The tail that doesn't wag: Why?

We know of countless traits that are due to mutations in single genes.  The same allele may not always confer exactly the same disease or other trait, because other genes or environmental factors may contribute, but for most purposes this additional variation is unimportant or at least tractable.  These traits appear in families in roughly Mendelian proportions, as has long been known.

However, most traits including most of the common diseases, do not segregate in families. Instead, it is clear that many different genetic factors are contributing, and most of these effects are individually small.  Further, lifestyle factors are usually even more important, in aggregate.

GWAS and other gene-searching methods have shown that this is likely to be the case for many important diseases, as we’ve written about here many times (and many others have written about elsewhere).  The state of such traits can be depicted schematically as in this figure, where just 3 different genes, each with two states, are shown.  The different combinations of variants are shown with their average associated trait value.  Here, the capital letter allele at each gene confers greater trait value.  This is highly simplified but represents our usual reconciliation of Mendelian genetic effects and the complex traits we observe.  The simplification, as far as it goes, doesn’t affect the main point.  In essence the causal model is that a huge (in many applications, essentially infinite) number of contributing genes is involved, each making a minor contribution.

Schematic distribution of stature and contributing genotypes

The causal complexity so often observed is a problem for the understandable yearning for simple causation—for the promise of ‘precision’ based or highly predictive medicine based on genotype.  We won’t here yet again belabor the reasons this is a culpable fantasy being perpetrated on the paying public, because we’re after a different point. 

If most individuals are in the middle of the population’s trait distribution (e.g., are of roughly average height in the figure), you can see that there are many different genotypes that confer the same trait value.  The sample size is large for the middle group, the bulk of the population, but no single genotype stands out as ‘causing’ average height.  But this is a general model of equal effects, and perhaps there are ways to go beyond such population averages and mine the data for those individual variants that do have identifiably strong effects.

In this figure, as in most textbook illustrations of the point, only a few genes (and those with only two variants each) are shown. But really we know there are tens, hundreds, or even thousands of genome regions that may contribute. This might suggest some strategies. Perhaps the extremes—the tails—can be used to inform us about what is going on in the whole distribution. We can let the tail wag the genetic causal dog in a few ways, perhaps.

The idea of tractability
The search for causal relationships is necessarily reductionist and naturally leads to the search for study design or analysis 'tricks' to turn complexity into simplicity, or at least tractability by some meaningful standard.  Can things be found by some approaches not to be so complex, with at least for some segments of the population, simpler genomic causation?  The following are instances in which this is thought possibly to be so.

1.  Rare variants in families.  Rare variants with strong effect can sometimes be found in close relatives with the same trait.  This may mean a clear-cut and hence typically also rare trait--something far from the norm.  Buried in a population sample, they could simply not be frequent enough to generate a statistical signal.  But, close relatives share big chunks of their genome as well as environments, so one must have some criterion for assigning causation to a genetic variant. A variant that creates a stop codon in a physiological relevant ('candidate'?) gene would be one such.  Even in the huge general population samples that are being collected, families will be identifiable, so a once-old-now-new family-based approach may be able to find some important variants.

2.  Multiple rare variants in the same gene, but different variants in different affected persons, especially when found in a gene known by more common variants or for some other physiological or functional reason to be a plausible causal factor.  The figure only shows two variants per each (A,B,C) gene. But different people may have different variants in the same gene.  They aren't likely to be found in simple whole-population studies, at least not initially.   But if in some way a strong variant identifies the gene as a possible candidate, and then an examination of population data shows other people with similar traits having other variants in the same gene, the gene gains causal plausibility, even if these variants don't seem to be sufficient on their own to be detected in association studies..

3.  Tail-wagging.  If concentrations of individually rare variants are found in individuals with phenotypes at the extremes of a trait distribution, they may be suspected as being causal.  People in the tail might share similar multi-site genotypes, even if the individual variants are generally rare. If the variants consistently contribute in non-trivial ways to the trait, then maybe if we look at those individuals who clearly would have collections of such variants, we might find them.  The tail of the distribution will show us the genes and then we can search for their effects in individuals with less extreme trait values.

In the figure this is clear.  All those individuals with very low or with very high trait values have the same or nearly the same genotype (e.g., AaBBCC, AABbCC, AABBCc, and AABBCC for the larger trait values).  The same thing goes for the lower tail (arrows in the figure).  The role of the 'capital letter' variants in these genes would be clear.  Of course, this classroom figure only shows 3 different genes but one can easily imagine the same sort of thing if there were 10 or hundreds of such contributors.  Environmental effects will of course make this less clear, but the tail might still wag the causal dog for us.  So what has been found?

Checking for the wag
Unfortunately, studies of extreme phenotypes have not yielded much, except for those already long-known because they are basically single-gene traits (CF, PKU, Tay-Sachs, MS,....), which mainly didn't require GWAS etc. to find.  Once they have been found, other variants of lesser effects have indeed been found, and though the story is more complex than that, for our purposes the single-gene effects did their job.

More importantly, for common traits one might hope to find clearer causation in very-high phenotype individuals.  However, where this has been looked for, such as in studies to map the genetic effects on traits like intelligence and stature, investigators have not found much tail-vs-middle difference, as far as we are aware.  A new study of supercentenarians, people in the extreme of the longevity distribution, did not find anything that explained their long survivorship.  The tail is not wagging the dog!

How can this be?  Is environment obscuring things even in the extremes?  Is it the reason those few people are in the extremes?  Or are we making some other mistaken assumption?  How can the extremes not be causally simpler than in the bulk of the population?  This seems a conundrum.

If we believe the evidence, there seem to be as many ways to be in the tails as to be in the middle of the trait distribution, with each person being genotypically unique.  The tails are not wagging the causal dog.  But why not?

What might this mean?  
This is curious because if there are finite numbers of contributing genes, with a distribution of allelic effects, the normal (unimodal, or central tendency) trait distribution, with most people in the middle, would suggest that there are more ways to get there than there are to be in the extremes.  This should also be true of mixtures of rare variants, shouldn't it?  Maybe not!

The figure is a simplified representation of the classical model for polygenic traits due to RA Fisher that basically was essentially of an infinite number of sites each contributing infinitesimal amounts.  In the limit, there are infinitely many ways to be in any part of the distribution. The model is powerful in its applications to various areas of genetics and seems to be basically right, but perhaps there are some key problems with the infinities and infinitesimals underlying the model.

Historically these 'infinities' play a major role in reconciling discrete Mendelian inheritance with quantitative traits and their inheritance, which was an important factor in the 'modern evolutionary synthesis' in the 1930s.  The general theory has served evolutionary and experimental breeders very well for nearly a century.  But is it correct or have we now found a problem area?

