just returned from and now recuperating from coronary bypass surgery, I have to ask the 'complexity' question--a very personal one in this case: Why me? I've lived a physically and physiologically vigorous life. My diet may not have always been the very best for cardio health (though, for reasons we've discussed here many times over the years, it's not completely clear what that diet should actually be), but it wasn't particularly bad, given what's thought these days to be a "healthy" diet.
The surgeon who remodeled me at Penn State's fine medical complex in Hershey, said he knows the risk factors in a population but couldn't know why any given individual developed clogged coronary arteries, nor which artery would be affected. His job was to replace, not explain them, one might say. So, he didn't even attempt to tell me why I was now in need of bypass surgery.
As he said, there are five known major risk factors: obesity, unhealthy diet, high cholesterol, genetic predisposition, and smoking. Yes, having diabetes and high blood pressure are risk factors as well, but correlated enough with obesity that perhaps he considers these two conditions to be side effects of obesity. In any case, these risk factors have been determined by looking at associations between possible causal variables and heart disease in populations. Resulting statistics describe the population, not identifying specific high-risk individuals within it. Indeed, some people with heart disease have all the risk factors, some have a combination of a few, and some have none. And even then, it's not possible to say which was the cause of the disease in most individual cases.
I have none of these risk factors -- though, I could make up a story. I smoked when I was young, my father had a pacemaker when he was old, but he lived to 99. Still, I have done vigorous exercise my whole life, thinking that was my "get cancer program" since it meant, I thought, that I would go not out with a coronary. What caused my artery to clog? Indeed, why in my case was the clog in an unstentable artery location, and hence required major surgery?
This brings up, again, the question of whether one's individual risk can even be known with any sort of 'precision'. Or is that an illusion? Is it a culpably false promise made by the calculating Dr Collins at NIH, to get NIH funding, rather than to give the public a realistic understanding of what we know and what we can hope to know based on research investment of the type he favors?
How, based on current methods of science, can it really be individual? What kind of information would that require, just considering actual, i.e., past effects, assuming they could really be ascertained to any reasonable measurement standard? What would you need to consider? Diet, exercise, personality (temperament, for example). Climate? Profession? The effects of war, drought, epidemic? Genes, even?
Of course, the gross and inexcusable BS of promising 'precision genomic medicine' based on very costly, open-ended genomic (and other 'omic) data collection enterprises is culpable. It is an often openly acknowledged way of getting, and keeping, mega-funding without having any real ideas (and understandable since medical schools culpably don't pay faculty salaries or basic research costs as part of their jobs). Focused science has chances of finding things out; blind data enumeration, far less so--and what we've done of that so far shows this quite clearly.
We often say 'family history', and clinically this may be the most useful piece of predictive information, but what does that actually explain? Did Dad or Uncle Jane have the same trait because of genes, or because of their shared family habits and lifestyles? How could you really tell? A surgeon need not care, as their job is to fix the clogged pipes, and if heart disease runs in a family the physician will treat his or her patient as high risk. Still, to prevent this sort of thing, we need to know what causes it.
This is a central biomedical question! It is hard enough to know, much less accurately measure, all factors in life that might in this or that way be a 'risk' factor for a given disease, like clogged coronary plumbing. Is it a delusion to think we could identify, much less measure all the factors? If, as seems obvious, there isn't just a single factor, and probably everyone's exposure set is different (and their effects need not be 'additive'), how on earth can we even know how well we are measuring, or ascertaining, such factors?
And, if we can do this, it only applies directly to current cases and their past lifestyle exposures. But what we would like to do, for individuals and for public health, is to predict the future to lower risks. However, there is no way, not even in principle, no reasonable chance of knowing what future exposures will be, not even for populations. Diets and lifestyles change in ways we cannot predict, nor can we predict major future events--climate, war, pestilence, food types and availability, etc., that would be highly relevant.
So what should we do with our understanding of these unpredictable factors? Perhaps just level with patients and the public, and stop using the public to endow a particular, and particularly costly, part of the university research empire. Maybe a return to focused, hypothesis--based research--actual science--in my view.
Showing posts with label precision medicine. Show all posts
Showing posts with label precision medicine. Show all posts
Sunday, August 11, 2019
Wednesday, November 28, 2018
Induction-deduction, and replicability: is there any difference?
In what sense--what scientific sense--does the future resemble the past? Or perhaps, to what extent does it? Can we know? If we can't, then what credence for future prediction can we give to results of studies today, necessarily from the past experience of current samples? Similarly, in what sense can we extrapolate findings on this sample to some other sample or population? If these questions are not easily answerable (indeed if they are answerable at all!), then much of current, and currently very widespread and expensive science, is at best of unclear, questionable value.
We can look at these issues in terms of a couple of standard aspects of science: the relationship between induction and deduction; and the idea of replicability. Induction and deduction basically come from the Enlightenment time in western history, when it was found in a formal sense that the world of western science--which at that time meant physical science--followed universal 'laws' of Nature. At that time, life itself was generally excluded from this view, not least because it was believed to be the result of ad hoc creation events by God.
The induction--deduction problem
-----------------
Some terminology: I will make an important distinction between two terms. By induction I mean drawing a conclusion from specific observed data (e.g., estimating some presumed causal parameter's value). Essentially, this means inferring a conclusion from the past, from events that have already occurred. But often what we want to do is to predict the future. We do that, often implicitly, by equating observed past values as estimates of causal parameters, that apply generally and therefore to the future; I refer to that predictive process, derived from observed data, as deduction. So, for example, if I flip a coin 10 times and get 5 Heads, I assume that this is somehow built into the very nature of coin-flipping so that the probability of Heads on any future flip is 0.5 (50%).
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If we can assume that induction implies deduction, then what we observe in our present or past observations will persist so that we can predict it in the future. In a law-like universe, if we are sampling properly, this will occur and we generally assume this means with complete precision if we had perfect measurement (here I speculate, but I think that quantum phenomena at the appropriate scale have the same universally parametric properties).
Promises like 'precision genomic medicine', which I think amount to culpably public deceptions, effectively equate induction with deduction: we observe some genomic elements associated in some statistical way with some outcome, and assume that the same genome scores will similarly predict the future of people decades from now. There is no serious justification for this assumption at present, nor quantification of by how much there might be errors in assuming the predictive power of past observations, in part because mutations and lifestyle clearly have major effects, but especially because these are unpredictable--even in principle. Indeed, there is another, much deeper problem of a similar kind, that has gotten recent--but to me often quite naive attention: replicability.
The replicability problem
Studies, perhaps especially in social and behavioral fields, report findings that others cannot replicate. This is being interpreted as suggesting that (ignoring the rare outright fraud), there is some problem with our decision-making criteria, other forms of bias, or poor study designs. Otherwise, shouldn't studies of the same question agree? There has been a call for the investigators involved to improve their statistical analysis (i.e., keep buying the same software!! but use it better), report negative results, and so on.
But this is potentially, and I think fundamentally, naive. It assumes that such study results should be replicable. It assumes, as I would put it, that at the level of interest, life = physics. This is, I believe not just wrong but fundamentally so.
The assumption of replicability is not really different from equating induction to deduction, except in some subtle way applied to a more diverse set of conditions. Induction of genomic-based disease risk is done on a population like, say, case-control samples, and then applied to the same population in terms of its current members' future disease risks. But we know very well that different genotypes are found in different populations, so it is not clear what degree of predictability we should, or can, assume.
Replicability is similar except that in general a result is assumed to apply across populations or samples, not just to the same sample's future. That is, I think, an even broader assumption than the genomics-precision promise that does, at least nominally, now recognize population differences.
The real, the deeper problem is that we have absolutely no reason to expect any particular degree of replicability between samples for these kinds of things. Evolution is about variation, locally responsive and temporary, and that applies to social behavior as well. We know that 'distance' or difference accumulates (generally) gradually over time and separation as a property of cultural as well as biological evolution. The same obviously applies even more to psychological and sociological samples and inferences from them.
Not only is it silly to think that samples of, say, this year's college seniors at X University will respond to questionnaires in the same way as samples of some other class or university or beyond. Of course, college students come cheap to researchers, and they're convenient. But they are not 'representative' in the replicability sense except by some sort of rather profound assumption. This is obvious, yet it is a tacit concept of very much research (biological, psychological, and sociological).
Even social scientists acknowledge the local and temporary nature of many of the things they investigate, because the latter are affected by cultural and historical patterns, fads, fashions, and so much more. Indeed, the idea of replicability is to me curious to begin with. Thus, a study that fails to replicate some other study may not reflect failings in either, and the idea that we should replicate in this kind of way is a carryover of physics envy. Perhaps in many situations, a replication result is what should be examined most closely! The social and even biological realms are simply not as 'Newtonian', or law-like, as is the real physical realm in which our notions of science--especially the very idea of a law-like replicability, arose. Not only is failure to replicate not necessarily suspect at all, but replicability should not generally be assumed. Or, put an other way, a claim that replicability is to be expected is a strong claim about Nature that requires very strong evidence!
This raises the very deep problem that in the absence of replicability assumptions, we don't know what to expect of the next study, after we've done the first.....or is this a justification for just keeping the same studies going (and funded) indefinitely? That's of course the very rewarding game being played in genomics.
We can look at these issues in terms of a couple of standard aspects of science: the relationship between induction and deduction; and the idea of replicability. Induction and deduction basically come from the Enlightenment time in western history, when it was found in a formal sense that the world of western science--which at that time meant physical science--followed universal 'laws' of Nature. At that time, life itself was generally excluded from this view, not least because it was believed to be the result of ad hoc creation events by God.
The induction--deduction problem
-----------------
Some terminology: I will make an important distinction between two terms. By induction I mean drawing a conclusion from specific observed data (e.g., estimating some presumed causal parameter's value). Essentially, this means inferring a conclusion from the past, from events that have already occurred. But often what we want to do is to predict the future. We do that, often implicitly, by equating observed past values as estimates of causal parameters, that apply generally and therefore to the future; I refer to that predictive process, derived from observed data, as deduction. So, for example, if I flip a coin 10 times and get 5 Heads, I assume that this is somehow built into the very nature of coin-flipping so that the probability of Heads on any future flip is 0.5 (50%).
-----------------
If we can assume that induction implies deduction, then what we observe in our present or past observations will persist so that we can predict it in the future. In a law-like universe, if we are sampling properly, this will occur and we generally assume this means with complete precision if we had perfect measurement (here I speculate, but I think that quantum phenomena at the appropriate scale have the same universally parametric properties).
Promises like 'precision genomic medicine', which I think amount to culpably public deceptions, effectively equate induction with deduction: we observe some genomic elements associated in some statistical way with some outcome, and assume that the same genome scores will similarly predict the future of people decades from now. There is no serious justification for this assumption at present, nor quantification of by how much there might be errors in assuming the predictive power of past observations, in part because mutations and lifestyle clearly have major effects, but especially because these are unpredictable--even in principle. Indeed, there is another, much deeper problem of a similar kind, that has gotten recent--but to me often quite naive attention: replicability.
The replicability problem
Studies, perhaps especially in social and behavioral fields, report findings that others cannot replicate. This is being interpreted as suggesting that (ignoring the rare outright fraud), there is some problem with our decision-making criteria, other forms of bias, or poor study designs. Otherwise, shouldn't studies of the same question agree? There has been a call for the investigators involved to improve their statistical analysis (i.e., keep buying the same software!! but use it better), report negative results, and so on.
But this is potentially, and I think fundamentally, naive. It assumes that such study results should be replicable. It assumes, as I would put it, that at the level of interest, life = physics. This is, I believe not just wrong but fundamentally so.
The assumption of replicability is not really different from equating induction to deduction, except in some subtle way applied to a more diverse set of conditions. Induction of genomic-based disease risk is done on a population like, say, case-control samples, and then applied to the same population in terms of its current members' future disease risks. But we know very well that different genotypes are found in different populations, so it is not clear what degree of predictability we should, or can, assume.
Replicability is similar except that in general a result is assumed to apply across populations or samples, not just to the same sample's future. That is, I think, an even broader assumption than the genomics-precision promise that does, at least nominally, now recognize population differences.
The real, the deeper problem is that we have absolutely no reason to expect any particular degree of replicability between samples for these kinds of things. Evolution is about variation, locally responsive and temporary, and that applies to social behavior as well. We know that 'distance' or difference accumulates (generally) gradually over time and separation as a property of cultural as well as biological evolution. The same obviously applies even more to psychological and sociological samples and inferences from them.
Not only is it silly to think that samples of, say, this year's college seniors at X University will respond to questionnaires in the same way as samples of some other class or university or beyond. Of course, college students come cheap to researchers, and they're convenient. But they are not 'representative' in the replicability sense except by some sort of rather profound assumption. This is obvious, yet it is a tacit concept of very much research (biological, psychological, and sociological).
Even social scientists acknowledge the local and temporary nature of many of the things they investigate, because the latter are affected by cultural and historical patterns, fads, fashions, and so much more. Indeed, the idea of replicability is to me curious to begin with. Thus, a study that fails to replicate some other study may not reflect failings in either, and the idea that we should replicate in this kind of way is a carryover of physics envy. Perhaps in many situations, a replication result is what should be examined most closely! The social and even biological realms are simply not as 'Newtonian', or law-like, as is the real physical realm in which our notions of science--especially the very idea of a law-like replicability, arose. Not only is failure to replicate not necessarily suspect at all, but replicability should not generally be assumed. Or, put an other way, a claim that replicability is to be expected is a strong claim about Nature that requires very strong evidence!
