Rare Disease Day and the promises of personalized medicine

Our daughter Ellen wrote the post that I republish below 3 years ago, and we've reposted it in commemoration of Rare Disease Day, February 29th, each year since. I wish I could include an update reporting that the cause of her rare disease has been identified. She would very much like to know, not only because it would explain this thing that has defined so much of her life, but also because, in this genetics age, being able to tell a new doctor the cause of her condition would mean they'd have no doubts. Sometimes a diagnosis isn't enough, and when you have a rare disease doubt can remain a frequent aspect of encounters with the medical system.

It's not there there has been no action. After a lengthy, ultimately failed attempt by a previous lab, which was unsuccessful for reasons unclear to us but probably technology-related, Ellen is currently included in another large sequencing project, and we're hopeful that we'll get some kind of an answer. They've done whole genome sequencing of her DNA as well as Ken's and mine, and are about to begin to look for her causal variant. To date, we know that she hasn't been found to have one of the known variants associated with her disease. There are occasional reports of new variants in other families with the same disease, and that could help identify hers, but what if she doesn't have one of these, either?

Finding a causal gene variant is easiest when a disease is rare and there are multiple cases in one family but Ellen is the only person in our family, for as far back as we can trace on both sides, with HKPP. When the disease is rare and only one family member has it, there's not really a peg to hang your hat on -- where do you start to look for the causal variant?

Ellen has classic hypokalemic periodic paralysis (HKPP), a disease for which causal DNA variants in a small number of ion channel genes have been identified in a number of families, where they essentially act as classical Mendelian variants. There are several possibilities here -- she could have a de novo mutation, a mutation new to her that she inherited from neither parent. If it's one that is shared by other people with HKPP, that would be easy to identify, but if not, even if it's on one of the three genes, to date, that have been found to be associated with the disease, how could it be shown that it is causal, rather than simply a mutation with no effect? And searches of 'her' genome are based on blood samples, and what if she carries a somatic mutation that arose after the embryonic separation of blood-related tissues from other tissues?

Some families with HKPP have members with the supposed causal variant who are symptom-free. This isn't unusual in genetics -- it's been called "incomplete penetrance" for a century, which basically means that one can have a causal mutation without the condition it apparently does cause in others. There can be various explanations for this. For example, when a disease responds to environmental triggers, as does HKPP, it's possible that gene by environment interaction at some critical age is required to set up the cascade of events that lead to paralytic episodes. Curiously, HKPP generally begins at puberty, for some unidentified reason -- perhaps some triggering event doesn't happen in disease-free family members with a causal variant, or perhaps the disease is polygenic rather than monogenic and those who are disease-free don't have the required critical mass of variants. This means that it's possible that Ken or I could have "the" causal variant but, because of incomplete penetrance -- whatever effect that would mean -- we don't have the disease. Or, we gave Ellen a mix of variants that together cause her disease but neither of us had the same mix that came together in her. But, at the very least, neither of us carries a known or plausibly relevant variant in the known HKPP-related genes that have been tested.

Ellen isn't the only person with HKPP whose cause is not known. Perhaps there are other ion channel genes associated with the disease, that are not yet identified. Or, perhaps in some people it's too genetically complex for causation to be parsed. Because of all these possible difficulties, identifying the cause of Ellen's disease is not likely to be straightforward. We are hopeful that the geneticists currently working on this will have something to tell her in the end, but whether it's something simple that she'll be able to tell her doctors we don't yet know.

This is one personal story for Rare Disease Day, but I think it's very relevant to all the promises of "personalized medicine" being made these days. Having your DNA sequenced isn't a magic answer. Sometimes the technology is limiting, sometimes the problem is actually impossible to solve.

By Ellen Weiss

Despite being the product of two of the authors of this blog – two people skeptical about just how many of the fruits of genetic testing that we've been promised will ever actually materialize  – I have been involved in several genetic studies over the years, hoping to identify the cause of my rare disease.

February 29 is Rare Disease Day; the day on which those who have, or who advocate for those who have, a rare disease publicly discuss what it is like to live with an unusual illness, raise awareness about our particular set of challenges, and talk about solutions for them.

I have hypokalemic periodic paralysis, which is a neuromuscular disease; a channelopathy that manifests itself as episodes of low blood potassium in response to known triggers (such as sodium, carbohydrates, heat, and illness) that force potassium from the blood into muscle cells, where it remains trapped due to faulty ion channels.  These hypokalemic episodes cause muscle weakness (ranging from mild to total muscular paralysis), heart arrhythmias, difficulty breathing or swallowing and nausea.  The symptoms may last only briefly or muscle weakness may last for weeks, or months, or, in some cases, become permanent.

I first became ill, as is typical of HKPP, at puberty.  It was around Christmas of my seventh grade year, and I remember thinking to myself that it would be the last Christmas that I would ever see.  That thought, and the physical feelings that induced it, were unbelievably terrifying for a child.  I had no idea what was happening; only that it was hard to breathe, hard to eat, hard to walk far, and that my heart skipped and flopped all throughout the day.  All I knew was that it felt like something terrible was wrong.

Throughout my high school years I continued to suffer. I had numerous episodes of heart arrhythmia that lasted for many hours, that I now know should've been treated in the emergency department, and that made me feel as if I was going to die soon; it is unsettling for the usually steady, reliable metronome of the heart to suddenly beat chaotically. But bound within the privacy teenagers are known for, my parents struggled to make sense of my new phobic avoidance of exercise and other activities as I was reluctant to talk about what was happening in my body.

HKPP is a genetic disease and causal variants have been found in three different ion channel genes.  Although my DNA has been tested, the cause of my particular variant of the disease has not yet been found.  I want my mutation to be identified.  Knowing it would likely not improve my treatment or daily life in any applicable way.  I'm not sure it would even quell any real curiosity on my part, since, despite having the parents I have, it probably wouldn't mean all that much to this non-scientist.  

But I want to know, because genetics has become the gold standard of diagnostics.  Whether it should be or not, a genetic diagnosis is considered to be the hard-wired, undeniable truth.  I want that proof in my hand to give to physicians for the rest of my life.  And of course, I would also like to contribute to the body of knowledge about HKPP in the hopes that future generations of us will not have to struggle with the unknown for so many years.

For many people, having a rare disease means having lived through years of confusion, terrible illness, misdiagnoses, and the pressure to try to convince skeptical or detached physicians to engage in investigating their suffering.

I was sick for all of my adolescent and young adult years; so sick that I neared the edge of what was bearable.  The years of undiagnosed, untreated chaos in my body created irrevocable changes in how I viewed myself and my life.  It changed my psychology, induced serious anxiety and phobias, and was the backdrop to every single detail of every day of my life.  And yet, it wasn't until I was 24 years old that I got my first clinical clues of what was wrong.  An emergency room for arrhythmia visit revealed very low blood potassium.  Still, for 4 more years I remained undiagnosed, and there was horrible suffering during which my loved ones had to take care of me like a near-infant, accompanying me to the hospital, watching me vomit, struggle to eat or walk to the bathroom, and waking up at 3am to take care of me.  For 4 more years I begged my primary physician and countless ER doctors during desperate visits to investigate what was going wrong, asked them to believe that anxiety was a symptom not a cause, and scoured medical information myself, until I was diagnosed.  It wasn't until I was 28 that I found a doctor who listened to me when I told him what I thought I had, made sense of my symptoms, recognized the beast within me, and began to treat me.

