Thursday, October 27, 2016

Causal complexity in life

Evolution is the process that generates the relationships between genomes and traits in organisms.  Although we have written extensively and repeatedly about the issues raised by causal complexity,  we were led to write this post by a recent paper, in the 21 October 2016 issue of Science, which discusses molecular pathways to hemoglobin (Hb) gene function.  Although one might expect this to be rather simple and genomically direct, it is in fact complex and there are many different ways to achieve comparable function.

The authors, C Nataragan et al.,  looked at the genetic basis of adaptation to habitats at different altitude, focusing on genes coding for Hb molecules, that transport oxygen in the blood to provide the body's tissues with this vital fuel.  As a basic aspect of our atmosphere, oxygen concentrations differ at different altitudes, being low in mountainous regions compared to lowlands.  Species must somehow adapt to their localities, and at least one way to to this is for oxygen transport efficiency mechanisms to differ at different elevations.  Bird species have moved into and among these various environments on many independent occasions.

The affinity of Hb molecules for, that is, ability to bind oxygen, depends on their amino acid sequence, and the authors found that this varies by altitude.  The efficiency is similar among species at similar altitudes, even if due to independent population expansions. But when they looked at the Hb coding sequences in different species, they found a variety of species-specific changes.  That is, there are multiple ways to achieve similar function, so that parallel evolution at the functional level, which is what Nature detects, is achieved by many different mutational pathways.  In that sense, while an adaptation can be predicted, a specific genetic reason cannot be.

The authors looked only at coding regions, but of course evolution also involves regulatory sequences (among other functional regions in DNA), so there is every reason to expect that there is even more complexity to the adaptive paths taken.

Important specific documentation....but not conceptually new, though unappreciated
The authors also looked at what they call 'resurrected ancestral' proteins, by experimentally testing the efficacy of some specific Hb mutations, and they found that genomic background made a major difference in how, or whether, a specific change would affect oxygen binding.  This shows that evolution is contingent on local conditions, and that a given genomic change depends on the genomic background.  The ad hoc, locally contingent nature of evolution is (or should be) a central aspect of evolutionary world views, but there is a widespread tendency to think in classical Mendelian terms, of a gene for this and a gene for that, so that one would expect similar results in similar, if independent areas or contexts.  This is a common, if often tacit, view underlying much of genome mapping to find genes 'for' some human trait, like important diseases.  But it is quite misleading, or more accurately, is very wrong.

In 2008 we wrote about this in Genetics, as we've done before and since here on MT and in other papers.  In the 2008 article we used the following image to suggest metaphorically the nature of this complex causation, with its alternative pathways and the like, where the 'trait' is the amount of water passing New Orleans on the Mississippi River.  The figure suggests how difficult it would be to determine 'the' causal source of the water, how many different ways there are to get the same river level.

Drainage complexity as a metaphor for genomic causal complexity.  Map by Richard Weiss and ArcInfo
One can go even further, and note that this is exactly the kind of findings that are to be expected from and documented by the huge list of association studies done of human traits.  These typically find a great many genome regions whose variation contributes to the trait, usually each with a small individual effect, and mainly at low frequency in the population.  That means that individuals with similar trait values (say, diabetes, obesity, tall, or short stature, etc.) have different genotypes, that overlap in incomplete and individually unique ways.

We have written about aspects of this aspect of life, in what we called evolution by phenotype, in various places.  Nature screens on traits directly and only on genes very indirectly in most situations in complex organisms.  This means that many genotypes yield the same phenotype, and these will be equivalent in the face of natural selection and will experience genetic drift among them even in the fact of natural selection, again because selection screens the phenotype.  This is the process we called phenogenetic drift.  These papers were not 'discoveries' of ours but just statements of what is pretty obvious even if inconvenient for those seeking simple genetic causation.

The Science paper on altitude adaptation shows this by stereotypical sequences from one individual each from a variety of different species, rather than different individuals within each species, but that one can expect must also exist.  The point is that a priori prediction of how hemoglobin adaptation will occur is problematic, except that each species must have some adaptation to available oxygen.  Parallel phenotype evolution need not be matched by parallel genotypic evolution because selection 'sees' phenotypes and doesn't 'care' about how they are achieved.

The reason for this complexity is simple: it is that this is how evolution working via phenotypes rather than genotypes molds the genetic aspects of causation.

