It's no secret I love evolution.
But I usually feel like such an outsider when it comes both to how it's done professionally and in pop culture. I think it's my tendency to see proximate rather than ultimate causes and it's the ultimate causes that seduce and bedazzle. I've learned that if you question ultimate evolutionary narratives, you're a party pooper. I'm a party pooper.
Here I am
Typing to myself
I've got the outsider's blues
Let's start with some recent fish science. These guppies of the same species, born big and born little, have been very nicely shown to grow at the same pace. The big ones are born later and into a competitive food environment. Researchers offer that it's due to selection for context-dependent control over gestation length/birth timing. But why? What about a proximate view? Surely the mother's context and its impact on her biology and on her eggs and babies is important. There may be no need to imagine a fancy adaptation that switches birth timing so that babies are badass food competitors ... Like there is no need to imagine a fancy adaptation that switches birth timing so that human babies escape the birth canal in time.
And, also today, there's news of a conference paper on human inbreeding. Most everyone believes inbreeding is bad, especially evolutionary scientists, many of whom rely on it being bad to make sense of animal behavior through their own culturally-tinted, taboo-tainted goggles. It's also foundational to how many evolutionary scientists explain cooperation with non-kin and our taboos against inbreeding. The news report linked above describes an enormous study of parents, all over the world, who are cousins who produce children. There's a list of biological trends for the outcomes of inbreeding that are assumed to be less than ideal (e.g. these kids are 1 cm shorter than average and less than 1 kg lighter at birth) and it's explained by genetics, of combining genomes of close relatives. Included in these traits of interest is age at first sex (delayed in offspring of inbreeding), age at first birth (same), number of opposite-sex partners (fewer in the inbred), number of offspring (fewer begat by the inbred). Sooo, I trend with the inbred. Am I inbred? No. To me, these trends don't scream bad genes from naughty parents. These outcomes look like they'd be influenced pretty heavily by complex cultural conditions and socioeconomic status, which may be intimately linked with conditions that pair-up cousins in the first place. Did these factors enter into the analysis? We'll have to wait and see when the paper's published.
And another news item today has me kicking a can out here. What if, rather than it being due to a fancy adaptation to seasonal fluctuation in resources, shrews' skulls shrink over winter as they experience the pressure and temperature of hard, cold dirt?
For some reason today--and maybe it's because my life writ-large lacks much opportunity to hold these discussions with people in real life, and my life writ-small has me pulled hard away from learning and doing evolution, period--I'm feeling nostalgic. The guppies, the inbreeding, and the shrew skulls awoke some ghosts of my past...
What if perpetual evolution due to mutation* causes speciation, rather than natural selection?
There's no way that everything that differs between males and females is explained by sexual selection. So what if body size and strength differences are a bigger story than that?
In that vein, what if women are smart BECAUSE HUMANS ARE SMART, and not to outfox rapists?
What if man's big penis is due to man's big vagina and not so much due to survival of the biggest?
What if the same mutation in multiple individuals can be induced by a virus? That kind of head start would seem to make it much easier for a mutation to go to fixation whether due to drift or selection.
I'm more similar, genetically, than 50% to my mom, to you, and to every single person on this planet. So what are we actually supposed to learn from all these fancy evolutionary equations that insist I'm only 50% similar to my parents, and less and less similar to everyone else, including you, in the tree?
And, I realize this may sound silly and obvious, but animals don't know where babies come from. Given the words we use, reading about the evolution of animal behavior is so confusing, in this light.
To those who get it
To evolution's outsiders
Do you wanna form a band?
* (and, in the myriad species who have it, the coin-flip of extinction or inheritance for each part of the genome, known as recombination and segregation during the halving of the genome during sperm and egg production)
Tuesday, October 24, 2017
Sunday, October 15, 2017
Understanding Obesity? Fat Chance!
Obesity is one of our more widespread and serious health-threatening traits. Many large-scale mapping as well as extensive environmental/behavioral epidemiological studies of obesity have been done over recent decades. But if anything, the obesity epidemic seems to be getting worse.
There's deep meaning in that last sentence: the prevalence of obesity is changing rapidly. This is being documented globally, and happening rapidly before our eyes. Perhaps the most obvious implication is that this serious problem is not due to genetics! That is, it is not due to genotypes that in themselves make you obese. Although everyone's genotype is different, the changes are happening during lifetimes, so we can't attribute it to the different details of each generation's genotypes or their evolution over time. Instead, the trend is clearly due to lifestyle changes during lifetimes.
