From scroll to screen: the 500 year academic speed-up

How do academics deal with the chaos of online publishing these days, when it comes to evaluations for professional performance?  Deans, chairs and grant reviewers must make hiring, tenure, promotion, and funding decisions based on academic track records.  But what should be in that record?

We have been advocating our view that the social media, including things like blogs, Twitter, and so on as well as online publishing and open reviewing should count.  Indeed, perhaps the successful professor should be expected to work in this rapid, open mode of spreading his/her ideas, results, and influence.

How that could be done is an important question.  Chairs and deans tend to be conservative.  They want to be fair, and not to be bowled over by chaff and resume-padding.  We know some of the  issues that relate to 'peer' review and so on that lead to just that.  But the online world is chaotic, even as it's exciting, breathtaking, and vibrant.  Many are and more will be considering ways to get away from simple bean-counting (publications, citations, etc.) and move towards more substantial criteria (we've posted on this before, e.g. here).

Galen. De pulsibus. (Manuscript; Venice, ca. 1550). This Greek manuscript of Galen’s treatise on the pulse is interleaved with a Latin translation.  Wikimedia Commons

Nothing New
Today we wanted just to put this in a different perspective.  I am reading a very fine new book by Susan Mttern, called The Prince of Medicine, which is a life of the classic Greek physician Galen.  This sentence, in its context, struck me:  "Almost all significant medical writers had, apparently, commented on Hippocrates..."

Hippocrates of Cos; Rubens, 1638, Wikipedia

Why this struck me is this:  Galen's time (130-200 AD) was roughly 500 years after Hippocrates (and here we skip over who Hippocrates actually was, or wasn't).  By then, all scholars worth their stethoscopes had commented on the works of the great master of Cos.  His ideas had been lauded, picked on, updated, and debated. Various schools of thought, often as vitriolic as differences about selectionism or genetic determinism, chimed in to advocate their view, and critique Hippocrates, and over those five centuries, to critique each other.

It was a vigorous field of play, and must have included a lot of chaff along with the ideas that stuck around.  One would have to know what's what to be able to evaluate who should be listened to, and  who was just ranting or hacking away gratuitiously at the great founder of medicine.

Sound familiar?

We're lucky.  The same is going on now, but we get to enjoy its variegated flavors every day.  We don't have to wait five centuries and only judge retrospectively over the misty generations.

Actually, this is nothing new and it's not specific to the sciences.  The humanities are going online as well, if more slowly.  But in the last thousands of years the idea of descending trees of commentary were certainly part of western culture.  This, as I understand it at least, was the Talmudic and Scholastic and Koranic scholarship through the millennia since the separation of these religions.  It is likely also characteristic of Asian thought, but that's not something I know about.

Again, this took place over centuries, with the usual back and forth, sometimes polite, sometimes vitriolic. Just as science develops its schools of thought, so have religion and philosophy, based on their respective commentariats. But as with science even back in the classical days, things took generations.

We are lucky to live in our faster age, even if it can be too fast sometimes.  Our professional world will have its shakeout.  Ideas will come and go, win or lose in this arena.  But we may all live to see it.  If we can learn to use it creatively.

Gene therapy -- a technological challenge, but we're good at meeting technological challenges

A friend told me recently that gene therapy for diseases of the skin was making a lot of progress.  Given the fits-and-starts history of the field of gene therapy, the fact that the US Institute of Medicine has recommended that most research in gene therapy no longer require the special review it has long been subject to (discussed in Nature this week here), as well as recent announcements of progress and hope, I was interested in looking into it.

Gene therapy, or more generally genetic engineering, is the replacement of a disease-causing allele with a working one, or the silencing of a causal allele.  Gene therapy has a long history, and not always a successful one.  It first looked possible in the early 1970's when Friedmann and Roblin proposed in Science that "gene therapy may ameliorate some human genetic diseases in the future", before much at all about the association between genes and disease was understood. 

There has been much progress since the 70's in understanding the essentials of gene function, of course; the genetic underpinnings of many diseases, primarily rare, have been identified and, while gene therapy has seen some tragic failings, progress is being made.  In theory, while promises in the past were too grandiose, medicine is likely to see many more successes in treating single-gene diseases.  After all, when faced with a clear problem, humans are very good at engineering solutions.