My existence, while still stained to a degree every day by my illness, has improved so immeasurably since being treated properly that the idea of returning to the uncontrolled, nearly unbearable sickness I once lived with frightens me very much.  I fear having to convince physicians of what I know of my body again.

What I went through isn't all that uncommon among the millions of us with a rare disease.  Lengthy periods of misdiagnoses, lack of diagnoses, begging well-meaning but stumped, disbelieving, or truly apathetic physicians to listen to us are common themes.  These lost years lay waste to plans, make decisions for us about parenthood, careers, and even whether we can brush our own teeth.  They induce mistrust, anxiety, exhaustion.

Each rare disease is, of course, by definition rare.  But having a rare disease isn't. Something like 10% of us has one.  It shouldn't be a frightening, frustrating, lengthy ordeal to find a physician willing to consider that what a patient is suffering from may be outside of the ordinary since it isn't all that unlikely at all.  Mathematically, it only makes sense for doctors to keep their eye out for the unusual.

I hope that one day the messages we spread on Rare Disease Day will have swept through our public consciousness enough that they will penetrate the medical establishment.  Until then, I will continue to crave the irrefutable proof of my disorder.  I will continue to worry about someday lying in a hospital bed, weak and verging on intolerably sick, trying to convince a doctor that I know what my body needs, a fear I am certain many of my fellow medically-extraordinary peers share.

And that is why I, this child of skeptics, seek answers, hope and proof through genetics.

Higher resolution discrimination: The GOP wants to allow employers to require genetic testing

This morning, Ed Yong published an article that takes on issues that we at the The Mermaid's Tale care very deeply about.

Link to article

The consequences for important medical research are not going to be pretty.

And I can't help but be angry about this for threatening to take away the fun of genetics too. If we can't have some control over our genetic testing, we can't do it for fun, for education, for finding out more about ourselves, for the awe of it, for innerspace exploration in the technology age. They're taking that away from us by eroding GINA.

I have lots of other thoughts... like about how this fits in so nicely with (not all of) the right's racist/eugenics inclinations.

And juxtapose this view from the political right where there is full-on acceptance of actually-more-than-genetics-can-even-deliver against their anti-science politics and policy...

It's like science is totally fine for Republicans as long as Mother Nature is a dictator.

If it's more complicated than that, then deny it, defund it, bulldoze it. The reality is, genetics is largely probabilistic; it is not a dictatorship. It's just so hard to convince people that it isn't. The ideological drive to justify behavioral differences and socioeconomic inequality with Nature above all is just too strong. If it's Nature, then we don't have to do the hard work of addressing the problems because Nature is Nature is Nature. This is really old thinking that really new knowledge (both through lots of science and lots of lived experience and lots of humanities and lots of art) has overturned but has not managed to catch on all that well. Along with new knowledge we get increasing understanding of genetics so these ancient beliefs can just be spouted by politicians using new-fangled science jargon.

This is really hard to write about today as all the stories about the proposed (and highly probable) budget cuts to science and the arts are blasting through my newsfeeds. It's overwhelming me today. I'm feeling hopeless and angry on behalf of science, art, knowledge, medicine, humanity, humans, children, teenagers, grown-ups, geezers. It's too much today.

But, back to Ed's article, I do need to put this here because it mentions that I have taught with 23andMe and longtime readers of the MT might know about that:

I don't teach with 23andMe anymore. I was doing it for as long as my university would pay for the kits. It was totally voluntary and students had to read Misha Angrist's book and endure long discussions and pass a quiz before deciding whether to go through with the testing. It was so powerful for teaching evolution, genetics, anthropology, etc... and we critiqued the hell out of it. My university said I needed to pay for the kits through course fees from now on. Before any of these threats to GINA, I decided not to do that and to stop using 23andMe. Now, even if my university reconsidered and funded the kits, I still wouldn't take it up again as a teaching tool.

Be afraid of fear, not personal genomics.

It's just the way it is now. This headline. This story.
(click to read/hear)

As Genetic Sequencing Spreads, 

Excitement, Worries Grow

It follows the recipe. (1) Start with a headline that demonstrates controversy. (2) Present a story about science-related news (which does not require controversy to be news). (3) End it ever-so briefly and vaguely with dissent, doubt, outcry or warning. 

This recipe applied to personal genomics is particularly bad.

If you read or hear that story you might be primed before you start to wonder, okay what's the worry? Glad this article will tell me, finally, what we should be concerned about concerning this brave new world of personal genomics.

But you'll be sorry when you reach the end and this is all you get:

But the idea of widespread sequencing is setting off alarm bells. How accurate are the results? How good are doctors at interpreting the results, which are often complicated and fuzzy? How well can they explain the subtleties to patients? The fear is that a lot of people could end up getting totally freaked out for no reason. And there are concerns about privacy. Scientists recently even sequenced a fetus in the womb, raising the possibility of everyone getting sequenced before or at birth — a prospect with a whole new set of questions and concerns. "I think there are lots of populationwide and individual dangers," said Mark Rothstein, a bioethicist at the University of Louisville. "We're basically not ready for a society in which very exquisite, detailed genomic information about every individual, potentially, is out there."

Why? Tell us? And I don't mean the "us" who have access to the academic journals. Or the "us" who have the patience to bushwhack through the jargon. I mean, here is your chance to share with the public who you're concerned about: Since you brought it up, tell us why we should worry.

It's unclear who deserves the complaints and the criticism for producing pieces like this, since much of the "telling us" that I'm begging for might be lying on the cutting room floor.

I'm clenched about this because right now about 20 students in my Human Varition (Anthropology 350) course at the University of Rhode Island are voluntarily participating in genotyping through 23andMe. And I'm using this curriculum for the second semester now. After last spring, where over 100 students in both Human Variation and also the introductory level Human Origins (Anthropology 201) did 23andMe, not one student got "totally freaked out." This along with much of my experience with genotyping and undergraduates indicates that, with education and with understanding, personal genomics does not induce fear. Not coincidentally, participating in personal genomics aides in education.

And the same fear that I'm trying to mitigate through education is the same fear that some journalists and ethicists seem to be perpetuating if not creating.

In my experience, if you're informed, you're likely to appreciate biological complexity rather than cling to genetic determinism. If you're informed, you understand the positive and negative consequences and aspects of personal genomics. If you're informed, you don't get lured into personal genomics for all the wrong reasons. You don't order an expensive 23andMe spit kit as if it's snake oil. You don't send your vial of saliva to California, along with 300 of your precious bucks, because you think it will help you to live a longer, healthier life, or because you think it will show you your future.  Spit kits are not crystal balls, are not medicine, are not cures. Plus, the results will also most certainly change! Not your genotypes, but how they're interpreted. That genomes must even be "interpreted" should be a flag shouldn't it?