Of course, if you see everything through gene-colored lenses, you might argue (as people have) that sure, it's lifestyles, but only some key nutrient-responding genes are responsible for the surge in obesity. These are the 'druggable' targets that we ought to be finding, and it should be rather easy since the change is so rapid that the genes must be few, so that even if we can't rein in McD and KFC toxicity, or passive TV-addiction, we can at least medicate the result. That was always, at best, wishful thinking, and at worst, rationalization for funding Big Data studies. Such a simple explanation would be good for KFC, and an income flood for BigPharma, the GWAS industry, DNA sequencer makers, and more.....except not so good for those paying the medical price, and those who are trying to think about the problem in a disinterested scientific way. Unfortunately, even when it is entirely sincere, that convenient hope for a simple genetic cause is being shown to be false.
A serious parody?
Year by year, more factors are identified that, by statistical association at least and sometimes by experimental testing, contribute to obesity. A very fine review of this subject has appeared in the mid-October 201 Nature Reviews Genetics, by Ghosh and Bouchard, which takes seriously not just genetics but all the plausible causes of obesity, including behavior and environment, and their relationships as best we know them, and outlines the current state of knowledge.
Ghosh and Bouchard provide a well-caveated assessment of these various threads of evidence now in hand, and though they do end up with the pro forma plea for yet more funding to identify yet more details, they provide a clear picture that a serious reader can take seriously on its own merits. However, we think that the proper message is not the usual one. It is that we need to rethink what we've been investing so heavily on.
To their great credit, the authors melded behavioral, environmental, and genetic causation in their analysis. This is shown in this figure, from their summary; it is probably the best current causal map of obesity based on the studies the authors included in their analysis:
If this diagram were being discussed by John Cleese on Monty Python, we'd roar with laughter at what was an obvious parody of science. But nobody's laughing and this isn't a parody! And it is by no means of unusual shape and complexity. Diagrams like this (but with little if any environmental component) have been produced by analyzing gene expression patterns even just of the early development of the simple sea urchin. But we seem not to be laughing, which is understandable because they're serious diagrams. On the other hand, we don't seem to be reacting other than by saying we need more of the same. I think that is rather weird, for scientists, whose job it is to understand, not just list, the nature of Nature.
We said at the outset of this post that 'the obesity epidemic seems to be getting worse'. There's a deep message there, but one essentially missing even from this careful obesity paper: it is that many of the causal factors, including genetic variants, are changing before our eyes. The frequency of genetic variants changes from population to population and generation to generation, so that all samples will look different. And, mutations happen in every meiosis, adding new variants to a population every time a baby is born. The results of many studies, as reflected in the current summary by Ghosh and Bouchard, show the many gene regions that contribute to obesity, their total net contribution is still minor. It is possible, though perhaps very difficult to demonstrate, that an individual site might account more than minimally for some individual carriers in ways GWAS results can't really identify. And the authors do cite published opinions that claim a higher efficacy of GWAS relative to obesity than we think is seriously defensible; but even if we're wrong, causation is very complex as the figure shows.
The individual genomic variants will vary in their presence or absence or frequency or average effect among studies, not to mention populations. In addition, most contributing genetic variants are too rare or weak to be detected by the methods used in mapping studies, because of the constraints on statistical significance criteria, which is why so much of the trait's heritability in GWAS is typically unaccounted for by mapping. These aspects and their details will differ greatly among samples and studies.
Relevant risk factors will come or go or change in exposure levels in the future--but these cannot be predicted, not even in principle. Their interactions and contributions are also manifestly context-specific, as secular trends clearly show. Even with the set of known genetic variants and other contributing factors, there are essentially an unmanageable number of possible combinations, so that each person is genetically and environmentally unique, and the complex combinations of future individuals are not predictable.
Risk assessment is essentially based on replicability, which in a sense is why statistical testing can be used (on which these sorts of results heavily rely). However, because these risk factor combinations are each unique they're not replicable. At best, as some advocate, the individual effects are additive so that if we just measure each in some individual add up each factor's effect, and predict the person's obesity (if the effects are not additive, this won't work). We can probably predict, if perhaps not control, at least some of the major risk factors (people will still down pizzas or fried chicken while sitting in front of a TV). But even the known genetic factors in total only account for a small percentage of the trait's variance (the authors' Table 2), though the paper cites more optimistic authors.
The result of these indisputable facts is that as long as our eyes are focused, for research strategic reasons or lack of better ideas, on the litter of countless minor factors, even those we can identify, we have a fat chance of really addressing the problem this way.
