Hyman Minsky, Charles Darwin, and descent into the cover of minutiae

The financial crisis was basically not predicted by our leading lights in the academic and intellectual economics community.  They had their very technical theories about how markets work, and how people behave economically--the rational, coolly calculating Homo economicus.  They had their 19th century and even earlier theoretical heroes, who are always cited.  There were somewhat differing schools of thought, but in fact they were, so to speak, more like different classrooms in the same building. Even with these differences, but they were alike in one thing: they were basically all wrong!  The wildly unstable speculation that led to the disaster of the 2000's was a policy result of this universal body of trusted advisors, Those Who Knew.

Well, not entirely.  There was a curmudgeonly economist named Hyman Minsky (1919-1996).  We're not economists and have only learned about him second-hand, after the fact, when what he said before the fact was born out by the facts.  A source we recently listened to was the BBC Radio program called Analysis (listen to or download the March 24 program).

Minsky; Levy Economics Institute

While fancy economists were building their mathematical 'models' of economic behavior, which were very intricate and detailed, ordinary people and the bankers who misled them were venturing hither and thither for the quick kills.  Minsky, basically out of the mainstream, was warning in less technical but actually far more relevant and correct ways that stability builds instability. As the Levy Economics Institute described his ideas in brief,

Minsky held that, over a prolonged period of prosperity, investors take on more and more risk, until lending exceeds what borrowers can pay off from their incoming revenues. When overindebted investors are forced to sell even their less-speculative positions to make good on their loans, markets spiral lower and create a severe demand for cash—an event that has come to be known as a "Minsky moment."

In the recent crisis, confidence in quick-profit investments was so great that people became careless and built their hopes and McMansions of sand. When what amounted to a grand, expanding Ponzi scheme finally collapsed, disaster struck for many (except those who could use the legal system to basically buy their way out of going to jail).

Minsky was just independent-thinking enough to be definitely out of what policy and university circles generally tolerate, and had died before the 2008 crash so he never saw his ideas vindicated.  They were subsequently adopted with post hoc enthusiasm, of course, by the very same prophets whose wisdom had led us to what actually happened (that is they didn't lose their university, bank, or think-tank jobs). Minsky is now apparently appearing with some prominence in new editions of economics textbooks (the idea of publishing books is perhaps a sign of total professional shamelessness, but that's another story).

On the radio discussion, the point was made that the Professionals, those Who Know have become ever more enamored of computer modeling, mathematical theory, simulations, and all the paraphernalia of technical 'science'.  In our highly risk-averse, technophilic, bureaucratized world, this passes for wisdom rather than soft-headed mainly verbal arguments (like Minsky's).  If you want to be published, get tenure or reach the next step on the think-tank or Wall Street status ladder, you better be very technical, and do things very narrowly and with elegant mathematics.  That that doesn't work, and it's known that it doesn't work, doesn't seem to matter ("well, it will work this time!").

This is a characteristic of our culture in our scientific age.  Reduction to technicality is what our institutions, reporters, governments, funders, advisors, and the like admire.  And that viewpoint has its tentacles elsewhere, too.

The same in evolution and genetics.
Like 19th century economists, Charles Darwin gave biologists their version of the truth.  It was a very broad theory, based on the traits of organisms.  This was what counted, not the underlying biological mechanism of the traits.  The argument was conceptual, with an implied quantitative basis.  Darwin actually viewed it as a mathematical theory much as Newton's theory of universal gravitation, but the mathematical details were unimportant.

Many scientists want to formalize such theory to give it support and the elegance of mathematics, but in fact, Darwin's own idea about the underlying basis ('gemmules' and 'pangenesis') was basically wrong.  Evolutionary theory proceeded well without any such basis and, indeed, today most biologists don't know or understand the mathematical claimant for the theory (called population genetics).

What the last 50 years have done is to attempt to reduce evolution to molecular and mathematical precision.  In particular, as genomic technologies have themselves evolved as dramatically as anything that ever happened to life, there has been a love-affair, or infatuation, with technology as if it were answering the basic questions about life.  Genetics does, indeed, illuminate many fundamentals about some aspects of life, but as we and many others have written extensively, it does not provide the global or precise kind of prediction that physics-envy would suggest.  Still, despite many facts being ignored or dismissed, such as the often poor predictive power from genotype to trait, contrary to the unstated causal assumption of genes as the fundamental 'instructions' of life, an enormous superstructure based on molecular and computer technology is being built on countless studies of minute details. Again, what we are seeing is reduction to technicality.

Hiding behind minutiae
Both areas shared the same sort of retreat to the depths of minutiae to establish their apparent profundity of understanding, wisdom, and influence.   Over-arching larger-scale understanding, rather unrelated to much of the minutiae, gets no attention: it's not technical and hence not glamorous enough. It sounds deeply important and so both the professions themselves and those who report their activities to the general public, and those who provide the funds for these activities, are impressed, buffaloed, intimidated, or otherwise persuaded.  But the diving into technical minutiae is a kind of bathos, that often does not seem to be much constrained by, or basically just bypasses, what we know and may even be obvious (as in economics).