I raise this question because in the limit we must be reaching different levels of 'infinity' if our notion is that the reason for the central tendency is that there are more ways to get there than there are to be in the tails--just the assumption we are testing.  But in the limit, to get a smooth distribution and its properties, we essentially assume a greater infinity of ways to be modal than the infinity of ways to be in the tails, and this may be an approximation that makes little sense in the genomic enumeration era--or else that tells us something we need to know.  Infinities are approximations, but maybe the idea of very many contributors runs into practical issues in the kinds of data that GWAS and other studies are using, even their enormous samples.

Could the lag of 'wag' be that we are not dealing with what mathematicians or physicists would call 'well-posed' questions? Maybe stature, obesity, diabetes, or heart disease are not biologically unitary traits.  Then, if they are instead complexes of multiple partly independent (and separately evolved) traits, maybe being simple in the tail is not what we should expect.  Maybe what we are calling a trait is not what evolution 'called' any such thing.

Or it could be that 'infinity' here just means a great many, so that the gist of things is that even in the tails, there really is an essentially unlimited number of ways to inherit few or many small (left tail) or large-effect (right tail) alleles.  And since in any case the number of different combinations is large, and the presence of specific variants small, statistical methods can't enumerate them very well.  One may get into the tails because his/her huge collection of rare or even unique variants have in aggregate more 'large' effect than the collections of those in the middle of the distribution, but each person is unique and the extra individual effects are trivially small.  Our methods are not suited to detect this.  Or maybe the same variants are found across the distribution, but they are slightly more individually common in those with traits in the tail.

Maybe these differences, and/or even the specific variants involved, are so small or the variants uniquely rare, that aggregate, statistical, probabilistic or distribution-based kinds of approaches just won't find them, or just can't find them.  If that is the case, then neither our questions nor our methods are well-posed.

It is perhaps relevant that for many traits we really do have a simplified tail in the population: the very rare, very pathological, usually early onset and severe instances of traits that do turn out to be largely single-gene in their causation.  But they are not a sufficient part of the samples being studied by most mapping efforts.  At the same time, it is all too easy to forget or conveniently ignore, the massive effects lifestyle factors can make in achieved traits be they physical or behavioral.  No wonder, even in the tails of the distribution, we don't get a clear genetic wag!

Here, at least, there are things to think about.  These are real questions.  Technical statisticians (which we're not!) may have explanations--but if so, they haven't led to much in the way of clear causal tractability or these general issues about mapping would not be of such widespread concern. How can the tail not be notably simpler in its causation?  Has our explanation here missed something important, or are geneticists as a community missing something?

The questions have both empirical and theoretical meaning.  Whatever one's view about GWAS and massive whole genome sequencing with the goal of predictive medicine, at least the issues raised are perhaps things we all could agree about.

Should we eat eggs?

Ken and I are in Seattle this week, Ken meeting with people at the Institute for Systems Biology, and having many interesting conversations about different approaches to complex problems.  We had dinner with Ken's host Sui Huang and others from his lab the other night.  It was good to talk with people who think about many of the issues we often blog about, such as why it's so hard to identify the cause of many complex diseases.

Indeed, we don't know the answers to what seem to be simple questions, such as whether eggs, or sugar, or fats, or carbs are good or bad for us.  Why don't we know, people asked at dinner.  I tried to quickly sum up my thinking on this, but did so very inadequately. Wish I could blame it on the wine.  But I thought I'd expand here.

The gold standard for determining cause and effect is, of course, randomized controlled trials (RCT's).  Randomly assign half your study population a treatment, and the other half a placebo and see whether the treatment has a statistically identifiable effect.  The groups should differ systematically only on whether they receive the treatment or not, so in theory the only effect you will see is of the treatment. The best studies are double blind, that is, even those administering the treatment don't know which individuals are getting which.

This can work well when you're testing something like a quick-acting drug, something with strong, immediate effects, but when what you want to know is the effect of, say, eating eggs, or chocolate, it's more complicated.  Yes, you could give one group a daily dose of chocolate for 6 weeks, say, or even 6 months, but placebo chocolate -- or eggs, or acupuncture, or marijuana -- has to be so disguised, or reduced to a pill containing what you decide is the single component of interest, that it no longer mimics the context in which it's actually eaten, and RCTs may well become much less informative.

And there are other issues.  A daily dose for how long?  And, how do you decide?  And, does the effect depend on what else you eat with your chocolate, whether, say, you take it with coffee or wine? Or, could it be that the age you start eating chocolate determines its effect?  That is, assumptions are necessarily built into your choice of study duration, age of subjects, how more or less reductionist your study is, and the contextual effects of the foods we eat, and so on.

Red licorice; Wikipedia

Ok, maybe you decide that you don't know how long it would take for chocolate consumption to have an effect, and so shouldn't build that into your study but instead you want to figure out how long it takes to raise HDL cholesterol/protect against cavities/prevent cancer/whatever you think its effect is.  An RCT isn't going to be useful for answering such questions because it takes too long, maybe decades, for the effect to be observed in a practicable sense; so you need a different study design.  Let's say a retrospective study, in which you ask people about their past diets.

Unfortunately, dietary recall is notoriously unreliable.  How often did you eat broccoli last year?  Or even last month?  Or in concoctions with many ingredients, like, say, soup, where you may not know what the ingredients were?  And, how much?  Maybe you tell me your serving was three spears -- but how much of the stem did you cut off, and does that matter?  And do you add butter?  Salt?  Do you stirfry it?

And, if you're being asked about something to which there might be a certain amount of guilt attached (damned Puritans!), how honest are you going to be when you're asked how much chocolate you eat, or how much alcohol you drink (physicians routinely round up to another beer, drink or glass of wine when their patients' answer that question)?

And, of course, the further back you go, the less reliable the answers will be.  But, what if it's the chocolate we ate before we were 10 that predisposes to an effect?  Sure, that's unlikely, but it does make the point that we're still building assumptions into our study design.  Necessarily.

And, no one eats chocolate, and only chocolate.  Or hardly anyone.  Or, only whatever component of a given food that's thought to have whatever effect we're after -- such as resveratrol in red wine, which may, or may not, protect against heart disease.  Does it matter what else we eat with the offending food?  Which of course could be anything.  Or how the nutrient is packaged; whether in grapes or wine?  How do we account for that?  It's the same problem geneticists have with respect to genomic context; a gene variant may be deleterious in one context, and not in another.

Further, it's possible that people who eat chocolate are healthier, or less healthy, in general than people who don't.  That is, there may be other factors that interfere with, or even explain, the direct effect of eating chocolate that you're trying to identify.  Or, people who eat a lot of eggs also eat a lot of, I don't know, red licorice and it's the dye in the licorice that's really causing whatever effect you're measuring.  You might take, say, (control for) weight or exercise or smoking into account, but it's unlikely you'll think to ask about red licorice consumption.

And so forth.  There are many similarities between these kinds of epidemiological questions and genetics, including assumptions about the kinds of factors we'd like to be identifying.  Ideally, we'd all love to find single genes or single nutrients, with large effects, large enough to drown out the inevitable noise from the rest of the diet, genome, exercise routine, etc. that is likely to influence the action of the single factor we're looking for.  But these are rare, and aren't going to explain the bulk of the complex diseases most of us will eventually get.