This raises the very deep problem that in the absence of replicability assumptions, we don't know what to expect of the next study, after we've done the first.....or is this a justification for just keeping the same studies going (and funded) indefinitely? That's of course the very rewarding game being played in genomics.
Thursday, October 18, 2018
When is a consistent account in science good enough?
We often want our accounts in science to be consistent with the facts. Even if we can't explain all the current facts, we can always hope to say, truthfully, that our knowledge is imperfect but our current theory is at least largely true....or something close to that....until some new 'paradigm' replaces it.
It is also only natural to sneer at our forebears' primitive ideas, of which we, naturally, now know much better. Flat earth? Garden of Eden? Phlebotomy? Phlogiston? Four humors? Prester John, the mysterious Eastern Emperoro who will come to our rescue? I mean, really! Who could ever have believed such nonsense?
In fact, leaders among our forebears accepted these and much else like it, took them as real, sought them for solace from life's cares not just because they were promised (as in religious figures) but as earthly answers. Or, to seem impressively knowledgeable, found arcane ways to say "I dunno" without admitting it. And, similarly, many used ad hoc 'explanations' for personal gain--as self-proclaimed gurus, promisers of relief from life's sorrows or medical woes (usually, if you cross their palms with silver first).
Even in my lifetime in science, I've seen forced after-the-fact 'explanations' of facts, and the way a genuine new insight can show how wrong those explanations were, because the new insight accounts for them more naturally or in terms of some other new facts, forces, or ideas. Continental drift was one that had just come along in my graduate school days. Evolution, relativity, and quantum mechanics are archetypes of really new ideas that transformed how our forebears had explained what is now our field of endeavor.
Such lore, and our more broad lionizing of leading political, artistic or other similarly transformative figures, organizes how we think. In many ways it gives us a mythology, or ethnology, that leads us to order success into a hierarchy of brilliant insights. This, in turn, and in our careerist society, provides an image to yearn for, a paradigm to justify our jobs, indeed our lives, make them meaningful--make them important in some cosmic sense, and really worth living.
Indeed, even ordinary figures from our parents, to the police, generals, teachers, and politicians have various levels of aura as idols or savior figures, who provide comforting answers to life's discomfiting questions. It is natural for those burdened by worrisome questions to seek soothing answers.
But of course, all is temporary (unless you believe in eternal heavenly bliss). Even if we truly believe we've made transformative discoveries or something like that during our lives, we know all is eventually dust. In the bluntest possible sense, we know that the Earth will some day destruct and all our atoms scatter to form other cosmic structures.
But we live here and now and perhaps because we know all is temporary, many want to get theirs now, and we all must get at least some now--a salary to put food on the table at the very least. And in an imperfect and sometimes frightening world, we want the comfort of experts who promise relief from life's material ills as much as preachers promise ultimate relief. This is the mystique often given to, or taken by, medical professionals and other authority figures. This is what 'precision genomic medicine' was designed, consciously or possibly just otherwise, to serve.
And we are in the age of science, the one True field (we seem to claim) that delivers only objectively true goods; but are we really very different from those in similar positions of other sorts of lore? Is 'omics any different from other omnibus beliefs-du-jour? Or do today's various 'omical incantations and promises of perfection (called 'precision') reveal that we are, after all, even in the age of science, only human and not much different from our typically patronized benighted forebears?
Suppose we acknowledge that the latter is, at least to a considerable extent, part of our truth. Is there a way that we can better use, or better allocate, resources to make them more objectively dedicated to solving the actually soluble problems of life--for the public everyday good, and perhaps less used, as from past to today, to guild the thrones of those making the promises of eternal bliss?
Or does sociology, of science or any other aspect of human life, tell us that this is, simply, the way things are?
It is also only natural to sneer at our forebears' primitive ideas, of which we, naturally, now know much better. Flat earth? Garden of Eden? Phlebotomy? Phlogiston? Four humors? Prester John, the mysterious Eastern Emperoro who will come to our rescue? I mean, really! Who could ever have believed such nonsense?
 |
| Prester John to the rescue (from Br Library--see Wikipedia entry) |
Even in my lifetime in science, I've seen forced after-the-fact 'explanations' of facts, and the way a genuine new insight can show how wrong those explanations were, because the new insight accounts for them more naturally or in terms of some other new facts, forces, or ideas. Continental drift was one that had just come along in my graduate school days. Evolution, relativity, and quantum mechanics are archetypes of really new ideas that transformed how our forebears had explained what is now our field of endeavor.
Such lore, and our more broad lionizing of leading political, artistic or other similarly transformative figures, organizes how we think. In many ways it gives us a mythology, or ethnology, that leads us to order success into a hierarchy of brilliant insights. This, in turn, and in our careerist society, provides an image to yearn for, a paradigm to justify our jobs, indeed our lives, make them meaningful--make them important in some cosmic sense, and really worth living.
Indeed, even ordinary figures from our parents, to the police, generals, teachers, and politicians have various levels of aura as idols or savior figures, who provide comforting answers to life's discomfiting questions. It is natural for those burdened by worrisome questions to seek soothing answers.
But of course, all is temporary (unless you believe in eternal heavenly bliss). Even if we truly believe we've made transformative discoveries or something like that during our lives, we know all is eventually dust. In the bluntest possible sense, we know that the Earth will some day destruct and all our atoms scatter to form other cosmic structures.
But we live here and now and perhaps because we know all is temporary, many want to get theirs now, and we all must get at least some now--a salary to put food on the table at the very least. And in an imperfect and sometimes frightening world, we want the comfort of experts who promise relief from life's material ills as much as preachers promise ultimate relief. This is the mystique often given to, or taken by, medical professionals and other authority figures. This is what 'precision genomic medicine' was designed, consciously or possibly just otherwise, to serve.
And we are in the age of science, the one True field (we seem to claim) that delivers only objectively true goods; but are we really very different from those in similar positions of other sorts of lore? Is 'omics any different from other omnibus beliefs-du-jour? Or do today's various 'omical incantations and promises of perfection (called 'precision') reveal that we are, after all, even in the age of science, only human and not much different from our typically patronized benighted forebears?
Suppose we acknowledge that the latter is, at least to a considerable extent, part of our truth. Is there a way that we can better use, or better allocate, resources to make them more objectively dedicated to solving the actually soluble problems of life--for the public everyday good, and perhaps less used, as from past to today, to guild the thrones of those making the promises of eternal bliss?
Or does sociology, of science or any other aspect of human life, tell us that this is, simply, the way things are?
Tuesday, October 16, 2018
Where has all the thinking gone....long time passing?
Where did we get the idea that our entire nature, not just our embryological development, but everything else, was pre-programmed by our genome? After all, the very essence of Homo sapiens compared to all other species, is that we use culture--language, tools, etc.--to do our business rather than just our physical biology. In a serious sense, we evolved to be free of our bodies, our genes made us freer from our genes than most if not all other species! And we evolved to live long enough to learn--language, technology, etc.--in order to live our thus-long lives.
Yet isn't an assumption of pre-programming the only assumption by which anyone could legitimately promise 'precision' genomic medicine? Of course, Mendel's work, adopted by human geneticists over a century ago, allowed great progress in understanding how genes lead at least to the simpler of our traits, with discrete (yes/no) manifestations, traits that do include many diseases that really, perhaps surprisingly, do behave in Mendelian fashion, and for which concepts like dominance and recessiveness been applied and that, sometimes, at least approximately hold up to closer scrutiny.
Even 100 years ago, agricultural and other geneticists who could do experiments, largely confirmed the extension of Mendel to continuously varying traits, like blood pressure or height. They reasoned that many genes (whatever they were, which was unknown at the time) contributed individually small effects. If each gene had two states in the usual Aa/AA/aa classroom example sense, but there were countless such genes, their joint action could approximate continuously varying traits whose measure was, say, the number of A alleles in an individual. This view was also consistent with the observed correlation of trait measure with kinship-degree among relatives. This history has been thoroughly documented. But there are some bits, important bits, missing, especially when it comes to the fervor for Big Data 'omics analysis of human diseases and other traits. In essence, we are still, a century later, conceptual prisoners of Mendel.
'Omics over the top: key questions generally ignored
Let us take GWAS (genomewide association studies) on their face value. GWAS find countless 'hits', sites of whatever sort across the genome whose variation affects variation in WhateverTrait you choose to map (everything simply must be 'genomic' or some other 'omic, no?). WhateverTrait varies because every subject in your study has a different combination of contributing alleles. Somewhat resembling classical Mendelian recessiveness, contributing alleles are found in cases as well as controls (or across the measured range of quantitative traits like stature or blood pressure), where the measured trait reflects how many A's one has: WhateverTrait is essentially the sum of A's in 'cases', which may be interpreted as a risk--some sort of 'probability' rather than certainty--of having been affected or of having the measured trait value.
We usually treat risk as a 'probability,' a single value, p, that applies to everyone with the same genotype. Here, of course, no two subjects have exactly the same genotype so some sort of aggregate risk score, adding up each person's 'hits', is assigned a p. This, however, tacitly assumes something like that each site contributes some fixed risk or 'probability' of affection. But this treats these values as if they were essential to the site, each thus acting as a parameter of risk. That is, sites are treated as a kind of fixed value or, one might say 'force', relative to the trait measure in question.
One obvious and serious issue is that these are necessarily estimated from past data, that is, by induction from samples. Not only is there sampling variation that usually is only crudely estimated by some standard statistical variation-related measure, but we know that the picture will be at least somewhat different in any other sample we might have chosen, not to mention other populations; and those who are actually candid about what they are doing know very well that the same people living in a different place or time would have different risks for the same trait.
No study is perfect, so we use some conveniently assumed well-behaved regression/correction adjustments to account for the statistical 'noise' due to factors like age, sex, and unmeasured environmental effects. Much worse than these issues, there are clearly factors of imprecision, and the obvious major one, taboo even to think about much less to mention, that relevant future factors (mutations, environments, lifestyles) are unknowable, even in principle. So what we really do, are forced to do, is extend what the past was like to the assumed future. But besides this, we don't count somatic changes (mutation arising in body tissues during life, that were not inherited), because they'd mess up our assertions of 'precision', and we can't measure them well in any case (so just shut one's eyes and pretend the ghost isn't in the house!).
All of these together mean that we are estimating risks from imperfect existing samples and past life-experience, but treating them as underlying parameters so that we can extend them to future samples. What that does is equate induction with deduction, assuming the past is rigorously parametric and will be the same in the future; but this is simply scientifically and epistemologically wrong, no matter how inconvenient it is to acknowledge this. Mutations, genotypes, and environments of the future are simply unpredictable, even in principle.
None of this is a secret, or new discovery, in any way. What it is, is inconvenient truth. These things should have been enough, by themselves and without badgering investigators about environmental factors that (we know very well, typically predominate) prevent all the NIH's precision promises from being accurate ('precise'), or even to a knowable degree. Yet this 'precision' sloganeering is being, sheepishly, aped all over the country by all sorts of groups who don't think for themselves and/or who go along lest they get left off the funding gravy train. This is the 'omics fad. If you think I am being too cynical, just look at what's being said, done, published, and claimed.
These are, to me, deep flaws in the way the GWAS and other 'omics industries, very well-heeled, are operating these days, to pick the public's pocket (pharma may, slowly, be awakening-- Lancet editorial, "UK life science research: time to burst the biomedical bubble," Lancet 392:187, 2018). But scientists need jobs and salaries, and if we put people in a position where they have to sing in this way for their supper, what else can you expect of them?
Unfortunately, there are much more serious problems with the science, and they have to do with the point-cause thinking on which all of this is based.
Even a point-cause must act through some process
By far most of the traits, disease or otherwise, that are being GWAS'ed and 'omicked these days, at substantial public expense, are treated as if the mapped 'causes' are point causes. If there are n causes, and a person has an unlucky set m out of many possible sets, one adds 'em up and predicts that person will have the target trait. And there is much that is ignored, assumed, or wishfully hidden in this 'will'. It is not clear how many authors treat it, tacitly, as a probability vs a certainty, because no two people in a sample have the same genotype and all we know is that they are 'affected' or 'unaffected'.
The genomics industry promises, essentially, that from conception onward, your DNA sequence will predict your diseases, even if only in the form of some 'risk'; the latter is usually a probability and despite the guise of 'precision' it can, of course, be adjusted as we learn more. For example, it must be adjusted for age, and usually other variables. Thus, we need ever larger and more and longer-lasting samples. This alone should steer people away from being profiteered by DNA testing companies. But that snipe aside, what does this risk or 'probability' actually mean?
Among other things, those candid enough to admit it know that environmental and lifestyle factors have a role, interacting with the genotype if not, usually, overwhelming it, meaning, for example, that the genotype only confers some, often modest, risk probability, the actual risk much more affected by lifestyle factors, most of which are not measured or not measured with accuracy, or not even yet identified. And usually there is some aspect that relates to age, or some assumption about what 'lifetime' risk means. Whose lifetime?
Aspects of such a 'probability'
There are interesting issues, longstanding issues, about these probabilities, even if we assume they have some kind of meaning. Why do so many important diseases, like cancers, only arise at some advanced age? How can a genomic 'risk' be so delayed and so different among people? Why are mice, with very similar genotypes to humans (which is why we do experiments on them to learn about human disease) only live to 3 while we live to our 70s and beyond?