These are just two areas in which one can draw some parallels.  They are undoubtedly widespread across many areas of our society, in science, semi-science, the arts and so on.  It does seem to be true that every culture has its traits, or themes, or belief systems.  In ours, it's a belief in technology and in particular computing technology.   Technology changes our lives, mainly for the better. But that it can solve many technical problems does not mean it leads to greater understanding.  Mathematics, despite Galileo's claim that it's the language with which God wrote the universe, is fantastically useful and precise when you can write equations whose assumptions are sufficiently accurate for your needs.  It can lead to outcomes that can be tested specifically.

But if the number and sorts of assumptions and structures (e.g., equations) that are constructed yield exact outcomes, those outcomes really are nothing more than the rewording of the assumptions.  That is, the deductions are contained within the assumptions and structures one choose to begin with.  There is no guarantee that the deductions represent the real world, unless the assumptions do.  Indeed, inaccuracies in assumptions and choice of structures can easily lead to unconstrained inaccuracies in the deductions, relative to the actual world.  The appearance of elegance and insight can be illusory even in theory.  (We might note here that the current kerfuffle over attempts to reinstate scientific racism also exemplify this kind of selective invocation of technical details or methods, while ignoring of more general countervailing facts that are well-known or obvious.)

This formal testability of mathematical predictions is often equated to--or confused with--proof of the assumptions on which it is based.  But they are assumptions, and if they are inaccurate your results will be precisely inaccurate.  Even matching predictions under such circumstances can, but need not, imply underlying truth.  This assumed to be causal can be correlated with what's truly causal, for example.

Further, when mathematical models and theories are thought to be precisely true--that is, assumed to be so--results from actual studies will rarely match predictions perfectly.  There will be human measurement and other technical errors, for example.  So how do we deal with these?  We use statistical or other sorts of tests, to judge whether the results match the predictions.  As we've written about before, we must rely on subjectively chosen tests of adequacy, like statistical significance level.  Superficial aspects of truth may pass such tests in a convincing way, but that doesn't mean the deeper, broader truths are being understood.

Worse than assuming that deviation of results from predictions are just technical errors, is the natural tendency to design studies and interpret results, in obliviousness to or willing ignoring of countervailing knowledge or facts. We do this all the time in science, even though we shouldn't.  Economists pretended everyone was a rational, perceptive value-calculating machine, when it was manifestly obvious that we are not.  Evolutionary geneticists assume Nature is a perfect screening machine, when it manifestly is not.

Verbal arguments can be global and true, but are not so easy to turn into specific predictions, hence their lower status than high-level technology. But ultimately science rests on verbal--conceptual--understanding.  Clearly in both economics and genetics (and who knows how many other fields?), we are in love with technology and use it for many reasons, delving deeper than our actual understanding allows.  Often that will generate findings or surprising facts that stimulate broader thinking, but just as often even scientists, enmeshed in the daily routine (rut?) of our careers,  have a hard time telling the difference.

We're human and we need our self-respect, sense of importance, salaries and retirement benefits, ego-stroking, and just plain sense that we are doing something of value and importance to our fellow humans.  We are all vulnerable to overlooking or circumventing deeper truths by hiding in minutiae that masquerade as truth, in order to attain those needs.  It happens all the time.  Usually, it doesn't matter very much.  But when misplaced claims of insight are uttered too charismatically, intercalate into too many societal vested interests, or are taken too seriously, then society can be in for a very rough ride to pay for its credulousness.  None of this is new, but if we are creatures who learn from experience, why don't we, or can't we, learn from our long history?

We are products of our culture.  One law of Nature may be that we cannot over-ride that law.

Sometime geneticist Joe Terwilliger on genetics

We may recently have given the false impression that geneticist Joe Terwilliger gives less priority to science, or at least good science, than to other perhaps more frivolous pursuits (he is Abe Lincoln every February, for example, and tuba player the rest of the time -- unless he's cleaning up bean debacles as a diplomat, or being a basketball and language coach to Dennis Rodman), so we wanted to help correct any such misconceptions here.  Perhaps to that end, Joe (now known in South Korea, we're afraid, as "sometime geneticist Joe Terwilliger") suggested we republish a blog post he first posted on his own short-lived blog in 2008.  He recently dug this up again and says that few could disagree, even 5 years later.

Joe as Abe on the balcony (but not of Ford's Theater)

The point is that sometimes there is a lot of convenient hard-of-hearing even in science, which fancies itself to be an objective search for truth. Some of the details in Joe's post are out-of-date but we, and he, think that the basic thrust is not.  In a sense that makes the conclusion all the more cogent, because the same modes of thinking about genomic causation are still predominant, despite the vastly costly but essentially consistent results in the five years since 2008.  And, as Joe points out, he and Ken had much the same message in 2000.

One not-so-subtle change, we will note, is that promises by NIH Director Francis Dr Collins, and many others in presumably responsible positions, have steadily altered  their due date, which recedes into the distance like, say, an oasis as you grope for water, the fences if you want your pitchers to have a better earned run average, or a preacher's promises of ultimate salvation, if you weekly plunk coins into the basket.