Informed citizens and consumers don't buy into personal genomics thinking it's their one and only answer-- their key to "me"-- because "me" will be increasingly different the more we learn about genetics and the links between genotypes and phenotypes. "Me" is, for most, too stubborn and conservative, while at once too big and too free, to be dictated by genotypes and probabilistic phenotypes.

All that is guaranteed with a 23andMe spit kit is that you will see parts of yourself that you haven't seen before. There's not a whole lot on the planet that's cooler than that. For most of us who will never go to Mars, at least we've got this, at least we've got innerspace.

Even if you don't get an ounce of joy from the experience, when you're informed you don't fall uncritically for claims that spit kits are dangerous or venomous.

Considering the engaging educational opportunites provided by personal genomics, considering its power to inform, spit kits may just be much-needed anti-venom.

In my experience education diminishes fear about genetic determinism because it diminishes genetic determinism. That leads me to see fear of personal genomics as a symptom of ignorance. And that's something worth being afraid of.

**

Note: Ken, Anne and I have differing views on direct-to-consumer (DTC) personal genomics like 23andMe so please remember that I speak only for myself when I write. Also, I am not paid  or sponsored by 23andMe to endorse their product. I use their product, at the educational rate, to teach anthropology at the University of Rhode Island.

Probability does not exist! Part IV. Here's to your health!

Probability and unique events
Probability and statistics are very sophisticated, technical, often very mathematical sciences.  The field is basically about the frequency of occurrence of different possible outcomes of repeatable events.

When events can in fact be repeated, a typical use of statistical theory is to estimate the properties of what's being observed and assume, or believe, that these will pertain to future sets of similar observations.  If we know how a coin flipped in repeated observations in the past, we extrapolate that to future flips of that coin--or even to flips of other 'similar' coins.  If we observe thousands of soup cans coming off an assembly line, and know what fraction were filled slightly below specified weight, we can devise tests for efficiency of the machinery, or methods for detecting and rejecting under-weight cans.  And there are countless other situations in which repeatable events are clearly amenable to statistical decision-making.

When events cannot be or haven't been repeated, a common approach is to assume that they could be, and use the observed single-study data to infer the likely outcomes of possible repetitions.  As before, we extend our inference to to new situations in which similar conditions apply. In both truly and singular events there is similar reasoning, regardless of the details about which statisticians vigorously argue.

Everyone acknowledges that there is a fundamentally subjective element in making judgments, as we've described in the previous parts of this series of posts.  They are called, for example, significance tests from which one must choose a cutoff level or decision level.  But in well-controlled, relatively simple, especially repeatable situations, the theory at least provides some rigorous criteria for making the subjective choices.

The issues become much more serious and problematic when the situation we want to understand is either not replicable, not simple, not well understood, or in which even our idea of the situation is that the probabilities of different possible outcomes are very similar to each other.  Unfortunately, these are basic problems in much of biology.

Like dice, outcome probabilities are estimated from empirical data--past experience or experiments and finite (limited) samples.  Estimation is a mathematical procedure that depends on various assumptions and values, like averages of some measured trait, have measurement error and so on.  One might question these aspects of any study of the real world, but the issue for us here is that these estimates rest on some assumptions and are retrospective, because they are based on past experience.  But what we want those estimates for is to predict, that is to use them prospectively.

This is perhaps trivial for dice--we want to predict the probability of a 6 or 3 in the next roll, based on our observations of previous rolls.  We can be confident that the dice will 'behave' similarly.  Remarkably, we can also extrapolate this to other dice fresh from a new pack, that have never been rolled before, but only on the assumption that the new dice are just like the ones our estimates were derived from.  We can never be 100% sure, but it seems usually a safe bet--for coin-flips and dice.

Predicting disease outcomes
But this is far from the case in genetics, evolution, and epidemiology.  There, we know that no two people are genetically alike, no two have exactly the same environmental or lifestyle histories.  So that people are not exactly like dice.  Further, genes change (by mutation) and environments change, and these changes are inherently unpredictable as far as is known.  Thus, unlike dice, we cannot automatically extrapolate estimates from past experience such as genes or lifestyle factors and disease outcomes, to the future -- or from past observations to you.  That is, often or even typically, we simply cannot know how accurate an extrapolation will be, even if we completely believe in the estimated risks (probabilities) that we have obtained.

And, any risk estimation is inherently elusive anyway because people respond.  If you're told your risk of heart disease is 12%, that might make you feel pretty safe and you might stop exercising so much, or add more whipped cream to your cocoa, or take up smoking, but if you're told your risk is 30% you might do the opposite.  Plus, there's some thought that heart disease might have an infectious component, and that's never included in risk estimators, and is inherently stochastic anyway.  And, if there's a genetic component to risk, that can vary to the extent that many families might have an allele unique to them, which can't be included in the model because models are built on prior observations that won't apply to that family. 

A second issue is that even if the other things are orderly, in genetics and epidemiology and trying to understand natural selection and evolution, we are trying to understand outcomes whose respective probabilities are usually small and usually very similar.  As we've tried to show with the very similar (or identical?) probabilities of Heads vs Tails, or of 6 vs 3 on a die, this is very difficult even in highly controlled, easily repeatable situations.  But this simply is often not nearly the case in biology.

Here the risks of this vs that genotype, at many different genes simultaneously, are very indivdually small and similar, and that's why GWAS requires large samples, often gets apparently inconsistent results from study to study, accounts for small fractions of heritability (the estimated  overall genetic contribution).  This means that it is very difficult to identify genetic contributions that are statistically significant--that have strong enough effects to pass some subjective decision-making criterion.

This means it's very difficult to estimate a statistically reliable risk probability to persons based on their genotype, and certainly makes it difficult to assign a future risk. Or to know whether each person with that genotype has the same risk as the average for the group.  That is why many of us think that the current belief system, and that's what it is!, in personalized genomic medicine, is going to cost a lot for relatively low payoff, compared to other things that can be done with research funds---for example, to study traits that really are genetic: for which the risk of a given genotype is so great, relative to other genotypes, that we can reliably infer causation that is hugely important to individuals with the genotype, and for which the precision of risk estimates is not a big issue.


Probabilities and evolution
Similarly, in reconstructing evolution, if the differences among contemporary genotypes in terms of adaptive (reproductive) success are very similar, the actual success of the bearers of the different genotypes will be very similar, and these are probabilities (of reproduction or survival).  And if we want to estimate selection situations in the distant, unobserved past, from net results we see today, the problems are much more challenging even if we thoroughly believe in our theories about adaptive determinism or genetic control of traits.  Past adaptation also occurs, usually we think, very slowly over many many generations, making it very difficult to apply simple theoretical models.  Even to look for contemporary selection, other than in clear situations such as the evolution of antibiotic or pesticide resistance, is very challenging.  Selective differences must be judged only from data we have today, and directly observing causes for reproductive differences in the wild today is difficult and requires sample conditions rarely achievable.  So naturally it is hard to detect a pattern, hard to make causal assertions that are more than storytelling.


