Tuesday, November 21, 2017

The Knowledge Factory Crisis: A different, anthropological way to view universities

Nothing we humans do lives up to its own mythology. We are fallible, social, competitive, acquisitive, our understanding is incomplete, and we have competing interests to address, in our lives and as a society.  I posted yesterday about universities as 'knowledge factories, reacting to a BBC radio program that discussed what is happening in universities, when research findings seem unrepeatable.

That program, and my discussion of what is going on at universities, took the generally expressed view of what universities are supposed to be, and examined how that is working.  The discussion concerned technical aspects that related to the nature of scientific information universities address or develop.  That is, in this context, their 'purpose' for being.  How well do they live up to what they are 'supposed' to be?

Many of my points in the post were about the nature of faculty jobs are these days, and the way in which pressures lead to the over-claiming of findings, and so on.  I made some suggestions that, in principle, could help science live up to its ideal.

Here in this post, however, I want to challenge what I have said about this.  Instead, I want to take a somewhat distanced viewpoint, looking at universities from the outside, in a standard kind of viewpoint that anthropologists take, rather than simply accepting universities' own assessments of what they are about.

Doing poorly by their ideal standard
My post noted ways in which universities have become not just a 'knowledge factory', but more crass business factories, as making money blatantly increasingly over-rides their legitimate--or at least, stated--role as idea and talent engines for society.  Here's a story from a few years ago about that, that is still cogent.  The fiscal pursuit discussed in this post is part of the phenomenon.  As universities are run more and more as businesses, which happens even in state universities, they become more exclusive, belying their original objective which (as in the land-grant public universities) was to make higher education available to everyone.  In addition to becoming money makers themselves, academia has become a boon for student-loan bankers, too.

But this is a criticism of university-based science, and expressed as it relates to how universities are structured.  That structure, even in science, leads to problems of science.  One might think that something so fundamentally wrong would be easy to see and to correct.  But perhaps not, because universities are not isolated from society--they are of society, and therein lies some deep truth.

Excelling hugely as viewed anthropologically
If you stop examining how universities compare to their ideals, or to what most people would tell you universities were for, and instead look at them as parts of society, a rather different picture emerges.

Universities are a huge economic engine of society.  They garner their very large incomes from various sources: visitors to their football and basketball stadiums, students whose borrowed money pays tuition, and agencies private and public that pour in money for research.  Whether or not they are living up to some ideal function or nature, they are a major and rather independent part of our economy.

Their employees, from their wildly paid presidents, down to the building custodians, span every segment of society.  The money universities garner pays their salaries, and buys all sorts of things on the open commercial economy, thereby keeping many other people gainfully employed.  Their activities (such as the major breakthrough discoveries they announce almost daily) generate material and hence income for the media industries, print and electronic, which in turn helps feed those industries and their relevant commercial influences (such as customers, television sales, and more).

Human society is a collective way for we human organisms to extract our living from Nature.  We compete as individuals in doing this, and that leads to hierarchies.  Overall, over time, societies have evolved such that these structures extract ever more resources and energy.  Via various cultural ideologies we are able to keep things going smoothly enough, at least internally, so as not to disrupt this extractive activity.

Religion, ownership hierarchies, imperialism, military, and other groups have self-justifications that make people feel they belong.  This contributes to building pyramids--whether they be literal, or figurative such as religions, universities, armies, political entities, social classes, or companies.  Often the justification is religious--nobility by divine right, conquest as manifest destiny, and so on.  That not one of these resulting societal structures lives up to its own ideology has long been noted.  Why should we expect universities to be any different?  These are the cultural ways people organize themselves to extract resources for themselves.

Universities are parasites on society, very hierarchical with obscenely overpaid nobles at the top?  They show no limits on the trephining they do on those who depend on them, such as graduating students with life-burdening debt?  They churn through those who come to them for whom they claim to 'provide' the good things in life?  Of course!  Like it or not, by promising membership and a better life, they are just like religions or political classes or corporations!

Institutions may be so caught up in their belief systems that they don't adapt to the times or competitors, or they may change their actions (if not always their self-description).  If they don't adapt they eventually crumble and are replaced by new entities with new justifications to gain popular appeal or acceptance.  However, fear not, because relative to their actual (as opposed to symbolic) role in societies, universities are doing very well: at present, they very clearly show their adaptability.

In this anthropological sense, universities are doing exceedingly well, far better than ever before, churning resources and money over far faster than ever before.  Grumps (like us) may point out the failings of lacking to live up to our own purported principles--but how is that different from any other engine of society?

