In keeping with last week's farm theme.
Monday, May 11, 2009
Friday, May 8, 2009
On the Road
No, this isn't a book title blog that refers to Jack Kerouac's famous hippie cult book (seemed great at the time!). It's because it has been a slow news day, with no miraculous claims being made in the science pages, not even very much murder and mayhem in the world. So I [Ken] have been trying to put together a talk, as I'm about to go on the road to various places in the next couple of months, to speak to some audiences who will be forced to listen to me expostulating about the state of affairs in genetics today, and how things biological got to be the way they are.
The first talk, next week, will be in Calgary, in Alberta, Canada, where I've been invited to give the 2009 Bea Fowlow Lecture to the Dept of Medical Genetics. My talk will basically be to a medical audience, but I also have a friend and colleague there, who's a biological anthropologist, multifaceted life-science author and editor, and workaholic administrator (named Benedikt Halgrimsson). He struggles, as we do here, with the problem of understanding the dimensionality, and underlying genetic basis, of craniofacial morphology. So there are a lot of unanswered questions to talk about.
Giving this lecture is a real honor....but it's intimidating! Not because Canadians are likely to be hostile (though they are hockey players). It's because they might expect me to have the answers--about what to do next in genetics! Unfortunately for all concerned, there's gonna be a lot of disappointment! Because the landscape in genetics, as we try to discuss in this blog, is at once fascinating, and frustrating: sometimes it seems we have such incredible powers to find things from DNA sequence to developmental process and how they can go awry in disease. And just when we think we have the answers in hand....they slip complexly through our fingers.
Academics like to bring in speakers to talk. Going to a talk has the appearance of real work, but gets a person out of the office. And there are often free refreshments, too. Talks largely are a kind of vapor-ware, though, since the next day, almost no matter what the speaker says, it's back to work as usual. Even in genetics, where a lot of us are hungering for better ideas, it's hard to shift research momentum. So, unlike Kerouac's travels, which were a lark, a ride into freedom, this one feels more like a trap!
The first talk, next week, will be in Calgary, in Alberta, Canada, where I've been invited to give the 2009 Bea Fowlow Lecture to the Dept of Medical Genetics. My talk will basically be to a medical audience, but I also have a friend and colleague there, who's a biological anthropologist, multifaceted life-science author and editor, and workaholic administrator (named Benedikt Halgrimsson). He struggles, as we do here, with the problem of understanding the dimensionality, and underlying genetic basis, of craniofacial morphology. So there are a lot of unanswered questions to talk about.
Giving this lecture is a real honor....but it's intimidating! Not because Canadians are likely to be hostile (though they are hockey players). It's because they might expect me to have the answers--about what to do next in genetics! Unfortunately for all concerned, there's gonna be a lot of disappointment! Because the landscape in genetics, as we try to discuss in this blog, is at once fascinating, and frustrating: sometimes it seems we have such incredible powers to find things from DNA sequence to developmental process and how they can go awry in disease. And just when we think we have the answers in hand....they slip complexly through our fingers.
Academics like to bring in speakers to talk. Going to a talk has the appearance of real work, but gets a person out of the office. And there are often free refreshments, too. Talks largely are a kind of vapor-ware, though, since the next day, almost no matter what the speaker says, it's back to work as usual. Even in genetics, where a lot of us are hungering for better ideas, it's hard to shift research momentum. So, unlike Kerouac's travels, which were a lark, a ride into freedom, this one feels more like a trap!
Thursday, May 7, 2009
Two cultures and the farm
[Anne, whose sister and brother-in-law are dairy goat farmers in New England, wrote this post.]
I visit my sister three or four times a year. When I leave academia behind and am stacking hay or feeding goats up on the farm, the idea of CP Snow’s two cultures shrinks into insignificance, or even irrelevance. Viewed from so far away, those two cultures on isolated, protected university campuses – there are good reasons that academia has been known as “The Ivory Tower” for centuries – merge into one, dwarfed by a much greater chasm, that between the academy and the outside world. On one side, at the farm on the hill, it’s hard physical work with survival on the line, and on the other, in the Pennsylvania valley I call home, it’s the life of the mind, where the main thing at risk is whether our research papers will be published and where we make a ritual out of running, forcing ourselves to remember that we’ve got a body and that it needs some care.