The theory is straightforward: find the gene, figure out what's going wrong, and stop or replace it.  Once a faulty gene has been identified, though, the fundamental problem has been delivering the therapeutic DNA into the nucleus of cells in the affected organ, and then into the right site in the genome so that it can either halt coding for the defective protein or begin telling the cell how to make a working copy of the protein.

Gene therapy using an adenovirus vector: Wikipedia

One way to do the delivery is with a viral vector.  Viruses are naturals at delivering their own genes into host cells, so, in theory harnessing that ability for therapeutic purposes is ideal.  Target the right type of cell, and the introduced gene can be expressed directly or integrated into the host-cell's genome where it will be expressed.  However, most genetic diseases would require repeated delivery of the therapeutic DNA, which would mean repeated exposure to the virus.  Our immune systems are very good at identifying and targeting invading viruses; while this is usually a good thing, it's not so good for virus-based gene therapy that needs to be repeated.  And, if the patient was exposed to the virus before its use in therapy, it's less likely to be effective, again because of the immune response.

Gene therapy makes a comeback
Indeed, it was a severe inflammatory response to an adenovirus-delivered therapy that was responsible for the death of Jesse Gelsinger in 1999, a 19-year old subject in a clinical trial.  His death greatly reduced enthusiasm for gene therapy, and rightly so.  But it did eventually give researchers insight into safer and more effective DNA delivery systems, adeno-associated virus (AAV) being one, although the length of DNA that it can carry is limited, and thus so are the diseases it can eventually be used to treat.  Even so, as reported in a paper in ABBS last year ("Phoenix rising: gene therapy makes a comeback", ABBS (2012) 44 (8): 632-640, Lamberis), AAV-based therapy has been successful now for a genetic cause of blindness, LCA, and there were 80 on-going clinical trials testing the efficacy of AAV-based gene therapy in 2012. 

Retrovirus-based vectors are another approach.  An advantage over AAV is that these can incorporate much larger transgenes, but they have been associated with leukemia-like T lymphoproliferative disorder due to insertional mutagenesis, or random insertion of the virus and its engineered load into the target DNA.  Lentivirus-based vectors are another possibility, but they, too, have their downsides. 

Non-viral vectors consisting of "lipids, peptides, carbohydrates or nanoparticles that fuse with the cell membrane and release the therapeutic DNA in the cell cytoplasm" (Lamberis) depend on natural mechanisms of the target cell to take them in and transport them within the cell.  They aren't as efficient as viral systems, but are safer and less likely to trigger immune responses than virus-based vectors.  

A prime candidate for successful genetic engineering is skin disease.  The skin is an eminently accessible organ, so that if the problem of getting the therapeutic agent through the epidermal barrier is soluable, topical treatment of many skin diseases could be envisioned.  Amy Paller and colleagues reported success with this last year in PNAS.  They introduced "spherical nucleic acid nanoparticles conjugates" into the keratinocytes of mice, and in human skin grafted onto a mouse, with the ultimate goal of delivering RNA silencing systems that can inhibit the expression of faulty genes for a variety of keratin-associated skin diseases.

FIRST, the Foundation for Ichthyosis and Related Skin Types, awarded Paller with funding to continue with this work.  The foundation's announcement describes Paller's work this way: 

The blistering and thickening of skin seen in EI [epidermolytic ichthyosis] usually results from a change in a single letter of the DNA code (a mutation) in one copy of the gene that provides the codes for manufacture of a keratin protein in the upper layers of skin. Small interfering RNAs (siRNAs) are small pieces of genetic material that can identify DNA pieces and bind to them, preventing the gene from being translated into protein. siRNAs are able to distinguish the mutated DNA from the normal DNA, and thus are able to prevent only the abnormal keratin protein from being formed. The problem with siRNA has been getting it through the skin barrier to where it needs to go. Dr. Paller and her team have found a way to get the siRNAs through the skin, through nanotechnology. By putting about 30 copies of the siRNA all around a central gold nanoparticle (leading to what her group calls “spherical nucleic acids”), the siRNAs are able to be rubbed into skin in a simple moisturizer.

