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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.

The GWAS hoax....or was it a hoax? Is it a hoax?

A long time ago, in 2000, in Nature Genetics, Joe Terwilliger and I critiqued the idea then being pushed by the powers-that-be, that the genomewide mapping of complex diseases was going to be straightforward, because of the 'theory' (that is, rationale) then being proposed that common variants caused common disease.  At one point, the idea was that only about 50,000 markers would be needed to map any such trait in any global populations.  I and collaborators can claim that in several papers in prominent journals, in a 1992 Cambridge Press book, Genetic Variation and Human Disease, and many times on this blog we have pointed out numerous reasons, based on what we know about evolution, why this was going to be a largely empty promise.  It has been inconvenient for this message to be heard, much less heeded, for reasons we've also discussed in many blog posts.

Before we get into that, it's important to note that unlike me, Joe has moved on to other things, like helping Dennis Rodman's diplomatic efforts in North Korea (here, Joe's shaking hands as he arrives in his most recent trip).  Well, I'm more boring by far, so I guess I'll carry on with my message for today.....




There's now a new paper, coining a new catch-word (omnigenic), to proclaim the major finding that complex traits are genetically complex.  The paper seems solid and clearly worthy of note.  The authors examine the chromosomal distribution of sites that seem to affect a trait, in various ways including chromosomal conformation.  They argue, convincingly, that mapping shows that complex traits are affected by sites strewn across the genome, and they provide a discussion of the pattern and findings.

The authors claim an 'expanded' view of complex traits, and as far as that goes it is justified in detail. What they are adding to the current picture is the idea that mapped traits are affected by 'core' genes but that other regions spread across the genome also contribute. In my view the idea of core genes is largely either obvious (as a toy example, the levels of insulin will relate to the insulin gene) or the concept will be shown to be unclear.  I say this because one can probably always retroactively identify mapped locations and proclaim 'core' elements, but why should any genome region that affects a trait be considered 'non-core'?

In any case, that would be just a semantic point if it were not predictably the phrase that launched a thousand grant applications.  I think neither the basic claim of conceptual novelty, nor the breathless exploitive treatment of it by the news media, are warranted: we've known these basic facts about genomic complexity for a long time, even if the new analysis provides other ways to find or characterize the multiplicity of contributing genome regions.  This assumes that mapping markers are close enough to functionally relevant sites that the latter can be found, and that the unmappable fraction of the heritability isn't leading to over-interpretation of what is 'mapped' (reached significance) or that what isn't won't change the picture.

However, I think the first thing we really need to do is understand the futility of thinking of complex traits as genetic in the 'precision genomic medicine' sense, and the last thing we need is yet another slogan by which hands can remain clasped around billions of dollars for Big Data resting on false promises.  Yet even the new paper itself ends with the ritual ploy, the assertion of the essential need for more information--this time, on gene regulatory networks.  I think it's already safe to assure any reader that these, too, will prove to be as obvious and as elusively ephemeral as genome wide association studies (GWAS) have been.

So was GWAS a hoax on the public?
No!  We've had a theory of complex (quantitative) traits since the early 1900s.  Other authors argued similarly, but RA Fisher's famous 1918 paper is the typical landmark paper.  His theory was, simply put, that infinitely many genome sites contribute to quantitative (what we now call polygenic) traits.  The general model has jibed with the age-old experience of breeders who have used empirical strategies to improve crop, or pets species.  Since association mapping (GWAS) became practicable, they have used mapping-related genotypes to help select animals for breeding; but genomic causation is so complex and changeable that they've recognized even this will have to be regularly updated.

But when genomewide mapping of complex traits was first really done (a prime example being BRCA genes and breast cancer) it seemed that apparently complex traits might, after all, have mappable genetic causes. BRCA1 was found by linkage mapping in multiply affected families (an important point!), in which a strong-effect allele was segregating.  The use of association mapping  was a tool of convenience: it used random samples (like cases vs controls) because one could hardly get sufficient multiply affected families for every trait one wanted to study.  GWAS rested on the assumption that genetic variants were identical by descent from common ancestral mutations, so that a current-day sample captured the latest descendants of an implied deep family: quite a conceptual coup based on the ability to identify association marker alleles across the genome identical by descent from the un-studied shared remote ancestors.