In that anthropological sense, whether educating people 'properly' or not, whether claiming more discoveries that stand up to scrutiny, universities are doing very, very, very well.  And that, not the purported reason that an institution exists, is the measure of how and why societal institutions persist or expand.  Hypocrisy and self-justification, or even self-mythology, are always part of social organization. A long-standing anthropological technique for understanding distinguishes what are called emics, from etics: what people say they do, from what they actually do.

Yes, there will have to be some shrinkage with demographic changes, and fewer students attending college, but that doesn't change the fact that, by material measures, universities are incredibly successful parts of society.

What about the intended material aspect of the knowledge factory--knowledge?
But there is another important side to all of this, which takes us back to science itself, which I think is actually important, even if it is naive or pointless to crab at the hypocrisies of science that are explicable in deep societal terms.

This has to do with knowledge itself, and with science on its own terms and goals.  It relates to what could, at least in principle, advance the science itself (assuming such changes could happen without first threatening science's and scientists' and universities' assets).  That will be the subject of our next post.

Monday, November 20, 2017

The 'knowledge factory'

This post reflects much that is in the science news, in particular our current culture's romance with data (or, to be more market-savvy about it, Big Data).  I was led to write this after listening to a BBC Radio program, The Inquiry, an ongoing series of discussions of current topics.  This particular episode is titled Is The Knowledge Factory Broken?

Replicability: a problem and a symptom
The answer is pretty clearly yes.  One of the clearest bits of evidence is the now widespread recognition that too many scientific results, even those published in 'major' journals, are not replicable.  When even the same lab tries to reproduce previous results, they often fail.  The biggest recent noise on this has been in the social, psychological, and biomedical sciences, but The Inquiry suggests that chemistry and physics also have this problem.  If this is true, the bottom line is that we really do have a general problem!

But what is the nature of the problem?  If the world out there actually exists and is the result of physical properties of Nature, then properly done studies that aim to describe that world should mostly be replicable.  I say 'mostly' because measurement and other wholly innocent errors may lead to some false conclusion.  Surprise findings that are the luck of the draw, just innocent flukes, draw headlines and are selectively accepted by the top journals.  Properly applied, statistical methods are designed to account for these sorts of things.  Even then, in what is very well known as the 'winner's curse', there will always be flukes that survive the test, are touted by the major journals, but pass into history unrepeated (and often unrepentant).

This, however, is just the tip of the bad-luck iceberg.  Non-reproducibility is so much more widespread that what we face is more a symptom of underlying issues in the nature of the scientific enterprise itself today than an easily fixable problem.  The best fix is to own up to the underlying problem, and address it.

Is it rats, or scientists who are in the treadmill?
Scientists today are in a rat-race, self-developed and self-driven, out of insatiability for resources, ever-newer technology, faculty salaries, hungry universities....and this system can be arguably said to inhibit better ideas.  One can liken the problem to the famous skit in a candy factory, on the old TV show I Love Lucy.  That is how it feels to many of those in academic science today.

This Inquiry episode about the broken knowledge factory tells it like it is....almost.  Despite concluding that science is "sending careers down research dead-ends, wasting talent and massive resources, misleading all of us", in my view, this is not critical enough.  The program suggests what I think are plain-vanilla, clearly manipulable 'solutions.  They suggest researchers should post their actual data and computer program code in public view so their claims could be scrutinized, that researchers should have better statistical training, and that we should stop publishing just flashy findings.  In my view, this doesn't stress the root and branch reform of the research system that is really necessary.

Indeed, some of this is being done already.  But the deeper practical realities are that scientific reports are typically very densely detailed, investigators can make weaknesses hard to spot (this can be done inadvertently, or sometimes intentionally as authors try to make their findings dramatically worthy of a major journal--and here I'm not referring to the relatively rare actual fraud).

A deeper reality is that everyone is far too busy on what amounts to a research treadmill. The tsunami of papers and their online supporting documentation is far too overwhelming, and other investigators, including readers, reviewers and even co-authors are far too busy with their own research to give adequate scrutiny to work they review. The reality is that open-publishing of raw data and computer code etc. will not generally be very useful, given the extent of the problem.

Science, like any system, will always be imperfect because it's run by us fallible humans.  But things can be reformed, at least, by clearing the money and job-security incentives out of the system--really digging out what the problem is.  How we can support research better, to get better research, when it certainly requires resources, is not so simple, but is what should be addressed, and seriously.