This indeed sounds as though it has potential.

CRISPR -- the next big thing
We can't end this post without mentioning another technological advance getting a lot of attention these days.  It exploits a portion of some prokaryotic immune systems, CRISPR, Clustered Regularly Interspaced Short Palindromic Repeats.  Found in bacterial and archaeal genomes, these short repeat DNA sequence loci, from invading structures called phage or plasmids, get incorporated into the prokaryote's genome between CRISPR repeats, as a 'search' sequence.  The CRISPR resulting structure is then the basis for recognition of exogenous invading genomes. The structure moves along an incomer's DNA till it detects a match to the incorporated 'search' sequence.  Then, aided by genes called CAS genes, it cuts the detected DNA.  Normally that destroys it, but other enzymes can be targeted to this cut, and repair it.  Here's where genetic engineering comes in -- the repair process can insert a user-designed sequence into the targeted break.

As Elizabeth Pennisi described in her piece, "The CRISPR Craze", (Science 23 August 2013 341(6148), 833-836), CRISPR are also showing potential for use in gene therapy if they can be used to "delete, add, activate or suppress targeted genes in human cells". The idea in general is that a harmful sequence could be detected, the DNA cut at this point, and a 'good' sequence inserted to replace the harmful part. Here's a video that shows the idea.  Mark Wanner at Jackson Labs also nicely describes the potential uses of CRISPR on his blog. 

There is much excitement about CRISPR's potential.  A story in The Independent quoted several scientists the other day, among them George Church, a geneticist at Harvard, who was one of the first to use CRISPR to actually edit nucleotides in human sequence. "“The efficiency and ease of use is completely unprecedented. I’m jumping out of my skin with excitement,” said Church."  Ok.  But there is still a lot of work to be done.  Getting the edited gene to the right place in the target genome without undesirable effects elsewhere will be a challenge, as with conventional gene therapy.

But, if humans are good at anything, it's technology, and gene therapy and genetic engineering are primarily technological challenges.  We often criticize the science of genetics as it portrays itself and its successes, but if these technologies live up to their potential, they can change a lot of lives for the better.

Hang him! Get it over with and worry about 'justice' later!

Buckminster Fuller famously said, “You never change things by fighting the existing reality.  To change something, build a new model that makes the existing model obsolete.”

We hear this all the time in the context of genetics.  Over and over and over, we've written about issues that we think are clear, obvious, certainly not secret; problems with genetics that we think explain why we pretty much can't expect to predict phenotype from genotype, or vice versa.  Many of the same issues apply to identifying environmental causes of disease.  This is the amassed evidence that some defendant (some 'theory' in the case of science) is being wrongly accused (wrongly attributed or applied).

It's more complicated than that, of course.  Mendelian genetics had its day, and the cause of many single gene diseases has been identified, but these are largely rare, often congenital conditions, and while these successes are priceless for families with these diseases, the same kind of success hasn't panned out for common, complex diseases.  And there are even more seriously troubled waters,  if not dangerous rapids, for Mendelian ideas up ahead--as we'll describe in upcoming posts!

The same problems apply to epidemiology -- when infectious diseases were more prevalent in the West, when the field was just coming into its own, infectious agents were readily identified, leading to prevention and cures.  Similarly, tobacco was identified long ago as a cause of disease, as was asbestos in buildings, and other chemical toxins.

Our methods work well when the causal agent has a strong effect, that one could see in a dark room while wearing dark glasses.  Extensive experimental and observational data show that mutations in the CFTR gene seem to cause cystic fibrosis, inhaling coal dust causes black lung disease.  But when multiple genes with small effects, or an interacting network of genes and environmental factors, or a complex diet rather than one component of a single food, or a combination of diet and exercise are the risk factors, identifying cause is harder.  Or impossible.

Courtroom inside Tombstone Courthouse State Historic Park, Photo by Matthew A. Lynn; Wikimedia

We've blogged a number of times in the last month or so about these issues.  About why we think genetics is bogged down with, generally, diminishing returns on ever-larger studies, why its promises of personalized genomic medicine, and the benefits of whole genome sequencing and so on have turned out to be over-promises and won't be attained nearly to their advertised extent. 