In addition, epidemiologists and geneticists share similar confounders -- everyone's dietary history (or genome, if we're doing genetics) is unique, as is their genome (or, again for geneticists, history of environmental exposures), and thus everyone gets to their disease in their own way, with unique interactions and pathways.  But we're collecting and making sense of data on a population of people.  Indeed, we can't hope to understand the effect, or association, at all without looking at large groups of people, but then we're stuck trying to figure out how to apply what we think we learned from the group to individuals who are probably as unalike as they are alike.

In reality, given the number of possible interacting factors, and the general weakness of their effects, we would quickly get to a point where we've got to take so much into account that our analysis becomes untenable.  There may be so many combinations to test that you can't replicate things enough to get a signal, or can't get nearly adequate samples.  The larger the sample, the more heterogeneity you may have in your data (the signal-to-noise ratio may not increase with sample size).  You have to correct for doing huge numbers of tests in order to evaluate your statistical significance of the findings, and that may make strong findings impracticable unless there are some individual causes in the data that are common enough to see them.  These and many other statistical issues are involved in multifactorial causal studies.  Even Big Data can't solve these problems, because everyone is still unique, effects are still small, and we still can't collect all unknowably-to-us relevant variables.

Researchers design the best studies they can, and peer review presumably assures that most of the studies that are funded are state-of-the-art.  But state-of-the-art epidemiology still can't assuredly overcome the many potential biases, or confounders or unknown risk factors, and so forth, that it needs to in order to reliably identify the causes of complex chronic diseases.  These aren't faults unless investigators or reporters of results don't fully acknowledge them and submit to the limitations they pose on the findings.  They are just realities.  When assumptions are built in to the study that force a particular kind of answer, that's a problem.

What causes disease X?  It seems to be a simple, well-posed question.  When the answer is a virus, or a bacterium, or a toxin, or smoking, or a gene with large effect, it is a well-posed question, with a single, or small number of identifiable answers.  But, when the process takes years or decades, and each factor has a small, hardly statistically identifiable effect, environments change, and everyone has a unique history of exposures anyway, the question is no longer well-posed, there's unlikely to be a single answer, and epidemiologists have a problem.  'Cause' may not even be an appropriate concept in this context.

On Exactitude in Science
Jorge Luis Borges, Collected Fictions, translated by Andrew Hurley.

...In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography.
—Suarez Miranda, Viajes de varones prudentes, Libro IV,Cap. XLV, Lerida, 1658 

Borges' short story is a parable, and not completely applicable to the Arts of Epidemiology and Genetics, not least because they have not yet attained Perfection with respect to complex disease.  Still, I think it nicely frames the problem: in our Empire, the Arts of Epidemiology and Genetics are trying to greatly reduce the size of the causal, predictive map, but the problem is that the 1:1 map of the Province is what's useful for explaining each individual's disease.  Yet, such exactitude does exactly nothing to allow us to generalize about causation, or predict disease.

So, should we eat eggs?  If we like them, eat them.

Seattle's finest (thinkers, not coffee)

We're currently in Seattle, not blogging, but where I gave a presentation about genomic causation at the Institute for Systems Biology. I was hosted by Sui Huang and his group, and had many good conversations. This is a research institute where a lot of clever people are working hard, in various ways, to understand the causal complexity of genomic variation.

Many are dedicated at present to a Big Data informatics or computational approach to causation, using GWAS or similar kinds of data.  My talk did critique that approach in the sense of things we've posted about many times here, and it probably didn't go down well with those at ISB for whom this is a GWAS world.  Still, my point was the extensive genome mapping has shown that traits are complex in ways we had reason to expect before the GWAS era began 20 or so years ago.  We've learned a lot, but one point is the disappointment that mapping would  not find 'the', or the few, genes for common disease traits.

The 'precision' individual predictive medicine is here, whether one likes it or not, because that's where the money is, from NIH, thanks to the PR spin director Francis Collins' has managed to sell. The romance with heavily inductive Big Data, computationally extensive and without strong hypotheses is what's afoot.  Nonetheless, reservations not being dismissed, there are people here at ISB and elsewhere who are trying to exploit what is in this sort of data.

One major 'new' approach is to use extensive genome sequencing in, yes, families.  Without any sense of embarrassment at how GWASers have for a long time sneered at pedigree data, the greater power of families to reveal strong genetic causal factors is being realized.  To be fair, most of the younger people involved were not the sneerers (that was their elders).  Whether major high-risk genes or variants will be found in numbers justifying this way of analyzing Big Data remains to be seen, and one needs to beware of claims for this or that success as showing that this approach gives value. Such claims will be made, because that's how we operate these days.  But the question isn't asked whether we could get more bang for the buck with other approaches.

Anyway, we've said these sorts of things many times, and here there are people trying to think hard about what can be learned from Big Data and mapping approaches.  My talk was on something different, namely, to point out that genes function only by interacting with other genes and 'environmental' factors.  DNA is inert by itself, and only works by interacting molecules in its environment, many of which are coded by DNA elsewhere in the genome.  If a mutation arises, does it have an 'allelic effect?'  If so, what is it?  Essentially, and fundamentally, it depends on its context in the individual--the rest of the genome and its net effects, plus environmental conditions.

I asked this in the context of an evolutionary simulation program, called ForSim, that I and colleagues have developed and used over the years.  When a new mutation arises in a simulation, one has to specify how it affects traits (phenotypes) being simulated. How to assign such an effect is by no means trivial and leads to many important issues about genetic causation.

As importantly, differentiated organisms exist because their cells are partially isolated from each other, so they can specialize, but connected through communications like signaling, to the other cells (and to the external environment as perceived by the chemical and other sensory systems, etc.).  These interactions are in a sense not working in cis as the term is used, meaning along a given DNA molecule, but are trans effects, meaning involving DNA elsewhere in the genome.  In addition, it is local combinations of factors, their location and timing and presence, absence, or concentrations that, together, bring about biological effects.

The cis-trans distinction is not at all new, but there are many phenomena that are very 'trans' in nature.  For example, a given cell responds to environmental factors like genetically coded signal molecules, that are produced elsewhere.  The local cell is the location for the interactions among many factors arriving from various places.  That is how tissue behavior and differentiation respond to changed conditions, and/or are produced initially in embryos as they differentiate their various organ systems.

However, there are many aspects of what goes on genomically within a given cell that involve important trans effects that are basically not yet understood--and often hardly investigated or, as one might say, that do not give pause to the Big Data train racing right past, paying little attention. Monoallelic expression of various sorts are examples.  I talked with various people who found these issues interesting to think about, and in general we discussed strange facts that might be given higher priority in trying to understand genetic causation.  For example, a single gene may be associated with one type of disease. (The Huntingtin protein and Huntington's Disease, or BRCA1 and breast/ovarian cancer, are examples).  These are interesting because the gene is expressed in all cells, and the dangerous variants seem to have particular roles to play that would lead one to expect that they would have negative effects on all tissues, not just the 'major' ones the genes are known for, yet they don't.  Similarly, these sorts of genes often do not cause the same problem in mice, even though the genes are in mice and actively used.