Thursday, October 13, 2016

Genomic causation....or not

By Ken Weiss and Anne Buchanan

The Big Story in the latest Nature ("A radical revision of human genetics: Why many ‘deadly’ gene mutations are turning out to be harmless," by Erika Check Hayden) is that genes thought to be clearly causal of important diseases aren't always (the link is to the People magazine-like cover article in that issue.)  This is a follow-up on an August Nature paper describing the database from which the results discussed in this week's Nature are drawn.  The apparent mismatch between a gene variant and a trait can be, according to the paper, the result of technical error, a mis-call by a given piece of software, or due to the assumption that the identification of a given mutation in affected but not healthy individuals means the causal mutation has been found, without experimentally confirming the finding--which itself can be tricky for reasons we'll discuss.  Insufficient documentation of 'normal' sequence variation has meant that the frequency of so-called causal mutations hasn't been available for comparative purposes.  Again we'll mention below what 'insufficient' might mean, if anything.

People in general and researchers in particular need to be more than dismissively aware of these issues, but the conclusion that we still need to focus on single genes as causal of most disease, that is, do MuchMoreOfTheSame, which is an implication of the discussion, is not so obviously justified.   We'll begin with our usual contrarian statement that the idea here is being overhyped as if it were new, but we know that except for its details it clearly is not, for reasons we'll also explain.  That is important because presenting it as a major finding, and still focusing on single genes as being truly causal vs mistakenly identified, ignores what we think the deeper message needs to be.

The data come from a mega-project known as ExAC, a consortium of researchers sharing DNA sequences to document genetic variation and further understand disease causation, and now including data from approximately 60,000 individuals (in itself, rather small compared to the need for purpose). The data are primarily exome sequences, that is, from protein-coding regions of the human genome, not from whole genome sequences, again a major issue.  We have no reason at all to critique the original paper itself, which is large, sophisticated, and carefully analyzed as far as we can tell; but the excess claims about its novelty are we think very much hyperbolized, and that needs to be explained.

Some of the obvious complicating issues
We know that a gene generally does not act alone.  DNA in itself is basically inert.  We've been and continue to be misled by examples of gene causation in which context and interactions don't really matter much, but that leads us still to cling to these as though they are the rule.  This reinforces the yearning for causal simplicity and tractability.  Essentially even this ExAC story, or its public announcements, doesn't properly acknowledge causal context and complexity because it is critiquing some simplistic single-gene inferences, and assuming that the problems are methodological rather than conceptual.

There are many aspects of causal context that complicate the picture, that are not new and we're not making them up, but which the Bigger-than-Ever Data pleas don't address:
1.  Current data are from blood-samples and that may not reflect the true constitutive genome because of early somatic mutation, and this will vary among study subjects,
2.  Life-long exposure to local somatic mutation is not considered nor measured, 
3.  Epigenetic changes, especially local tissue-specific ones, are not included, 
4.  Environmental factors are not considered, and indeed would be hard to consider,
5.  Non-Europeans, and even many Europeans are barely included, if at all, though this is  beginning to be addressed, 
6.  Regulatory variation, which GWAS has convincingly shown is much more important to most traits than coding variation, is not included. Exome data have been treated naively by many investigators as if that is what is important, and exome-only data have been used a major excuse for Great Big Grants that can't find what we know is probably far more important, 
7.  Non-coding regions, non-regulatory RNA regions are not included in exome-only data,
8.  A mutation may be causal in one context but not in others, in one family or population and not others, rendering the determination that it's a false discovery difficult,
9.  Single gene analysis is still the basis of the new 'revelations', that is, the idea being hinted at that the 'causal' gene isn't really causal....but one implicit notion is that it was misidentified, which is perhaps sometimes true but probably not always so,
 10.  The new reports are presented in the news, at least, as if the gene is being exonerated of its putative ill effects.  But that may not be the case, because if the regulatory regions near the mutated gene have no or little activity, the 'bad' gene may simply not be being expressed.  Its coding sequence could falsely be assumed to be harmless, 
11. Many aspects of this kind of work are dependent on statistical assumptions and subjective cutoff values, a problem recently being openly recognized, 
12.  Bigger studies introduce all sorts of statistical 'noise', which can make something appear causal or can weaken its actual apparent cause.  Phenotypes can be measured in many ways, but we know very well that this can be changeable and subjective (and phenotypes are not very detailed in the initial ExAC database), 
13.  Early reports of strong genetic findings have well known upward bias in effect size, the finder's curse that later work fails to confirm.