So perhaps the lesson is that under these circumstances, rather than just dismiss critics, science--actual science as it's supposed to be--should feel a need to take stock of what it's doing.  But we leave it to you to judge.

And if you hear about Joe in other contexts in weeks to come, remember that he was a sometime geneticist here first:


The Rise and Fall of Human Genetics and the Common Variant - Common Disease Hypothesis
By Joe Terwilliger
Nov 2008

There is an enormity of positive press coverage for the Human Genome Project and its successor, the HapMap Project, even though within the field the initial euphoric party when the first results came out has already done a full 180 to be replaced by the hangover that inevitably follows such excesses.

For those of you not familiar with the history of this field and the controversies about its prognosis which were present from the outset, I refer you to a review paper I and a colleague wrote back in 2000 at the height of the controversy - Nature Genetics 26, 151 - 157 . The basic gist of the argument put forward for the HapMap project was the so-called common variant/common disease hypothesis (CV/CD) which proposed that "most of the genetic risk for common, complex diseases is due to disease loci where there is one common variant (or a small number of them)" [Hum Molec Genet 11:2417-23]. Under those circumstances it was widely argued that using the technologies being developed for the HapMap project, that one would be able to identify these genes using "genome-wide association studies" (GWAS), basically by scoring the genotype for each individual in a cross sectional study for each of 500,000 to 1,000,000 individual marker loci - the argument being that if common variants explained a large fraction of the attributable risk for a given disease, that one could identify them by comparing allele frequencies at nearby common variants in affected vs unaffected individuals. This point was contested by researchers only with regard to how many markers you might have to study for this to work if that model of the true state of nature applied. Many overly optimistic scientists initially proposed 30,000 such loci would be sufficient, and when Kruglyak suggested it might take 500,000 such markers people attacked his models, yet today the current technological platforms use 1,000,000 and more markers, with products in the pipelines to increase this even more, because it quickly became clear that the earlier models of regular and predictable levels of linkage disequiblrium were not realistic, something that should have been clear from even the most basic understanding of population genetics, or even empirical data from lower organisms.

Today such studies are widespread, having been conducted for virtually every disease under the sun, and yet the number of common variants with appreciable attributable fractions that have been identified is miniscule. Scientists have trumpetted such results as have been found for Crohn's disease, in which 32 genes were detected using panels of thousands of individuals genotyped at hundreds of thousands of markers - this sounds great until you start looking at the fine print, in which it is pointed out that all of these loci put together explain less than 10% of the attributable risk of disease, and for various well-known statistical reasons, this is a gross overestimate of the actual percentage of the variance explained. Most of these loci individually explain far less than half a percent of the risk, meaning that while this may be biologically interesting, it has no impact at all on public health as most of the risk remains unexplained. This is completely opposite to the CV/CD theory proposed as defined above. In fact, this is about the best case for any complex trait studied, with virtually every example dataset I have personally looked at there is absolutely nothing discovered at all.

At the beginning of the euphoria for such association studies, the example "poster child" used to justify the proposal was the relationship between variation at the ApoE gene and risk of Alzheimer disease. In an impressively gutsy paper recently, a GWAS study was performed in Alzheimer disease and published as an important result, with a title that sent me rolling on the floor in tears laughing: "A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease" [ J Clin Psychiatry. 2007 Apr;68(4):613-8 ] - in an amazingly negative study they did not even have the expected number of false positive findings - just ApoE and absolutely nothing else... And the authors went on to describe how important this result was and claimed this means they need more money to do bigger studies to find the rest of the genes. Has anyone ever heard of stopping rules, that maybe there aren't any common variants of high attributable fraction??? This was a claim that Ken Weiss and I put forward many times over the past 15 years, and Ken has been making this point for a decade before that even, in his book, "Genetic variation and human disease", which anyone working in this field should read if they are not familiar with the basic evolutionary theory and empirical data which show why noone should ever have expected the CV/CD hypothesis to hold...

In many other fields, the studies that have been done at enormous expense have found absolutely nothing, and in what Ken Weiss calls a form of Western Zen (in which no means yes), the failure of one's research to find anything means they should get more money to do bigger studies, since obviously there are things to find but they did not have big enough studies with enough patients or enough markers - it could not possibly be that their hypotheses are wrong, and should be rejected... It is a truly bizarre world where failure is rewarded with more money - but when it comes to promising upper-middle-aged men (i.e. Congress) that they might not die if they fund our projects, they are happy to invest in things that have pretty much now been proven not to work...

While in a truly bizarre propaganda piece, Francis Collins, in a parting sycophantic commentary (J Clin Invest. 2008 May;118(5):1590-605) claimed that the controversy about the CV/CD hypothesis was "... ultimately resolved by the remarkable success of the genetic association studies enabled by the HapMap project." He went on to list a massive table of "successful" studies, including loci for such traits as bipolar, Parkinson disease and schizophrenia, and of course the laughable success of ApoE and Alzheimer disease. To be objective about these claims, let me quote from what researchers studying those diseases had to say.