Why is this?  The generic answer is that the genomic background or other life-course factors differ among species, or among cells.  But generic answers at this stage are rather hand-waving by nature. To me, these are examples of 'strange' facts that could, potentially, provide far more important answers if they were understood, than what we'll get by increasing the scale of the same sorts of studies we've been doing.

Another topic we had very good talks was about the way that genomic causal complexity has evolved to enable organisms to achieve biological success (survival and reproduction) under varying conditions.  The fact that there are many causal paths to traits like, say, glucose levels or behavior, means that different genotypes can succeed in a given environment, or the species can succeed in diverse environments.  Thus, the genomic complexity being clearly shown by mapping makes evolutionary sense.

In that context, another important point is that some genes, especially perhaps those involved in early development, are highly conserved evolutionarily.  Why is that, and how might that channel aspects of biology, keeping them from differing too much even over eons of geologic history?  These are important points.

We've discussed other things even in the 2 days we've been here, and hope to comment on them in the near future (but we're on holiday as of tomorrow, for a few days, so we may not get to that til next week).  We also want to mention that we met and had dinner with another of our regular, thoughtful correspondents, Manoj Samanta, whose Homolog.us blog often has topics MT readers might find interesting.

My final time at ISB ended with several hours of extremely stimulating discussions with a number of their people, students and staff alike.  What a refreshing and invigorating experience!  I intend to keep in touch with many of the people there.

Not yet, on sustainability, it seems....

This post is triggered by the beginning of the biking season here.  Joy of joys!!  When the snow's gone and the temperature gets far enough above freezing for weaklings like me to venture out on my bike, I like to see what's changed along the many various paths that are here, since the previous autumn when I put my bike in the basement to wait out the long, icy winter.  My usual reaction is that an important message hasn't gotten through at all.

We live in a land-grant university town.  Penn State is in fact a very good university.  We have an ag school and they have some active conservation and sustainability groups.  But overall it is a money-first institution.  Each year as I venture out I see more former agricultural land being urbanized:  soil and the prior year's farmland is being paved over to make room for condos or suburban-style McMansions, or shrink-wrap or fast-food franchise outlets, or banks.  Oh, and more bars, of course.

A few years ago our President and trustees sold many, as I recall hundreds, of acres right near the edge of campus, upon which hundreds of condos are being built.  That land had been used by the ag school and its students.  Bye bye, or perhaps one would better say $ye $ye to that old-fashioned notion!

View of Circleville Farm, 2005; Penn State Daily Collegian

Circleville Farm now: US Framing

Even here it seems that the message hasn't gotten through that what's important here should be beyond the interests of the short-term 'developers' (that is, the realtors and destruction companies) and that there should instead be at least a modicum of consideration not just for the long-term future but also the quality of life.  Thousands more people come in, and what we get is more demand on water, more light, noise, and air pollution, more crime, more sewage and electricity demand, more litter.  And, of course, more business for the local banks, shrink-wrap, fast-food, and alcohol merchants.  Every new development seems to be named for what the developers destroyed to build it -- "Pheasant Glen", "Acacia Woods", "Pine Hollow".

Urban sprawl envy
It is difficult for me to grasp why, even here, the need for restrained growth and long-term sustainability, especially in regard to agriculture, is basically not felt at all.  I think that's the appropriate word for it.  Humans are humans, and we're a short-term thinking species.  We're very good at long-term awareness, but it's in the abstract.  What we feel is of the here-and-now, and in our society this means quantity more than quality of life.  Or, to be fair, quantity of life is seen as quality of life.  Until the threat looms palpably on the horizon, we will carry on with what we know and are used to.

Even here, even in a university town with an ag school, intellectual awareness of issues has little 'bite'.  It is depressing.  Even as we see stories in the news of droughts and soil loss and climate change issues, we do essentially nothing.  Without a wolf right at the door blowing hard, complacency yields to or generates deniers and other arguers-of-convenience, who reflect the general human pattern of short-term thinking.  We daily hear arguments about how to insure 'growth' in business and the economy.  Population growth is hardly on the agenda at all, either locally or globally.  Here the operative word is growth, and it's a mainstay of international development efforts as well as economics professors.  There was just an episode of the  BBC Radio4 program The Inquiry, that dealt with this problem--and asked if we've just all got tired of hearing about climate change. The same goes for population growth, and resources exhaustion.

I can ride my bike all over town, which is a very good thing.  But at night it's hard to see the stars. We may think our consumerist world is expanding, but light pollution keeps us from realizing how finite we really are.

If State College with a major university, that even has sustainability programs, can't do it, who can or will?  Maybe in the western US, where drought stares one in the face?  We shouldn't denigrate climate deniers if we act in a comparable manner.  It seems that there is only one word in our language:  "More!"  Instead, Nancy Reagan had it right: when it comes to 'development' what we need to learn is how to "Just say No!".

But if deniers want to keep their big vehicles and whatever else makes them feel righteous, climate change advocates have their own hypocrisies.  They and their publicity advocates overstate the various aspects of the science itself.  Sustainability advocates use efficient LED light bulbs and recycle their milk bottles, but they still live in big air conditioned houses and drive to work and to the store to get those milk bottles.  Climate scientists and relevant policy-makers in large numbers gambol around the world attending meetings (flying, nice hotels with fancy meals, ....) where they debate how people should cut back on greenhouse gases, rather than the boring alternative of using Skype or Google Hangout for their conferences.  This is do-as-I-say-not-as-I-do behavior and no matter how correct the climate and sustainability arguments are--and they seem essentially urgent--the threat is just not looming enough.

I, too, am a hypocrite and probably, no certainly, don't do enough to truly recognize the problem. Neither do I have any idea how, or even if, it would be possible for a people, even an educated people, to become enough aware of long-term issues to do something really major about it. I like my car (and driving to the store to get my bottles of milk)!  But I also know that as long as everyone carries on as usual, each year there will be more bike paths for me to ride, because they put them in and around all the new condo complexes, and that's great!  So, I think I'll close now, and head out....

Have gorillas really inbred themselves into the future?

By Anne Buchanan and Ken Weiss

NOTE:  This is a revision of our original post, because a mistake on our part was pointed out by a commenter, to whom we offer thanks.  Our main point hasn't changed....unless there are still misperceptions on our part.

BBC Earth headline: "Inbreeding Makes Mountain Gorillas Genetically Healthy." We are so tempted to add an exclamation point to that, but we won't.  Anyway, you know it's there, whether we add it or not. Everyone 'knows' that inbreeding is bad; what a juicy story!