We're often told that we're too negative.  And we're told that if we don't have an alternate model, we should not criticize the status quo.  But we believe we've got a positive message, and that is that we've learned a tremendous amount in the last 20 or 30 years about what genes do.  The science wouldn't be where it is if we hadn't that knowledge.  There's still a lot we don't understand, but we think that at this stage, much of that is because we're constrained in our thinking by a prevailing model that doesn't accommodate all observations.  We think new thinking is in order, but, no, we can't personally offer it up.  We think about these things all the time, and work with others to explore the  issues, but the lesson of history is that the field, or some new young thinker will have to do that.

The positive message is that we have tons of what appears to be reliable knowledge, but knowledge that points to the possibility (or, depending on how you view things, high likelihood) that some very fundamental facts of life are missing, and that current methods are not suited to detect.  And the fact that the same sorts of things apply to modern genetics and to evolutionary reconstructions and interpretations, reinforces this:  what the current methods can do, and have done, is to show where the confusing, contrary, perplexing, and sometimes paradoxical issues are. 

So, it's odd to hear that if we can't produce a new model we should shut up.  Or, worse, that by constantly saying these things somebody, like a congressperson, might hear them and wonder if we really need as much money or as many university jobs, as we've been fortunate to have.  But maybe such a threat should exist, and clearly!  What a stimulus to serious thought!

Imagine a 12 person jury evaluating the evidence on a murder case.  Eleven vote to convict, but one juror doesn't, saying that the evidence doesn't add up.  She points out the contradictions and the bits of evidence the others have ignored and so on.  No, she doesn't know who did commit the murder, but she's convinced the defendant didn't.  But the others don't budge.  They tell her that if she can't tell them who did do it, they won't change their vote. Hang the accused!

There are problems in genetics....and to press this point is to do a service, not a disservice, to the elusive truth that confronts us.  Whether or not anybody cares to listen, we will continue to express our message, fallible as we are, as we see it.

But we're also trying, within our poor powers to add or detract (to paraphrase the Gettysburg Address), to find better ideas.

[This post contributed to equally by both Ken and Anne.]

We should be more circumspect about animal models in research

Mice can be cute and cuddly, or if they come into view unexpectedly they can trigger the stereotypical leap to a chair and the "Eeeek!!!" cry for help.  But many of us come across mice in a very different, less cute setting--in our research labs.  There, our mice surprise us in other ways, that ought to be just as scary.  The general issues and their ethical and scientific import are discussed in a nice article by Jennifer Couzin-Frankel in Science.  She makes the point that there are far fewer rules when it comes to running, analyzing and interpreting studies using animal models than for human clinical trials, and thus it's often difficult to evaluate their importance.

From: http://cdn.toonvectors.com/images/35/16985/toonvectors-16985-940.jpg

Animal studies generally are based on far fewer individuals than clinical trials.  If somehow we justify requiring hundreds of thousands of cases (say, diabetics) and at least as many unaffected controls, from multiple global studies, to claim to identify the genes 'for' a trait (GWAS and other similar mapping efforts), and even then don't find much, how is it that we expect to learn about this by mapping studies in a small study of mice? 

There are many examples in which serious and clear high-risk mutational effects found in humans do not arise in mice with similar mutations induced transgenically.  Altered BRCA1 and breast cancer is one of legions of examples.  Much is learned even in those cases, about aspects of the gene's biology, but not necessarily about why the difference in the trait between mice and humans.  Mouse skulls develop differently in many ways from those of humans, in particular involving the sutures (joints) between the bones in the skull vault that protects the brain.  Yet mutations causing abnormal suture closure in humans have quite similar effects in mice--that is, the mouse model seems to work, but why that is is somewhat unclear.  We study dental or limb development in mice, but their teeth and limbs are quite different from ours--however some of the same genes and gene interactions apply in both cases.

When one gene on its own doesn't usually account for human traits like disease (not even those with strongest effect, such as BRCA1 and breast cancer) why do we think we'll understand diabetes by making single-gene knockouts?  The rationale, and it's true to some extent, is that we are trying to work out, with animal models, how the gene works and why a mutant version can lead to disease. But this is so very incomplete that it's curious how much we invest in the approach (which, we must immediately acknowledge, we have been doing in our own lab for many years).  The challenge is to know when and why the differences exist--and not to over-extrapolate from mouse to human.