If you pick any of the arrows (links) in this diagram, you can ask how strong or necessary that link is, how much it may vary among samples or depend on the European nature of the data used here, or to what extent even its identification could be a sampling or statistical artifact. Links like 'smoking' or 'medication', not to mention specific genes, even if they're wholly correct, surely have quantitative effects that vary among people even within the sample, and the effect sizes probably often have very large variance. Many exposures are notoriously inaccurately reported or measured, or change in unmeasured ways. Some are quite vague, like 'lifestyle', 'eating behavior', and many others--both hard to define and hard to assess with knowable precision, much less predictability. Whether their various many effects are additive or have more complex interaction is another issue, and the connectivity diagram may be tentative in many places. Maybe--probably?--in such traits simple behavioral changes would over-ride most of these behavioral factors, leaving those persons for whom obesity really is due to their genotype, which would then be amenable to gene-focused approaches.
If this is a friable diagram, that is, if the items, strengths, connections and so on are highly changeable, even if through no fault of the authors whatever, we can ask when and where and how this complex map is actually useful, no matter how carefully it was assembled. Indeed, even if this is a rigidly accurate diagram for the samples used, how applicable is it to other samples or to the future?Or how useful is it in predicting not just group patterns, but individual risk?
Our personal view is that the rather ritual plea for more and more and bigger and bigger statistical association studies is misplaced, and, in truth, a way of maintaining funding and the status quo, something we've written much about--the sociopolitical economics of science today. With obesity rising at a continuing rate and about a third of the US population recently reported as obese, we know that the future health care costs for the consequences will dwarf even the mega-scale genome mapping on which so much is currently being spent, if not largely wasted. We know how to prevent much or most obesity in behavioral terms, and we think it is entirely fair to ask why we still pour resources into genetic mapping of this particular problem.
There are many papers on other complex traits that might seem to be simple like stature and blood pressure, not to mention more mysterious ones like schizophrenia or intelligence, in which hundreds of genomewide sites are implicated, strewn across the genome. Different studies find different sites, and in most cases most of the heritability is not accounted for, meaning that many more sites are at work (and this doesn't include environmental effects). In many instances, even the trait's definition itself may be comparably vague, or may change over time. This is a landscape 'shape' in which every detail is different, within and between traits, but is found in common with complex traits. That in itself is a tipoff that there is something consistent about these landscapes but we've not yet really awakened to it or learned how to approach it.
Rather than being skeptical about these Ghosh and Bouchard's' careful analysis or their underlying findings, I think we should accept their general nature, even if the details in any given study or analysis may not individually be so rigid and replicable, and ask: OK, this is the landscape--what do we do now?
Is there a different way to think about biological causation? If not, what is the use or point of this kind of complexity enumeration, in which every person is different and the risks for the future may not be those estimated from past data to produce figures like the one above? The rapid change in prevalence shows how unreliable these factors must be, at prediction--they are retrospective of the particular patterns of the study subjects. Since we cannot predict the strengths or even presence of these or other new factors, what should we do? How can we rethink the problem?
These are the harder question, much harder than analyzing the data; but they are in our view the real scientific questions that need to be asked.
There's deep meaning in that last sentence: the prevalence of obesity is changing rapidly. This is being documented globally, and happening rapidly before our eyes. Perhaps the most obvious implication is that this serious problem is not due to genetics! That is, it is not due to genotypes that in themselves make you obese. Although everyone's genotype is different, the changes are happening during lifetimes, so we can't attribute it to the different details of each generation's genotypes or their evolution over time. Instead, the trend is clearly due to lifestyle changes during lifetimes.
Of course, if you see everything through gene-colored lenses, you might argue (as people have) that sure, it's lifestyles, but only some key nutrient-responding genes are responsible for the surge in obesity. These are the 'druggable' targets that we ought to be finding, and it should be rather easy since the change is so rapid that the genes must be few, so that even if we can't rein in McD and KFC toxicity, or passive TV-addiction, we can at least medicate the result. That was always, at best, wishful thinking, and at worst, rationalization for funding Big Data studies. Such a simple explanation would be good for KFC, and an income flood for BigPharma, the GWAS industry, DNA sequencer makers, and more.....except not so good for those paying the medical price, and those who are trying to think about the problem in a disinterested scientific way. Unfortunately, even when it is entirely sincere, that convenient hope for a simple genetic cause is being shown to be false.
A serious parody?