And, to summarize, the story is this: Mountain gorillas are an endangered species, surviving now in just two small groups in central Africa, a total population of only about 800 individuals.  Their numbers had fallen to just under 300 in the 1980's, for multiple reasons including poaching and loss of habitat, but Diane Fossey made their conservation her life's work, and the population more than doubled since its lowest point.

                  Location of eastern and western gorillas; Xue et al., Science 2015

But, their small numbers led to extensive inbreeding, which is always worrisome to conservationists because it may reduce a population's ability to adapt to changing environments.  But, the BBC writes:

Now scientists have discovered inbreeding has actually benefitted mountain gorillas by removing many harmful genetic variations. They are also genetically adapted to living in small populations.

And,

Fewer harmful genetic mutations, which stop genes functioning and can cause serious health conditions, were found in the mountain gorilla population than in the western gorilla populations.

Ok, let's step over to the actual paper ("Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding," Xue et al.), in Science last week, to get the story without the go-between.  So, the investigators sequenced the whole genomes of 13 eastern gorillas, including seven mountain gorillas and six eastern lowland gorillas.  They compared these sequences with published sequences of lowland gorillas further west, and found lower genetic diversity in both the mountain and lowland gorillas from the east, which they report as consistent with the smaller population sizes there.  Their analysis, they report, confirms that the eastern lowland and mountain gorillas are two genetically distinct populations.  Genome wide linkage disequilibrium was higher in the eastern gorillas than the western, evidence of different demographic histories of these populations, and suggesting a recent population bottleneck in the eastern gorillas.

Foraging gorilla, Congo; Wikipedia, Pierre Fidenci

In eastern gorillas, chromosomes were found to be homozygous across 34 to 38% of their length, while in western lowland gorillas, they were 13% homozygous, indicating that the eastern gorillas have a recent history of several generations of close inbreeding.  Xue et al. also report that the eastern and western populations diverged perhaps 150,000 years ago, with no mating history in the last 20,000 years or so.  And, overall, it seems that gorilla population sizes have been small for thousands of years, and thus probably have been inbreeding for all of that time.

Again comparing mountain with lowland gorillas, Xue et al found no evidence for natural selection or adaptation favoring functional genes in either group.

Such adaptation might be expected from the fact that mountain gorillas range over high altitudes (1500 to 4000 m), with consequences for diet, morphology, and physiology. However, we found no significant enrichment in any functional category of genes, although there are interesting examples related to nervous system morphology, immunoglobulin quantity, and red blood cell morphology. Mountain gorillas carry a significant excess of variants in genes associated with blood coagulation in humans (fig. S21), perhaps linked to high-altitude living. We also identified variants associated with cardiomyopathy, including in one deceased individual (Kaboko) in whom post mortem analysis revealed evidence of muscular hypertrophy. Cardiovascular disease has been identified as a notable cause of death in captive western lowland gorillas.

With respect to unfavorable effects of inbreeding, the authors report the opposite, saying that inbreeding seems to have purged deleterious mutations from the genome.  They suggest that gorillas have found workarounds for inbreeding effects, as well, such as by "natal dispersal and gene flow between isolated populations."

Xue told the BBC that gorillas have been coping with small populations for thousands of years, and,

"While comparable levels of inbreeding contributed to the extinction of our relatives, the Neanderthals, mountain gorillas may be more resilient. There is no reason why they should not flourish for thousands of years to come."

No reason? 
But, we can think of a number of reasons.  The Ebola virus has been devastating to chimps and gorillas, wiping out 95% of some groups of gorillas in which it has spread.  And, there's always the possibility that other infectious diseases may emerge, or reach these animals, and be equally, or even more devastating.

And, poaching continues to be a problem, and hunting for bushmeat.  Loss of habitat continues to be a problem.  Climate change will surely have consequences for these animals.  As with any other animal, including humans, environmental change and its consequences are unpredictable.  Whether or not any species has the genetic wherewithal to adapt to that change is unpredictable; it's impossible to know what any single gene will do in every possible environment, never mind what every gene, and every genetic interaction will do.  This is, of course, true with respect to predicting our own futures from our genomes as well. 

What is 'inbreeding' and what does it mean?
There are several things about this paper aside from the apparent obliviousness of the research report to the real threat to gorilla 'fitness', namely that they're widely projected to become extinct because of human incursion and predation, in addition to disease. We might also ask, if the western gorillas have so little relative homozygosity, why they aren't plagued with the sorts of defects that the easterners have already purged, and on the verge of collapse -- or long gone?  

The answer is that both populations (not just the eastern) did well enough to be here today.  Both low and high homozygosity are obviously good enough, because neither wiped out either population structure in their past.  So why tell the story as if one way's better? It seems to be the tired old evolutionary trope that we cannot seem to escape: To be different from is to be better than, to evolve away from is because it's a better way.  But, mutation is always happening, genetic drift is always happening, and if a variant works, it works. It isn't necessarily selected because it's better, or more adaptive, than anything that came before. 

This paper is in a sense an exaggeration of, and in a way confusion about inbreeding and its effects.  There are several meanings of 'inbreeding' that are relevant here** . The classical meaning refers to mating with close relatives relative to random-mating. The issue there is the classical one of increased incidence of recessive disorders with inbreeding.  In that context, the probability of an allele being homozygous more than just by chance: if the latter is p^2 when there is random mating, the former is p^2 + Fp(1-p), where p is the frequency of the variant in question, and F is the excess probability of being homozygous due to non-random mating. That may be because of socially constructed preferential kin-mating or just a deviation from random mating. In many, if not historically most, human populations, mating was prescribed as to be between cousins of various types. If variants are harmful but recessive so that their harm is only seen in homozygotes (both copies in a person being defective), then mating between close relatives can increase the frequency of such events, and the loss of the harmful variant from the population, but of course only at the expense of the carriers of those harmful genotypes. One can argue that if something like close-relative mating were so dangerous it would never have evolved to be, in a sense, the ancestral human way as it has.  Or, one can note that the reason for local group endogamy or exogamy (how mates are chosen in any population, human or otherwise) has to do with social structure, resource distribution, and control of internecine and intergroup strife--not because of disease genes.

The authors appear not to have done this kind of calculation, however, and samples would have been too small for it to make sense.  Instead, they looked along the genome to see what fraction was homozygous (that is, variant sites along the region in the sequenced animals).  This reflects a different use of the term 'inbreeding', and we think what this paper is referring to, is the rise in homozygosity due to genetic drift in small populations.   In a small population, rarer alleles (genetic variants) are lost more rapidly from the population, mainly just by chance. Homozygosity at a given site is an inevitable reflector of population size, and in a small population the region of a chromosome that is homozygous (not varying) would be larger than in a large outbred population. That is not an automatic indicator of a history of loss of harmful mutations, recessive or otherwise. In any population harmful variants have a shorter staying time than helpful ones, but their duration depends on many different factors that can't be inferred from the stretches of homozygosity alone.