We've made some of these points before, but with apology, they really do bear stressing and repeating.  Appropriate measures should include:

     (1) Stop paying faculty salaries on grants (have the universities who employ them, pay them);

     (2) Stop using manipulable score- or impact-factor counting of papers or other counting-based items to evaluate faculty performance, and try instead to evaluate work in terms of better measures of quality rather than quantity;

     (3) Stop evaluators considering grants secured when evaluating faculty members;

     (4) Place limits on money, numbers of projects, students or post-docs, and even a seniority cap, for any individual investigator;

     (5) Reduce university overhead costs, including the bevy of administrators, to reduce the incentive for securing grants by any means;

     (6) Hold researchers seriously accountable, in some way, for their published work in terms of its reproducibility or claims made for its 'transformative' nature.

     (7) Grants should be smaller in amount, but more numerous (helping more investigators) and for longer terms, so one doesn't have to start scrambling for the next grant just after having received the current one.

     (8) Every faculty position whose responsibilities include research should come with at least adequate baseline working funds, not limited to start-up funds.

     (9)  Faculty should be rewarded for doing good research that does not require external funding but does address an important problem.

     (10)  Reduce the number of graduate students, at least until the overpopulation ebbs as people retire, or, at least, remove such number-counts from faculty performance evaluation.

Well, these are snarky perhaps and repetitive bleats.  But real reform, beyond symbolic band-aids, is never easy, because so many people's lives depend on the system, one we've been building over more than a half-century to what it is today (some authors saw this coming decades ago and wrote with warnings). It can't be changed overnight, but it can be changed, and it can be done humanely.

The Inquiry program reflects things now more often being openly acknowledged. Collectively, we can work to form a more cooperative, substantial world of science.  I think we all know what the problems are.  The public deserves better.  We deserve better!

PS.  P.S.:  In a next post, I'll consider a more 'anthropological' way of viewing what is happening to our purported 'knowledge factory'.

Even deeper, in regard to the science itself, and underlying many of these issues are aspects of the modes of thought and the tools of inference in science.  These have to do with fundamental epistemological issues, and the very basic assumptions of scientific reasoning.  They involve ideas about whether the universe is actually universal, or is parametric, or its phenomena replicable.  We've discussed aspects of these many times, but will add some relevant thoughts in the near future.

Friday, November 10, 2017

33 Syllabi for Intro to BioAnth/ Intro to Human Origins and Evolution

Two years ago, many of you generously sent me your syllabi for your introductory biological anthropology courses when I put out a call here at The Mermaid's Tale. Thank you! Four teaching assistants who are also anthropology majors worked with me on a little study of these syllabi. My collaborators are Alexa Bracken, Katherine Burke, Nadine Kafeety, and Molly Jane Tartaglia and I am grateful for their work on this.

Here are our results...

  • n = 33 syllabi, from 2015 or before, gathered mostly from your helpful submissions and also collected from AAA and departmental websites, though not extensively. Institutions in 3 different nations and at least 17 U.S. states are represented
  • 29/33 require a textbook (as opposed to other readings/resources) 
  • 14/33 have separate labs/recitations
  • 18/33 teach natural selection before learning the genetic basis for variation [this 2017 study supports doing the opposite] 
  • 2/33 mention genetic drift and/or neutral evolution
  • 2/33 mention epigenetics
  • 3/33 mention evo-devo and/or development
  • 3/33 mention controversy/controversies
  • 0/33 mention creationism and/or creation
  • 4/33 mention 'racism' 
  • 1/33 mention 'sexism'

I've typed and deleted a lot of words here and can't seem to avoid sentences that read like I'm telling a bunch of my brilliant friends and colleagues that we're doing it wrong. I don't believe we are.

I understand that syllabi aren't perfect or even great representations of what we do in our courses.

But maybe we could be better at highlighting some of the more complicated and significant terrain we cover in class, in the syllabus. Syllabi are posted publicly; they're seen by countless faculty reviewers and administrators. I think that we biol/evol/physical anthropologists could do better at getting the word out that our courses are not simply the human equivalent of "Intro to walrus origins and evolution."

Anthropology is what makes human evolution different from walrus evolution. And now that we're freed, mostly, from having to teach that evolution is true, why don't we really go for it and teach that it's also okay that evolution is true? Why not face the cultural controversies, recognize the sordid (and worse) history of our discipline and evolutionary science, and that history's massive influence on our culture and society to this day? We are! I know. But let's put it on the syllabus to make it official.