Richard Peto, raised some of these questions many decades ago. But they were never really addressed, even in an era when NIH et al were spending much money on 'aging' research including studies of lifespan. There were generic theories that suggested from an evolutionary theory why some diseases were deferred to later ages (it is called 'negative pleiotropy'), but nobody tried seriously to explain why that was from a molecular/genetic point of view. Why do mice only live only 3 years, anyway? And so on.
These are old questions and very deep ones but they have not been answered and, generally, are conveniently forgotten--because, one might argue, they are inconvenient.
If a GWAS score increases the risk of a disease, that has a long delayed onset pattern, often striking late in life, and highly variable among individuals or over time, what sort of 'cause' is that genotype? What is it that takes decades for the genes to affect the person? There are a number of plausible answers, but they get very little attention at least in part because that stands in the way of the vested interests of entrenched too-big-to-kill Big Data faddish 'research' that demands instant promises to the public it is trephining for support. If the major reason is lifestyle factors, then the very delayed onset should be taken as persuasive evidence that the genotype is, in fact, by itself not a very powerful predictor.
Why would the additive effects of some combination of GWAS hits lead to disease risk? That is, in our complex nature why would each gene's effects be independent of each other contributor? In fact, mapping studies usually show evidence that other things, such as interactions are important--but they are at present almost impossibly complex to be understood.
Does each combination of genome-wide variants have a separate age-onset pattern, and if not, why not? And if so, how does the age effect work (especially if not due to person-years of exposure to the truly determining factors of lifestyle)? If such factors are at play, how can we really know, since we never see the same genotype twice? How can we assume that the time-relationship with each suspect genetic variant will be similar among samples or in the future? Is the disease due to post-natal somatic mutation, in which case why make predictions based on the purported constitutive genotypes of GWAS samples?
Obviously, if long delayed onset patterns are due not to genetic but to lifestyle exposures interacting with genotypes, then perhaps lifestyle exposures should be the health-related target, not exotic genomic interventions. Of course, the value of genome-based prediction clearly depends on environmental/lifestyle exposures, and the future of these exposure is obviously unknowable (as we clearly do know from seeing how unpredictable past exposures have affected today's disease patterns).
The point here is that our reliance on genotypes is a very convenient way of keeping busy, bringing in the salaries, but not facing up to the much more challenging issues that the easy one (run lots of data through DNA sequencers) can't address. I did not invent these points, and it is hard to believe that at least the more capable and less me-too scientists don't clearly know them, if quietly. Indeed, I know this from direct experience. Yes, scientists are fallible, vain, and we're only human. But of all human endeavors, science should be based on honesty because we have to rely on trust of each other's work.
The scientific problems are profound and not easily solved, and not soluble in a hurry. But much of the problem comes from the funding and careerist system that shackles us. This is the deeper explanation in many ways. The paint on the House of Science is the science itself, but it is the House that supports that paint that is the real problem.
A civically responsible science community, and its governmental supporters, should be freed from the iron chains of relentless Big Data for their survival, and start thinking, seriously, about the questions that their very efforts over the past 20 years, on trait after trait, in population after population, and yes, with Big Data, have clearly revealed.
Yet isn't an assumption of pre-programming the only assumption by which anyone could legitimately promise 'precision' genomic medicine? Of course, Mendel's work, adopted by human geneticists over a century ago, allowed great progress in understanding how genes lead at least to the simpler of our traits, with discrete (yes/no) manifestations, traits that do include many diseases that really, perhaps surprisingly, do behave in Mendelian fashion, and for which concepts like dominance and recessiveness been applied and that, sometimes, at least approximately hold up to closer scrutiny.
Even 100 years ago, agricultural and other geneticists who could do experiments, largely confirmed the extension of Mendel to continuously varying traits, like blood pressure or height. They reasoned that many genes (whatever they were, which was unknown at the time) contributed individually small effects. If each gene had two states in the usual Aa/AA/aa classroom example sense, but there were countless such genes, their joint action could approximate continuously varying traits whose measure was, say, the number of A alleles in an individual. This view was also consistent with the observed correlation of trait measure with kinship-degree among relatives. This history has been thoroughly documented. But there are some bits, important bits, missing, especially when it comes to the fervor for Big Data 'omics analysis of human diseases and other traits. In essence, we are still, a century later, conceptual prisoners of Mendel.
'Omics over the top: key questions generally ignored
Let us take GWAS (genomewide association studies) on their face value. GWAS find countless 'hits', sites of whatever sort across the genome whose variation affects variation in WhateverTrait you choose to map (everything simply must be 'genomic' or some other 'omic, no?). WhateverTrait varies because every subject in your study has a different combination of contributing alleles. Somewhat resembling classical Mendelian recessiveness, contributing alleles are found in cases as well as controls (or across the measured range of quantitative traits like stature or blood pressure), where the measured trait reflects how many A's one has: WhateverTrait is essentially the sum of A's in 'cases', which may be interpreted as a risk--some sort of 'probability' rather than certainty--of having been affected or of having the measured trait value.
We usually treat risk as a 'probability,' a single value, p, that applies to everyone with the same genotype. Here, of course, no two subjects have exactly the same genotype so some sort of aggregate risk score, adding up each person's 'hits', is assigned a p. This, however, tacitly assumes something like that each site contributes some fixed risk or 'probability' of affection. But this treats these values as if they were essential to the site, each thus acting as a parameter of risk. That is, sites are treated as a kind of fixed value or, one might say 'force', relative to the trait measure in question.
One obvious and serious issue is that these are necessarily estimated from past data, that is, by induction from samples. Not only is there sampling variation that usually is only crudely estimated by some standard statistical variation-related measure, but we know that the picture will be at least somewhat different in any other sample we might have chosen, not to mention other populations; and those who are actually candid about what they are doing know very well that the same people living in a different place or time would have different risks for the same trait.
No study is perfect, so we use some conveniently assumed well-behaved regression/correction adjustments to account for the statistical 'noise' due to factors like age, sex, and unmeasured environmental effects. Much worse than these issues, there are clearly factors of imprecision, and the obvious major one, taboo even to think about much less to mention, that relevant future factors (mutations, environments, lifestyles) are unknowable, even in principle. So what we really do, are forced to do, is extend what the past was like to the assumed future. But besides this, we don't count somatic changes (mutation arising in body tissues during life, that were not inherited), because they'd mess up our assertions of 'precision', and we can't measure them well in any case (so just shut one's eyes and pretend the ghost isn't in the house!).
All of these together mean that we are estimating risks from imperfect existing samples and past life-experience, but treating them as underlying parameters so that we can extend them to future samples. What that does is equate induction with deduction, assuming the past is rigorously parametric and will be the same in the future; but this is simply scientifically and epistemologically wrong, no matter how inconvenient it is to acknowledge this. Mutations, genotypes, and environments of the future are simply unpredictable, even in principle.
None of this is a secret, or new discovery, in any way. What it is, is inconvenient truth. These things should have been enough, by themselves and without badgering investigators about environmental factors that (we know very well, typically predominate) prevent all the NIH's precision promises from being accurate ('precise'), or even to a knowable degree. Yet this 'precision' sloganeering is being, sheepishly, aped all over the country by all sorts of groups who don't think for themselves and/or who go along lest they get left off the funding gravy train. This is the 'omics fad. If you think I am being too cynical, just look at what's being said, done, published, and claimed.
These are, to me, deep flaws in the way the GWAS and other 'omics industries, very well-heeled, are operating these days, to pick the public's pocket (pharma may, slowly, be awakening-- Lancet editorial, "UK life science research: time to burst the biomedical bubble," Lancet 392:187, 2018). But scientists need jobs and salaries, and if we put people in a position where they have to sing in this way for their supper, what else can you expect of them?
Unfortunately, there are much more serious problems with the science, and they have to do with the point-cause thinking on which all of this is based.
Even a point-cause must act through some process
By far most of the traits, disease or otherwise, that are being GWAS'ed and 'omicked these days, at substantial public expense, are treated as if the mapped 'causes' are point causes. If there are n causes, and a person has an unlucky set m out of many possible sets, one adds 'em up and predicts that person will have the target trait. And there is much that is ignored, assumed, or wishfully hidden in this 'will'. It is not clear how many authors treat it, tacitly, as a probability vs a certainty, because no two people in a sample have the same genotype and all we know is that they are 'affected' or 'unaffected'.
The genomics industry promises, essentially, that from conception onward, your DNA sequence will predict your diseases, even if only in the form of some 'risk'; the latter is usually a probability and despite the guise of 'precision' it can, of course, be adjusted as we learn more. For example, it must be adjusted for age, and usually other variables. Thus, we need ever larger and more and longer-lasting samples. This alone should steer people away from being profiteered by DNA testing companies. But that snipe aside, what does this risk or 'probability' actually mean?
Among other things, those candid enough to admit it know that environmental and lifestyle factors have a role, interacting with the genotype if not, usually, overwhelming it, meaning, for example, that the genotype only confers some, often modest, risk probability, the actual risk much more affected by lifestyle factors, most of which are not measured or not measured with accuracy, or not even yet identified. And usually there is some aspect that relates to age, or some assumption about what 'lifetime' risk means. Whose lifetime?
Aspects of such a 'probability'
There are interesting issues, longstanding issues, about these probabilities, even if we assume they have some kind of meaning. Why do so many important diseases, like cancers, only arise at some advanced age? How can a genomic 'risk' be so delayed and so different among people? Why are mice, with very similar genotypes to humans (which is why we do experiments on them to learn about human disease) only live to 3 while we live to our 70s and beyond?
Richard Peto, raised some of these questions many decades ago. But they were never really addressed, even in an era when NIH et al were spending much money on 'aging' research including studies of lifespan. There were generic theories that suggested from an evolutionary theory why some diseases were deferred to later ages (it is called 'negative pleiotropy'), but nobody tried seriously to explain why that was from a molecular/genetic point of view. Why do mice only live only 3 years, anyway? And so on.
These are old questions and very deep ones but they have not been answered and, generally, are conveniently forgotten--because, one might argue, they are inconvenient.
If a GWAS score increases the risk of a disease, that has a long delayed onset pattern, often striking late in life, and highly variable among individuals or over time, what sort of 'cause' is that genotype? What is it that takes decades for the genes to affect the person? There are a number of plausible answers, but they get very little attention at least in part because that stands in the way of the vested interests of entrenched too-big-to-kill Big Data faddish 'research' that demands instant promises to the public it is trephining for support. If the major reason is lifestyle factors, then the very delayed onset should be taken as persuasive evidence that the genotype is, in fact, by itself not a very powerful predictor.
Why would the additive effects of some combination of GWAS hits lead to disease risk? That is, in our complex nature why would each gene's effects be independent of each other contributor? In fact, mapping studies usually show evidence that other things, such as interactions are important--but they are at present almost impossibly complex to be understood.
Does each combination of genome-wide variants have a separate age-onset pattern, and if not, why not? And if so, how does the age effect work (especially if not due to person-years of exposure to the truly determining factors of lifestyle)? If such factors are at play, how can we really know, since we never see the same genotype twice? How can we assume that the time-relationship with each suspect genetic variant will be similar among samples or in the future? Is the disease due to post-natal somatic mutation, in which case why make predictions based on the purported constitutive genotypes of GWAS samples?
Obviously, if long delayed onset patterns are due not to genetic but to lifestyle exposures interacting with genotypes, then perhaps lifestyle exposures should be the health-related target, not exotic genomic interventions. Of course, the value of genome-based prediction clearly depends on environmental/lifestyle exposures, and the future of these exposure is obviously unknowable (as we clearly do know from seeing how unpredictable past exposures have affected today's disease patterns).
The point here is that our reliance on genotypes is a very convenient way of keeping busy, bringing in the salaries, but not facing up to the much more challenging issues that the easy one (run lots of data through DNA sequencers) can't address. I did not invent these points, and it is hard to believe that at least the more capable and less me-too scientists don't clearly know them, if quietly. Indeed, I know this from direct experience. Yes, scientists are fallible, vain, and we're only human. But of all human endeavors, science should be based on honesty because we have to rely on trust of each other's work.
The scientific problems are profound and not easily solved, and not soluble in a hurry. But much of the problem comes from the funding and careerist system that shackles us. This is the deeper explanation in many ways. The paint on the House of Science is the science itself, but it is the House that supports that paint that is the real problem.
A civically responsible science community, and its governmental supporters, should be freed from the iron chains of relentless Big Data for their survival, and start thinking, seriously, about the questions that their very efforts over the past 20 years, on trait after trait, in population after population, and yes, with Big Data, have clearly revealed.
Wednesday, August 15, 2018
On the 'probability' of rain (or disease): does it make sense?
We typically bandy the word probability around, as if we actually understand it. The term, or a variant of it like probably, can be used in all sorts of contexts that, on the surface seem quite obvious and related to some sense of uncertainty; e.g., "That's probably true," or "Probably not." But is it so obvious? Are the concepts clear at all? When are they, actually, more than just informally and subjectively, meaningful?
Will it rain today? Might it? What is the chance of rain?
One of the typical uses of probabilistic terms in daily life has to do with weather predictions. As a former meteorologist myself, I find this a cogent context in which to muse about these terms, but with extensions that have much deeper relevance.
Here is an episode of a generally very fine BBC Radio 4 program called More or Less, whose mission is to educate listeners on the proper use and understanding of numbers, statistics, probabilities and the like. This episode deals, somewhat unclearly and to me quite vaguely, unsatisfactorily, and even somewhat defensively, about the use and interpretation of weather forecasts.