Well, yes, we're always critical, but this new finding isn't really a surprise
To some readers we are too often critical, and at least some of us have to confess to a contrarian nature.  But here is why we say that these new findings, like so many that are by the grocery checkout in Nature, Science, and People magazines, while seemingly quite true, should not be treated as a surprise or a threat to what we've already known--nor a justification of just doing more, or much more of the same.

Gregor Mendel studied fully penetrant (deterministic) causation.  That is what we now know to be 'genes', in which the presence of the causal allele (in 2-allele systems) always caused the trait (green vs yellow peas, etc.; the same is true of recessive as dominant traits, given the appropriate genotype). But this is generally wrong, save at best for the exceptions such as those that Mendel himself knowingly and carefully chose to study.  But even this was not so clear!  Mendel has been accused of 'cheating' by ignoring inconsistent results. This may have been data fudging, but it is at least as likely to have been reacting to what we have known for a century as 'incomplete penetrance'.  (Ken wrote on this a number of years ago in one of his Evolutionary Anthropology columns.)  For whatever reason--and see below--the presence of a 'dominant' gene or  'recessive' homozyosity at a 'causal' gene doesn't always lead to the trait.

In most of the 20th century the probabilistic nature of real-world as opposed to textbook Mendelism has been completely known and accepted.  The reasons for incomplete penetrance were not known and indeed we had no way to know them as a rule.  Various explanations were offered, but the statistical nature of the inferences (estimates of penetrance probability, for example) were common practice and textbook standards.  Even the original authors acknowledge incomplete penetrance, but this essentially shows that what the ExAC consortium is reporting are details but nothing fundamentally new nor surprising.  Clinicians or investigators acting as if a variant were always causal should be blamed for gross oversimplification, and so should hyperbolic news media.

Recent advances such as genomewide association studies (GWAS) in various forms have used stringent statistical criteria to minimize false discovery.  This has led to mapped 'hits' that satisfied those criteria only accounting for a fraction of estimated overall genomic causation.  This was legitimate in that it didn't leave us swamped with hundreds of very weak or very rare false positive genome locations.  But even the acceptable, statistically safest genome sites showed typically small individual effects and risks far below 1.0. They were not 'dominant' in the usual sense.  That means that people with the 'causal' allele don't always, and in fact do not usually, have the trait.  This has been the finding for quantitative traits like stature and qualitative ones like presence of diabetes, heart attack-related events, psychiatric disorders and essentially all traits studied by GWAS. It is not exactly what the ExAC data were looking at, but it is highly relevant and is the relevant basic biological principle.

This does not necessarily mean that the target gene is not important for the disease trait, which seems to be one of the inferences headlined in the news splashes.  This is treated as a striking or even fundamental new finding, but it is nothing of that sort.  Indeed, the genes in question may not be falsely identified, but may very well contribute to risk in some people under some conditions at some age and in some environments.  The ExAC results don't really address this because (for example) to determine when a gene variant is a risk variant one would have to identify all the causes of 'incomplete penetrance' in every sample, but there are multiple explanations for incomplete penetrance, including the list of 1 - 13 above as well as methodological issues such as those pointed out by the ExAC project paper itself.

In addition, there may be 'protective' variants in the other regions of the genome (that is, the trait may need the contribution of many different genome regions), and working that out would typically involve "hyper astronomical" combinations of effects using unachievable, not to mention uninterpretable, sample sizes--from which one would have to estimate risk effects of almost uncountable numbers of sequence variants.  If there were, say, 100 other contributing genes, each with their own variant genotypes including regulatory variants, the number of combinations of backgrounds one would have to sort through to see how they affected the 'falsely' identified gene is effectively uncountable.

Even the most clearly causal genes such as variants of BRCA1 and breast cancer have penetrance far less than 1.0 in recent data (here referring to lifetime risk; risk at earlier ages is very far from 1.0). The risk, though clearly serious, depends on cohort, environmental and other mainly unknown factors.  Nobody doubts the role of BRCA1 but it is not in itself causal.  For example, it appears to be a mutation repair gene, but if no (or not enough) cancer-related mutations arise in the breast cells in a woman carrying a high-risk BRCA1 allele, she will not get breast cancer as a result of that gene's malfunction.