Do western lowland gorillas, with their lack of a history of 'inbreeding' as presented by this story, show some detectable load of sub-par individuals? If so, that would be relevant news. In fact, both groups have coefficients higher than human cousins relative to each other, as a commentary on this paper notes. But so what?  In fact, and perhaps to the contrary, being too inbred in the small-population sense could, as far as just-so stories go, mean there would not be enough variation in the population to respond to environmental challenges.

What the study does no doubt actually show is that the two gorilla populations have had different demographic histories. That is ecologically interesting and perhaps useful for understanding wildlife conservation issues.  But in itself it says basically nothing about purging harmful variation except that it would be somewhat faster, on average, in one group than the other -- but only slightly so, because if that were not the case the burden of loss could have threatened the very survival of the group in the past so that it never made it to the present, which obviously isn't the case.  'Inbreeding' in headlines may have a juicy sound and catch the lascivious eye, and that's why the news media go for it so readily.

It should also be noted that extensive, detailed, biomedically documented studies in human isolate populations have found each to have particular instances of elevated recessive diseases or other traits due to inbreeding effects, but the overall burden of genetic disease has not been particularly increased, if at all.

***The often and perhaps still confusing issue of inbreeding have been clarified long ago, e.g., by Albert Jacquard in 1975, in J. Theoretical Biology, "Inbreeding: one word, several meanings", by various wrtiting of Warren Ewens back in the 70s, or see Templeton and Read, Conservation Genetics, 1994; they are discussed in any good population genetics text.

Yanomami blood sample return: Some update information

On March 25 we posted a discussion of research ethics that was brought about by the return of some blood samples we had for many years housed in our lab at Penn State.  We explained why this had been contentious, and why many aspects of the demand for return were based on reasons wholly unrelated to anything done by the investigators with these samples.  Rather, the concern had more to do with experiences of the Yanomami tribal members over many years, due to outside interference with their lives.

There was a flurry of publicity from various news sources, showing the return of the samples (links below), and we would like to correct some of what was said.  In many of the stories, the source of the samples was mis-attributed to the University of Pennsylvania, when in fact it was Penn State.  There also seems to be a misapprehension or allegations in the news stories that the samples were collected without permission and implying that we were the ones who collected them.  Whatever one's views about how the Yanomami were treated and their experience of the outside world, or how they currently feel about the samples, these involve quite irresponsible mistakes.  My lab has housed a set of these samples for many years, after the person (JV Neel) whose group collected them had retired, he divided the samples up for safekeeping and curation into three sets, one for the Anthropology department at Penn State, one for an investigator now at Penn, and one for the National Cancer Institute, where they had various potential research interests in them, in case new methods became available that could enable things to be learned about cancer that the original methods, of the '60s and '70s, were incapable of resolving.  Michigan would have destroyed them otherwise.

There is plenty of room for debate about scientific studies by the industrialized nations, of indigenous or dependent populations.  The nature of informed consent was discussed in our earlier post on this problem.  Feelings expressed now by Yanomami representatives may be entirely sincere and even justified from their point of view.  At the time, the filming of the collections and the trade goods and whatever else was involved offered in exchange as part of the Yanomami's participation, including the provision of the blood (and other biomedically related samples and information) made the collection seem totally voluntary.  The degree to which cultural and/or power differences and the like led to misunderstanding about the samples and what was to be done with them is impossible for us to know, and there are differences of views for many reasons.

Not the least of the problems is the passage of decades of time, and of the lives of both investigators and subjects.  Retrospective judgments about informed consent, coercion, recompense, and relevance of the anthropological studies to the Yanomami experience with the outside world, are important issues, but not ones we ourselves can judge.

It should also be pointed out that the issues about the Amazonian indigenes and the outside world are not new.  Indeed, around 1800, when Alexander von Humboldt visited the Yanomami general area (and one of the main sites of the work now in question), there were already long-established mission stations with a lot of western culture already brought into the area.  So external influence, helping, and/or meddling with the lives of the indigenous populations have a deep ancestry.  Hopefully, newer ethics or protections will prevent further problems of this kind.

A few of the stories (in English, Spanish and Portuguese):
http://g1.globo.com/rr/roraima/noticia/2015/04/sangue-yanomami-repatriado-dos-eua-e-enterrado-em-aldeia-indigena.html

http://www.lavoixdunord.fr/france-monde/bresil-des-yanomami-rendent-a-la-terre-leur-sang-ia0b0n2755248

- BBC: http://www.bbc.com/news/world-latin-america-32178286
- American Indian and Friends (BBC): http://americanindiansandfriends.com/news/indigenous-tribe-s-blood-returned-to-brazil-after-decades
- News 24 (AFP): http://m.news24.com/news24/World/News/Indian-tribe-buries-blood-samples-taken-by-US-researchers-20150405
- 9 News, Austrália (AFP): http://www.9news.com.au/world/2015/04/06/02/11/brazilian-native-tribe-holds-ceremony-for-blood-samples-taken-by-us-researchers

IF: Impact Factor....or inflation factor?

As in many departments, our graduate students and post-docs here in the Penn State Anthropology Department hold weekly 'journal clubs' where recent interesting papers are discussed. Last week, the students discussed the nature, value and importance of tabulations of journal impact factors (IF), basically the citation rate per published paper. There have been many papers and commentaries on this subject in recent years, but this session focused on a paper by Brembs, Button and Munafo entitled "Deep impact: unintended consequences of journal rank," published in 2013 in Frontiers in Neuroscience.

The IF scandal

This article assesses the assigned IF of journals relative to their retraction, error or fraud, and reliability or replicability rates. The objective picture of the IFs is not encouraging. Statistical analysis shows that the 'major' journals--the expensive, exclusive, snobbish high-status ones are, in terms of the quality and accuracy of their content no better, and arguably worse than the less prestigious journals. We won't go into the details but basically there is a rush to publish dramatic results in those status journals, the journals are in business to attract and generate attention, and that leads them to receive, and publish, splashier claims. In this self-reinforcing pattern they garner the honors, so it is worrisome that they appear to do this without actually publishing the best research or, worse, systematically publishing unreliable work.

It is of course not wrong to publish in such journals, nor is all they publish of questionable merit. If you can get good papers in those journals, of course, do it! The point for us here is that faculty, and young faculty in particular, are judged by granting agencies, department heads and deans to a substantial extent by the IFs of the publications on their CVs. It is bad enough for the competition of over-populated academe to be based on the mythology of unending exponential growth, but it's worse if the system is such that it can be effectively gamed or used to reinforce those at the top so they can stay at the top, often without their work being substantially better than that of their peers. This squeezes those not at the top down into the lower-status strata of the disciplines -- and when it comes to jobs, if they can't manage high IFs or high numbers of publications, it may well mean they are out of work!