Couzin-Frankel has identified many issues and reports on NIH efforts to look at them.  They are serious and perhaps should threaten funding of such studies and diverting funds to better ways--and making the environment suitable for people to find them.  There are elements of scandal and misrepresentation in the drug area that she reports.  Perhaps at least more transparency can result, but forgetting that, the scientific issues are serious in their own right.

Of mice and not-men in many more ways
This article and those cited in it don't begin to touch many of the serious issues, that go way beyond small sample sizes, non-randomization, and so on.  Here are some others, that we have seen in our own personal experience with our own work, or those of colleagues:

1.  Doing very unpleasant things to mice and getting them approved by your local research ethical review board ("IRB"), torment if not torture often justified on the grounds of preventing disease, an argument often stretched to the limit to justify things with only the remotest connection to health.

2.  Reporting the most extensive transgenic effect because that one (author says either privately or even in the paper) is the 'most representative' of the engineered effect.

3.  Using statistical techniques to get more results than there is blood in a turnip from small samples, which have all sorts of unreported but potentially relevant nuances.

4.  Assuming that an experimental effect done on one mouse strain represents 'the mouse' and, worse, 'the human'.  In fact, many if not most transgenic manipulations yield different results for different test strains, and even worse than that, often the chosen strain is one chosen because it has relevant characteristics--more likely to get cancer, more responsive to genetic manipulation, and so on.

5.  Assuming that inbred mice are genetically homogeneous--that is, they have no variation within their own genomes (that is, both copies of their chromosomes, inherited one from each parent, are of identical sequence) and, just as bad, no variation among individuals of the same inbred strain.  Those who pay attention know that this is not accurate.  Getting the whole genome sequence of one mouse from a strain and using that to represent all mice of the strain is an example.

In our own lab we have used our computer program ForSim to do simulations of the inbreeding and inter-breeding process that is involved in work to identify transgenic effects or to identify ('map') genes whose variation affects some trait of interest.  It is easy to show that mouse strains, crosses, and representative sequences are not reliable indicators of all the potentially relevant variation that may exist in a particular study design.

There is no easy answer.  First, despite reservations and some public opposition, the rationale that we'll save children from horrible diseases if we give those diseases to mice is compelling.  We are, after all, in charge. We eat pigs and cows, make chickens live shoulder to shoulder indoors for their entire lives, and so on, so what we do to mice isn't all that different.

Second, despite knowing that mice are different from humans (we usually don't yell "Eeeeek!!" when we see another human), we have a very understandable tendency to assume they're the same. That leads us to make dramatic discovery announcements to the press (and granting agencies).  Sometimes these discoveries do, in fact, pan out as advertised.  Again, there is no obvious way to know in advance of, or even after, an experimental result is in how or whether it will work on humans.

But at least we should be vastly more circumspect about this sort of animal research. That's only fair to the voting public that pays for it based on what we promise them....and for the poor mice who have no vote in the matter.

The 'Oz' of medicine: look behind the curtain or caveat emptor!

There has been quite a stir over recent attempts to provide a general calculator of heart disease risk and associated recommendations.  In particular, how reliable and how independent are the offered recommendations relative to the prescription of lifetime use of medicine like statins?  How skeptical should the general public be about the reliability of the risks and the disinterestedness--lack of potential gain or self-interest--behind recommendations that a high fraction of the population go on life-long meds?

We've commented on aspects of this general issue before, and in particular about the shaky aspects of the GetOnStatins push.  Here's another article in the NYTimes about this, raising issues about the accuracy of the risk estimates--the predictions--based on the chosen risk factors (cholesterol levels, age, sex, and numerous others). These calculators give wide-ranging results and are now widely viewed to be inaccurate.  And the attacks we've seen are not the accusation that, while conflicts of interest seem likely to be a factor, the main problem is that pharma is pushing the recommendations to increase sales.

There are a couple of issues that are deeply problematic across a wide spectrum of the kinds of biomedical (and, indeed, evolutionary) research that is getting so much funding and public play.