And, finally
We hope to have shown in this series of posts why we think we have to accept that 'probability' is an elusive notion, often fundamentally subjective and not different from 'belief'.  We set up criteria for believability (statistical significance cutoff values) upon which decisions--and in health, lives--depend.  The stability of the evidence and vagaries of cutoff-criteria, and our often reluctance to accept results we don't like (treating evidence that doesn't pass our cutoff criterion but is close to it as 'suggestive' of our idea rather than rejecting our idea), all conspire to raise very important issues for science.  The issues have to do with allocation of resources, egos, and other vested interests upon which serious decisions must be made.

In the end, causation must exist (we're not solopsists!), but randomness and probability may not exist other than in our heads.  The concept provides a tool for evaluating things that do exist, but in ways that are fundamentally subjective.  But we are in such a hurry in the system of science and its use that has evolved that we are not nearly humble enough in regard to what we know about what we don't know.   That is a fact that exists, whether probability does or not!

It is for these kinds of reasons that we feel research investment should concentrate on areas where the causal 'signal' is strong and basically unambiguous--traits and diseases for which a specific genetic causation is much more 'probable' than for the complex traits that are soaking up so many resources.  Even the 'simple' genetic traits, or simple cases of evolutionary signal, are hard enough to understand.

"Multikulti is dead"

Now
Sometimes we ask ourselves if we're way out in left field when we occasionally caution that the wave of new genetic determinism needs to be watched lest it be a harbinger of a new era of eugenics.  Objections to this say that the new genetics is for biomedical and other improvements, not negative discrimination--but that, of course, privacy is needed to protect against the misuse of data by, for example, insurance companies.

And then an Angela Merkel will declare, to a standing ovation, that multiculturalism has 'utterly failed' in Germany.  Germany, of all places.

At "the beginning of the 1960s our country called the foreign workers to come to Germany and now they live in our country," said Ms. Merkel at the event in Potsdam, near Berlin. "We kidded ourselves a while. We said: 'They won't stay, [after some time] they will be gone,' but this isn't reality. And of course, the approach [to build] a multicultural [society] and to live side by side and to enjoy each other ... has failed, utterly failed."

The crowd gathered in Potsdam greeted the above remark, delivered from the podium with fervor by Ms. Merkel, with a standing ovation. And her comments come just days after a study by the Friedrich Ebert Foundation think tank (which is affiliated with the center-left Social Democratic Party) found that more than 30 percent of people believed Germany was "overrun by foreigners" who had come to Germany chiefly for its social benefits.

The study also found that 13 percent of Germans would welcome a "Führer" – a German word for leader that is explicitly associated with Adolf Hitler – to run the country “with a firm hand.” Some 60 percent of Germans would “restrict the practice of Islam,” and 17 percent think Jews have “too much influence,” according to the study. 

And the far-right is gaining all over Europe from an anti-immigrant backlash -- largely, but not entirely, anti-Muslim.  Many Roma have been deported from France in the last few months.  And of course this anti-immigration fervor is not restricted to Europe.  Here in the US we've got a level of rage we haven't seen for decades, as Frank Rich writes in the Sunday NYTimes.  And we've got the Tea Party movement, which officially is calling for smaller government, lower taxes, and strict interpretation of the Constitution, but the group is apparently most attractive to those with, shall we say, less than inclusive views.  Here we've lifted a paragraph from the Wikipedia description of the Tea Party:

Various polls have also probed Tea Party supporters for their views on a variety of political and controversial issues. A University of Washington poll of 1,695 registered voters in the State of Washington reported that 73% of Tea Party supporters disapprove of Obama's policy of engaging with Muslim countries, 88% approve of the controversial immigration law recently enacted in Arizona, 82% do not believe that gay and lesbian couples should have the legal right to marry, and that about 52% believed that "lesbians and gays have too much political power."

So the background of us vs. them is building.  What about the will to translate that to genetics?  Well, here's an excerpt from in an interview published in New Scientist with Slavoj Žižek, Slovenian philosopher and commentator:

You were in China recently and got a glimpse of what’s happening in biogenetics there.

         In the west, we have debates about whether we should
         intervene to prevent disease or use stem cells, while the
         Chinese just do it on a massive scale. When I was in China,
         some researchers showed me a document from their
         Academy of Sciences which says openly that the goal of
         their biogenetic research is to enable large-scale medical
         procedures which will “rectify” the physical and physiological
         weaknesses of the Chinese people.


Is this true?  We can't confirm it, but it wouldn't be a surprise.  Not because it's China, but because we are in an age of belief in biotech and our ability to harness it to our will.  If it isn't true yet, it will be -- somewhere.

The New York Times is reporting that there is now a new museum exhibit open in Germany that shows that the holocaust was not something Hitler and his henchmen foisted off on a benign, unaware populace.   Instead, the populace put him into power.

Then
There are many parallels between the rhetoric of the early Darwinian age, that started out piously voicing the idea that science could now improve humankind via genetics, and the kinds of rhetoric, such as we're citing here, that we hear so often today.  It started out mainly benign or even positive in the early eugenics era (encouraging the best of us to reproduce, and discouraging voluntary restraint on the rest of us unwashed).  But of course it turned coercive -- first in medicine, by the way -- and then murderously hateful.

Historically, this kind of thing happens most when a society is under stress.  We're seeing a version of that stress in the current recession -- the anger is palpable.  Is democracy robust to the schemes of the demogogues who would like power and would use emotive, anti-immigrant or religious crusading arguments to start a 21st century version of the eugenics era?  Hopefully so.  But there is much general societal parallel, including much of the rhetoric and even invocation of Darwinian concepts (or their pious, medicalized, benign-sounding equivalent), so that one can't just dismiss the possibility.

Personalized genomic medicine can become personalized genomic discrimination.  If concepts like 'racial profiling' take hold, 'personalized' can become 'personalized + race'.  And we have to realize that if we believe that genome sequence can predict risk for essentially all disease traits -- and that's basically the claim, or hope -- then there's no reason to also believe that other traits, including socially sensitive traits, will not be equally predictable.  This is what happened before, except that specific genes were not known (other criteria were used, such as family patterns).

And if you believe that you can predict complex disease effectively and if you believe in 'Darwinian' medicine, then you'll also be prone to argue that normal traits -- whatever suits your personal interests to advocate -- also reflect natural selection.  And that by definition means you place value differences on different versions of the trait -- like IQ. And if you believe these were selected differently to an important extent between  human populations (i.e., 'races'), then there's a short line from there to drawing value judgments about races.  And then you can worry about the inferior individuals out-reproducing the superior and being a danger to society down the road.  This is exactly the trail or reasoning that we've been through before.

Now?
We aren't saying that this is all happening now, as we write.  We're just saying that, for those with antennae for this sort of them vs us view of the world, the antennae are picking up signal. The lesson from the history of eugenics and what it spawned is that, like the fog in Carl Sandberg's poem, this stuff creeps in on little cat feet.