Our student seminar discussed this problem and the effect it may have on their careers, if their work is going to be judged by a somewhat rigged, and inaccurate, scoring system. If they can't get into the high-IF elite publishing club, which is somewhat self-reinforcing, how can they compete for jobs and grants and get their work known?

We have several reactions to this. For students and others who may have a stake in (if not in the heart of!) the system, here are a few thoughts on alternatives to the high IF journals. The picture is grim, but in some surprising ways, not at all hopeless.

Some thoughts for students:
First, TheWinnower, founded by Josh Nicholson, a graduate student at Virginia Tech, is a new online site where one can send papers but also where blogs and other such new-media communications can be published, and these publications given a DOI (formal document identifier) and hence be more regularly citable and permanently archived. It's but one of many new communication venues. A lot of what is on these media is of course superficial fluff, but (Sssh!! don't tell anyone!) so is a lot of any sort of publication, even (believe it or not!) in the 'major' journals and so has it always been, even in the old-time printed journals of yore.

Secondly, there are allies in any movement towards change, not just from the grass roots where pressure for social change usually arises. There are thoughtful and progressive administrators, and serious scholars and scientists, who are resisting the pressure to use IF score-counting, in career evaluations, purportedly to make them more 'objective'.

And, there are many people making their way largely and in various ways on blogs, open-access publishing, online teaching, communicating with people via Twitter and other outlets (most of which we, being quite senior, probably don't even know of!). Writing for public media of all sorts has always been a mainline, legitimate way to build careers in anthropology, especially its sociocultural sides. But generally, critiques of the system at all levels, such as repeated revelations about score-counting bureaucracies and IF biases, as well as objections to closed access publishing, will have their impact if they are repeated often and loudly enough.

Thirdly, ironically and reassuringly perhaps, the tightening of the grant prospects and the well-documented concentration of funding in the hands of senior investigators, means more people will have to rely less on grants, and their university employers will simply have to recognize that. Teaching and other forms of service, scholarship, and outreach will simply have to be reinvigorated. Universities aren't just going to close shop because their grant funds shrink. They're not even going to be able to keep shifting towards hiring poorly paid Instructors. So the field is open for innovation and creativity.

Fourthly, also ironically, the greater the rush to the Big Journals, the better it may be for the job prospects of current grad schools? Why? Well, fewer people in the running for each job will have such publications on their CVs than perhaps was the case in the past. As long as applicants realize that others will want the same jobs they do, and they develop their skills and depth of thought accordingly, they'll compete well. After all, colleges and universities will simply not be able to hold out for those few with BigName publications, even if they wanted to. They'll be 'stuck' having to evaluate people on their actual merits. And, not so trivial as you might think, most of their faculty haven't got BigName papers either, and might not want to be outshone by adding a junior hyper-achiever to their midst. Indeed, many less research-intensive but wholly academically serious places feel, correctly, that applicants for faculty positions who have BigName publications don't really want to work there and will move on as soon as they can get a ‘better’ job, and/or in the meantime won't be dedicated to teaching, students and the local institution. So things aren't always as dire or as one-sided as they seem--even if times are relatively difficult right now.

Fifth, if the intense rigors of the research-intensive Fast Lane appeal to you, well, you know the gig and its competitive nature, and if you get your advanced degree from a fine and well-regarded program that will give you a chance at getting the brass ring. Those avenues are of course open, even if highly competitive.

"Painted Pony Bean" by Liveon001 © Travis K. Witt - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons -

But why does anyone even tally such things as impact factors?
An obvious question one should be why anybody would tally impact factors in the first place? Who has what to gain? The answer has to be that it is in someone's interest and someone will gain by it. After all, when some of us started our careers, there was no such thing (or, the earlier version Science Citation Index, was remote, in the library, laborious to look through and then usually only for legitimate resource searching). Scholarship itself was on average at least as good as now, careers were made without bean-counting but more on merit and substance, and bean-counting expectations were lower (and respect for teaching higher), the grant game much, much less intense.

IF scores are computed by a commercial company, Thompson-Reuters, as---what? As a favor to the publishing industry, and for what we would call a kind of academic bourgeois market for baubles and vanity. Journals self-promote by gaming their IFs, universities self-promote by gaming their faculty's IF ratings. They have money to make by promoting and, yes, manipulating their IFs (see the above article for just some of the ways). One can ask whether there is even a single reason for such score-keeping to be done other than for reasons of artificially constructed status hierarchies.

One motivation for this bean-counting is the heavy proliferation of online journals. Some of these are very highly respected, and deservedly, while others are chaff or, worse, scams for making money playing on fears and insecurities of faculty members needing advancement. IFs will at least be some assistance to an administrator or grant reviewer who wants to have an idea of a faculty candidate's record. But if the IFs are systematically unreliable, or manipulated, or even reverse indicators of actual work quality as some articles like the one above have suggested, that is a rather lame rationale for using IFs. Administrators evaluating their faculty members' careers should look at the actual work, not just some computer-tallied score about it. That may not be easy, but administrators are well-paid and accepted their jobs, after all.

There was in the past an insider Old Boy network in academe, that discriminated more arbitrarily in terms of funding, over-powerful Editors who controlled who published and what they published, and less opportunity for women and cultural minorities (based on ethnic as well as university status hierarchies). To increase fairness, but also to avoid discrimination lawsuits, and to play the self-promotion PR spin game, universities and their administrative officials learned the value of being 'objective' and hiding behind Excel spreadsheets rather than judgment. More objectivity did in many ways dislodge the older elite insider networks, but a system of elites has clearly re-established itself, and manipulable IF factors and their associated commercial incentives have helped reestablish some dominance in the academic system. It may still be wide open in many ways, but is heavily burdened by the game because the corporate university has become so money-oriented. This is very well documented. Things like IFs serve those interests.

The academic world will experience change, and is changing, and the new ways of communication are better and faster and more open-structured than ever. They make life more frenetic, but that will probably calm down because it's exhausting everyone. There will of course always be an elite, and for some that's a happy community to be part of. But it's not to everyone's taste. How long it will take coup-counting administrators to accept these other venues such as online communications, is unclear, but it's happening.

Social change requires resistance to the status quo, usually organized resistance (or else money-based leverage). Bureaucracies do need to be pressured, by faculty, graduate students and post-docs, and people like Department Heads and Chairs. But, it has to happen, and it will.

Redpolls: genetically similar, phenotypically different

Redpolls are a group of small birds in the finch family, members of the genus Acanthis.  They breed in the far north, but sometimes migrate as far south as the central US in winter, when food is scarce further north.  They rely on a small variety of seeds, and sometimes travel a remarkable thousands of miles to find them.

Range of the Common Redpoll; Source: Cornell Lab of Ornithology

All redpolls share characteristic red markings on their heads, but otherwise these birds vary enough that they've been thought to comprise as many as six separate species, based on plumage and morphology.  Most commonly, ornithologists have treated them as three species; the Common Redpoll, the Hoary (or Arctic), and the Lesser.  Now a new paper ("Differentially expressed genes match bill morphology and plumage despite largely undifferentiated genomes in a Holarctic songbird," Mason and Taylor) reports a DNA sequencing study that suggests that the redpolls are in fact a single species.