"What's Jack up to today?" I asked my sister.
"He's out tedding the fields," she replied.
When we had this conversation I had never heard of tedding. Every word I don't know is a window into the cultural divide, attached as it is to a set of practices I know little if anything about. Some words describe equipment I have seen when I drive by farm fields but never thought about – tedders, diskers, seeders – and some evoke a history as old as agriculture itself, since soil has been prepared for tillage as long as people have been planting crops, whether with hand held sticks, crude implements pulled by animals or mega-machines specialized to a single task.
The vocabulary of farming is a constant indicator of the divide, but there are many other landmarks. Separate calendars, for instance; academics measure their year by semester and holiday breaks, farmers measure theirs by season – planting, haying, breeding, birthing, harvesting. Or even by weather report – if it’s going to rain tomorrow, there will be no mowing of standing hay today because it won’t dry (but class will still be held). And, the seasons are likely to be delimited by events academics have no way to notice; my sister text messaged me one late April evening to say that the barn swallows had returned that afternoon.
The kinds of risks that farmers and academics are exposed to scarcely even overlap; farming has one of the highest accident rates in America and life expectancy of a farmer is on the
order of four years less than that of a professor. Society has decided we’ve got very different economic worth, too; small farmers on average earn far less than half of what professors do. Farmers are at the mercy of unpredictable events beyond their control – drought, rain, animals contracting disease, the price of grain, the ever declining price the farmer earns for produce sold at market, the cost of health insurance – while unpredictability has been fairly well eliminated from a professor’s working life. A professor with tenure, at least.
I recognize that I could make similar comparisons between academics and miners, or soldiers, or athletes or musicians or visual artists and the lists would differ only in detail. The singular difference is that farmers provide the rest of us, including miners and artists, with sustenance. They are tied to the land and the seasons in ways that most of the rest of us can, and do, ignore.
I was at the farm when Ken heard that he and colleagues had gotten a large grant to study the genetics of the evolution of skull shape in primates. This project has nothing to do with disease, so doesn’t claim to be contributing to future health – a claim that sells a lot of science, particularly modern genetics, but this study is basic science.
When I told my sister about the project, she looked at me skeptically and asked, “Why do we care?”
And, surrounded as I was by goats and hard work, I couldn’t help but see her point, even though I work on this project myself. Why should her tax money be spent to fund a project to further knowledge that will have no practical application in any of our lifetimes, if ever, when she can’t even afford health insurance, and just barely the grain to keep her animals alive? Academics do provide knowledge, edification, and social advance for students. We also provide much highly technical knowledge that starts out very abstract and theoretical and some of it does, eventually, work its way to farm and field. Still, I felt a whiff of sympathy with Chairman Mao: send all the professors to the countryside, let them learn the value of real work. Though, in my version, they wouldn’t have to stay ten years; a week or two would do.
I visit my sister three or four times a year. When I leave academia behind and am stacking hay or feeding goats up on the farm, the idea of CP Snow’s two cultures shrinks into insignificance, or even irrelevance. Viewed from so far away, those two cultures on isolated, protected university campuses – there are good reasons that academia has been known as “The Ivory Tower” for centuries – merge into one, dwarfed by a much greater chasm, that between the academy and the outside world. On one side, at the farm on the hill, it’s hard physical work with survival on the line, and on the other, in the Pennsylvania valley I call home, it’s the life of the mind, where the main thing at risk is whether our research papers will be published and where we make a ritual out of running, forcing ourselves to remember that we’ve got a body and that it needs some care.
"What's Jack up to today?" I asked my sister.
"He's out tedding the fields," she replied.
When we had this conversation I had never heard of tedding. Every word I don't know is a window into the cultural divide, attached as it is to a set of practices I know little if anything about. Some words describe equipment I have seen when I drive by farm fields but never thought about – tedders, diskers, seeders – and some evoke a history as old as agriculture itself, since soil has been prepared for tillage as long as people have been planting crops, whether with hand held sticks, crude implements pulled by animals or mega-machines specialized to a single task.