First, the research and interpretations are based on what amounts to a deep belief system--yes, that's an apt word for it--an assumption that raw data collection, handed to high-speed computers, can find the patterns that will properly estimate risk, given various observed traits of the sampled person. That in turn amounts to assuming that such data do, in fact, contain and will reveal the nature of causal truth.  That is essentially the rationale for the glamorous touting of what is now catch-phrased as 'Big Data' (we need our branding, apparently).  It sounds impressive, and it generates attention and large, long-term studies that are the boon of professors who need to fund their own salaries and their research empires.

Whether the belief in computer analysis of uncritically collected data is scientifically appropriate or not is hard to separate from the thicket of vested career interests and the glamour of technology in our current society.  But there are a few clear-cut problems that a few write about but that the system as a whole dare not think too hard about because it might slow down the train.

Problem 1:  Risks are estimated retrospectively--from the past experience of sampled individuals, whether in a properly focused study or in a Big Data extravaganza.  But risks are only useful prospectively: that is, about what will happen to you in your future, not about what already happened to somebody else (which, of course, we already know).

We frequently mention this issue because it's a fundamental problem, and in the case of the heart disease calculator, it is that the people from whom the risks have been estimated lived importantly different lifestyles from people now using the calculator (and it's regardless of the unknown future risk exposures).  Smoking is lower and exercise higher.  Drug (legal) exposures are different, diagnoses and treatment options, etc.  Not to mention changes in prevalence of unknown risk factors.

This reveals something more that is deeply at the inescapable heart of the problem:  We respond to 'news' about risks by altering our behavior, companies market different things to us as a result, and we undertake other behavior whose relation to the disease is not known or whose exposure levels can't be predicted....not even in principle.  Thus we are saying essentially that "if you live like your grandparents did, this is your risk."  But in truth we are not seers, nor is any Oz behind the curtain, who know, or can know, how you will live or who controls all the outcomes--if only we could discover him. 

Problem 2:  The idea is that by doing statistical association studies (correlation or regression analysis, for example) on what happened in the past we are revealing the causal understructure of the results we care about.  Symbolically, we write  

DiseaseProb = RiskExposureAmount x Dose-responseEffect + otherStuffIincludingErrors.

Such equations are routinely referred to as constituting a causal 'model', but in truth it's nothing of the kind.  Instead it's generic rather than being developed from or tied in any serious way to the actual causation--the mechanism--that may apply to the risk factor.  And 'may' is decided by a subjective statistical test of some kind that we choose to apply to any associations we find in the form of data we choose to collect (our study or sample design).

We are usually not actually applying any serious form of 'theory' to the model or to the reesults.  We are just searching for non-random associations (correlations) that may be just chance, may be due to the measured factor, or may be due to some other confounding but unmeasured factors.  And even this depends on how 'randomness' plays into our sampling and to causation, and how important rare events that cannot be captured by most samples (e.g., very rare genetic variants, or somatically arising variants that are not transmitted).

It is by such reasoning that we feel we can assume that the same association we observe in our sample will apply to the other people whom we haven't observed.  Since we don't know how different your life will be from the lives from whom even these estimates were derived, we can't know how different your risks will be, even if we've identified the right factors.

Problem 3:  Statistical analysis is based on probability concepts, which in turn are (a) based on ideas of repeatability, like coin flipping, and (b) that the probabilities can be accurately estimated.  But people, not to mention their environments, are not replicable entities (not even 'identical' twins).  We are not all totally unlike each other, but are never exactly alike.  No two populations or two samples are identical (that fact ironically is based on a real theory, that of evolution).  National exhaustive data bases will always have such problems, and the extent of them is essentially unknown, and we have no theory for it, largely because of Problem 2: we have no real theory for the disease causation problem.

Problem 4:  Competing causes inevitably gum up the works.  Your risk of a heart attack depends on your risk of completely unrelated causes, like car crashes, drug overdoses, gun violence, cancer or diabetes, etc.  If those change, your risk of heart disease will change.  If you're killed in a crash today you can't have a stroke tomorrow.  But we cannot know if any such exposure factors will change or by how much.  The car-crash illuistration is perhaps trivial.  But environmental changes on a large scale certainly are not:  War or pestilence are extreme examples that we know are regular types of occurrence.