So what does a forecast calling for an x% chance of rain mean? Let's think of an imaginary chessboard laid over a particular location. It is raining under the black, but not under the white squares. There is nothing probabilistic about this. 50% of people in the area will experience rain. If I don't know where you live, exactly, I'd have to say that you have a 50% chance of rain, but that has nothing to do with the weather itself but rather with my uncertainty of where you live. Even then it's misleadingly vague since people don't live randomly across a region (they are, for example, usually clustered in some sub-regions).
Another interpretation is that I don't know where the black and white squares will be exactly, at any given time, but my weather models predict that in about half of the region, rain will fall. This could be because my computer models, necessarily based on imperfect measurement and imperfect theory, are therefore imperfect--but I run them many times, making small random changes in various values to account for that imperfection, and I find that among these model runs, 50% of the time at any given spot, or 50% of the entire area under consideration, experiences rain.
Or, is it that there is an imaginary chessboard moving overhead and so the 50% of the land will be under the black and hence getting rain at any given time, and thus that any given area will only get it 50% of the time, but every area will certainly get rain at some time during the forecast period, indeed every area will be getting rain half of the period? Then the best forecast is that you will get wet if you stay outside all day, but if you only run out to get the mail you might not? Might??
Or is it that my models are imperfect but theory or experience tell me that there is a 50% chance of any rain in the area--that is, my knowledge can tell me no more than that. In that case, any given place will have this guesstimated chance of rain. But does that mean at any given time during the forecast period, or at every time during it? Or is it that my knowledge is very good, but the meteorological factors--the nature of atmospheric motion and so on--only probabilistically form droplets that are large enough not just to be clouds but to fall to earth? That is, is it the atmospheric process itself that is probabilistic--at least based on the theory, since I can't observe every droplet.
If a rain-generating front is passing through the area, it could rain everywhere along the front, but only until the front has moved past the area. Thus, it may rain with 100% certainty, but only 50% of the specified time, if the front takes that amount of time to pass through.
I've undoubtedly only mentioned some of the many ways that weather forecast probabilities can be intended or interpreted as meaning. It is not clear--and the BBC program shows this--that everyone or perhaps even anyone making them actually understands, or is thinking clearly about, what these probability forecasts mean. Even meteorologists themselves, especially when dumbing down for the average Joe who only wants to know if he should carry his brolly with him, are likely ('probably'?!) unclear about these values. Probably they mean a bit of this and a bit of that. I wonder if anyone can know which of the meanings are being used in any given forecast.
Well, fine, everyone knows that nobody really knows everything about the weather. Anyway, it's not that big of a deal if you get an unexpected drenching now and then, or more often haul your raincoat to work but never need it.
But what about things that really matter, like your future health? My doc takes my blood pressure and looks at my weight, and may warn me that I am at 'risk' of a heart attack or stroke--that without taking some preventive measures I may (or probably will) have such a fate. That's a lot more important than a soaked shirt. But what does it mean? Isn't everybody at some risk of these diseases?Does my doc actually know? Does anybody? Who is thinking clearly about these kinds of risk pronouncements?
OK, caveats, caveats: but will I get diabetes?
In genomics 'precision' genomic medicine is one of the genomics marketing slogans of the day, the very vague (I would say culpably false) promise that from your genotype we can predict your future--that's what 'precision' implies. The same applies even if weaseling now would include environmental factors as well as genomic ones. And the idea implies knowledge not just of some vague probability, but by implication it means perfection--prediction with certainty. But to what extent--if any at all--is the promise, or can the promise be true? What would it mean to be 'true'? After all, anyone might get, say type 2 diabetes, mightn't they? Or, more specifically, what does such a sentence itself even mean, if anything?
We know that, today at least, some people get diabetes sometime in their lives, and even if we don't know why or which ones, that seems like a safe assertion. But to say that any person, not specifically identified, might become diabetic is rather useless. We want a reason--a cause--and if we have that we assume it will enable us to identify specifically vulnerable individuals. Even then, however, we don't know more than to say, in some sense that we may not even understand as well as we think we do, that not all the vulnerable will get the disease: but we seem to think that they share some probability of getting it. But what does that mean, and how do we get such figures?
Does it mean that among all those with a given GWAS! genotype, (1) a fraction f will get diabetes?(2) a fraction f will get diabetes if they live beyond some specified age? (3) a fraction f will get diabetes before they die if they live the same lifestyle diet as those from whom the risk was estimated? (4) a net fraction f will get diabetes, pro-rated year by year as they age; (5) a net fraction related to f will get diabetes, but that is adjusted for current age, sex, race, etc.?
What about each individual consulting their Big Data genomic counselor? Are these fractions f related to each individual as a probability p=f that s/he will get diabetes (conditional on things like items 1-5 above)? That is, is every person at the same risk?
Only if we can equate our past sample, from which we estimated f by induction to the probability p used by deduction to assert for each new individual might this, even in principle, lead to 'precision genomic medicine'. It is prediction, not just description that we are being promised. Even if we were thinking in public health terms, this is essentially the same, because it would relate to the fraction of individuals who will be affected in the future, because each person is exposed to the same probability.
Of course, we might believe that each person has some unique probability of getting diabetes (related, again, to the above items), and that f reflects the mix (e.g., average) of these probabilities. But then, we have to assume that all the genotypes and lifestyles and so on in the current group whose future we're offering 'precision' predictions is exactly like the sample from which the predictions were derived, that this mix of risks is, somehow, conserved. How can such an assumption ever be justified?
Of course, we know very well that no current sample whose future we want to be precise about will be exactly the same as the past sample from which the probabilities (or fractions) were derived. Obviously, much will differ, but we also know that we simply have no way to assess by how much it will differ. For example, future diets, sociopolitical, and other factors that affect risk will not be the same as those in the past, and are inherently unpredictable. So, on what meaningful basis can 'precision' prediction be promised?
Just for fun, let's take the promise of precision genomic medicine at its face value. I go to the doc, who tells me
I mean, what if there are no such true probabilities, because even if there were, not just knowledge, but also circumstances (cultural, not to mention mutations) continually change, and what if we have no way whatever to know how they're gonna change? Then what is the use of these 'precision' predictions? They, at best, only apply to a single, current instance. So what (if anything at all) does 'precision' mean?
It only takes a tad of thinking to see how precisely imprecise these promises all are--must be, except very short-term extrapolations of what past data showed, and extrapolations of unknown (and unknowable) 'precision'. Except, of course, the very precise truth that you, as a taxpayer, are going to foot the bill for a whole lot more of this sort of promises.
Unlike the weather, we don't have anything close to as rigorous an understanding of human biology and cultures as we do of the behavior of gases and fluids (the atmosphere). We might want to say, self-protectingly and more honestly modest, that our use of 'probability' is very subjective and really just means an extrapolated rough average of some unspecifiable sort. But then that doesn't sound like the glowing promise of 'precision', does it? One has to wonder what sort of advice would make scientifically proper, and honorable, use of the kind of probabilistic, vague, ephemeral evidence we have when we rely on 'omics approaches, or even when it's the best we can do at present.
In meteorology, it used to be (when I was playing that game) that we'd joke "persistence is the best forecast". This was, of course, for short range, but short range was all we could do with any sort of 'precision'. We are pretty much in that situation now, in regard to genomics and health.
The difference is, weather forecasters are honest, and admit what they don't know.
Will it rain today? Might it? What is the chance of rain?
One of the typical uses of probabilistic terms in daily life has to do with weather predictions. As a former meteorologist myself, I find this a cogent context in which to muse about these terms, but with extensions that have much deeper relevance.
Here is an episode of a generally very fine BBC Radio 4 program called More or Less, whose mission is to educate listeners on the proper use and understanding of numbers, statistics, probabilities and the like. This episode deals, somewhat unclearly and to me quite vaguely, unsatisfactorily, and even somewhat defensively, about the use and interpretation of weather forecasts.
So what does a forecast calling for an x% chance of rain mean? Let's think of an imaginary chessboard laid over a particular location. It is raining under the black, but not under the white squares. There is nothing probabilistic about this. 50% of people in the area will experience rain. If I don't know where you live, exactly, I'd have to say that you have a 50% chance of rain, but that has nothing to do with the weather itself but rather with my uncertainty of where you live. Even then it's misleadingly vague since people don't live randomly across a region (they are, for example, usually clustered in some sub-regions).
Another interpretation is that I don't know where the black and white squares will be exactly, at any given time, but my weather models predict that in about half of the region, rain will fall. This could be because my computer models, necessarily based on imperfect measurement and imperfect theory, are therefore imperfect--but I run them many times, making small random changes in various values to account for that imperfection, and I find that among these model runs, 50% of the time at any given spot, or 50% of the entire area under consideration, experiences rain.
Or, is it that there is an imaginary chessboard moving overhead and so the 50% of the land will be under the black and hence getting rain at any given time, and thus that any given area will only get it 50% of the time, but every area will certainly get rain at some time during the forecast period, indeed every area will be getting rain half of the period? Then the best forecast is that you will get wet if you stay outside all day, but if you only run out to get the mail you might not? Might??
Or is it that my models are imperfect but theory or experience tell me that there is a 50% chance of any rain in the area--that is, my knowledge can tell me no more than that. In that case, any given place will have this guesstimated chance of rain. But does that mean at any given time during the forecast period, or at every time during it? Or is it that my knowledge is very good, but the meteorological factors--the nature of atmospheric motion and so on--only probabilistically form droplets that are large enough not just to be clouds but to fall to earth? That is, is it the atmospheric process itself that is probabilistic--at least based on the theory, since I can't observe every droplet.
If a rain-generating front is passing through the area, it could rain everywhere along the front, but only until the front has moved past the area. Thus, it may rain with 100% certainty, but only 50% of the specified time, if the front takes that amount of time to pass through.
I've undoubtedly only mentioned some of the many ways that weather forecast probabilities can be intended or interpreted as meaning. It is not clear--and the BBC program shows this--that everyone or perhaps even anyone making them actually understands, or is thinking clearly about, what these probability forecasts mean. Even meteorologists themselves, especially when dumbing down for the average Joe who only wants to know if he should carry his brolly with him, are likely ('probably'?!) unclear about these values. Probably they mean a bit of this and a bit of that. I wonder if anyone can know which of the meanings are being used in any given forecast.
Well, fine, everyone knows that nobody really knows everything about the weather. Anyway, it's not that big of a deal if you get an unexpected drenching now and then, or more often haul your raincoat to work but never need it.
But what about things that really matter, like your future health? My doc takes my blood pressure and looks at my weight, and may warn me that I am at 'risk' of a heart attack or stroke--that without taking some preventive measures I may (or probably will) have such a fate. That's a lot more important than a soaked shirt. But what does it mean? Isn't everybody at some risk of these diseases?Does my doc actually know? Does anybody? Who is thinking clearly about these kinds of risk pronouncements?
OK, caveats, caveats: but will I get diabetes?
In genomics 'precision' genomic medicine is one of the genomics marketing slogans of the day, the very vague (I would say culpably false) promise that from your genotype we can predict your future--that's what 'precision' implies. The same applies even if weaseling now would include environmental factors as well as genomic ones. And the idea implies knowledge not just of some vague probability, but by implication it means perfection--prediction with certainty. But to what extent--if any at all--is the promise, or can the promise be true? What would it mean to be 'true'? After all, anyone might get, say type 2 diabetes, mightn't they? Or, more specifically, what does such a sentence itself even mean, if anything?
We know that, today at least, some people get diabetes sometime in their lives, and even if we don't know why or which ones, that seems like a safe assertion. But to say that any person, not specifically identified, might become diabetic is rather useless. We want a reason--a cause--and if we have that we assume it will enable us to identify specifically vulnerable individuals. Even then, however, we don't know more than to say, in some sense that we may not even understand as well as we think we do, that not all the vulnerable will get the disease: but we seem to think that they share some probability of getting it. But what does that mean, and how do we get such figures?
Does it mean that among all those with a given GWAS! genotype, (1) a fraction f will get diabetes?(2) a fraction f will get diabetes if they live beyond some specified age? (3) a fraction f will get diabetes before they die if they live the same lifestyle diet as those from whom the risk was estimated? (4) a net fraction f will get diabetes, pro-rated year by year as they age; (5) a net fraction related to f will get diabetes, but that is adjusted for current age, sex, race, etc.?
What about each individual consulting their Big Data genomic counselor? Are these fractions f related to each individual as a probability p=f that s/he will get diabetes (conditional on things like items 1-5 above)? That is, is every person at the same risk?
Only if we can equate our past sample, from which we estimated f by induction to the probability p used by deduction to assert for each new individual might this, even in principle, lead to 'precision genomic medicine'. It is prediction, not just description that we are being promised. Even if we were thinking in public health terms, this is essentially the same, because it would relate to the fraction of individuals who will be affected in the future, because each person is exposed to the same probability.
Of course, we might believe that each person has some unique probability of getting diabetes (related, again, to the above items), and that f reflects the mix (e.g., average) of these probabilities. But then, we have to assume that all the genotypes and lifestyles and so on in the current group whose future we're offering 'precision' predictions is exactly like the sample from which the predictions were derived, that this mix of risks is, somehow, conserved. How can such an assumption ever be justified?
Of course, we know very well that no current sample whose future we want to be precise about will be exactly the same as the past sample from which the probabilities (or fractions) were derived. Obviously, much will differ, but we also know that we simply have no way to assess by how much it will differ. For example, future diets, sociopolitical, and other factors that affect risk will not be the same as those in the past, and are inherently unpredictable. So, on what meaningful basis can 'precision' prediction be promised?