The vocabulary of farming is a constant indicator of the divide, but there are many other landmarks. Separate calendars, for instance; academics measure their year by semester and holiday breaks, farmers measure theirs by season – planting, haying, breeding, birthing, harvesting. Or even by weather report – if it’s going to rain tomorrow, there will be no mowing of standing hay today because it won’t dry (but class will still be held). And, the seasons are likely to be delimited by events academics have no way to notice; my sister text messaged me one late April evening to say that the barn swallows had returned that afternoon.
The kinds of risks that farmers and academics are exposed to scarcely even overlap; farming has one of the highest accident rates in America and life expectancy of a farmer is on the
order of four years less than that of a professor. Society has decided we’ve got very different economic worth, too; small farmers on average earn far less than half of what professors do. Farmers are at the mercy of unpredictable events beyond their control – drought, rain, animals contracting disease, the price of grain, the ever declining price the farmer earns for produce sold at market, the cost of health insurance – while unpredictability has been fairly well eliminated from a professor’s working life. A professor with tenure, at least.I recognize that I could make similar comparisons between academics and miners, or soldiers, or athletes or musicians or visual artists and the lists would differ only in detail. The singular difference is that farmers provide the rest of us, including miners and artists, with sustenance. They are tied to the land and the seasons in ways that most of the rest of us can, and do, ignore.
I was at the farm when Ken heard that he and colleagues had gotten a large grant to study the genetics of the evolution of skull shape in primates. This project has nothing to do with disease, so doesn’t claim to be contributing to future health – a claim that sells a lot of science, particularly modern genetics, but this study is basic science.
When I told my sister about the project, she looked at me skeptically and asked, “Why do we care?”
And, surrounded as I was by goats and hard work, I couldn’t help but see her point, even though I work on this project myself. Why should her tax money be spent to fund a project to further knowledge that will have no practical application in any of our lifetimes, if ever, when she can’t even afford health insurance, and just barely the grain to keep her animals alive? Academics do provide knowledge, edification, and social advance for students. We also provide much highly technical knowledge that starts out very abstract and theoretical and some of it does, eventually, work its way to farm and field. Still, I felt a whiff of sympathy with Chairman Mao: send all the professors to the countryside, let them learn the value of real work. Though, in my version, they wouldn’t have to stay ten years; a week or two would do.
Wednesday, May 6, 2009
Monday, May 4, 2009
How complex is 'complex'?
The word 'complex' is frequently used, though not always as clearly as it might be. In today's genetics arena it means a trait that is the result of multiple genetic elements as well as environmental factors that are usually unknown or not specified, but can include the genetic element's genomic background. Can we get a clearer understanding of this interaction in some way that has not yet been well-explored?
Most complex traits, whose genetic contributors GWAS and related mapping methods are designed to find (see earlier posts) show substantial evidence of being 'genetic' in some sense: there is correlation of the trait among relatives or an association of risk of the trait--like a disease--among family members.
The problem is that despite evidence for genetic involvement, GWAS and other methods have only been able to identify a small fraction of the contributing elements. One response is that we need larger studies. Another is that the objective is not to account for the disease in terms of genes, but to find genetic pathways that are involved.
Most common diseases have increased substantially, if not dramatically, within living memory and more importantly within the time since trustworthy epidemiological data on incidence (rate of new cases per year) or prevalence (fraction of persons affected) have been available.
This would suggest to reasonable people, even including some geneticists, that at least for preventive purposes the major responsible (and avoidable) factors for the disease are environmental, such as exposures to risk factors like toxins, lifestyle changes such as in diet, etc.
A few years ago, the molecular technology infrastructure for mapping studies was laid down, and paid for on the rationale that common genetic variants were likely responsible for these common diseases--and hence that genetics was a right way to approach them. Common variants for common disease (CVCD) became a mantra.
In response to the environmental and other arguments raised even at the time, proponents of CVCD and the investment in the gene-mapping infrastructure (e.g., the HapMap project) said that, yes, environmental factors clearly were involved, but the increase in prevalence was due to their interaction with common genetic susceptibility variants.
Subsequent mapping, including numerous, often huge genomewide association studies, has generally failed to find such variants. The meaning of 'common' can of course be adjusted to fit results, but the bulk of the heritability of these many studied traits remains unexplained. It's a fair question whether these traits are truly complex and largely unmappable, or whether we just haven't studied them enough.