Francis Collins, personalized genomic medicine, and the nature of probabilistic risk

Probability and the weather
Personalized forecasts
People regularly complain about the weather forecast in a way that misunderstands probability.  It's a sensitive subject for Ken, as a one-time meteorologist in a long-ago former life.  If the forecast is for a 30% chance of showers, and it stays dry, it's often viewed as a bad forecast.  You should have played golf after all.  If it rains, it's often viewed as a bad forecast.  Dammit, they ruined your golf outing, by driving you prematurely into the 19th hole bar!

Neither conclusion is right.

One can only really tell whether the forecast was right the next day, by totaling up the area that received rain, or by averaging the fraction of each area that received rain at any given time, or something like that.

The key point is that this is a 'personalized' forecast in the same way that genomic medicine, and Direct to Consumer (DTC) genetic risk estimation are personalized.  For a given genotype, or given golf course, whether it actually rains or not, or whether you get the disease in question, is almost irrelevant to whether the forecast was a good one.

Probability and risk of disease
Personalized genomics
We said on Saturday that Francis Collins reactions to his genotypic diagnosis of being at elevated risk for diabetes were, at his diabetes-free status at age 60, irrelevant to the genotype-testing result.  If the population average risk of diabetes is, say, 10%, that means 10% of the population will get the disease at some point in their lives.  If Francis' genotype-based relative risk is elevated by, say, 20% relative to  the risk of the average Joe, his absolute risk is raised to 12% (and we're exaggerating risks here to make the point, since most of the genotype-based  risks of common diseases add considerably less than 20% of the average lifetime relative risk, and most lifetime risks are less than 10%).

But in fact he doesn't have diabetes.  That says almost nothing about the accuracy or usefulness of his risk estimate.  12% risk means that 88% of people with the same genotype don't get diabetes, and his status is wholly consistent with that (or with his being at the average risk), and says nothing about whether the risk estimate was accurate.  The only risk estimate that should affect his behavior (he reports slimming down etc. in response to the genotype data), is the 'conditional' risk of getting diabetes at some future age, for a white male who has already survived until age 60.  That is probably an unknown value, for many reasons, such as lack of enough data.   Indeed even if the genetic risk estimate is accurate, a person with his genotype could, if diabetes-free at age 60, actually be at lower than average risk from his age onward.  Why?  Because the bulk of those who were born with the same genotype might already have gotten diabetes at a younger age: earlier onset is a typical way that elevated-risk genotypes work.

The issues are subtle, and just like weather forecasts, easy to misunderstand.  Another reason people should shun DTC services, and just adopt health lifestyles to the extent they can.

More profoundly, if Dr Collins is at our hypothetical 20%  relative risk, that means that the average person with his detected genotype is at 20% increased risk, but does not mean that everyone with that genotype is!  For example, those with the genotype who eat too much McFastfood may be at a 90% risk of diabetes, hugely elevated over the general population risk perhaps, while vegans with the same genotype could even be at lower risk than the population average.  We rarely know enough about interacting or confounding variables, like other genes or lifestyles, relative to the one we know about (the tested genotype) to say more than what's average for that genotype.  But we have every reason to believe that not everyone with the genotype is at its average risk.

When risk differences are less than huge in absolute terms, personalized medicine cannot judge the relevance of these issues except at best in a here-and-now population context, such as by a case-controls study, and these exposure contexts are always changing.  That's why it's important to understand what probabilistic predictions mean, when it comes both to your golfing decisions, and to your life.

In fact for most diseases if you want to know much, much more about your risk than any genotype service can tell you,  just look at the health history of your relatives. That's been known since Darwin's time.  And unlike DTC services, that information's free!  No doctor bills, no unnecessary testing, no specific genes need to be identified.  If we learn of specific gene-based therapies, and you're at high familial risk, then it would be worthwhile to have a proper genetic counseling service do the test.

A Snake Oil factor, but it cuts both ways
The DTC business is cowboy capitalism at this stage, and selling this kind of snake oil to people totally unable to understand the actual meaning of the data is an ethical as well as policy issue, and of course there is disagreement about where the ethical lines are where they should be drawn.  Even most doctors--even most geneticists--are helpless to understand the nature, accuracy, or stability of these risks.  Of course there is always a lot of selling going on, as a glance at Parade or an airline magazine will clearly show (shrink your prostate!  get rid of age wrinkles! have a 20 year-old body at age 70!).  As P T Barnum said, there's a sucker sitting in every airplane seat.  But this is why regulation is important, in our view, in something so closely involved with life, death, and health-care costs.

We've suggested in our posts that the FDA should treat this kind of DTC 'advice' as practicing medicine, and license it only as a part of genetic counseling where it could in principle be properly constrained and regulated to stick closer to truths that we can generally agree on and that consumers (and counselors and physicians) can make reliable sense of.

However we must admit that this is partly a political stance by us.  If the entire business were just shifted to medical clinics, the same testing would probably take place, with the same level of understanding.  After all, the issues are complex even for those with sophisticated knowledge.

In fact, transferring the business (doubtlessly to be supplied, probably on a larger scale, by the same companies) and perhaps establishing it part of what would be lobbied into routine medical exams, the genomic testing could be much more costly to the health-care system, and more lucrative for the companies in the future, and hence perhaps even more problematic than it is now.  Still, we think regulation is better than caveat emptor.

Bottom line?
So the simple conclusion for us is that it's best to follow advice we've only known since Hippocrates a mere 2400 years ago (moderation in all things), and skip both that extra dessert and the personalized DTC genotyping.

The complexities of complexity

I've just returned from co-teaching a course in logical reasoning in genetics in Helsinki (whether or not everyone agrees that my reasoning is logical is not for me to say). I worked with Joe Terwilliger from New York, Markus Perola from Finland, and Patrik Magnusson from Sweden. Students were mainly Finns, but others from the US, Peru, Sweden and perhaps elsewhere that I've forgotten to mention. Hopefully they got something from the course (along with some fun times in snowy, dark, but very socially hospitable Finland, a very nice place, with very good food).

Naturally a lot of attention was paid to ideas, activities, results, and interpretation of GWAS and other whole-genome studies, and we discussed various study designs for inferring the genetic contributions to complex traits.

A picture is emerging that is wholly consistent with theoretical expectations based on basic genetic and evolutionary conceptions that have been around for a long time, and that we present in detail (though in a different, nonbiomedical context) in Mermaid. It's that for many traits, perhaps even most traits, a large number of genes contribute, along with 'environments' (still an elusive term in many biomedical contexts). Sometimes, one or a few genes are far more important in the biological process generating the normal trait or whose mis-firing can lead to disease. Or many genes may be important but, for various reasons, in any population only one or a few may contain variants in the population that have strong effect on their own.

In these cases, unless lifestyle factors are exceedingly important, the genetic variants can be inferred from family or case-control studies, of which GWAS studies are one scaled-up instance. The strong effects are typically repeatable, and focus attention on one or a few genes. Cystic fibrosis is an example of a usually single-gene trait, and breast cancer is a trait in which variants in a few genes have differentially important effect. In the latter, however, only a small fraction of all cases are accounted for.