Parkinson disease: "Taken together, studies appear to provide substantial evidence that none of the SNPs originally featured as PD loci (sic from GWAS studies) are convincingly replicated and that all may be false positives...it is worth examining the implications for GWAS in general." Am J Hum Genet 78:1081-82

Schizophrenia: "...data do not provide evidence for involvement of any genomic region with schizophrenia detectable with moderate [sic 1500 people!] sample size" Mol Psych 13:570-84

Bipolar AND Schizophrenia: "There has been great anticipation in the world of psychaitric research over the past year, with the community awaiting the results of a number of GWAS's... Similar pictures emerged for both disorders - no strong replications across studies, no candidates with strong effect on disease risk, and no clear replications of genes implicated by candidate gene studies." - Report of the World Congress of Psychiatric Genetics.

Ischaemic stroke: "We produced more than 200 million genotypes...Preliminary analysis of these data did not reveal any single locus conferring a large effect on risk for ischaemic stroke." Lancet Neurol. 2007 May;6(5):383-4.

And the list goes on and on of traits for which nothing was found, with the authors concluding they need more money for bigger studies with more markers. It is really scary that people are never willing to let go of hypotheses that did not pan out. Clearly CV/CD is not a reasonable model for complex traits. Even the diseases where they claim enormous success are not fitting with the model - they get very small p-values for associations that confer relative risks of 1.03 or so - not "the majority of the risk" as the CV/CD hypothesis proposed.

One must recall that in the intial paper proposing GWAS by Risch and Merikangas (Science 1996 Sep 13;273(5281):1516-7) - a paper which, incidentally, pointed out that one always has more power for such studies when collecting families rather than unrelated individuals - the authors stated that "despite the small magnitude of such (sic: common variants in)genes, the magnitude of their attributable risk (the proportion of people affected due to them) may be large because they are quite frequent in the population (sic: meaning >>10% in their models), making them of public health significance." The obvious corollary of this is that if they are not quite frequency, they are NOT having high attributable fraction and are therefore NOT of public health significance.

And yet, you still have scientists claiming that the results of these studies will lead to a scenario in which "we will say to you, 'suppose you have a 65% chance of getting prostate cancer when you're 65. If you start taking these pills when you're 45, that percent will change to 2". Amazing claims when the empirical evidence is clear that the majority of the risk of the majority of complex diseases is not explained by anything common across ethnicities, or common in populations... (Leroy Hood, quoted in the Seattle Post-Intelligencer). Francis Collins recently claimed that by 2020, "new gene-based designer drugs will be developed for ... ALzheimer disease, schizophrenia and many other conditions", and by 2010, "predictive genetic tests will be available for as many as a dozen common conditions". This does not jibe with the empirical evidence... In Breast Cancer for example, researchers claimed that knowledge of the BRCA1 and BRCA2 genes (which confer enormously high risk of breast cancer to carriers) was uninteresting as it had such a small attributable fraction in the population. Of course now they have performed GWAS studies and examined tens of thousands of individuals and have identified several additional loci which put together have a much smaller attributable fraction than BRCA1 and BRCA2, yet they claim this proves how important GWAS is. Interesting how the arguments change to fit the data, and everything is made to sound as if it were consistent with the theory.

I suggest that people go back and read "How many diseases does it take to map a gene with SNPs?" (2000) 26, 151 - 157. There are virtually no arguments we made in that controversial commentary 8 years ago which we could not make even stronger today, as the empirical data which has come up since then basically supports our theory almost perfectly, and refutes conclusively the CV/CD hypothesis, despite Francis Collins' rather odd claims to the contrary...

In the end, these projects will likely continue to be funded for another 5 or 10 years before people start realizing the boy has been crying wolf for a damned long time... This is a real problem for science in America, however, as NIH is spending big money on these rather non-scientific technologically-driven hypothesis-free projects at the expense of investigator-initiated hypothesis-driven science. Even more tragically training grants are enormously plentiful meaning that we are training an enormous number of students and postdocs in a field for which there will never be job opportunities for them, even if things are successful. Hypothesis-free science should never be allowed to result in Ph.D. degrees if one believes that science is about questioning what truth is and asking questions about nature, while engineering is about how to accomplish a definable task (like sequencing the genome quickly and cheaply). The mythological "financial crisis" at NIH is really more a function of the enormous amounts of money going into projects that are predetermined to be funded by political appointees and government bureaucrats rather than the marketplace of ideas through investigator-initiated proposals. Enormous amounts of government funding into small numbers of projects is a bad idea - one which began with Eric Lander's group at MIT proposing to build large factories for the sequencing of the genome rather than spreading it across sites, with the goal of getting it done faster (an engineering goal) instead of getting more sites involved so that perhaps better scientific research could have come along the way. This has led to a scenario years later in which the factories now want to do science and not just engineering, which is totally contrary to their raison d'etre, and leads to further concentrations of funding in small numbers of hands when science is better served, perhaps by a larger number of groups receiving a smaller amount of money so that more brains are working in different directions thinking of novel and innovative ideas not reliant on pure throughput. Human genetics has transformed from a field with low funding, driven by creative thinking into a field driven by big money and sheep following whatever shepherd du jour is telling them they should do (i.e. innovative means doing what they current trend is rather than something truly original and creative). This is bad for science, and also is bad science. GWAS has been successful technologically, and it has resoundingly rejected the CV/CD hypothesis through empirical data. If we accept this and move on, we can put the HapMap and HGP where it belongs, in the same scientific fate as the Supercollider, and let us get back to thinking instead of throwing money at problems that are fundamentally biological and not technological!