A kind of widespread relevant evidence may be the following. The substantial heritability of common disease as well as normal traits suggests that many genes contribute; the traits are often called 'polygenic' for that reason. But these many genes might individually vary in their effects. For many theoretical and empirical reasons, one would expect some alleles (genetic variants) at one or a few genes, to interact or respond more strongly to changing environmental factors.
If that is the case, then the more important genes that were not identifiable in case-control or family samples before the environmental change, should be mappable afterward. That's because those variants that would be the main responders to the environmental change, whatever it was. Their individual effects, modest before the change, should be major after it.
Yet, today, after a long list of diseases have had large, rapid increases in prevalence, the GWAS findings are as we have seen: they are not identifying much that is of population-scale importance. On the surface, this suggests that the argument about complexity really is correct: there are, indeed, many genes involved, but they each make very small net effect on risk. A few are detected whose effects are greater, but they are few and even their effects are only modest.
From this perspective, which is based on data, not theory, secular trends in risk and the failure of GWAS to find CVCD's is relevant data, suggest that complex traits really are basically homogeneous in terms of genetic causation.
Now, if this is true it constitutes material evidence that should change our understanding of the nature of these traits: why would it be that there are generally no major alleles waiting for environmental changes to give them a chance to be expressed? Indeed, isn't that just how natural selection is supposed to work, with environmental change favoring 'good' genetic variants in the population and raising them to high frequency? Those variants should have substantial effect on the trait so the organism carrying the variants would reproduce more successfully.
If our thinking is correct, then this tells us something. Perhaps the networks of which biological traits are built are internally adjusting--strong changes in one part of a pathway network lead to slowing down of others. Yet, secular trends show that the net result can involve major change. It is indeed somewhat difficult to believe that the genetic responses to environmental changes are so internally homogeneous that even after major stimulus none really stands out even when studied in large samples. There must be a message there--if we can but figure it out!
These are just superficial ideas at this point, but they could help direct changes in what we look for, or how we look. We are starting to use an evolutionary simulation program that Brian Lambert in our group has written (see the description of ForSim on Ken's web page for details) to see if this point is correct as we think, or if there is some aspect of genetic control that we are overlooking. Stay tuned for results.
Most complex traits, whose genetic contributors GWAS and related mapping methods are designed to find (see earlier posts) show substantial evidence of being 'genetic' in some sense: there is correlation of the trait among relatives or an association of risk of the trait--like a disease--among family members.
The problem is that despite evidence for genetic involvement, GWAS and other methods have only been able to identify a small fraction of the contributing elements. One response is that we need larger studies. Another is that the objective is not to account for the disease in terms of genes, but to find genetic pathways that are involved.
Most common diseases have increased substantially, if not dramatically, within living memory and more importantly within the time since trustworthy epidemiological data on incidence (rate of new cases per year) or prevalence (fraction of persons affected) have been available.
This would suggest to reasonable people, even including some geneticists, that at least for preventive purposes the major responsible (and avoidable) factors for the disease are environmental, such as exposures to risk factors like toxins, lifestyle changes such as in diet, etc.
A few years ago, the molecular technology infrastructure for mapping studies was laid down, and paid for on the rationale that common genetic variants were likely responsible for these common diseases--and hence that genetics was a right way to approach them. Common variants for common disease (CVCD) became a mantra.
In response to the environmental and other arguments raised even at the time, proponents of CVCD and the investment in the gene-mapping infrastructure (e.g., the HapMap project) said that, yes, environmental factors clearly were involved, but the increase in prevalence was due to their interaction with common genetic susceptibility variants.
Subsequent mapping, including numerous, often huge genomewide association studies, has generally failed to find such variants. The meaning of 'common' can of course be adjusted to fit results, but the bulk of the heritability of these many studied traits remains unexplained. It's a fair question whether these traits are truly complex and largely unmappable, or whether we just haven't studied them enough.
A kind of widespread relevant evidence may be the following. The substantial heritability of common disease as well as normal traits suggests that many genes contribute; the traits are often called 'polygenic' for that reason. But these many genes might individually vary in their effects. For many theoretical and empirical reasons, one would expect some alleles (genetic variants) at one or a few genes, to interact or respond more strongly to changing environmental factors.