Year by year, more factors are identified that, by statistical association at least and sometimes by experimental testing, contribute to obesity. A very fine review of this subject has appeared in the mid-October 201 Nature Reviews Genetics, by Ghosh and Bouchard, which takes seriously not just genetics but all the plausible causes of obesity, including behavior and environment, and their relationships as best we know them, and outlines the current state of knowledge.
Ghosh and Bouchard provide a well-caveated assessment of these various threads of evidence now in hand, and though they do end up with the pro forma plea for yet more funding to identify yet more details, they provide a clear picture that a serious reader can take seriously on its own merits. However, we think that the proper message is not the usual one. It is that we need to rethink what we've been investing so heavily on.
To their great credit, the authors melded behavioral, environmental, and genetic causation in their analysis. This is shown in this figure, from their summary; it is probably the best current causal map of obesity based on the studies the authors included in their analysis:
If this diagram were being discussed by John Cleese on Monty Python, we'd roar with laughter at what was an obvious parody of science. But nobody's laughing and this isn't a parody! And it is by no means of unusual shape and complexity. Diagrams like this (but with little if any environmental component) have been produced by analyzing gene expression patterns even just of the early development of the simple sea urchin. But we seem not to be laughing, which is understandable because they're serious diagrams. On the other hand, we don't seem to be reacting other than by saying we need more of the same. I think that is rather weird, for scientists, whose job it is to understand, not just list, the nature of Nature.
We said at the outset of this post that 'the obesity epidemic seems to be getting worse'. There's a deep message there, but one essentially missing even from this careful obesity paper: it is that many of the causal factors, including genetic variants, are changing before our eyes. The frequency of genetic variants changes from population to population and generation to generation, so that all samples will look different. And, mutations happen in every meiosis, adding new variants to a population every time a baby is born. The results of many studies, as reflected in the current summary by Ghosh and Bouchard, show the many gene regions that contribute to obesity, their total net contribution is still minor. It is possible, though perhaps very difficult to demonstrate, that an individual site might account more than minimally for some individual carriers in ways GWAS results can't really identify. And the authors do cite published opinions that claim a higher efficacy of GWAS relative to obesity than we think is seriously defensible; but even if we're wrong, causation is very complex as the figure shows.
The individual genomic variants will vary in their presence or absence or frequency or average effect among studies, not to mention populations. In addition, most contributing genetic variants are too rare or weak to be detected by the methods used in mapping studies, because of the constraints on statistical significance criteria, which is why so much of the trait's heritability in GWAS is typically unaccounted for by mapping. These aspects and their details will differ greatly among samples and studies.
Relevant risk factors will come or go or change in exposure levels in the future--but these cannot be predicted, not even in principle. Their interactions and contributions are also manifestly context-specific, as secular trends clearly show. Even with the set of known genetic variants and other contributing factors, there are essentially an unmanageable number of possible combinations, so that each person is genetically and environmentally unique, and the complex combinations of future individuals are not predictable.
Risk assessment is essentially based on replicability, which in a sense is why statistical testing can be used (on which these sorts of results heavily rely). However, because these risk factor combinations are each unique they're not replicable. At best, as some advocate, the individual effects are additive so that if we just measure each in some individual add up each factor's effect, and predict the person's obesity (if the effects are not additive, this won't work). We can probably predict, if perhaps not control, at least some of the major risk factors (people will still down pizzas or fried chicken while sitting in front of a TV). But even the known genetic factors in total only account for a small percentage of the trait's variance (the authors' Table 2), though the paper cites more optimistic authors.
The result of these indisputable facts is that as long as our eyes are focused, for research strategic reasons or lack of better ideas, on the litter of countless minor factors, even those we can identify, we have a fat chance of really addressing the problem this way.
If you pick any of the arrows (links) in this diagram, you can ask how strong or necessary that link is, how much it may vary among samples or depend on the European nature of the data used here, or to what extent even its identification could be a sampling or statistical artifact. Links like 'smoking' or 'medication', not to mention specific genes, even if they're wholly correct, surely have quantitative effects that vary among people even within the sample, and the effect sizes probably often have very large variance. Many exposures are notoriously inaccurately reported or measured, or change in unmeasured ways. Some are quite vague, like 'lifestyle', 'eating behavior', and many others--both hard to define and hard to assess with knowable precision, much less predictability. Whether their various many effects are additive or have more complex interaction is another issue, and the connectivity diagram may be tentative in many places. Maybe--probably?--in such traits simple behavioral changes would over-ride most of these behavioral factors, leaving those persons for whom obesity really is due to their genotype, which would then be amenable to gene-focused approaches.