Human evolution is fundamentally different from the rest of evolutionary biology and I believe it's dangerous to pretend it isn't, or to unintentionally give the impression that it isn't. I hope you agree.

Thursday, November 9, 2017

What we can learn from the birds and why there are birds


Evolution is a fact of life, but there are many different interpretations of how it works. There is the persistent classically Darwinian view, in which natural selection explains everything as a deterministic 'force'--clearly the kind of imagery Darwin himself had.  This is nowadays focused around genes as the metaphor for the competing deterministic causal factors that are responsible.  We know that even clearly adaptive traits we see today evolved through earlier stages of adaptation that may have had nothing to do with current functions.

We know now that this is a deeply important factor about the origins of the major functional traits of organisms, but also that life is complex and chance plays a major role in its dynamics.  In one sense this means selection cannot literally be force-like: it must have some 'probabilistic' aspects, even if there isn't a fixed probability, or probability process like coin-flipping, at work.  That aspect, due to competing selection and so on, is more like a series of one-off effects.  At the same time, the fast fox doesn't always catch the fleeing rabbit, so that even if selection is favoring 'fast' genes, there is an element of what would appear afterwards to have been probabilism in the change of fast-gene frequencies.

Every organism is subjected to functional challenges on all of its traits, all of the time, so that even if natural selection acted as a force (which it cannot really precisely be), which adaptive functions among this array of competing constraints win out will be affected by chance, because from trait A's viewpoint, the relative impact of selection on the other traits will always be changing.

We also know that there is complex genetic control of complex functions, and this involves gene duplication and multiple more or less equivalent pathways to similar outcomes.  So any given gene's effect on the trait will be affected by the other redundant genes it carries.

There is still a widespread, almost ritualistic view of evolution, informally at least, in terms of the genes 'for' some trait whose favorable variation was driven essentially in a deterministic, force-like way to replace other genetic alternatives in their species.  This can easily be seen even among biologists, who should know better, and especially in the biomedical community, in which at least some pratctitioners have actually been taught the premises of evolution at a serious level--beyond, for example, what is often purveyed in medical schools. 

A typical habit is to today's functions and traits, and the past's traits (only rarely the past's genes as well), and to extrapolate from then to now, using reasoning--typically informal reasoning--to connect the dots with steady lines, the way we treat objects falling to earth or planets orbiting stars.

However, much of this is because evolutionary change is highly subject to time-compression that both reflects and is caused by these assumptions.  The 'million' aspect of a million years is skipped over as if it were just a few days.  Yet, we are wholly aware of the immense timescales that apply to most evolutionary changes in complex functions, like, say, our brainpower or our upright posture.  One way to try, at least, to unhitch ourselves from these illusory lapses into physics-like determinism, is to look at things over a much more vast time scale, for which we actually have evidence.

An instructive case
It is probably impossible for us to really grasp the meaning of evolution's timescale.  That's the enormous value of mathematical modeling, if it is used properly.  Our 'ancient modern human' ancestors in the fossil record existed around 100,000 years ago, or arguably much less.  Our species has occupied the world since then, but even much of that well within the last 20,000 or so years (only around 12,000 in the Americas).

But we have some really good evidence of things on spans of times a thousand times as long--that is, on the order of 100,000,000 (a hundred million) years.  This example has to do with the evolution of flight.  A very fine discussion of feathered dinosaurs can be heard on the podcast of the BBC Radio 4 program "In Our Time", that can be downloaded as a podcast or listened to online; here is the link: http://www.bbc.co.uk/programmes/b099v33p.

How did dinosaurs or their precursors develop the complexly rearranged bodies, and the feathered exteriors that were required for flight and the evolution of birds?  What adaptations occurred and when, and can we know why?  Major recent fossil finds, largely in China, have opened these questions for much closer examination than was possible when the first bird fossil, archaeopteryx, was found in Europe in around 1861, right after Darwin's Origin of Species (1859).

This BBC discussion, even expressed implicitly in a strong selectionistic viewpoint, shows the subtleties of the issues, when 100 million years is the span and large the number of specimens.  If you listen carefully, you can see the many nuances, small changes, rudimentary beginnings and so on that were involved--and the nature of speculation and attempts to guess at the nature of the reasons for the existence of these small steps that eventually led to feathered flight--but that, in themselves, were mainly unrelated to flight.  It is a sobering lesson in evolutionary interpretation, and even this discussion necessarily lapses into speculation.

Tuesday, October 24, 2017

My so-called view of life

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)

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.

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.