If that is the case, then the more important genes that were not identifiable in case-control or family samples before the environmental change, should be mappable afterward. That's because those variants that would be the main responders to the environmental change, whatever it was. Their individual effects, modest before the change, should be major after it.
Yet, today, after a long list of diseases have had large, rapid increases in prevalence, the GWAS findings are as we have seen: they are not identifying much that is of population-scale importance. On the surface, this suggests that the argument about complexity really is correct: there are, indeed, many genes involved, but they each make very small net effect on risk. A few are detected whose effects are greater, but they are few and even their effects are only modest.
From this perspective, which is based on data, not theory, secular trends in risk and the failure of GWAS to find CVCD's is relevant data, suggest that complex traits really are basically homogeneous in terms of genetic causation.
Now, if this is true it constitutes material evidence that should change our understanding of the nature of these traits: why would it be that there are generally no major alleles waiting for environmental changes to give them a chance to be expressed? Indeed, isn't that just how natural selection is supposed to work, with environmental change favoring 'good' genetic variants in the population and raising them to high frequency? Those variants should have substantial effect on the trait so the organism carrying the variants would reproduce more successfully.
If our thinking is correct, then this tells us something. Perhaps the networks of which biological traits are built are internally adjusting--strong changes in one part of a pathway network lead to slowing down of others. Yet, secular trends show that the net result can involve major change. It is indeed somewhat difficult to believe that the genetic responses to environmental changes are so internally homogeneous that even after major stimulus none really stands out even when studied in large samples. There must be a message there--if we can but figure it out!
These are just superficial ideas at this point, but they could help direct changes in what we look for, or how we look. We are starting to use an evolutionary simulation program that Brian Lambert in our group has written (see the description of ForSim on Ken's web page for details) to see if this point is correct as we think, or if there is some aspect of genetic control that we are overlooking. Stay tuned for results.
Sunday, May 3, 2009
Geneticist as cowboy?
This is a great cartoon, but .... what does it mean?? The geneticist is a cowboy? The cowboy was cloned?? Lab results show the guy's a cowboy? Any ideas??
Friday, May 1, 2009
GWAS on my mind
There's a classic blues song Georgia on My Mind that is ironically appropriate. The current Nature has two reports of mapping studies of autism traits (and there is reference to a third study published elsewhere). The study gained a lot of attention (or was given a lot of hype, depending on your view of these things). It isn't the first such story to get attention. These studies are treated as if they are major findings, and tend to reinforce the idea that GWAS are a powerful approach to understanding the genetics of complex disease. Despite the evidence, we can't seem to get GWAS off our mind.
Of course 'major' is a subjective judgment but after filtering the rhetoric in the media, and looking at what the actual papers and authors say, it turns out that, as before, these new findings account for less than 1% of autism cases (and such estimates are often upwardly biased for various statistical reasons). And the genes were known before as potentially relevant. And the different studies did not find the same genes.
This is par for the course, unfortunately, because autism is a sadly damaging disease both to the persons affected and those who care about them. One doesn't want to dismiss any findings that might materially help. But as even the authors of one of the studies pointed out in comments to the media (e.g., here), this is further evidence that GWAS are a fading tool, though that is not how the news media portrayed the result.
The sad fact is that in autism, as in many other diseases, there is plenty of generic evidence for genetic risk factors playing some role (because the disorder seems to cluster somewhat in families), yet the prevalence has grown rapidly only in a recent few decades. Yes, there is the lingering question of whether prevalence has actually risen, or just the probability that a child will be diagnosed with one of a broadened spectrum of disorders, but prevalence has continued to rise rapidly even in the last few years, while the definition of the spectrum has been the same, which makes it seem less likely that rates are simply reflecting increased or altered diagnosis. This shows that even if there are gene-environment interactions, the trait is preponderantly due to environmental factors--unfortunately, despite all sorts of guesses and wild guess, the factors are not yet known.
GWAS and other mapping approaches held out hope to many, and even to some extent to skeptics, that diseases like autism for which there was no good physiological understanding could reveal genes, and hence mechanisms that would lead both to understanding and eventually to treatment. But this hope really hasn't been borne out.