Most of the time, although genetic variation is clearly contributing to variation in the trait, be it normal variation in stature or pathologic levels of, say, blood pressure, there is clear family risk: if a close family member is affected, your risk is substantially increased. This implies genes, yet.....mapping can't find most of them. This is called polygenic variation.

As far as predicting your value for polygenic traits, the original method of relating your trait to the value in your relatives, that was due to Francis Galton in the late 1800s, still works best. That's because, when genes are contributing to the trait substantially, similarities among relatives follow known relationships, that reflect the action of all genetic variability in the individuals, and you don't gain much by trying to identify or use all the specific genes. But GWAS efforts are attempting to go further, and at least to identify collections or combinations of known variants that may give you a risk 'score' that has at least some predictive power. That means, the identification of many of the polygenes (genes with individually small effect).

Extremely large sets of data, such as whole population biobanks, with full genome sequence, will become available in the predictable future. Such risk scores seem therefore in the offing. But even those who are writing papers proposing such personalized genetic risk scores recognize that the predictive power for disease may remain low in most instances, and it may be a long time before it shows clinical value.

One of the complicating ironies is that, in these conditions, the vast majority of cases of a disease of this sort will be the only cases in their families! That is, the trait is substantially genetic, but with so many possible contributing genotypes that rarely will close relatives inherit enough 'risk variants' also to be affected. That happens only in the subset in which one or a few strong-effect variants are being transmitted. This is similar to the statement above that Galton's classical prediction of trait values among relatives is better by far than trying to enumerate the contributing variants.

There is much food for thought here, with serious implications for what it means to say a case of a disease is 'genetic', or how and when using genetic information will be particularly useful. There are many other issues that are worth discussing in the future, too, but this at least summarizes the essence of what the pro-GWAS advocates are discussing these days, even while recognizing that the kind of promise offered for this approach is not going to be realized.

*References to some of these points are technical so I didn't include them here, but they could be sent on request.*

GWAS revisited: vanishing returns at expanding costs?

We've now had a chance to read the 4 papers on genomewide association studies (GWAS) in the New England Journal of Medicine last week, and we'd like to make a few additional comments. Basically, we think the impression left by the science commentary in the New York Times that GWAS are being seriously questioned by heretofore strong adherents was misleading. Yes, the authors do suggest that all the answers are not in yet, but they are still believers in the genetic approach to common, complex disease.

David Goldstein (whose paper can be found here) makes the point that SNPs (single nucleotide polymorphisms, or genetic variants) with major disease effects have probably been found by now, and it's true that they don't explain much of the heritability (evidence of overall inherited risk) of most diseases or traits. He believes that further discoveries using GWAS will generally be of very minor effects. He concludes that GWAS have been very successful in detecting the most common variants, but now have reached the point of diminishing returns. He says that "rarer variants will explain missing heritability", and these can't be identified by GWAS, so human genetics now needs to turn to sequencing whole genomes to find these.

Joel Hirschhorn (you can find his paper here) states that the main goal of GWAS has never been disease prediction, which indeed they've only had modest success with, but rather the discovery of biologic pathways underlying polygenic disease or traits. GWAS have been very successful at this--that is, they've confirmed that drugs already in use are, as was basically also known, targeting pathways that are indeed related to the relevant disease, although he says that further discoveries are underway. Unlike Goldstein, he believes that larger GWAS will find significant rare variants associated with disease.

Peter Kraft and David Hunter (here) tout the "wave of discoveries" that have resulted from GWAS. They do say that by and large these discoveries have low discriminatory ability and predictive power, but believe that further studies of the same type (only much bigger) will find additional risk loci that will help explain polygenic disease and yield good estimates of risk. They suggest that, because of findings from GWAS, physicians will be able to predict individual risk for their patients in 2 to 3 years.

John Hardy and Andrew Singleton (here) describe the GWAS method and point out that people are surprised to learn that it's often just chromosome regions that this method finds, not specific genes, and that some of these are probably not protein-coding regions but rather have to do with regulating gene expression. Notably, unlike the other authors who all state that the "skeptics were wrong", but somehow don't bother to cite their work so that the reader could check that claim (they do cite the Terwilliger and Hiekkalinna paper we mentioned here last week, but that is on a specialized technical issue, not the basic issues related to health effects).

They also state that the idea of gene by environment interaction is a cliche, and has never been demonstrated. Whether they mean by this that there is no environmental effect on risk or simply that it's difficult to quantify (or, a technical point, that environmental effects are additive) is not clear. If the former, that's patently false--even risk of breast cancer in women who do carry the known and undoubted BRCA1 or BRCA2 risk alleles, varies significantly by decade of birth. Or the huge rise in diabetes, obesity, asthma, autism, ADHD, various cancers, and many other diseases just during the memory of at least some living scientists who care to pay attention. And, see our post of 4/18.

So, we find none of the supposed general skepticism here. Yes, these papers do acknowledge that risk explained by GWAS has been low, but they claim this as 'victory' for the method, and dismiss, minimize, or (worse) misrepresent problems that were raised long ago, and instead say either that risk will be explained with bigger studies, or GWAS weren't meant to explain risk in the first place (it's not clear that the non-skeptics agree with each other about the aim of GWAS, or about whether they have now served their purpose and it's time to move on--to methods that apparently actually do or also do explain heritability and predict risk.)

The 'skeptics' never said that GWAS would find nothing. What at least some of us said was that what would be found would be some risk factors, but that complex traits could not by and large be dissected into the set of their genetic causes in this way.

Rather than face these realities, we feel that what is being done now is to turn defeat into victory by claiming that ever-larger efforts will finally solve the problem. We think that is mythical. Unstable and hardly-estimable small, probabilistic relative risks will not lead to revolutionary 'personalized medicine', and there are other and better ways to find pathways. If a pathway (network of interacting genes) is so important, it should have at least some alleles that are common and major enough that they should already be known (or could easily be known from, say, mouse experiments); once one member is known, experimental methods exist to find its interacting gene partners.

In a way it's also a sad consequence of ethnocentric thinking to suppose that because we can't find major risk alleles in mainstream samples from industrialized populations, that such undiscovered alleles might not exist in, or even be largely confined to, smaller or more isolated populations, where they could be quite important to public health. They do and, ironically, mapping methods (a technical point: especially linkage mapping) can be a good way to find them.

But if we're in an era of diminishing, if not vanishing, returns, we're also in an era in which we think we will not only get less, but will have to spend and hence sequester much more research resources to get it. So there are societal as well as scientific issues at stake.

In any case, we already have strong, pathway-based personalized medicine! By far the major risk factor for most diabetes, for example, involves energy and fat metabolic pathways. Individuals at risk can already target those pathways in simple, direct ways: walk rather than taking the elevator, and don't over-eat!

If those pathways were addressed in this way, there would actually be major public impact, and ironically, what would remain would be a residuum of cases that really are genetic in the meaningful sense, and they would be more isolated and easier to study in appropriate genetic, genomic, and experimental ways.

The rear-view mirror and the road ahead

We've already posted some critiques of the current push for ever-larger genomewide association-style studies of disease (GWAS) which have been promoted by glowing promises that huge-scale studies and technology will revolutionize medicine and cure all the known ills of humankind (a slight exaggeration on our part, but not that far off the spin!). We want to explain our reasoning a bit more.