(most notably in terms of the big money NIH is sending into these non-scientific technologically-driven hypothesis-free studies, rather than investigator initiated hypothesis-driven science - one of the main causes of the "funding crisis" at NIH where a tiny portion of new grants are funded - get rid of the big science that is not working - like the supercollider! - and there is no funding crisis)

Why less malaria can be more of a problem

Most people who are interested in malaria are concerned with regions with really high transmission.  These are areas, such as sub-Saharan Africa, where malaria prevalence is high year round and where malaria related mortality is highest.  Sadly, that mortality generally seems to disproportionately affect children.  It’s no wonder then, that these are the areas that most people think about.  

Huge amounts of research money goes toward potential vaccines, toward genome sequencing of parasites, laboratory and field based science, and much of it is focused on the malaria situation in sub-Saharan Africa.  I’ve already questioned whether this is a wise use for a limited amount of money and that by changing socio-economic factors we would probably get more bang for our buck.   

Here I am also going to argue that for what most of us in tropical medicine, epidemiology, and disease ecology do, areas of low transmission are more important.  And while I can make this argument from a few different vantage points, today I’m going to base it off the fact that drug resistant malaria (see Note 1) seems to recurrently arise in areas of low transmission, only to spread to places like Africa where the implications of treatment failure at the population level are most severe (White, 2004).   

First, what do I mean by low or high transmission?  

In the malaria system there are always at least two hosts, humans and mosquitoes.  Humans get infected by infectious mosquitoes that have in turn been infected by infectious humans.  In areas of high transmission, people are bitten by infected and infectious mosquitoes more frequently in comparison to people in low transmission areas.  For example, in parts of sub-Saharan Africa people may be bitten by infectious mosquitoes more than 100 times in a single year.  In areas of low transmission people might only be bitten by an infectious mosquito once a year (Bousema & Drakeley, 2011). 

There are several ways to break the transmission cycle between humans and mosquitoes.  Some efforts are based on preventing mosquitoes from feeding on human blood (e.g. mosquito nets).  Others are based on treating infected humans with antimalarials; which should both cure the infected persons and halt the spread of parasites within the infected to others.  Suffice it to say that at least a huge component of modern malaria control efforts depends on the use of antimalarials.  Drug resistance is therefore a very large problem, and it is a problem with a relatively long history.  

For example, chloroquine was a wonder drug for malaria treatment, it was easy to manufacture, cheap, and compared to previous antimalarials it seemed to have fewer negative side effects.  However, it wasn’t long after it had been rolled out into the global scene that people in the field began to notice decreased sensitivity in parasites to chloroquine, almost simultaneously in parts of Southeast Asia and South America in the late 1950s and early 1960s (Payne, 1987).  The move was slow for chloroquine resistant parasites, but they eventually did make the passage to Africa.  Perhaps especially in Southeast Asia this story has been repeated over and over again (Parker et al., 2012), with each new antimalarial losing its efficacy shortly after its widespread use and subsequently parasites with the drug resistant mutation (or mutations) spread throughout the world (Anderson & Roper, 2005).  

I believe that I’m relatively safe in stating that there is a growing consensus among malariologists that drug resistant strains continue to emerge in certain areas and not in others because of the level of transmission intensity in those areas (Klein, 2013).  Take Thailand, for example: This is a nation that has very little malaria in the central plains regions and highly seasonal malaria along its international borders with Cambodia and Myanmar.  Those border sites appear to consistently be centers for the emergence of drug resistant parasites strains, most recently with decreased sensitivity to artemisinin (Noedl et al., 2008; Phyo et al., 2012).  

But what is it about low transmission areas that make them primed for drug resistant strains to pop up?  

Here there is certainly room for debate.  Several things must happen in order for drug resistant parasites to become prevalent enough to be a public health problem.  First there must be mutations that arise which confer some sort of resistance to antimalarial drugs.  However, some mutations are likely to be harmful for the parasite, even if offering some protection against antimalarials.  Therefore the mutation must not be so harmful as to provide an overall disadvantage in comparison to its advantage in the presence of drugs.  Then, that mutation (or mutations) must spread and be retained throughout the population.  