There are many other examples of mapping that identified genes that even if strongly and truly associated with a test trait have very far from complete penetrance.  A mutation in HFE and hemochromatosis comes to mind: in studies of some Europeans, a particular mutation seemed always to be present, but if the gene itself were tested in a general data base, rather than just in affected people, it had little or no causal effect.  This seems to be the sort of thing the ExAC report is finding.

The generic reason is again that genes, essentially all genes, work only in their context. That context includes 'environment', which refers to all the other genes and cells in the body and the external or 'lifestyle' factors, and also age and sex as well.  There is no obvious way to identify, evaluate or measure the effects of all possibly relevant lifestyle effects, and since these change, retrospective evaluation has unknown bearing on future risk (the same can be said of genomic variants for the same reason).  How could these even be sampled adequately?

Likewise, volumes of long-existing experimental and highly focused results tell the same tale. Transgenic mice, for example, in which the same mutation is introduced into their 'same' gene as in humans, very often show little or no, or only strain-specific effects.  This is true in other experimental organisms. The lesson, and it's by far not a new or very recent one, is that genomic context is vitally important, that is, it is person-specific genomic backgrounds of a target gene that affect the latter's effect strength--and vice versa: that is, the same is true for each of these other genes. That is why to such an extent we have long noted the legerdemain being foist on the research and public communities by the advocates of Big Data statistical testing.  Certainly methodological errors are also a problem, as the Nature piece describes, but they aren't the only problem.

So if someone reports some cases of a trait that seem too often to involve a given gene, such as the Nature piece seems generally to be about, but searches of unaffected people also occasionally find the same mutations in such genes (especially when only exomes are considered), then we are told that this is a surprise.  It is, to be sure, important to know, but it is just as important to know that essentially the same information has long been available to us in many forms.  It is not a surprise--even if it doesn't tell us where to go in search of genetic, much less genomic, causation.

Sorry, though it's important knowledge, it's not 'radical' nor dependent on these data!
The idea being suggested is that (surprise, surprise!) we need much more data to make this point or to find these surprisingly harmless mutations.  That is simply a misleading assertion, or attempted justification, though it has become the intentional industry standard closing argument.

It is of course very possible that we're missing some aspects of the studies and interpretations that are being touted, but we don't think that changes the basic points being made here.  They're consistent with the new findings but show that for many very good reasons this is what we knew was generally the case, that 'Mendelian' traits were the exception that led to a century of genetic discovery but only because it focused attention on what was then doable (while, not widely recognized by human geneticists, in parallel, agricultural genetics of polygenic traits showed what was more typical).

But now, if things are being recognized as being contextual much more deeply than in Francis' Collins money-strategy-based Big Data dreams, or 'precision' promises, and our inferential (statistical) criteria are properly under siege, we'll repeat our oft-stated mantra: deeply different, reformed understanding is needed, and a turn to research investment focused on basic science rather than exhaustive surveys, and on those many traits whose causal basis really is strong enough that it doesn't really require this deeper knowledge.  In a sense, if you need massive data to find an effect, then that effect is usually very rare and/or very weak.

And by the way, the same must be true for normal traits, like stature, intelligence, and so on, for which we're besieged with genome-mapping assertions, and this must also apply to ideas about gene-specific responses to natural selection in evolution.  Responses to environment (diet etc.) manifestly have the same problem.  It is not just a strange finding of exome mapping studies for disease. Likewise, 'normal' study subjects now being asked for in huge numbers may get the target trait later on in their lives, except for traits basically present early in life.  One can't doubt that misattributing the cause of such traits is an important problem, but we need to think of better solutions that Big Big Data, because not confirming a gene doesn't help, or finding that 'the' gene is only 'the' gene in some genomic or environmental backgrounds is the proverbial and historically frustrating needle in the haystack search.  So the story's advocated huge samples of 'normals' (random individuals) cannot really address the causal issue definitively (except to show what we know, that there's a big problem to be solved).  Selected family data may--may--help identify a gene that really is causal, but even they have some of the same sorts of problems.  And may apply only to that family.

The ExAC study is focused on severe diseases, which is somewhat like Mendel's selective approach, because it is quite obvious that complex diseases are complex.  It is plausible that severe, especially early onset diseases are genetically tractable, but it is not obvious that ever more data will answer the challenge.  And, ironically, the ExAC study has removed just such diseases from their consideration! So they're intentionally showing what is well known, that we're in needle in haystacks territory, even when someone has reported big needles.