Problem 5:  Theory in physics is in many ways the historic precedent on which we base our thinking.  This arose in the time of Galileo and Newton.  One advantage physics has is that its objects are highly replicable.  It is believed that every electron, everywhere in the universe, is identical.  If you want to know if certain factors can reveal, say, the Higgs Boson, you collide beams of gazillions of identical protons at each other, their splatter pattern tells you something about their makeup.  Things scatter at a predictable pattern and though its probabilistic, you know the expected distribution from your theory and can then estimate, to the closest possible extent, whether the result fits.

But life is not replicable in that way, and life is the product of an evolutionary process that not only involves imperfect replication (of DNA from one generation to the next) and differential proliferation depending on local circumstances.  Life is about difference not replicability.

Problem 6:  Big Data is proposed as the appropriate approach, not a focused hypothesis test.  Big Data are uncritical data--by policy!  This raises all sorts of issues such as nature of sample and accuracy of measurements (of genotypes and of phenotypes).  Somatic mutation and cell-specific interactions and so forth, for example, are not measured, nor is the microbiome, and can't really be retro-collected. If the Big Data, Computers Are Everything approach works, then we will have undergone a change in the basic nature of science--it could be, of course, but now it's more a belief, for various reasons, than anything with sound theory behind it (other than theories of computing and statistics, etc., of course).

There is far insufficient evaluation of how well whole-population-based data bases, including CDC and cancer registries, have actually identified things not knowable in other ways--or what kinds of things (eg other than major factors) they found.  Even posterchildren, like Framingham Heart Study of heart disease risks, and tests of the drug warfarin and clotting, have not been unambiguous successes.  Had this been done modestly without even in the '90s promises of magic bullets, and was done on a modest scale, followed by proof of principle in terms of actual cures etc., then the situation might be different.

Based on fundamental beliefs that the ultimate control in the cosmos is at the level of basic physics, science believes that the world must be predictable and that everything, from gravity on up to life, must follow universal laws.  That drives the belief that if we observe enough things we can find out how those laws apply to life, which is more complex than a hydrogen atom.  But there could be, say, uncomputably many arrangements of components that explain much of biological causation, that statistical models can't explain even when statistical studies can reveal them, or causal interactions too indirect for our current kinds of non-theory-based statistical study designs can reveal.

This is old news that nobody wants to acknowledge
We're disclosing no secrets here, except the secret that the profession simply won't stop foisting this sort of approach onto the funding agencies and media, associated with promises of major positive social impact, rather than reforming the way science is done.  The profession is not confessing that basically one writes regression equations because computers can do them, Big Data can be fed into them, and....really, .... that we usually haven't much of a clue how the causation actually works.  Even if, as is likely often the case, a measured risk factor really is a risk factor, its connection with the outcomes is typically not even mildly understood, at present.  Yet our daily proclamations to the media.

Even major environmental or genetic risk factors often take extensive, highly focused studies to work out in any quantitative way that is very useful in telling people what their risks are or helping them decide what to do about them.  If a particular gene gives you, say, a 50% risk of a nasty disease, you might want to be screened, or you'll stop some behavior that confers such level of risk. You don't care if it's 55% or 71%.

But most risk factors change you risk by only a few percent, or less, and hardly measurable with accuracy, except by invoking the belief we discussed in Problem 3.

None of this is new to science or to this particular era in science.  But in an age of impatience, where so many vested interests drive the system, communication is so rapid, and self-promotion so prevalent among professionals and the media etc., the issues are not given much attention.  It doesn't pay to face up to them.  They are too challenging.

The drumbeat of critiques of research results that emblazon the media is over-matched by the blare of excited promises and proclamations.  It's as if there's an Oz behind the curtain who pulls various levers and makes things happen (and the scientist seems to claim that s/he is that Oz, or has secret communication with him).  It's an understandable human tendency, it's our way of doing business, even if it's only somewhat connected to the problems we say we're doing our best to solve.  Often, our best would be to slow down, scale down, focus and think harder. That's no guarantee, and it doesn't mean we shouldn't use technology and even large-scale approaches.  But we should stop using them mainly as a way of keeping the tap open.