One potential explanation is that in low transmission areas people are unlikely to have developed immunity to malaria (White, 2004).  When they are infected they have high parasite densities in their blood and they are more likely to take antimalarials; meaning that more parasites are exposed to more antimalarials, therefore leading to more chances for resistance to emerge.  Then, parasites with resistant strains don’t have to survive their host’s immune responses, and are likely to have higher parasite densities, meaning that they are also more likely to be passed on to mosquitoes and ultimately other human hosts.  

Population genetics likely also plays an important role.  In areas with really high transmission, a single infected person is likely to be carrying multiple parasite strains at the same time.  Mosquitoes are also likely to be infected by multiple strains, as they have probably both fed off of individuals with more than one strain and potentially have fed on more than one infected individual.  Malaria parasites have quite complicated life cycles, undergoing several different life stages within human and mosquito hosts.  They reproduce asexually inside humans until some of them split off and become ready for sexual reproduction, basically developing into either males or females.  These sexual parasites are then picked up by the mosquito where they undergo sexual reproduction.  Therefore genetic recombination only occurs within the mosquito.  This has strong implications for drug resistance which is conferred through multiple genes.  Genetic recombination means that such mutant combinations can be lost through sexual recombination.  However, in areas of low transmission, such as in Southeast Asia, many parasites are actually reproducing with themselves; the mosquito isn’t picking up sexual parasites from multiple strains but instead from a single strain (Anderson et al., 2000).  Recombination still occurs, but it is occurring with a single parasite strain, meaning the gene combinations will not be lost.

Intrahost competition between parasites is probably also important.  There is evidence that, within individuals who are infected by multiple strains of parasites, not all parasite strains do equally as well.  Some appear to be more aggressive and to propagate themselves at higher levels than other strains.  In areas with high transmission, a parasite strain with a drug resistance mutation may still need to out-compete other parasite strains within the host (Klein, 2013).  Since a mutation isn’t likely to confer absolute resistance to antimalarials, and since it might actually be slightly harmful to the parasite strain, it is possible that aggressive parasite strains (even without drug resistance mutations) can out-compete those with such mutations.  Conversely, in low transmission areas this is less likely to be the case.   

What does all of this mean for the big picture though?   The obvious implication related to the drug resistant strains that keep emerging in Southeast Asia and then spreading throughout the world is only part of this story.  

Currently there are increased efforts for malaria control and, in some situations, even efforts directed toward malaria elimination.  But there are no easy fixes to the malaria problem.  That is, in some areas, malaria transmission will be greatly reduced but will continue to persist at very low levels of transmission.  Most likely, even in areas where complete eradication is achieved, there will be periods of time where malaria persists at low transmission levels.  The paradox, therefore, is that as the malaria situation improves, as transmission is reduced and less people become sick or even die, the situation with regard to effective antimalarials might simultaneously worsen.  

Previously I’ve argued that if we really want to fix the malaria problem we should look toward socio-economic factors.  Malaria remains a disease that mostly afflicts poor people in poor nations in the tropical world.  If we could increase economic wellbeing and improve sanitation, we would probably fix a large part of the problem, much like we did here in the U.S.A. over half a century ago.  Also, it might make more sense to dump the millions of dollars that are directed toward researching single diseases into funding primary health care for children in the industrializing world (which is the approach of groups such as Partners in Health).  That is, if children in poor nations received free health care regardless of the cause of their illness we will probably see huge improvements in global health.

But the situations that I describe above, where the conditions in low transmission areas lead to a type of perfect storm, are probably best addressed through epidemiological, ecological, and evolutionary approaches.  While I’m quite pessimistic about spending millions and millions of dollars on potential vaccines to save children in Africa from one (out of a lot) of pathogens, I do think there is a very important role for the life and biomedical sciences in malaria control.  Here are a few examples:

1.) Antimalarials will continue to be important, even if they are only used sparingly in areas of low transmission.  That means that evolutionary biologists will continue to have an important role in malaria control (Read, Day, & Huijben, 2011).  

2.) Another interesting factor that I completely avoided in this post has to do with areas with several different hosts (zoonosis and anthroponosis) – such as in Borneo where nonhuman primates carry malaria parasites that can also easily infect humans (Singh et al., 2004).  This type of situation can also lead to persistent low transmission and is perhaps best addressed by ecologists.  

3.) Finally, in areas with low, sporadic transmission the role of human migration in continued transmission is quite important; meaning that human demographers, geographers, and population ecologists are also important for addressing the malaria situation (Prothero, 1999; Tatem & Smith, 2010).  

Note 1: Drug resistance isn't really a binary trait.  It is probably more accurate to talk about levels of reduced sensitivity to drugs, but this is quite a mouthful.  Typically when I use the term drug resistance, I am talking about parasites that no longer respond well to antimalarials at the doses that are safe to give to humans.    

REFERENCES

Anderson, T. J. C., & Roper, C. (2005). The Origins and Spread of Antimalarial Drug Resistance: Lessons for Policy Makers. Acta Tropica, 94, 269–280.

Anderson, T. J., Haubold, B., Williams, J. T., Estrada-Franco, J. G., Richardson, L., Mollinedo, R., Bockarie, M., et al. (2000). Microsatellite markers reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum. Molecular biology and evolution, 17(10), 1467–82. 