The current typical response (and that of the authors of these studies) is that we need larger studies of various genetic sorts. As we've said a few times in this blog, and as even some GWAS proponents believe, that is most likely to mean much more work to find much less, and unlikely to really crack the problem. Larger studies will in principle find rarer things, but that only works if the increase in study size isn't accompanied by even greater risk heterogeneity (an improved signal to noise ratio). Even complete DNA sequence can't automatically pull the rabbit out of the hat, because the more DNA examined the more variable sites one will find.
Since it is likely that much of what we're looking for is regulatory, and we don't yet know very well how to identify such sequences, we'll be awash in DNA data with no clearer biological picture. There are some subtle points afoot here. They have to do with the statistical nature of these studies. It's possible in principle that even with densely-marked GWAS no marker will have detectably strong association with the true causal site (or, worse, sites) in a chromosome region. Full DNA sequence would in principle include the site(s) directly, but the plethora of data may well obscure it. Generally, if individual signal is strong enough to be breakthrough-generating, we should have seen it by now.
So, each new study like these recent ones presses home the dual points: large association studies simply do not seem to be the way to understand these complex traits, and instead we need some clever person to show us a better way.
We need to get GWAS off our mind (though it doesn't seem likely to happen any time soon). Being negative about GWAS may seem unseemly. But it is an attempt to be constructive. These problems are so worth studying that they are worth studying right rather than just studying again and again in the same unsatisfactory way.
Of course 'major' is a subjective judgment but after filtering the rhetoric in the media, and looking at what the actual papers and authors say, it turns out that, as before, these new findings account for less than 1% of autism cases (and such estimates are often upwardly biased for various statistical reasons). And the genes were known before as potentially relevant. And the different studies did not find the same genes.
This is par for the course, unfortunately, because autism is a sadly damaging disease both to the persons affected and those who care about them. One doesn't want to dismiss any findings that might materially help. But as even the authors of one of the studies pointed out in comments to the media (e.g., here), this is further evidence that GWAS are a fading tool, though that is not how the news media portrayed the result.
The sad fact is that in autism, as in many other diseases, there is plenty of generic evidence for genetic risk factors playing some role (because the disorder seems to cluster somewhat in families), yet the prevalence has grown rapidly only in a recent few decades. Yes, there is the lingering question of whether prevalence has actually risen, or just the probability that a child will be diagnosed with one of a broadened spectrum of disorders, but prevalence has continued to rise rapidly even in the last few years, while the definition of the spectrum has been the same, which makes it seem less likely that rates are simply reflecting increased or altered diagnosis. This shows that even if there are gene-environment interactions, the trait is preponderantly due to environmental factors--unfortunately, despite all sorts of guesses and wild guess, the factors are not yet known.
GWAS and other mapping approaches held out hope to many, and even to some extent to skeptics, that diseases like autism for which there was no good physiological understanding could reveal genes, and hence mechanisms that would lead both to understanding and eventually to treatment. But this hope really hasn't been borne out.
The current typical response (and that of the authors of these studies) is that we need larger studies of various genetic sorts. As we've said a few times in this blog, and as even some GWAS proponents believe, that is most likely to mean much more work to find much less, and unlikely to really crack the problem. Larger studies will in principle find rarer things, but that only works if the increase in study size isn't accompanied by even greater risk heterogeneity (an improved signal to noise ratio). Even complete DNA sequence can't automatically pull the rabbit out of the hat, because the more DNA examined the more variable sites one will find.
Since it is likely that much of what we're looking for is regulatory, and we don't yet know very well how to identify such sequences, we'll be awash in DNA data with no clearer biological picture. There are some subtle points afoot here. They have to do with the statistical nature of these studies. It's possible in principle that even with densely-marked GWAS no marker will have detectably strong association with the true causal site (or, worse, sites) in a chromosome region. Full DNA sequence would in principle include the site(s) directly, but the plethora of data may well obscure it. Generally, if individual signal is strong enough to be breakthrough-generating, we should have seen it by now.
So, each new study like these recent ones presses home the dual points: large association studies simply do not seem to be the way to understand these complex traits, and instead we need some clever person to show us a better way.
We need to get GWAS off our mind (though it doesn't seem likely to happen any time soon). Being negative about GWAS may seem unseemly. But it is an attempt to be constructive. These problems are so worth studying that they are worth studying right rather than just studying again and again in the same unsatisfactory way.
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