Now yesterday there was another story, reflecting a long-known finding that obesity is a risk factor for breast cancer.  This isn't far-fetched, if the idea is correct that obesity relates to cholesterol which is a molecule in pathways related to various steroid hormones which could stimulate growth in breast cancer cells.  The idea is that if one lowers cholesterol, this risk might be lowered.  And how does one lower cholesterol?  Taking statins is one way.  So, like so many things, this is a complicated story.

It could be that the way things are being done, including Big Data 'omics' approaches is the best approach to genomic causation that there is, and we just won't get to the kinds of rigor that physics and chemistry enjoy.  Or that something better is due, and it'll come along when somebody has the right insight.  But that will likely happen to that person who is banging his/her head against problems like those we've outlined here, and in other posts.

Our yelling-"Fire!"-in-a-crowded-theater line

We had an experience last week that must eventually come to all who blog.  Or at least to those who allow comments, and who, as we do, wish to stimulate discussion and thought. We wrote a post about evolutionary psychology, arguing that behavior is unlikely to be as hard-wired as seems so often to be the default assumption, because it can swing drastically over time, influenced by cultural changes and so on.  That doesn't even mean that 'genes' have no role, but it does show that the effects of culture are such that it's unclear what is hard-wired, and we usually can't make confident specific evolutionary assessment of the behavior.  We said that the challenge is not in figuring out how we're hard-wired but in figuring out how our brains were hard-wired not to be hard-wired.

We moderate comments to prevent publication of all the spam that would otherwise end up cluttering every post.  We very much appreciate comments, believing that if this whole endeavor isn't a conversation, there's little point.  We are glad we are enough of a small, niche blog that we don't have to deal with trolls.  We publish every non-spam comment.

But last week, in response to our ev psych blog, a reader wrote suggesting, among other things, that because rape and crime are universal, these behaviors must be genetic.  His examples were not only the standard ones, so favored by the eugenicists of old, but also of rape by non-Europeans.  The comment struck us as having more than a tinge of racism and sexism, and seemed, to us, to be a modern reprise of pro-eugenics arguments as well.  We do not know the commenter, and perhaps nothing like that was in his mind, and the post itself said nothing about rape or crime!  So we wondered why those examples, the usual ones that have been used by the powerful to decide who is worthy, would be mentioned.  But, we published it anyway.  I moderate most of the comments.  I didn't consider not publishing it initially. It was in our view an ugly comment, which I thought would be easy to deal with in a reply. Ken replied. I thought his reply was a good one.  He summarized the nub of the commenter's points.

Then Holly weighed in, saying she wanted it to be known publicly that she wouldn't have published the comment.  Holly is a co-contributor to MT, and we respect her greatly, so we took her comment seriously.  What happens here reflects on her, too.  Should we delete the initial comment?  Delete that, and Ken's and Holly's and pretend none of it ever happened, neither the original comment nor our censoring of it?

Censorship: Wikimedia

After a lot of thought, I pulled just the comment.  Ken would have preferred to leave it, and his reply, but by the time he let me know that, the deed was done.  I left Ken's response, and Holly's, because I wanted readers to know we had censored a comment. Ken's comments indicated the tenor of the initial message, so it was pretty clear what he had been responding to.

Yes, we censored a comment.  To his credit, when he saw what we'd done the author emailed Ken and explained his position, as well as his dismay at our intolerance and scientific elitism.  Ken wrote back, explaining why his comment struck us as so out of line.  I am still uncomfortable with what we did, but I was uncomfortable with every option.

It's the only time we've ever pulled a comment.  We're not averse to disagreement. Indeed, as a friend we respect very much asked, after seeing what we'd done here, "How can you have a productive conversation if you exclude particular points of view from being heard?"

But this experience has made us realize that we do have limits.  When the disagreement strikes us as racist, sexist, and tainted with eugenics, that seems, to Ken and me, like a pretty good line to not cross.  

Interesting discussion site...

Here is an interesting online tv website, the Institute for Arts and Ideas (IAI).  There are a variety of discussions involving societal issues, including those related to science.  They are on the philosophical or sociopolitical side, rather than the technical, but interesting.

I don't know if they've done a discussion on big ag and so on, but they have dealt with evolution and medicine.

We just thought we'd point it out.