Bousema, T., & Drakeley, C. (2011). Epidemiology and infectivity of Plasmodium falciparum and Plasmodium vivax gametocytes in relation to malaria control and elimination. Clinical microbiology reviews, 24(2), 377–410. doi:10.1128/CMR.00051-10

Klein, E. Y. (2013). Antimalarial drug resistance: a review of the biology and strategies to delay emergence and spread. International journal of antimicrobial agents, 41(4), 311–317. 

Noedl, H., Se, Y., Schaecher, K., Smith, B., Socheat, D., & Fukuda, M. (2008). Evidence of artemisinin-resistant malaria in western Cambodia. N Engl J Med, 359(24), 2619–2620.

Parker, D., Lerdprom, R., Srisatjarak, W., Yan, G., Sattabongkot, J., Wood, J., Sirichaisinthop, J., et al. (2012). Longitudinal in vitro surveillance of Plasmodium falciparum sensitivity to common anti-malarials in Thailand between 1994 and 2010. Malaria journal, 11(1), 290. 

Payne, D. (1987). Spread of chloroquine resistance in Plasmodium falciparum. Parasitology today (Personal ed.), 3(8), 241–6. 

Phyo, A. P., Nkhoma, S., Stepniewska, K., Ashley, E. A., Nair, S., McGready, R., ler Moo, C., et al. (2012). Emergence of artemisinin-resistant malaria on the western border of Thailand: a longitudinal study. The Lancet, 12.

Prothero, R. M. (1999). Malaria, forests and people in Southeast Asia. Singapore Journal of Tropical Geography, 20(1), 76–85.

Read, A. F., Day, T., & Huijben, S. (2011). The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proceedings of the National Academy of Sciences of the United States of America, 108 Suppl , 10871–7. 

Singh, B., Kim Sung, L., Matusop, A., Radhakrishnan, A., Shamsul, S. S. G., Cox-Singh, J., Thomas, A., et al. (2004). A large focus of naturally acquired Plasmodium knowlesi infections in human beings. Lancet, 363(9414), 1017–24. 

Tatem, A. J., & Smith, D. L. (2010). International population movements and regional Plasmodium falciparum malaria elimination strategies. Proceedings of the National Academy of Sciences, 107(27), 12222.

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In Memoriam: Jim Crow

On January 3rd, James F Crow died, at the ripe old age of 95.  Jim was one of the 20th century's most prominent population geneticists, training many other leaders in the field as well as providing much of the evolutionary theory we have today.  Madison, Wisconsin must be a healthy place to live because, among other things, Jim's predecessor and one of the ultimate founders of population genetics, Sewall Wright, lived and passed away there.  Wright lived til he was just shy of his 100th birthday, so perhaps Madison is just too tough a place to make it all the way.

I knew Jim from several meetings, though not as a close friend.  But all of us in our generation were weaned on Crow and Kimura (the latter, a founder of the 'neutral' theory of evolution as a counterweight to the prevailing strongly selectionist view, was a Crow student).  Many other of our most prominent population geneticists trained or worked with him.  His Wiki page stresses his role in teaching, suggesting it may have been his most important single contribution.  If one includes his books, and his very clear arguments about various subjects in his papers, then it would be hard to argue with that.

Crow may not be known for many specific major theorems or the like, but he worked extensively on the nature of natural selection and how to detect evidence of it, on the way interactions among genes were (or, he might insist) were not reflected in adaptive evolution, on the age effect of fathers on disease risk in their children, and on human population variation--these being things I knew him for (he also did experimental and fruit-fly work).

Jim Crow was personally a gentle man and a gentleman.  He was well-rounded personally and in his family.  I cribbed the picture, showing him playing the viola as he did for the Madison Symphony,  from John Hawkes' very fine post, where you can learn more about Crow (John is at Wisconsin).

As I personally witnessed, he could defend a point of view in discussions, but I never knew him to become aggressive about it, nor ad hominem, even when his protagonist was a bully (something I myself witnessed, the bully being alpha male Jim Neel).  He stuck to polite consideration of issues--even though he did have his points of views!

Like the other famous Jim (Watson), Crow did hold some views about human variation, that (I think) in naive ways conflated the facts that we're all genetically different, that genes affect our traits, with group (i.e., 'race') differences.  But we all have our blinders.

In his later, retired years, Jim continued to contribute and one noteworthy way was his editing of a column of retrospectives that ran in each issue of the prominent journal Genetics.  These looked back at major issues and historical figures in ways that might not be widely known among younger readers for whom history was not considered a very important part of science.

Jim had nowhere near the name-recognition of the great triumvirate of  population genetics, Wright, JBS Haldane, and RA Fisher.   In recent years, I've found that people in genetics often have not heard of him (to their knowledge).  But even the great troika, along with other giants in science of their time, are no longer remembered by name.  And Jim had the kind of career, and of kindness, that should satisfy anyone in or out of science.