If this is a friable diagram, that is, if the items, strengths, connections and so on are highly changeable, even if through no fault of the authors whatever, we can ask when and where and how this complex map is actually useful, no matter how carefully it was assembled. Indeed, even if this is a rigidly accurate diagram for the samples used, how applicable is it to other samples or to the future?Or how useful is it in predicting not just group patterns, but individual risk?
Our personal view is that the rather ritual plea for more and more and bigger and bigger statistical association studies is misplaced, and, in truth, a way of maintaining funding and the status quo, something we've written much about--the sociopolitical economics of science today. With obesity rising at a continuing rate and about a third of the US population recently reported as obese, we know that the future health care costs for the consequences will dwarf even the mega-scale genome mapping on which so much is currently being spent, if not largely wasted. We know how to prevent much or most obesity in behavioral terms, and we think it is entirely fair to ask why we still pour resources into genetic mapping of this particular problem.
There are many papers on other complex traits that might seem to be simple like stature and blood pressure, not to mention more mysterious ones like schizophrenia or intelligence, in which hundreds of genomewide sites are implicated, strewn across the genome. Different studies find different sites, and in most cases most of the heritability is not accounted for, meaning that many more sites are at work (and this doesn't include environmental effects). In many instances, even the trait's definition itself may be comparably vague, or may change over time. This is a landscape 'shape' in which every detail is different, within and between traits, but is found in common with complex traits. That in itself is a tipoff that there is something consistent about these landscapes but we've not yet really awakened to it or learned how to approach it.
Rather than being skeptical about these Ghosh and Bouchard's' careful analysis or their underlying findings, I think we should accept their general nature, even if the details in any given study or analysis may not individually be so rigid and replicable, and ask: OK, this is the landscape--what do we do now?
These are the harder question, much harder than analyzing the data; but they are in our view the real scientific questions that need to be asked.
Tuesday, October 10, 2017
An article in Issues in Science and Technology
Regular MT readers will know that some of us here have a very skeptical view of the obsession with genomewide association mapping (GWAS) for every trait under the sun. We think that mapping served a purpose once upon a time, to show that complex apparently polygenic traits really were complex and polygenic. Identifying many contributing genome regions showed that, and that each individual has a unique genotype and that many or most relevant variants were too rare or their effects too weak to be detected (most heritability wasn't accounted for by the mapping). When tens, hundreds, or even thousands (yes!) of genome sites were claimed to contribute, it has seemed we're lost in never-never land when it comes to sensible explanations of causation.
But the funding keeps flowing for this mostly useless sort of Big Data (sorry, we can't salivate over that phrase the way so many do, because we're no longer out hunting for Big Grants). Our view, expressed many times and in many ways here, is that we need better ideas about the relationships between genes and health, and between genes and our traits and their evolution.
We've written about this in the past, but rather than do that again, I've written some of these issues in a somewhat different way in a new paper. That paper, in the new, Fall 2017, Issues in Science and Technology, "Is precision medicine possible?", lays out some thoughts about genetic causal complexity vis-à-vis 'precision' genomic medicine, and the challenges we face.
Rather than rehashing here what you can see in that article, if you're interested, just go to the article. It's in a journal related to policy, but the odds that any policymaker will read it carefully much less do anything constructive in response are between slim and none. Still, blogs are for stating a point of view!
The people whose truly genetic disorders are not being alleviated because we're dumping so much resource into stale ideas are being shortchanged. However, until we've made the alternative investment, in attack rather than 'mapping' disease, we'll not know how preventable or treatable they may be.
But the funding keeps flowing for this mostly useless sort of Big Data (sorry, we can't salivate over that phrase the way so many do, because we're no longer out hunting for Big Grants). Our view, expressed many times and in many ways here, is that we need better ideas about the relationships between genes and health, and between genes and our traits and their evolution.
We've written about this in the past, but rather than do that again, I've written some of these issues in a somewhat different way in a new paper. That paper, in the new, Fall 2017, Issues in Science and Technology, "Is precision medicine possible?", lays out some thoughts about genetic causal complexity vis-à-vis 'precision' genomic medicine, and the challenges we face.
Rather than rehashing here what you can see in that article, if you're interested, just go to the article. It's in a journal related to policy, but the odds that any policymaker will read it carefully much less do anything constructive in response are between slim and none. Still, blogs are for stating a point of view!
The people whose truly genetic disorders are not being alleviated because we're dumping so much resource into stale ideas are being shortchanged. However, until we've made the alternative investment, in attack rather than 'mapping' disease, we'll not know how preventable or treatable they may be.