For many understandable reasons, geneticists would love to lock up huge amounts of research grant resources, for huge amounts of time, to generate huge amounts of data that will be deliciously interesting to play with. But such vast up-front cost commitments may not be the best way to eliminate the ills of humankind. It may not even be the best way to understand the genetic involvement in those ills.

In a recent post we cited a number of our own papers in which we've been pointing out problems in this area for many years, and while we didn't give references we did note that a few others have recently been saying something like this, too. The problem is that searching for genetic differences that may cause disease is based on designs such as comparing cases and controls, which don't work very well for common, complex diseases like diabetes or cancers. Among other reasons this is because, if the genetic variant is common, people without the disease, the controls, may still carry a variant that contributes to risk, but they might remain disease-free because, say, they haven't been exposed to whatever provocative environment is also associated with risk (diet, lack of exercise, etc.). And these designs don't work very well for explaining normal variation.

As we have said, the knowledge of why we find as little as we are finding has been around for nearly a century, and it connects us to what we know about evolutionary genetics. Since the facts apply as well to almost any species--even plants, inbred laboratory mice, and single-celled species like yeast--they must be telling us something about life that we need to listen to!

Part of the problem is that environments interact with many different genes to produce the phenotypes (traits, including disease) in ways that would be good to understand. However, our methods of understanding causation necessarily look backwards in time (they are 'retrospective'): we study people who have some trait, like diabetes, and compare them to age-sex-etc. matched controls, to see how they differ. Geneticists and environmental epidemiologists stress their particular kinds of risks, but the trend recently has strongly been to focus on genes, partly because environmental risk factors have proven to be devilishly hard to figure out, and genetics has more glamour (and plush funding) these days: it may have the sexy appearance of real science, since it's molecular!

Like looking in the rear-view mirror, we see the road of risk-factor exposures that we have already traveled. But what we really want to understand is the causal process itself, and for 'personalized medicine' and even public health we need to look forward in time, to current people's futures. That is what we are promising to predict, so we can avoid all ills (and produce perfect children).

We need to look at the road ahead, and what we see in the rear-view mirror may not be all that helpful. We know that the environmental component of most common diseases contributes far more to risk than any specific genetic factors, probably far more than all genetic factors combined do on their own. We know that clearly from the fact that many if not most common diseases have changed, often dramatically, in prevalence just in the last couple of generations, while we've had very good data and an army of investigators tracking exposures, lifestyles, and outcomes.

Those changes in prevalence are a warning shot across the genetics bow that geneticists have had a very convenient tin ear to. They rationalize these clear facts by asserting that changes in common diseases are due to interactions between susceptible genotypes and these environmental changes. Even if such unsupported assertions were true, what we see in the rear-view mirror does not tell us what the road ahead will be like, for the very simple, but important reason that there is absolutely no way to know what the environmental--the non-genetic--risk factor exposures will be.

No amount of Biobanking will change this, or make genotype-based risk prediction accurate (except for the small subset of diseases that really are genetic), because each future is a new road and risks are inevitably assessed retrospectively. Even if causation were relatively simple and clear, which is manifestly not the case. No matter how accurately we can identify the genotypes of everyone involved (and there are some problems there, too that we will have to discuss another time).

This is a deep problem in the nature of knowledge in regard to problems such as this. It is one sober, not far-out, not anti-scientific, reason why scientists and public funders should be very circumspect before committing major amounts of funding, for decades into the future, to try to track everyone, and everyone's DNA sequences. And here we don't consider the great potential for intrusiveness that such data will enable.

As geneticists, we would be highly interested in poking around in the data mega-studies would yield. But we think it would not be societally responsible data to generate, given the other needs and priorities (some of which actually are genetic), that we know we can address with available resources and on other problems or approaches.

We can learn things by checking the rear-view mirror, but life depends on keeping our eye on the road ahead.

GWAS: really, should anyone be surprised?

There's a story today in the New York Times about papers just published in the New England Journal of Medicine (embargoed for 6 months, so we can't link to it here) questioning the value of GWAS--genomewide association studies. We're interested because we, but largely Ken, often in collaboration with Joe Terwilliger at Columbia, have been writing for many years--in many explicit ways and for what we think are the right reasons, for 20 years or more--about why most common diseases won't have simple single gene, or even few gene explanations.

"The genetic analysis of common disease is turning out to be a lot more complex than expected," the reporter writes. Further, "...the kind of genetic variation [GWAS detect] has turned out to explain surprisingly little of the genetic links to most diseases." Of course, it depends on who's expectations you're talking about, and who you're trying to surprise. It may be a surprise for a genetics true-believer, but not for those who have been paying attention to the nature of genomic causation and how it evolved to be as it is (the critical facts and ideas have been known, basically, since the first papers in modern human genetics more than 100 years ago).

GWAS are the latest darling of the genetics community. Meant to identify common genetic factors that influence disease risk, the method scans the entire genome of people with and without the disease to look for genetic variants associated with disease risk. Many papers have been published claiming great success with this approach, and proclaiming that finally we're about to crack disease genetics and the age of personalized medicine is here. But upon scrutiny these successes turn out not to explain very much risk at all--often as little as 1%, 3% (even if the relative risk is, say 1.3--a 30% increase, or in a few cases 3.0 or more--there will always be excpetions). And this is for reasons that have been entirely predictable, based on what is known about evolution and genes.

Briefly, genes with major detrimental effects by and large are weeded out quickly by natural selection, most traits, including disease, except for the frankly single-gene diseases (which can stay around because they're partly recessive), are the result of many interacting genes, and, particularly for diseases with a late age of onset, environmental effects. And, there are many genetic pathways to any trait, so that the assumption that everyone with a given disease gets there the same way has always been wrong. Each genome is unique, with different, and perhaps rare or very rare genes contributing to disease risk and these will be difficult or impossible to find with current methods. Risk alleles can vary between populations, too--different genes are likely to contribute to diabetes in, say, Finns than in the Navajo. Or even the French. Or even different members of the same family! Inconvenient truths.

Now, news stories and even journal articles rarely point out any of these caveats. Indeed, David Goldstein is cited in the Times story as saying that the answer is individual whole genome sequencing, another very expensive and still deterministic approach (that surely will now be contorted by the proponents of big Biobanks to show that this is just the thing they've had in mind all along!). Nobody backs away from big long-term money just to satisfy what the science actually tells us. Now maybe there is the story, in the public interest, that a journalist ought to take on.

So, the push will be for ever-more complete DNA sequences to play with, but this is not in the public interest in the sense that it will not have major public health impact. Even if it identifies new pathways, one of the major rationales given in the face of the awkward epidemiological facts, which is unlikely to be a major outcome in public health terms. We have many ways to identify pathways, and more are becoming available all the time. While whole sequences can identify unique rare haplotypes or polygenotypes that some affected people share, that is really little more than trying the same method on new data, like Cinderella's wicked step-sisters forcing their feet into the glass slipper.