Just for fun, let's take the promise of precision genomic medicine at its face value. I go to the doc, who tells me
"Based on your genome sequence, I must advise you of your fate in regard to diabetes."I gather you, too, can imagine how to construct many different sorts of fantasy conversations like this, even rashly assuming that your doctor understood probability, had read his New England Journal regularly when not too sleepy after a day's work at the clinic--and that the article in the NEJM was actually accurate. And that NIH knew in sincerity what they were promising in the way of genomic predictability promises. But wait! The medical journals, and even the online genotyping scam companies--you can probably name one or two of them--change your estimated risks from time to time as new 'data' come in. So when can I assume case-closed and I (well, the Doc) really knows the true probabilities?
"Thanks, doc. Fire away!"
"You have a 23.5% chance of getting the disease."
"Wow! That sounds high! That means I have a 23.5% chance that I won't die in a car or plane crash, right? That's very comforting. And if about 10% of people get cancer, then of my 76.5% chance of not getting diabetes, it means only a 7.65% chance of cancer! Again, wow!"
"But wait, Doc! Hold on a minute. I might get diabetes and cancer, right? About a 7.65% percent chance of that, right?"
"Um, well, um, it doesn't work quite that way [to himself, sotto voce: "at least I think so..."].....that's because you might die of diabetes, so you wouldn't get cancer. Of course, the cancer could come first, but it would linger, because you have to live long enough to experience your 23.5% risk of diabetes. That would not be good news. And, of course, you could get diabetes and then get in a crash. I said get diabetes, not die of it, after all!"
I mean, what if there are no such true probabilities, because even if there were, not just knowledge, but also circumstances (cultural, not to mention mutations) continually change, and what if we have no way whatever to know how they're gonna change? Then what is the use of these 'precision' predictions? They, at best, only apply to a single, current instance. So what (if anything at all) does 'precision' mean?
It only takes a tad of thinking to see how precisely imprecise these promises all are--must be, except very short-term extrapolations of what past data showed, and extrapolations of unknown (and unknowable) 'precision'. Except, of course, the very precise truth that you, as a taxpayer, are going to foot the bill for a whole lot more of this sort of promises.
Unlike the weather, we don't have anything close to as rigorous an understanding of human biology and cultures as we do of the behavior of gases and fluids (the atmosphere). We might want to say, self-protectingly and more honestly modest, that our use of 'probability' is very subjective and really just means an extrapolated rough average of some unspecifiable sort. But then that doesn't sound like the glowing promise of 'precision', does it? One has to wonder what sort of advice would make scientifically proper, and honorable, use of the kind of probabilistic, vague, ephemeral evidence we have when we rely on 'omics approaches, or even when it's the best we can do at present.
In meteorology, it used to be (when I was playing that game) that we'd joke "persistence is the best forecast". This was, of course, for short range, but short range was all we could do with any sort of 'precision'. We are pretty much in that situation now, in regard to genomics and health.
The difference is, weather forecasters are honest, and admit what they don't know.
Monday, August 13, 2018
Big Data: the new Waiting for Godot
In Samuel Beckett's cryptic play, Waiting for Godot, two men spend the entire play anticipating the arrival of someone, Godot, at which point presumably something will happen--one can say, perhaps, that the wait will have been for some achieved objective. But what? Could it simply mean that they can then go somewhere else? Or, perhaps, there will be no end because Godot will never, in fact, arrive.
A good discussion of all of this is on the BBC Radio 4 The Forum podcast. Apparently, Beckett insisted that any such answers were in the play itself--he didn't imply that there was some external meaning, such as that Godot was God, or that the play was an allegory for the Cold War--which is one reason the play is so enigmatic.
Was the play written intentionally to be a joke, or a hoax? Of course, since the author refused to answer or perhaps even to recognize the legitimacy of the question, we'll never know. Or perhaps that in itself, is the tipoff that it really is a hoax. Or maybe (I think more likely) that because it was written in France in 1949, it's an existentialist era statement of the angst that comes from the recognition that the important questions in life don't have answers.
Waiting for the biomedical Promised Land
That was then, but today we are witnessing real-life versions of the play: things just as cleverly open-ended, with the 'What happens then?' question only having a vague, deferred answer, as in Beckett's title. And, as in the play, it is not clear how self-aware even some of the perpetrators are of what they are about.
I refer to the possibility that we are witnessing various Big Data endeavors, unknowingly imitative but as cleverly and cryptically open-ended as the implied resolution that will happen when Godot arrives. Big Data 'omics is a current, perhaps all too convenient, scientific version of the play, that we might call Waiting for God'omics. The arrival of the objective--indeed, not really stated, but just generically promised as, for example, 'precision genomic medicine' for 'All of Us'--is absolutely as slyly vague as what Vladimir and Estragon were presumably waiting for. The genomic Godot will never arrive!
This view is largely but not entirely cynical, for reasons that are at least a bit subtle themselves.
Reaching the oasis, the end of the rainbow, or the Promised Land is bad for business
One might note that if the 'omics Godot were ever to arrive, it would be the end of the Big Data (or should one say Big Gravy?) train, so obviously our Drs Vladimirs and Estragons must ensure that such a tragedy, arrival at the promised land, the elimination of all diseases in everyone, or whatever, never happens in real life. Is there any sense that anyone seriously thinks we would reach resolution of the cause of disease, with precision for all of us, say, and be able (that is, willing) to close down the Big Budget nature of our proliferating 'omictical me-too world?
We have entrenched the search for Godot, a goal so vague as to be unattainable. Even the proper use of the term 'precision' implies an asymptote, a truth that one never reaches but can get ever closer to. If we could get there, as is implied, we should have been promised 'exact' genomic medicine. And wouldn't this imply that then, finally, we'll divert the resources towards cures and prevention?
However, even if the perpetrators of the Big Promises never think or aren't aware of it, we must note that the goal cannot be reached even with the best and most honorable of intentions. Because of births and deaths, and environmental changes, and mutations and recombination, there truly never is the palm-draped oasis at which our venture could cease. There will never be an 'all' of us, and genetic causation is ever-changing (in part because of the similarly dynamic environment), meaning that there are no such things as risks to be approached with 'precision'. Risks are changeable and not stable, and indeed not fixed numerical values. At best, they are collective population (or sample) averages. So there is never a 'there' there, anywhere. There is only a different one everywhere.
But awareness of these facts doesn't seem to be part of the 'omicsalyptic promises with which we are inundated. They seem, by contrast, rote promises that are little if any different from political, economic, or religious promises--if only we do this, we'd get to a Promised Land. But such a land does not exist.
If we had, say, a real national health system, it would be properly and avowedly open-ended without anyone honorable objecting (if it were done well). And epidemiologically, of course, there will always be new mutations, recombinations, environments and the like to try to understand--disease with, or without strong genotype-phenotype causation. There will always be a need for health research (and basic science). But science, of all fields of human endeavor, should be honest. It should not hold out the promise that Godot will arrive, but in a sense, openly acknowledge that that can never happen.
But this doesn't let those off the guilty hook who are hawking today's implicit Big Data, big open-ended budget promise that by goosing up research now we'll soon eliminate genetic disease (I recall that Francis Collins did indeed, not all that long ago, promise that this Paradise would come soon--um, I think his date was something like 2010!) It's irresponsible, self-interested promising, of course. And those in genomics who are intelligent enough to deserve to be in genomics do, or should, know that very well.
Like Vladimir and Estragon, we'll always be told that we're waiting for Godot, and that he'll be coming soon.
NOTE: One might observe that Godoism is a firmly entrenched strategy elsewhere in our society, for examples, in regard to theoretical physics, where there will never be a collider big enough to answer the questions about fundamental particles: coming to closure would be as fiscally threatening to physics as it is to life sciences. Science is not alone in this, but our society does not pay it nearly enough skeptical heed.
 |
| www.mckellen.com |
A good discussion of all of this is on the BBC Radio 4 The Forum podcast. Apparently, Beckett insisted that any such answers were in the play itself--he didn't imply that there was some external meaning, such as that Godot was God, or that the play was an allegory for the Cold War--which is one reason the play is so enigmatic.
Was the play written intentionally to be a joke, or a hoax? Of course, since the author refused to answer or perhaps even to recognize the legitimacy of the question, we'll never know. Or perhaps that in itself, is the tipoff that it really is a hoax. Or maybe (I think more likely) that because it was written in France in 1949, it's an existentialist era statement of the angst that comes from the recognition that the important questions in life don't have answers.
Waiting for the biomedical Promised Land
That was then, but today we are witnessing real-life versions of the play: things just as cleverly open-ended, with the 'What happens then?' question only having a vague, deferred answer, as in Beckett's title. And, as in the play, it is not clear how self-aware even some of the perpetrators are of what they are about.
I refer to the possibility that we are witnessing various Big Data endeavors, unknowingly imitative but as cleverly and cryptically open-ended as the implied resolution that will happen when Godot arrives. Big Data 'omics is a current, perhaps all too convenient, scientific version of the play, that we might call Waiting for God'omics. The arrival of the objective--indeed, not really stated, but just generically promised as, for example, 'precision genomic medicine' for 'All of Us'--is absolutely as slyly vague as what Vladimir and Estragon were presumably waiting for. The genomic Godot will never arrive!
This view is largely but not entirely cynical, for reasons that are at least a bit subtle themselves.
Reaching the oasis, the end of the rainbow, or the Promised Land is bad for business
One might note that if the 'omics Godot were ever to arrive, it would be the end of the Big Data (or should one say Big Gravy?) train, so obviously our Drs Vladimirs and Estragons must ensure that such a tragedy, arrival at the promised land, the elimination of all diseases in everyone, or whatever, never happens in real life. Is there any sense that anyone seriously thinks we would reach resolution of the cause of disease, with precision for all of us, say, and be able (that is, willing) to close down the Big Budget nature of our proliferating 'omictical me-too world?
We have entrenched the search for Godot, a goal so vague as to be unattainable. Even the proper use of the term 'precision' implies an asymptote, a truth that one never reaches but can get ever closer to. If we could get there, as is implied, we should have been promised 'exact' genomic medicine. And wouldn't this imply that then, finally, we'll divert the resources towards cures and prevention?
However, even if the perpetrators of the Big Promises never think or aren't aware of it, we must note that the goal cannot be reached even with the best and most honorable of intentions. Because of births and deaths, and environmental changes, and mutations and recombination, there truly never is the palm-draped oasis at which our venture could cease. There will never be an 'all' of us, and genetic causation is ever-changing (in part because of the similarly dynamic environment), meaning that there are no such things as risks to be approached with 'precision'. Risks are changeable and not stable, and indeed not fixed numerical values. At best, they are collective population (or sample) averages. So there is never a 'there' there, anywhere. There is only a different one everywhere.
But awareness of these facts doesn't seem to be part of the 'omicsalyptic promises with which we are inundated. They seem, by contrast, rote promises that are little if any different from political, economic, or religious promises--if only we do this, we'd get to a Promised Land. But such a land does not exist.
If we had, say, a real national health system, it would be properly and avowedly open-ended without anyone honorable objecting (if it were done well). And epidemiologically, of course, there will always be new mutations, recombinations, environments and the like to try to understand--disease with, or without strong genotype-phenotype causation. There will always be a need for health research (and basic science). But science, of all fields of human endeavor, should be honest. It should not hold out the promise that Godot will arrive, but in a sense, openly acknowledge that that can never happen.
But this doesn't let those off the guilty hook who are hawking today's implicit Big Data, big open-ended budget promise that by goosing up research now we'll soon eliminate genetic disease (I recall that Francis Collins did indeed, not all that long ago, promise that this Paradise would come soon--um, I think his date was something like 2010!) It's irresponsible, self-interested promising, of course. And those in genomics who are intelligent enough to deserve to be in genomics do, or should, know that very well.
Like Vladimir and Estragon, we'll always be told that we're waiting for Godot, and that he'll be coming soon.
NOTE: One might observe that Godoism is a firmly entrenched strategy elsewhere in our society, for examples, in regard to theoretical physics, where there will never be a collider big enough to answer the questions about fundamental particles: coming to closure would be as fiscally threatening to physics as it is to life sciences. Science is not alone in this, but our society does not pay it nearly enough skeptical heed.
Sunday, December 3, 2017
What do ravens do?
"As behavioral ecologists, we try to reveal rules of behavior as though we were discovering truths. In reality, the word 'rule' as applied to animal behavior is a verbal shortcut. A 'rule' means nothing more than a consistency of response. It is not adherence to dictum. Animals adhere no more to rules than we do by showing up at the beach when its 110 degrees but not when it's 30 degrees. Rules are the sum of decisions made by individuals that are then exhibited by crowds, not vice versa. Rules are thus a result. They are the average behavior that we and many animals are programmed with, learn, or make up as we go along."
This is a cogent quote from Bernd Heinrich's book Mind of the Raven (1999, Ecco books), which I was given as a birthday gift. The idea was that I would like to read about the various capabilities of ravens, relative to our informal and even formal ideas about what 'mind' or 'consciousness' mean and how we might know, and whether these interesting birds might have it, whatever it is.
However, the quote I've given is more than just the author's views on what ravens' internal experiences might be. It applies to much that we have to deal with in science--at least, in biological and behavioral sciences themselves. I've used it because I think the observation also applies to something I've been writing about in recent posts--related to what may seem to be a very different topic, whether life is parametric or not.