Common Redpoll; Wikipedia Commons

Arctic Redpoll; Wikipedia Commons; (13667519855)" by Ron Knight from Seaford, East Sussex, United Kingdom -  Licensed under CC BY 2.0 via Wikimedia Commons

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Lesser Redpoll by Lawrie Phipps derivative work: MPF (talk) - Carduelis_cabaret.jpg. Licensed under CC BY 2.0 via Wikimedia Commons

A figure from the Mason and Taylor paper makes the differences more apparent:

From Figure 1, Mason and Taylor, 2015

As Gustave Axelson recently wrote in his post about this study for the Cornell Lab of Ornithology All About Birds blog, seeing a Hoary redpoll can be one of those Moby Dick-like quests for a birder intent on adding it to his or her lifelist.  But Mason and Taylor report, after sampling 77 redpolls of very different phenotype, and sequencing 20,000 SNPs in the genome, and 215,000 in the transcriptome (that is, mRNA transcribed from different genes), with gene expression data and ecological niche modeling, they find very little variation between the different redpolls. In contrast, as Axelson points out, genetic comparisons between other similar species of birds, such as black-capped and Carolina chickadees, has found substantial variation all across the genome.

Mason and Taylor write, "we present evidence of (i) largely undifferentiated genomes among currently recognized species; (ii) substantial niche overlap across the North American Acanthis range; and (iii) a strong relationship between polygenic patterns of gene expression and continuous phenotypic variation within a sample of redpolls from North America."

As evolutionary biologists, Mason and Taylor are interested in the processes that lead to phenotypic diversity and speciation. "The Holarctic redpoll finches (Genus: Acanthis) provide an intriguing example of a recent, phenotypically diverse lineage; traditional sequencing and genotyping methods have failed to detect any genetic differences between currently recognized species, despite marked variation in plumage and morphology within the genus."

Mason and Taylor write that interspecific breeding has been observed, as have birds with characteristics of two different species, though phenotypic variation has been observed to be continuous throughout the redpoll range.  But no one has been able to document significant variation in either nuclear or mitochrondrial DNA.  So, if they are genetically so similar, how is it that these birds look different enough to be considered separate species?  The authors propose three possible scenarios:

The paucity of genetic differentiation within the redpoll complex, despite marked phenotypic variation across a Holarctic distribution, could be the result of multiple evolutionary scenarios (Marthinsen et al. 2008): redpolls may be comprised of (i) a single, undifferentiated gene pool that exhibits phenotypic polymorphism, in which phenotypic differences reflect locally adapted demes or neutral phenotypic variation within a single metapopulation; (ii) multiple gene pools that have recently diverged, in which incomplete lineage sorting has hindered the capacity of previous studies to differentiate populations or species; or (iii) multiple divergent gene pools that are actively exchanging genes through hybridization and introgression via secondary contact.

They compared the niches of hoary and common redpolls and determined that hoary redpolls prefer higher latitudes while common redpolls show less of a preference and are more widespread, with much overlap.  But they don't believe that the difference was enough to explain morphological differences between the birds.  That is, geographic isolation, the usual explanation for speciation, doesn't explain the phenotypic variation observed among redpolls.

Mason and Taylor note that the lack of outlier SNPs suggests that the different redpoll species, as now recognized, share a very recent ancestry.  If there were outliers, this would suggest that the birds had had a long history of no contact, during which time genetic variants arose and spread, but then the species reunited, and the interbreeding would have dispersed much, but not all, of those variants between the entire family.  That is, option i above; this is a single undifferentiated gene pool that exhibits phenotypic polymorphism.

Intriguingly, we found novel differences in gene expression that are correlated with redpoll phenotypes, suggesting that gene expression might play an important role in generating phenotypic diversity among redpolls.

This is intriguing.  Mason and Taylor suggest that redpolls should now be considered a single species,  although as Axelson says, this is up to the American Ornithologists Union.  But, given the very low genetic diversity even between widely dispersed birds, and the fact that phenotypic variation is continuous within the genus, it makes sense.  They further suggest that gene expression differences could be due to environmental conditions which trigger phenotypic plasticity in traits like bill width or plumage coloration.

Without whole genome sequencing, these results remain suggestive.  There may be as of yet unknown regions of the genome that are responsible for the variation seen in this species, but the lack of variation in SNPs throughout the genome suggest this is probably not going so.

Evolutionary considerations and the species problem
Evolutionary biologists know that there is a 'species problem'.  That is, only individuals are clear-cut distinct natural units (and, given their colonization by bacteria and the like, even they aren't all that discrete).  Species would be next, but it is about group properties and there are many definitions.  The most commonly accepted is that a species is a group of individuals that can successfully mate and produce fertile offspring.  Similar individuals whose offspring are always sterile would be assigned different species.  Different appearance need not imply mating incompatibility (as, for example, people from Africa and Polynesia, who are inter-fertile).

Single genetic changes have been found to lead to mating incompatibility, as between populations of fruit flies.  Of course changes of any sort can do this in the case of individual human couples.  If there is mating incompatibility among groups, at least, we call them different species.  Among other reasons, the expectation is that over time they will diverge in their genes and traits, with or without the aid of natural selection, and become ever more different.  Only with shared mating would these differences be blended and circulated through a species' population.

We can note four important points here.  First, species can be defined in many ways, but the idea of genetic isolation as an enabler of separate adaptation and divergence, that goes back to Darwin, is important in accounting for the evolution of diversity.  Second, speciation is a separate phenomenon from diversity of traits.  The latter is found both within and between populations of the same species. These are obvious but subtle points, often missed or overlooked even by biologists who equate natural selection and trait differences with species differences.  Mating incompatibility enables the accumulation of trait differences, but trait differences do not in themselves enable speciation.

Thirdly, what we haven't mentioned yet, is polyphenism.  This is a well-known phenomenon in which the same genotype can yield very different phenotypes (traits) in different environments.  This can happen if something in the diet produces pigments, or it can happen if genes are expressed, or not, depending on environmental conditions, leading to environment-specific results, in different individuals with the same genotype or the same genotype in different environments.  For example, the brown goldfinches in our back yard are turning yellow as spring comes.

Fourth, individual groups whose members could physically and genetically mate successfully, but don't, either because they are isolated from each other, don't come into contact, or just simply don't do it even if they could, are sometimes considered to be different species.  Usage varies and it's a judgment call, with  no external 'law' necessitating the definition.

There is no one principle or rule about by which biological species can be defined by trait comparisons, or genomic comparisons alone.  Each case is different, and since genotypic differences  or trait differences can, but needn't indicate, species differences, one has to study each case on its own merits.  That's not always easy, but it's the nature of life.