If it's true as it seems to be, that most instances of a disease are due to individually rare polygenotypes, then the foot will not fit be any better than before. And it won't get around the relative vs absolute risk, nor the environmental, nor the restrospective/prospective epistemiological problem. These are serious issues, but we'll have to deal with them separately. And the rarer the genotype the harder to show how often it is found in controls, hence the harder to estimate its effect.

------------------------
A few reasons why no one should be surprised:

*Update* Here are a few of the papers that have made these points in various ways and contexts over the years. They have references to other authors who have recently made some of these points. The point (besides vanity) is that we are not opportunistically jumping on a new bandwagon, now that more people are (more openly) recognizing the situation. In fact, the underlying facts and reasons have been known for even a far longer time.

The basic facts and theory were laid out early in the 20th century by some of the leaders of genetics, including RA Fisher, TH Morgan, Sewall Wright, and others.

Kenneth Weiss, Genetic Variation and Human Disease, Cambridge University Press, 1993.

Joseph Terwilliger, Kenneth Weiss, Current Opinion in Biotechnology, 9(6), 578-594 (1998). Linkage disequilibrium mapping of complex disease: fantasy or reality?

Kenneth Weiss, Joseph Terwilliger, Nature Genetics 26, 151 - 157 (2000). How many diseases does it take to map a gene with SNPs?

Joseph Terwilliger, Kenneth Weiss, Annals of Medicine 35: 532-544 (2003). Confounding, ascertainment bias, and the quest for a genetic "Fountain of Youth".

Kenneth Weiss, Anne Buchanan, Genetics and the Logic of Evolution, Wiley, 2004.

Joseph Terwilliger, Tero Hiekkalinna, European Journal of Human Genetics, 14, 426–437 (2006). An utter refutation of the 'Fundamental Theorem of the HapMap'.

Anne Buchanan, Kenneth Weiss, Stephanie M Fullerton, International Journal of Epidemiology, 35(3):562-571 (2006). Dissecting complex disease: the quest for the Philosopher's Stone?

Kenneth Weiss, Genetics 179, 1741–1756 (2008). Tilting at Quixotic Trait Loci (QTL): An Evolutionary Perspective on Genetic Causation.

Anne Buchanan, Sam Sholtis, Joan Richtsmeier, Kenneth Weiss, BioEssays, What are genes for or where are traits from? What is the question? BioEssays 31:198-208 (2009).

Big Lobbying Week

This past week saw pushes on both sides of the Atlantic for funding for new mega-genomic projects. On the European side, this lobbying is for EU-wide national biobanks, of millions of peoples' records including personal health information and (of course) DNA samples. On the American side, it's to get federal funding for more large-scale genetics. Mass emailings are going out to anyone on potentially relevant science list-serves, asking them to get in touch with the incoming secretary of Health and Human Services. In both cases, the advertized benefit of these huge and expensive genomics projects is a revolutionary 'personalized medicine,' a cause that seems somewhat unsavory given that it will mainly be for wealthy patients, in an era when many millions are without basic living resources including health care.

Personalized medicine is code for a high-technology approach to genetics, to tailor treatment to each individual by predicting their susceptibilities (and potentials?) to suggest molecular interventions. Lifestyle advice is also mentioned, but the real push is genetic, and it's based on a faith in strong genetic determinism, because if individual genotypes don't have high predictive power, the dream of revolutionized medicine won't become a reality. And so this past week was a big one for lobbyists for the belief system that holds (sometimes explicitly, sometimes implicitly) that genes determine everything in life, which often goes hand-in-hand with the belief that anything organized about life has to be due to natural selection (for brevity, we are exaggerating--but not all that much). Inherent in both of these related beliefs is an assumed fundamental inherency about organisms and their traits. Such views have a history of being rationales for various sorts of inequality, but also discrimination, sometimes of the worst kinds. So this isn't just societally neutral science, and it would be naive to believe that such misuses of science are just historical relics.

Commercial interests as well as the self-interests of academics and the bureaucratic portfolios of science funding agencies are transparent in these efforts. An objective never stated publicly as such is to lock up huge amounts of funds, for open-ended time periods. That will certainly keep the vested interests in the pink of professional health for decades.....but will it keep the public that pays for it in the pink of health?

We think the evidence is clearly that it won't, and for several reasons. First, hundreds of diseases really are genetic. Cystic fibrosis and muscular dystrophy are well-known examples. They are actually quite complicated, but at least the genes are known, good targets for research, and tests for at least their major genetic variants with high predictive power are already available: they do not require targeted, sequestered funds nor nationwide biobanks. Secondly, even for complex traits, like cancer or diabetes, the majority of cases are manifestly not genetic in the usual sense. Thirdly, sequestered, targeted research pots are not needed to stimulate research into these common and important diseases: investigators will initiate research proposals to work on those problems that will compete just fine in the peer-reviewed system.

Actually, the scientists organizing and proposing these efforts know very well that they are unlikely to deliver their proposed benefits. They know, too, that when one mega-project ends, self-interest drives the need for a successor. That's the nature of the game these days--and not only in genetics by any means--it is largely what vested interests are all about, and naturally those who will gain are not going to speak against locking up hundreds of millions of funds for countless years to come to fund their playground. And we've not mentioned the many issues of confidentiality and other kinds of abuse of private information.

Of course genes are important. The overall genetic contribution to most biological traits in any species is substantial. That's why embryos can start as single cells and turn into predictable adults, resembling their parents, and so on. Clearly genomes play a major, if not the only, role in this. This is 'molecular' and materialistic causation. There is nothing mystical about it.

But prediction and understanding in science are more than making such statements about a genomic role in biological traits. Living organisms--even individual cells--are highly complex, with countless interacting factors, each variable in the population, and affected by contingency and chance. And there is the 'environment', which from any gene's point of view includes everything else, including the rest of the individual cell's genome. That means that we may not be able to have usefully high individual (personalized) prediction based on any one gene or its variants, or even on any reasonably enumerable set of them. And that is what the evidence, of which there is a huge amount, has clearly shown.

We'll comment at a later date on this lack of individually predictive determinism, and why looking from the 'gene' (itself an increasingly elusive notion these days) on up to the organism and its diseases, is not a cost-effective way to invest health resources. Science is a good thing to invest in, and large health data bases can be, too. But investment should be in proportion to the problems that need solving, not the research curiosities (or interests) of a small group of privileged people called 'scientists'. The history of such glowing promises by geneticists is older than many who will chance across this posting, and while there is a clear and important role for some large-scale genomic resources, biobanks and dreamy promises for gene-based 'personalized medicine' are highly exaggerated, self-interested lobbying tools that need to be recognized as such. When you see ads like the one linked to above, you should ask why would anyone need to pay for such ads? Who has what to gain? If as scientists we just want to keep the large-scale genomic industry in business, at least let's say so honestly and be done with it.

Societally responsible science requires that people speak up about the facts as they are known. The integrity of science depends on truthfulness, and there should be resistance when facts are distorted or dissembled, or exaggerated promises made, out of this kind of self-interest, especially when the target is public funds. At least, that is how we see what is going on in this regard today.