The physical world seems to be parametric, that is, driven ultimately by some universally true processes, like gravity, that are in turn reflections of underlying, universal, fixed parameters, or numerical values. Of course, 'numerical values' refers to human-derived mathematics and science, and might, from some wholly different point of view, be differently perceived or characterized.
But to us, phenomena like the speed of light, c, and various quantum phenomena etc., have fixed, universal values. The value is the same everywhere, even if its manifestation may be modified by local circumstances. For example, c is specified as in a vacuum. Whether or not there exists any true total vacuum, the idea--and the belief in its universality--are clear and important bedrock aspects of physics, chemistry, and cosmology. In some other substance, rather than a vacuum, the speed of light is altered in an orderly way.
But what about life?
We can ask whether, while life is a physical and molecular phenomenon, it is part and parcel of the same parametric cosmos, or if it has exceptions at the level at which we want answers to our basic questions. That would be analogous to physics adhering to a dictum, in the raven quote. But maybe life is not analogous to a vacuum. This, at least, is what I mean by asking whether life is a parametric phenomenon, and expression doubts that it could be so.
An a priori reason, in my mind, is that life is a molecular process of regular molecular activity (genes, proteins, and so on), but it evolves because the specifics are different--they vary. Without that, there would be no evolution, and organismal complexity, and the underlying genetic and proteinic complexity by which life, and its interacting ecosystems have come about, would not be here. In that sense, I think it is appropriate to suggest that life is not a parametric phenomenon.
This, to me, is not the same as saying that life is a kind of self-organized complexity. It certainly is that, but the phrase misses what I think is the underlying fact, which is that life is not parametric. Complexities like the mandelbrot set (figure below) are parametric: they repeat the same phenomenon in an evermore complex but always rigorously. This is a form of 'complexity' but it is very rigorously regular. Life is, if anything, rigorously irregular, among individuals, populations, species, and the structures within each of those.
Many people have written about life's complexity with analogies to things like the Mandelbrot set and many others of the sort. But while that sounds as if it acknowledges the complexity of life, it really is an implicit hunger for just the opposite: for regularity, tractability, and 'parametricity'. I think that is at best an ad hoc approximation but theoretically fundamentally wrong.
The consequences are obvious: we can describe existing data by various statistical and even mathematical data-fitting procedures. But we cannot make predictions or projections with known 'precision' and indeed that is why I think that rhetoric like 'precision genomic medicine' is strictly an advertising slogan, scientifically misleading (and culpably so), and misunderstood by most people even those who use it, and perhaps even by the NIH that proffer it as a funding or marketing ploy for its budgets. It is a false promise, as stated (saying instead that we want funds for research to make medicine more precise by including genomic information would be honest and appropriate).
Heinrich's description of ravens' behavior seemed an apt way to make my point, as I see things at any rate, clear by an easily digested analogy. Some ravens did what they were seen to do, but that was the net result of what some observed ravens did on some occasions, not what 'ravens do' in the parametric sense. The ravens are not all following a rule and even the 'consistency' of their responses is not like that (different ravens do different things, as Heinrich's book makes clear).
We want rules that explain 'truth' in genetics and evolution. We ought to be able to see that that may be a misleading way to view the nature of the living world. And, seeing that, to change what we promise to the public and, as important as what we promise to them, to change how we think.
Or, as quoth the raven: nevermore!
This is a cogent quote from Bernd Heinrich's book Mind of the Raven (1999, Ecco books), which I was given as a birthday gift. The idea was that I would like to read about the various capabilities of ravens, relative to our informal and even formal ideas about what 'mind' or 'consciousness' mean and how we might know, and whether these interesting birds might have it, whatever it is.
However, the quote I've given is more than just the author's views on what ravens' internal experiences might be. It applies to much that we have to deal with in science--at least, in biological and behavioral sciences themselves. I've used it because I think the observation also applies to something I've been writing about in recent posts--related to what may seem to be a very different topic, whether life is parametric or not.
The physical world seems to be parametric, that is, driven ultimately by some universally true processes, like gravity, that are in turn reflections of underlying, universal, fixed parameters, or numerical values. Of course, 'numerical values' refers to human-derived mathematics and science, and might, from some wholly different point of view, be differently perceived or characterized.
But to us, phenomena like the speed of light, c, and various quantum phenomena etc., have fixed, universal values. The value is the same everywhere, even if its manifestation may be modified by local circumstances. For example, c is specified as in a vacuum. Whether or not there exists any true total vacuum, the idea--and the belief in its universality--are clear and important bedrock aspects of physics, chemistry, and cosmology. In some other substance, rather than a vacuum, the speed of light is altered in an orderly way.
But what about life?
We can ask whether, while life is a physical and molecular phenomenon, it is part and parcel of the same parametric cosmos, or if it has exceptions at the level at which we want answers to our basic questions. That would be analogous to physics adhering to a dictum, in the raven quote. But maybe life is not analogous to a vacuum. This, at least, is what I mean by asking whether life is a parametric phenomenon, and expression doubts that it could be so.
An a priori reason, in my mind, is that life is a molecular process of regular molecular activity (genes, proteins, and so on), but it evolves because the specifics are different--they vary. Without that, there would be no evolution, and organismal complexity, and the underlying genetic and proteinic complexity by which life, and its interacting ecosystems have come about, would not be here. In that sense, I think it is appropriate to suggest that life is not a parametric phenomenon.
This, to me, is not the same as saying that life is a kind of self-organized complexity. It certainly is that, but the phrase misses what I think is the underlying fact, which is that life is not parametric. Complexities like the mandelbrot set (figure below) are parametric: they repeat the same phenomenon in an evermore complex but always rigorously. This is a form of 'complexity' but it is very rigorously regular. Life is, if anything, rigorously irregular, among individuals, populations, species, and the structures within each of those.
 |
| Mandelbrot set. From Wikipedia entry |
The consequences are obvious: we can describe existing data by various statistical and even mathematical data-fitting procedures. But we cannot make predictions or projections with known 'precision' and indeed that is why I think that rhetoric like 'precision genomic medicine' is strictly an advertising slogan, scientifically misleading (and culpably so), and misunderstood by most people even those who use it, and perhaps even by the NIH that proffer it as a funding or marketing ploy for its budgets. It is a false promise, as stated (saying instead that we want funds for research to make medicine more precise by including genomic information would be honest and appropriate).
Heinrich's description of ravens' behavior seemed an apt way to make my point, as I see things at any rate, clear by an easily digested analogy. Some ravens did what they were seen to do, but that was the net result of what some observed ravens did on some occasions, not what 'ravens do' in the parametric sense. The ravens are not all following a rule and even the 'consistency' of their responses is not like that (different ravens do different things, as Heinrich's book makes clear).
We want rules that explain 'truth' in genetics and evolution. We ought to be able to see that that may be a misleading way to view the nature of the living world. And, seeing that, to change what we promise to the public and, as important as what we promise to them, to change how we think.
Or, as quoth the raven: nevermore!
Thursday, June 22, 2017
Everything is genetic, isn't it?
There is hardly a trait, physical or behavioral, for which there is not at least some familial resemblance, especially among close relatives. And I'm talking about what is meant when someone scolds you saying, "You're just like your mother!" The more distant the relatives in terms of generations of separation, the less the similarity. So you really can resist when told, "You're just like your great-grandmother!" The genetic effects decline in a systematic way with more distant kinship.
The 'heritability' of a trait refers to the relative degree to which its variation is the result of variation in genes, the rest being due to variation in non-genetic factors we call 'environment'. Heritability is a ratio that ranges from zero when genes have nothing to do with the trait, to 1.0 when all the variation is genetic. The measure applies to a sample or population and cannot automatically be extended to other samples or populations, where both genetic and environmental variation will be different, often to an unknown extent.
Most quantitative traits, like stature or blood pressure or IQ scores show some amount, often quite substantial, of genetic influence. It often happens that we are interested in some trait that we think must be produced or affected by genes, but that no relevant factor, like a protein, is known. The idea arose decades ago that if we could scan the genome, and compare those with different manifestations of the trait, using mapping techniques like GWAS (genomewide association studies), we could identify those sites, genomewide, whose variation in our chosen sample may affect the trait's variation. Qualitative traits like the presence or absence of a disease (say, diabetes or hypertension), may often be due to the presence of some set of genetic variants whose joint impact exceeds some diagnostic threshold, and mapping studies can compare genotypes in affected cases to unaffected controls to identify those sites.
Genes are involved in everything. . . . .
Many things can affect the amount of similarity among relatives, so one has to try to think carefully about attributing ideas of similarity and cause. Some traits, like stature (height) have very high heritability, sometimes estimated to be about 0.9, that is, 90% of the variation being due to the effects of genetic variation. Other traits have much lower heritability, but there's generally familial similarity. And, that's because we each develop from a single fertilized egg cell, which includes transmission of each of our parent's genomes, plus ingredients provided by the egg (and perhaps to a tiny degree sperm), much of which were the result of gene action in our parents when they produced that sperm or egg (e.g., RNA, proteins). This is why traits can usually be found to have some heritability--some contribution due to genetic variation among the sampled individuals. In that sense, we can say that genes are involved in everything.
Understanding the genetic factors involved in disease can be important and laudatory, even if tracking them down is a frustrating challenge. But because genes are involved in everything, our society also seems to have an unending lust for investigators to overstate the value of their findings or, in particular, to estimate or declaim on the heritability, and hence genetic determination, of the most societally sensitive traits, like sexuality, criminality, race, intelligence, physical abuse and the like.
. . . . . but not everything is 'genetic'!
If the estimated heritability for a trait we care about is substantial, then this does suggest the obvious: genes are contributing to the mechanisms of the trait and so it is reasonable to acknowledge that genetic variation contributes to variation in the trait. However, the mapping industry implies a somewhat different claim: it is that genes are a major factor in the sense that individual variants can be identified that are useful predictors of the trait of interest (NIH's lobbying machine has been saying we'll be able to predict future disease with 'precision'). There has been little constraint on the types of trait for which this approach, sometimes little more than belief or wishful-thinking, is appropriate.
It is important to understand that our standard measures of genes' relative effect are affected both by genetic variation and environmental lifestyle factors. That means that if environments were to change, the relative genetic effects, even in the very same individuals, would also change. But it isn't just environments that change; genotypes change, too, when mutations occur, and as with environmental factors, these change in ways that we cannot predict even in principle. That means that we cannot legitimately extrapolate, to a knowable extent, the genetic or environmental factors we observe in a given sample or population, to other, much less to future samples or populations. This is not a secret problem, but it doesn't seem to temper claims of dramatic discoveries, in regard to disease or perhaps even more for societally sensitive traits.
But let's assume, correctly, that genetic variation affects a trait. How does it work? The usual finding is that tens or even hundreds of genome locations affect variation in the test trait. Yet most of the effects of individual genes are very small or rare in the sample. At least as important is that the bulk of the estimated heritability remains unaccounted for, and unless we're far off base somehow, the unaccounted fraction is due to the leaf-litter of variants individually too weak or too rare to reach significance.
Often it's also asserted that all the effects are additive, which makes things tractable: for every new person, not part of the study, just identify their variants and add up their estimated individual effects to get the total effect on the new person for whatever publishable trait you're interested in. That's the predictive objective of the mapping studies. However, I think that for many reasons one cannot accept that these variable sites' actions are truly additive. The reasons have to with actual biology, not the statistical convenience of using the results to diagnose or predict traits. Cells and their compounds vary in concentrations per volume (3D), binding properties (multiple dimensions), surface areas (2D) and some in various ways that affect how how proteins are assembled and work, and so on. In aggregate, additivity may come out in the wash, but the usual goal of applied measures is to extrapolate these average results to prediction in individuals. There are many reasons to wish that were true, but few to believe it very strongly.
Even if they were really additive, the clearly very different leaf-litter background that together accounts for the bulk of the heritability can obscure the numerical amount of that additivity from sample to sample and person to person. That is, what you estimated from this sample, may not apply, to an unknowable extent, to the next sample. If and when it does works, we're lucky that our assumptions weren't too far off.
Of course, the focus and promises from the genetics interests assume that environment has nothing serious to do with the genetic effects. But it's a major, often by far the major, factor, and it may even in principle be far more changeable than genetic variation. One would have to say that environmental rather than genetic measures are likely to be, by far, the most important things to change in society's interest.
We regularly write these things here not just to be nay-sayers, but to try to stress what the issues are, hoping that someone, by luck or insight, finds better solutions or different ways to approach the problem that a century of genetics, despite its incredibly huge progress, has not yet done. What it has done is in exquisite detail to show us what the problems are.
A friend and himself a good scientist in relevant areas, Michael Joyner, has passed on a rather apt suggestion to me, that he says he saw in work by Denis Noble. We might be better off if we thought of the genome as a keyboard rather than as a code or program. That is a good way to think about the subtle point that, in the end, yes, Virginia, there really are genomic effects: genes affect every trait....but not every trait is 'genetic'!
The 'heritability' of a trait refers to the relative degree to which its variation is the result of variation in genes, the rest being due to variation in non-genetic factors we call 'environment'. Heritability is a ratio that ranges from zero when genes have nothing to do with the trait, to 1.0 when all the variation is genetic. The measure applies to a sample or population and cannot automatically be extended to other samples or populations, where both genetic and environmental variation will be different, often to an unknown extent.