Until it was tried, we really didn't know how tractable such mapping of complex traits might be. Perhaps heritability estimates based on quantitative statistical models was hiding what really could be enumerable, replicable causes, in which case mapping could lead us to functionally relevant genes. It was certainly worth a try!

But it was quickly clear that this was in important ways a fool's errand.  Yes, some good things were to be found here and there, but the hoped-for miracle findings generally weren't there to be found. This, however, was a success not a failure!  It showed us what the genomic causal landscape looked like, in real data rather than just Fisher's theoretical imagination.  It was real science.  It was in the public interest.

But that was then.  It taught us its lessons, in clear terms (of which the new paper provides some detailed aspects).  But it long ago reached the point of diminishing returns.  In that sense, it's time to move on.

So, then, is GWAS a hoax?
Here, the answer must now be 'yes'!  Once the lesson is learned, bluntly speaking, continuing on is more a matter of keeping the funds flowing than profound new insights.  Anyone paying attention should by now know very well what the GWAS etc. lessons have been: complex traits are not genetic in the usual sense of being due to tractable, replicable genetic causation.  Omnigenic traits, the new catchword, will prove the same.

There may not literally be infinitely many contributing sites as in the original statistical models, be they core or peripheral, but infinitely many isn't so far off.  Hundreds or thousands of sites, and accounting for only a fraction of the heritability means essentially infinitely many contributors, for any practical purposes.  This is particularly so since the set is not a closed one:  new mutations are always arising and current variants dying away, and along with somatic mutation, the number of contributing sites is open ended, and not enumerable within or among samples.

The problem is actually worse.  All these data are retrospective statistical fits to samples of past outcomes (e.g., sampled individuals' blood pressures, or cases' vs controls' genotypes).  Past experience is not an automatic prediction of future risk.  Future mutations are not predicable, not even in principle.  Future environments and lifestyles, including major climatic dislocations, wars, epidemics and the like are not predictable, not even in principle.  Future somatic mutations are not predictable, not even in principle.

GWAS almost uniformly have found (1) different mapping results in different samples or populations, (2) only a fraction of heritability is accounted for by tens, hundreds, or even thousands of genome locations and (3) even relatively replicable 'major' contributors, themselves usually (though not always) small in their absolute effect, have widely varying risk effects among samples.

These facts are all entirely expectable based on evolutionary considerations, and they have long been known, both in principle, indirectly, and from detailed mapping of complex traits.  There are other well-known reasons why, based on evolutionary considerations, among other things, this kind of picture should be expected.  They involve the blatantly obvious redundancy in genetic causation, which is the result of the origin of genes by duplication and the highly complex pathways to our traits, among other things.  We've written about them here in the past.  So, given what we now know, more of this kind of Big Data is a hoax, and as such, a drain on public resources and, perhaps worse, on the public trust in science.

What 'omnigenic' might really mean is interesting.  It could mean that we're pressing up ever more intensely against the log-jam of understanding based on an enumerative gestalt about genetics.  Ever more detail, always promising that if we just enumerate and catalog just a bit (in this case, the authors say we need to study gene regulatory networks) more we'll understand.  But that is a failure to ask the right question: why and how could every trait be affected by every part of the genome?  Until someone starts looking at the deeper mysteries we've been identifying, we won't have the transormative insight that seems to be called for, in my view.

To use Kuhn's term, this really is normal science pressing up against a conceptual barrier, in my view. The authors work the details, but there's scant hint they recognize we need something more than more of the same.  What is called for, I think is young people who haven't already been propagandized about the current way of thinking, the current grantsmanship path to careers.

Perhaps more importantly, I think the situation is at present an especially cruel hoax, because there are real health problems, and real, tragic, truly genetic diseases that a major shift in public funding could enable real science to address.

Rare Disease Day and the promises of personalized medicine

O ur daughter Ellen wrote the post that I republish below 3 years ago, and we've reposted it in commemoration of Rare Disease Day, Febru...