Genetic leaf-litter

There are many ways in which everyone is a conceptual prisoner, encaged in culturally based limits. We are born to, and trained in and entrained by our circumstances, and these in turn are a legacy of history. We can try to escape from this but probably the most we can hope for is to keep subtle assumptions and constraints at bay. In genetics, there is a pervasive concept of the 'wild type', a concept that goes back into the history of genetic research, referring to the natural allele at a gene, that was favored by a history of selection, relative to which other alleles (mutational variants) were viewed as generally rare and harmful (waiting to be shortly removed by natural selection).

There is a tacit extension of this gene-specific concept to the whole genome (or even organism) as when 'normal' inbred laboratory mice are referred to as the 'wild type' relative to an experimental modification such as a transgenic gene knockout mouse of the same strain. Sometimes this is clear shorthand, but beware of conceptual shorthand! An implication of this kind of genetic thinking is that in regard to human traits, including especially disease, there is the normal human genome as represented by 'the' human genome sequence available in genome data bases, and the disease-causing mutants. But in fact genomes are very large sequences of DNA that serve as targets for mutation in every cell, every individual, every generation.

We know that biological traits are the result of developmental processes that include countless genes (of the classical protein-coding type as well as many other functional DNA sequence elements). Species contain large numbers of members--there are about 7 billion of us humans stalking the Earth. What this means is that there is a potentially huge amount of variation at most if not all viable spots in our genome. After a mutation occurs, it may proliferate if its bearer successfully reproduces. Over time, some of these alleles grow in frequency to become quite common.

When genomic DNA is sequenced in a number of individuals, this variation is easily detected. But whether affected by natural selection or just by the chance aspects of reproductive success or failure, most allelic variation that is present in genomes at any given time is rare. Relative to the more common variants, this genetic variation is a kind of leaf-litter of variation. Even with hundreds of thousands or, indeed, hundreds of millions of very rare variants present in our species, any small sample will pick up some of them by chance.

In a small sample, those will seem to be more common than they are; so if we sequenced 5 people (10 copies of the genome) the lucky variants whose true population frequency is only a few in a billion that by chance are in the 5 people we sample, will seem to have a frequency of at least 10% (one copy of the 10 we sampled being the variant). The tip-off that this genomic leaf-litter exists is that most of the variants are not seen in other samples, or if common enough to be sampled more than once, usually only seen in samples from the same geographic region (because that's where they arose as new mutations, and were transmitted to descendants who remained living in the same continent). In developed countries, variants that cause disease will show up in specialty clinics at major medical centers.

In trying to find variants by mapping, as in genomewide association studies (GWAS) that compare sequences between cases and controls, we may feel that we have so far detected the common, but not all the rare causal variants that exist. But we may also feel that if we can just enlarge our samples, we'll get a much better handle on the nature of the effects of these variants, or we'll detect the remaining variants that haven't yet been detected.

This is likely to be an illusion, as the growing number of those of us who argue that very large GWAS will not bring a big payoff of the kind envisioned and promised by those who argue for this kind of project. There are several reasons for this skepticism. First, it is hard to detect rare things with statistical significance, much less to get a good idea of their effects and action. One needs huge samples to get enough instances to show that the variant is meaningfully more common in cases than controls.

But second, the leaf-litter phenomenon means that as sample sizes increase, more and more rarer and rarer variants will be picked up. It will be difficult to show clearly that they are causally involved with our trait, but even if they are they will have less and less effect on public health. They will vary from population to population, and sample to sample from the same population. Environments may affect whether carriers of the variant manifest the disease, and most such variation will at most have minor effect on risk of disease (if the effect were stronger, the allele would have been removed by selection, or we would have been able to detect it in family studies).

And if it requires more than one such variant, or even many of them, to combine to produce disease, the detection and evaluation situation will be that much more challenging, if not pointless. There will always be exceptions, as is true about the nature of life. But the leaf-litter phenomenon is real and there is plenty of evidence for it. It is predicted by population genetics theory. And it is consistent with results of mapping studies that have been done to date. Ironically, perhaps, while the individual rare alleles have little detectable effects, their aggregate effects in the population may account for the observed heritability (familial aggregation of risk, or similarity of trait values) of most traits, including disease. That heritability, which is clearly there, is what has been considered mysterious given the failure of linkage or GWAS studies to find the genes that are responsible.

We are presented with a kind of epistemological paradox: the genetic variation exists, but we may have insurmountable challenges to find most of it. Indeed, it is somewhat mystical even to argue that it exists as individual effects, if they cannot be found or replicated by current statistical genetic methods. Evolution 'cares' about reproductive success, not about simplicity in genetic causation. From a population perspective, evolution occurs because mutations occur generating variation that selection and chance can effect from one generation to the next.

Genetic leaf-litter is thus the fuel for evolution. We may care to know the cause of each instance of a trait or disease, but Nature has only cared about viability and success, and tolerates the leaf-litter. As massive amounts of human DNA sequence are produced, we will see this. It will be an incredible playground for population and evolutionary geneticists. But what we do with it, in terms of identifying disease causation, is not clear.