Most quantitative traits, like stature or blood pressure or IQ scores show some amount, often quite substantial, of genetic influence. It often happens that we are interested in some trait that we think must be produced or affected by genes, but that no relevant factor, like a protein, is known. The idea arose decades ago that if we could scan the genome, and compare those with different manifestations of the trait, using mapping techniques like GWAS (genomewide association studies), we could identify those sites, genomewide, whose variation in our chosen sample may affect the trait's variation. Qualitative traits like the presence or absence of a disease (say, diabetes or hypertension), may often be due to the presence of some set of genetic variants whose joint impact exceeds some diagnostic threshold, and mapping studies can compare genotypes in affected cases to unaffected controls to identify those sites.
Genes are involved in everything. . . . .
Many things can affect the amount of similarity among relatives, so one has to try to think carefully about attributing ideas of similarity and cause. Some traits, like stature (height) have very high heritability, sometimes estimated to be about 0.9, that is, 90% of the variation being due to the effects of genetic variation. Other traits have much lower heritability, but there's generally familial similarity. And, that's because we each develop from a single fertilized egg cell, which includes transmission of each of our parent's genomes, plus ingredients provided by the egg (and perhaps to a tiny degree sperm), much of which were the result of gene action in our parents when they produced that sperm or egg (e.g., RNA, proteins). This is why traits can usually be found to have some heritability--some contribution due to genetic variation among the sampled individuals. In that sense, we can say that genes are involved in everything.
Understanding the genetic factors involved in disease can be important and laudatory, even if tracking them down is a frustrating challenge. But because genes are involved in everything, our society also seems to have an unending lust for investigators to overstate the value of their findings or, in particular, to estimate or declaim on the heritability, and hence genetic determination, of the most societally sensitive traits, like sexuality, criminality, race, intelligence, physical abuse and the like.
. . . . . but not everything is 'genetic'!
If the estimated heritability for a trait we care about is substantial, then this does suggest the obvious: genes are contributing to the mechanisms of the trait and so it is reasonable to acknowledge that genetic variation contributes to variation in the trait. However, the mapping industry implies a somewhat different claim: it is that genes are a major factor in the sense that individual variants can be identified that are useful predictors of the trait of interest (NIH's lobbying machine has been saying we'll be able to predict future disease with 'precision'). There has been little constraint on the types of trait for which this approach, sometimes little more than belief or wishful-thinking, is appropriate.
It is important to understand that our standard measures of genes' relative effect are affected both by genetic variation and environmental lifestyle factors. That means that if environments were to change, the relative genetic effects, even in the very same individuals, would also change. But it isn't just environments that change; genotypes change, too, when mutations occur, and as with environmental factors, these change in ways that we cannot predict even in principle. That means that we cannot legitimately extrapolate, to a knowable extent, the genetic or environmental factors we observe in a given sample or population, to other, much less to future samples or populations. This is not a secret problem, but it doesn't seem to temper claims of dramatic discoveries, in regard to disease or perhaps even more for societally sensitive traits.
Often it's also asserted that all the effects are additive, which makes things tractable: for every new person, not part of the study, just identify their variants and add up their estimated individual effects to get the total effect on the new person for whatever publishable trait you're interested in. That's the predictive objective of the mapping studies. However, I think that for many reasons one cannot accept that these variable sites' actions are truly additive. The reasons have to with actual biology, not the statistical convenience of using the results to diagnose or predict traits. Cells and their compounds vary in concentrations per volume (3D), binding properties (multiple dimensions), surface areas (2D) and some in various ways that affect how how proteins are assembled and work, and so on. In aggregate, additivity may come out in the wash, but the usual goal of applied measures is to extrapolate these average results to prediction in individuals. There are many reasons to wish that were true, but few to believe it very strongly.
Even if they were really additive, the clearly very different leaf-litter background that together accounts for the bulk of the heritability can obscure the numerical amount of that additivity from sample to sample and person to person. That is, what you estimated from this sample, may not apply, to an unknowable extent, to the next sample. If and when it does works, we're lucky that our assumptions weren't too far off.
Of course, the focus and promises from the genetics interests assume that environment has nothing serious to do with the genetic effects. But it's a major, often by far the major, factor, and it may even in principle be far more changeable than genetic variation. One would have to say that environmental rather than genetic measures are likely to be, by far, the most important things to change in society's interest.
We regularly write these things here not just to be nay-sayers, but to try to stress what the issues are, hoping that someone, by luck or insight, finds better solutions or different ways to approach the problem that a century of genetics, despite its incredibly huge progress, has not yet done. What it has done is in exquisite detail to show us what the problems are.
A friend and himself a good scientist in relevant areas, Michael Joyner, has passed on a rather apt suggestion to me, that he says he saw in work by Denis Noble. We might be better off if we thought of the genome as a keyboard rather than as a code or program. That is a good way to think about the subtle point that, in the end, yes, Virginia, there really are genomic effects: genes affect every trait....but not every trait is 'genetic'!
Tuesday, July 7, 2015
Separating science and science politics?
Some people feel that scientific issues should be kept separate from science politics. But is that even possible? The fear is often voiced that even if science does often work largely as a
business even with its self-promotional components, that is just how things are, and that if anybody listened to what people like ourselves say (which is, of course, unlikely!), many large-scale projects, which are
older than your parents' first car (and just as rusty), might be phased out, which would be unfortunate, because it would be losing all that valuable information
after decades of careful effort.
But we think it is not reason enough to maintain large projects that may once have yielded valuable results but that are now running on fumes, kept running for legacy reasons. Nobody would suggest that the data be discarded, but it could be made available while funds moved to more promising approaches.
The unlimited insult
The widely touted 'new' idea of genomic 'precision' medicine is an example. Here is a quote we ran across from George Eliot's 1871-2 book Middlemarch:
"I believe that you are suffering from what is called fatty degeneration of the heart, a disease which was first divined and explored by Laennec, the man who gave us the stethoscope, not so vary many years ago. A good deal of experience--a more lengthened observation--is wanting on the subject. But after what you have said, it is my duty to tell you that death from this disease is often sudden. At the same time no such result van be predicted. Your condition may be consistent with a tolerably comfortable life for another fifteen years, or even more. I could add no information to this, beyond anatomical or medical details, which would leave expectation at precisely the same point."
What's the point of this quote? After all,
anyone can mine almost anything for juicy quotes that support their biases or points they want to make, and invoking some prior author--not to mention a
fiction writer!--is a form of rhetorical trick that really has little actual
cogency. Who gives a hoot what George Eliot thought, after all?
When our NIH Director proclaimed a billions of
dollar project that we would finally do 'precision' based medicine, it was an
insult to every physician who has ever practiced medicine, back to Hippocrates
and of course all the others who did not write books and are thus not known to
us today. The reason is, of
course, that every honorable physician throughout history was
doing his/her best to be as precise and (to borrow a previous advertising
slogan for NIH) to do 'personalized' medicine.
We've written before about the vacuous or
transparently lobbying nature of words like 'precision', and the point here is
not that being as precise in medical diagnosis, prevention, prediction, and
treatment as is possible at any time is anything other than wholly noble. The point is that blanket
statements suggesting that genotypes are going to predict everything about a
person is a costly way to divert funds from being directed more precisely, one might say, where that word is actually appropriate.
There are many disorders that are highly predictable from genetic data (sickle cell anemia, cystic fibrosis, muscular dystrophy, and many, many others). The genetics community should show that knowing this can lead to effective gene-based approaches to what is truly and clearly 'genetic', before we just spew resources out across the entire genomic landscape.
There are many disorders that are highly predictable from genetic data (sickle cell anemia, cystic fibrosis, muscular dystrophy, and many, many others). The genetics community should show that knowing this can lead to effective gene-based approaches to what is truly and clearly 'genetic', before we just spew resources out across the entire genomic landscape.
Even if it were clearly important to assemble genomic data as part of a unified health-care and health-research system, new large-scale databases should start fresh without the legacy
of past work imposing various frameworks on the future resource. Various rationales, almost amounting to 'we need practice building data bases' have been suggested in defense of keeping elderly projects afloat, but these seem, to us, as much political rationale for holding on to resources as it is truly the best way to start making a national resource. But rather than just being cranky about this, we have various thoughts to offer, about
the current inertia that is built into our system.
The idea of better ways of identifying risk groups, for example, especially far in advance of when their risk becomes manifest as disease, is a proper major goal of public health research. Medicine usually deals with people when they are already ill or at high risk, but research can benefit from partitioned analysis of low and high risk individuals, where that is possible and as early as possible. This can be useful, e.g., in determining risk alleles or environmental exposures, or who will respond well to a given drug or therapy. The earlier the better--and when genotypes at conception do truly have high predictive power that is a proper kind of data to collect. But how often is that? After about a generation's worth of mapping studies, the answer is rather clear--if the politics is separated from the actual science.
We think there are several reasons why building huge data bases to partition populations into high and low risk groups or, much harder, individuals is right-minded in principle, but will have problems.
1. Inertia, and Momentum. Without real change, in personnel and in projects, the gravitational pull of business as usual is huge. A system of science Patricians is established, and they become Geriatric Patricians. They are, as you know, getting the bulk of the grants (e.g., first-time NIH grant recipients are about age 45 according to some recently published data, the percentage age 36 and younger has been plummeting and the percentage of PIs over age 66 growing steadily since 1980, as the graph below shows). Any savvy scientist knows that big long-term projects are politically hard to phase out. That is science politics, not science, though isn't specific to science. However, in science it can impose inertia on current methods and concepts.
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| Source |
Occasionally a new idea, technology, method, or, today, 'omic, does come along, and there is a swell of momentum as the herd rushes to adopt it. Again, however, the goal is to establish too-big-to-stop longterm projects. Of course, such change may sometimes be a very good thing, if the method, idea, or technology is truly beneficial. But often it is not much of an improvement, or perhaps the questions being asked are themselves conveniently changed as a funding stratagem. Do you not think this is an important part of the current system? Perhaps that's only to be expected, since real innovation is clearly hard to come by, which is nobody's fault. But the more inertially entrenched this system, the less may be the opportunity for real innovation.
For example, when everyone uses the same data sources or sequencing approach or statistical packages for analyzing data, there is a kind of channeling conformism. This is in part because lab equipment and software are complex and highly technical, and developing one's own is generally not feasible. That requires large, long-term funding, so the problem is not an easy one to solve. There are of course always innovators, and we should be grateful for that. But when struggling for one's career, or to keep continued funding, it is easier, for many reasons--in science as in other fields--to simply do what others are doing.
2. The 'Quantum Mechanical Effect.' In quantum mechanics, when a basic property of a primary particle like an electron or photon is measured, it affects the particle's other properties. The combination is probabilistic and measuring is a form of interference, which generates change. You can't know the exact nature of the change without re-measurement--which then creates the same problem.
This Quantum Mechanical Effect has a kind of analogy in biomedical genetics and epidemiology. When even a bad study's findings are trumpeted to the media by the investigators and the journals, in full-throated self-promotion mode, and the media report is more or less without serious circumspection, ordinary people may change their behavior accordingly. One reason is that most doctors themselves cannot keep up, and since the findings are so oscillating and fickle, even researchers may not have a grasp on complex causation. As a result, diets and other habits change, companies change their products, advertising, and even their labels (because, believing the results, the FDA insists). So the pattern of exposure to the purported risk factors changes and hence so does the risk itself. That is, the effects of retrospective statistical data-fitting themselves constitute a kind of 'measurement' that itself affect the risks being measured.
3. The unknown unknowns. Donald Rumsfeld doesn't really deserve the ridicule he gets for his quip about the unknown unknowns (even if he may deserve ridicule for other things). It is very clear from recent history, not just remotely distant lore, that lifestyles change in major ways, and very quickly. If it were just a matter of differing doses of the same old risk factors, then we're faced with problem #2 above that the exposure levels will change in unknowable ways.
This is true if the mix of exposures act in additive ways (just add up the estimated risk of a change in this behavior, and then of that behavior). But even if this additivity assumption were accurate, what is more likely to be important, is that exposures to entirely unforeseeable factors will arise. Nobody in the 1950s could have predicted the number of hours we'd spend watching flickering images, or flying in jets or being CT-scanned, or eating new fad foods, or new manufactured foods, or the kinds of infection or antibiotic exposures we would experience. Yet changes like these have made a huge difference in disease patterns just in the lifetime of us seniors: obesity, lung cancer (down in men, up in women), diabetes, autism, asthma, psychiatric disorders of all sorts. Some of these have gone from being not so important, to being pandemic.
Some of course defend the system not just as the game we know but also arguing that it is the best way to generate good science. This is where the serious debate should take place. Whether real change can be forced on the system is an open question.
Wednesday, June 10, 2015
The new religion: 'next gen' hype?
Ken and I were visiting with a woman the other day whose elderly husband is dying. In his 90's, he has long had seriously impaired vision and hearing, and in recent years has become less and less mobile. And, he has been losing his memory. Now everything else is failing. This he seems to know, at least at some level, and as he languishes in a nursing home, usually asleep but sometimes not, in many of his more lucid moments he says depressingly that he has had enough. But, of course, he can't do anything about that now. Now he must wait for the Fates to decide when he has had enough.
Perhaps you read the thought-provoking story by Robin Marantz Henig in the New York Times Magazine a few weeks ago about Sandy Bem, a woman with a diagnosis of Alzheimer's disease who decided that when she felt she was no longer the self she recognized, she would commit suicide. She lived with her declining memory as long as she felt she wanted to, and then ended her life. Of course there is disagreement about whether people should have a right to make a decision like this, but we think there is a lot to be said for our freedom to make that choice.
So when our friend said that it was a relief to know that someday people won't have to go through what her husband was going through, we thought we agreed with her. We assumed she meant humane assisted suicide should be legal and available. But no. "Soon," she said, "because of genetics, people won't have to die."
She is an atheist, and has no belief that her husband, or she herself, or anyone is going to a better place when they die. So clearly it's comforting to her to think that, while she herself may not benefit, in the future life truly won't need to end once geneticists have sorted out the science. I think Ken muttered something about how genetics is a long way from that, and we quickly changed the subject to talking about the weather. Who are we to take away this comforting thought?
The new religion
But where did she get this idea? She certainly didn't invent it. She is thoughtful and educated, reads, she watches the news; this idea came from scientists. All that human genome project hype, before and after it was 'finished', reputable scientists promising us the end of disease, and even that one day genetic knowledge would let us live forever, or at least as old as Masuthelah. Now the new million genome talk is much the same. Our genes will predict the diseases we'll get, precisely, and because of genetic engineering, gene therapy and targeted pharmaceuticals, we'll be able to prevent or cure them. Worth every penny of the billions that are going to be spent on setting up the infrastructure, collecting all the genomic information, doing the analysis.
We've already blogged about this new phase of very expensive research (including here: "What's 'precise' about 'precision' medicine (besides desperate spin)?"). So the iffy payoff is not our point today. Today we want to imagine that the promises all come true -- your future is written in your genes, and whatever you are destined to get is predictable from your genome. And then prevented because gene therapy will soon be routine, like having your oil changed, and bad genes will be replaceable. Or, if you do get your disease, it will be treatable with personalized pharmaceuticals, targeted at, well, we're not really sure what they'll be targeted at but they'll work that out.
And it's not clear what the ultimate goal actually is, either (other than the 21st century version of the Gold Rush, when the Levi's makers, the gold pan producers and the saloon owners got very rich). Disease prevention? Treatment? Immortality?
But let's think this through
Unless every conceptus is sequenced, most pediatric genetic diseases won't be preventable by precision medicine that much better than they are today with genetic counseling and early tests like ultrasound, unless every potential set of parents is vetted for carrier status for every known genetic disorder, and IVF is used to conceive and produce the perfect-child. Maybe people with money will do just that (indeed, direct-to-consumer genetic testing companies now offer testing for carrier status for a number of diseases) but who will pay for it to be done routinely for everyone?
And, we're a long long way from treating genetic diseases routinely with gene therapy, never mind with other, non-genetic treatments. And, somatic mutations that occur in the fetus are responsible for some pediatric genetic diseases, and they aren't predictable or preventable. So, we'll still have pediatric diseases.
And, we'll still have accidental deaths, and deaths from infectious diseases, because no antibiotic strategy will be perfect, and bugs will always outrun them anyway, and we'll probably have wars and suicides, so we won't all live forever. (Speaking of unequal access to medical knowledge and let's throw in care as well, shouldn't we be thinking about the unfairness of who gets those infectious diseases, and will continue to do so, as we dedicate billions of dollars to the promise of preventing genetic disease? Or who goes to war?)
So, presumably it's those of us whose fate is late-onset chronic disease that precision medicine is aiming at. Presumably those are written in our genome. But, what about our friend's 90+ year old husband, who is dying of old age? Would his death have been preventable, in theory? Nothing there to prevent, except wearing out. Oh, wait, telomeres. Right, he'd have had his lengthened long ago.
What about diseases that are largely due to lifestyle? Heart disease or type 2 diabetes caused by obesity, which let's say is due to inactivity and poor eating choices (this week sugar, last week high cholesterol animal proteins)? Even people who believe GWAS is showing us the cause of these kinds of late onset diseases acknowledge that they are polygenic, and that genes don't explain all the risk. How will they be prevented with increased genetic knowledge? Oh, not to worry--computers will do it if we turn enough statisticians onto the job!
But, ok, let's say that despite our sneering, it really is possible
We really can predict and prevent genetic disease. Then what? Either telomere therapy will be keeping us all young forever, or more and more healthy but old people will be stacking up at the other end of life. (Will we have prevented dementia? Joints wearing out? Not clear.)
Neither of these options looks good to us. How will the young-in-years ever get jobs if the young-at-heart keep them for 2, 3 hundred years, not to mention forever? Or, who will take care of all the healthy elders, and where will they live? We will have to feed, clothe, house, heat and cool, transport, and entertain them, or they will stay in the labor force and we'll have to figure out what new generations of new people will do for a living. If the elderly and super-elderly become feeble and need special homes and care, well, that will at least provide jobs for the young.
'Housing' has hidden implications. Housing takes space, uses energy and water, generates sewage, and that must come from somewhere. If we stop plowing under former people, we'll have to plow under farm land. Or maybe we'll just stack condos on top of each other until they reach as high as those skyscrapers oil-rich regions are building. Oh, of course, we'll put roof-top gardens on them. And, perhaps we'll either build some on Mars or one of Neptune's moons, or we'll grow beans and cattle there and fly them 'home'.
And, what about the extreme inequality of maintaining more and more old people, at huge cost, in rich countries, as they consume more and more resources, while people in poor countries continue on as now, with no access to the brave new world of precision medicine?
Demographic inevitability looms over any promises of genetic nirvana. It leads not just to population growth, but generally to exponential growth, that gets wildly out of any realistic sense of control rather quickly. Yet demographic unconstraint looms silently behind all the rosy promises that health research hyperbole make. Like death, immortality is something we don't really want to think about.
Rosy reassuring promises have been a long-standing strategy of politicians and preachers and it's no surprise that geneticists, being intelligent people, see the gains to be made by making them. But even if they don't, the more one thinks about this, the more of a social wrong the promises of precision medicine seem to be. Someone needs to tell the people.
Late news flash!
As if to help us make this point, as this post was just being finished, a news item appeared reporting that the FDA has just approved what appears to be a major new drug to combat high 'bad' cholesterol levels, LDL. Clinical trials have shown the drug (which mimics the action of a genetic mutation that blocks LDL production) to drastically lower LDL cholesterol, but whether this leads to drastic reduction in heart attacks is not yet known. Assuming it does, then what we'll get over the next few decades is more and more people living long enough to become like our friend's husband. How many will wish they'd had a mercifully quick heart attack instead of the lingering decay they will suffer as a consequence?
There is no easy answer. Preventing disease is surely good. Over-promising is surely not -- except for the beneficiaries of the resources that go their way as a result. And the makers of the gold pans. But, with less disease and more and more people dying at older ages come profound social implications, and these should be part of the discussion.
Perhaps you read the thought-provoking story by Robin Marantz Henig in the New York Times Magazine a few weeks ago about Sandy Bem, a woman with a diagnosis of Alzheimer's disease who decided that when she felt she was no longer the self she recognized, she would commit suicide. She lived with her declining memory as long as she felt she wanted to, and then ended her life. Of course there is disagreement about whether people should have a right to make a decision like this, but we think there is a lot to be said for our freedom to make that choice.
So when our friend said that it was a relief to know that someday people won't have to go through what her husband was going through, we thought we agreed with her. We assumed she meant humane assisted suicide should be legal and available. But no. "Soon," she said, "because of genetics, people won't have to die."
She is an atheist, and has no belief that her husband, or she herself, or anyone is going to a better place when they die. So clearly it's comforting to her to think that, while she herself may not benefit, in the future life truly won't need to end once geneticists have sorted out the science. I think Ken muttered something about how genetics is a long way from that, and we quickly changed the subject to talking about the weather. Who are we to take away this comforting thought?
The new religion
But where did she get this idea? She certainly didn't invent it. She is thoughtful and educated, reads, she watches the news; this idea came from scientists. All that human genome project hype, before and after it was 'finished', reputable scientists promising us the end of disease, and even that one day genetic knowledge would let us live forever, or at least as old as Masuthelah. Now the new million genome talk is much the same. Our genes will predict the diseases we'll get, precisely, and because of genetic engineering, gene therapy and targeted pharmaceuticals, we'll be able to prevent or cure them. Worth every penny of the billions that are going to be spent on setting up the infrastructure, collecting all the genomic information, doing the analysis.
We've already blogged about this new phase of very expensive research (including here: "What's 'precise' about 'precision' medicine (besides desperate spin)?"). So the iffy payoff is not our point today. Today we want to imagine that the promises all come true -- your future is written in your genes, and whatever you are destined to get is predictable from your genome. And then prevented because gene therapy will soon be routine, like having your oil changed, and bad genes will be replaceable. Or, if you do get your disease, it will be treatable with personalized pharmaceuticals, targeted at, well, we're not really sure what they'll be targeted at but they'll work that out.
 |
| "Gold Pan" by Nate Cull from Christchurch, New Zealand - http://flickr.com/photos/64857724@N00/2876115. Licensed under CC BY 2.0 via Wikimedia Commons |
And it's not clear what the ultimate goal actually is, either (other than the 21st century version of the Gold Rush, when the Levi's makers, the gold pan producers and the saloon owners got very rich). Disease prevention? Treatment? Immortality?
But let's think this through
Unless every conceptus is sequenced, most pediatric genetic diseases won't be preventable by precision medicine that much better than they are today with genetic counseling and early tests like ultrasound, unless every potential set of parents is vetted for carrier status for every known genetic disorder, and IVF is used to conceive and produce the perfect-child. Maybe people with money will do just that (indeed, direct-to-consumer genetic testing companies now offer testing for carrier status for a number of diseases) but who will pay for it to be done routinely for everyone?
And, we're a long long way from treating genetic diseases routinely with gene therapy, never mind with other, non-genetic treatments. And, somatic mutations that occur in the fetus are responsible for some pediatric genetic diseases, and they aren't predictable or preventable. So, we'll still have pediatric diseases.
And, we'll still have accidental deaths, and deaths from infectious diseases, because no antibiotic strategy will be perfect, and bugs will always outrun them anyway, and we'll probably have wars and suicides, so we won't all live forever. (Speaking of unequal access to medical knowledge and let's throw in care as well, shouldn't we be thinking about the unfairness of who gets those infectious diseases, and will continue to do so, as we dedicate billions of dollars to the promise of preventing genetic disease? Or who goes to war?)
So, presumably it's those of us whose fate is late-onset chronic disease that precision medicine is aiming at. Presumably those are written in our genome. But, what about our friend's 90+ year old husband, who is dying of old age? Would his death have been preventable, in theory? Nothing there to prevent, except wearing out. Oh, wait, telomeres. Right, he'd have had his lengthened long ago.
What about diseases that are largely due to lifestyle? Heart disease or type 2 diabetes caused by obesity, which let's say is due to inactivity and poor eating choices (this week sugar, last week high cholesterol animal proteins)? Even people who believe GWAS is showing us the cause of these kinds of late onset diseases acknowledge that they are polygenic, and that genes don't explain all the risk. How will they be prevented with increased genetic knowledge? Oh, not to worry--computers will do it if we turn enough statisticians onto the job!
But, ok, let's say that despite our sneering, it really is possible
We really can predict and prevent genetic disease. Then what? Either telomere therapy will be keeping us all young forever, or more and more healthy but old people will be stacking up at the other end of life. (Will we have prevented dementia? Joints wearing out? Not clear.)
Neither of these options looks good to us. How will the young-in-years ever get jobs if the young-at-heart keep them for 2, 3 hundred years, not to mention forever? Or, who will take care of all the healthy elders, and where will they live? We will have to feed, clothe, house, heat and cool, transport, and entertain them, or they will stay in the labor force and we'll have to figure out what new generations of new people will do for a living. If the elderly and super-elderly become feeble and need special homes and care, well, that will at least provide jobs for the young.
'Housing' has hidden implications. Housing takes space, uses energy and water, generates sewage, and that must come from somewhere. If we stop plowing under former people, we'll have to plow under farm land. Or maybe we'll just stack condos on top of each other until they reach as high as those skyscrapers oil-rich regions are building. Oh, of course, we'll put roof-top gardens on them. And, perhaps we'll either build some on Mars or one of Neptune's moons, or we'll grow beans and cattle there and fly them 'home'.
And, what about the extreme inequality of maintaining more and more old people, at huge cost, in rich countries, as they consume more and more resources, while people in poor countries continue on as now, with no access to the brave new world of precision medicine?
Demographic inevitability looms over any promises of genetic nirvana. It leads not just to population growth, but generally to exponential growth, that gets wildly out of any realistic sense of control rather quickly. Yet demographic unconstraint looms silently behind all the rosy promises that health research hyperbole make. Like death, immortality is something we don't really want to think about.
Rosy reassuring promises have been a long-standing strategy of politicians and preachers and it's no surprise that geneticists, being intelligent people, see the gains to be made by making them. But even if they don't, the more one thinks about this, the more of a social wrong the promises of precision medicine seem to be. Someone needs to tell the people.
Late news flash!
As if to help us make this point, as this post was just being finished, a news item appeared reporting that the FDA has just approved what appears to be a major new drug to combat high 'bad' cholesterol levels, LDL. Clinical trials have shown the drug (which mimics the action of a genetic mutation that blocks LDL production) to drastically lower LDL cholesterol, but whether this leads to drastic reduction in heart attacks is not yet known. Assuming it does, then what we'll get over the next few decades is more and more people living long enough to become like our friend's husband. How many will wish they'd had a mercifully quick heart attack instead of the lingering decay they will suffer as a consequence?
There is no easy answer. Preventing disease is surely good. Over-promising is surely not -- except for the beneficiaries of the resources that go their way as a result. And the makers of the gold pans. But, with less disease and more and more people dying at older ages come profound social implications, and these should be part of the discussion.
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