Finally, we have to add that these points have been made by various authors for many years, often based on principles that did not require mega-studies to show.  Put another way, we had reason to expect what we're seeing, and years of studies supported that expectation.  This doesn't even consider the deep problems about statistical inference that are being widely noted and the deeply entrenched nature of that approach's conceptual and even material invested interests (see this week's Aeon essay, e.g.).  It's time to change, but doing so would involve deeply revising how resources are used--of course one of our common themes here on the MT--and that is a matter almost entirely of political economy, not science.  That is, it's as much about feeding the science industry as it is about medicine and public health.  And that is why it's mainly about business as usual rather than real reform.

Saturday, October 8, 2016

....nor tolerate those who do

This Presidential campaign has been as bizarre, sad, or even sick as any that has happened in living memory, or perhaps, in the entire history of major political parties in our country.  In many ways a lot of us feel left without a really savory voting choice or feeling really being included in our national politics with elections so influenced by monied interests.  And then of course, politicians often say what's expedient and opportunistic, so it's difficult to know what they actually believe or how accurate the facts they repeat from what their advisors have told them.

In normal times, except for the legacy problem of having been First Lady and giving an aura of entitlement, Hillary Clinton would be accepted by Democrats and Republicans both as an entirely qualified candidate.  She has extensive experience and a track record.  That doesn't mean one need agree with her policies, though I generally do, but at least she's for real.  But her bona fides are being questioned in what would have been inconceivable ways not long ago.  Unfortunately for all of us, the Republican party has come off the rails, and I think it deserves a good electoral whacking for that.

It's hard to know how and where to ridicule their Trumpty Dumpty fall.  There's his history of racism, sexism, sexual abuse, and infidelity, his decision not to disavow white supremacist supporters like David Duke, his dirty business dealings, his pathological inability to adhere to truth, his bizarre and destructive adherence to unfounded conspiracy theories, his sense of entitlement and his enormous need for attention and approval.

And here's an example that strikes home particularly hard to me, as a military veteran.  It's hard to believe this, or indeed any of what passes for politics in the name of Trump, is for real.

Military honor
It's mind-boggling to imagine Trump as Commander-in-Chief.  Any organization as large as the US military will have its bad apples.  But I think I am not alone in believing that, overall, the US military is among the most highly honorable segments of our society or, perhaps, of any society.

Our military has a vital if very unpleasant task in defending us and our interest.  But beyond that, and one of our great national credits, is that those who represent us in uniform are not an unprincipled rapine rabble.  They have standards, and generally they live up to them.

When I became a US Air Force officer, back in the Viet Nam era,we had to pledge to the US military honor code.  The military still has that code. Its wording varies depending on the situation or service, but it basically goes like this:  I will not lie, cheat, or steal, nor tolerate those who do.

I and my fellows took that oath, and we took it seriously, and proudly in defense of our country.  It's very important, because the military's mission can depend on the integrity of its members.  In my nearly 5 years' active duty experience, the officer corps (and the enlisted personnel as well, by the way) lived up to that high standard, to the extent humanly reasonable. My own personal faults and foibles notwithstanding, that oath represented not just what one says in a military enlistment, but as a way to live, and I've tried to do that even though my service years were long ago.  I think I am not unusual in that.

Now, as part of our current political circus, Mr Trump recently trumpeted a number of military officers who somehow found it within themselves to endorse him for the presidency--the Commander in Chief of our armed forces. That they could support someone who is among other things boastful draft-dodger who disses combat veterans who struggle with psychiatric trauma, and prisoners of war (like Republican Senator McCain), and who is proud to have wriggled out of paying the very taxes that are necessary to support the military, is itself rather remarkable--but we'll let that pass.

Obey the General: A sad misunderstanding!
I did not care to look for these officers' names, or ranks, but one of the most honorable things about our military, that cannot be said of many countries' military, is that on active duty they stay clear of politics.  They have their private ballot, of course, but they do not campaign etc.  They serve whoever we as a nation elect.  Thus, I assume these Trumpistas are retired rather than active duty (as were other former officers who endorsed Clinton).  However, in supporting Mr Trump, they revealed that they took their honor code rather lightly, perhaps as something that was job-related, but not part of their personal lifetime standard.

One could try to give these officers at least a small amount of credit and think their endorsement was due to an honorable, but sad, misunderstanding. Perhaps in their older years, these officers are hard of hearing or of understanding, and thought they were outranked and endorsed Trump because they still feel the must fall in line and obey orders--and they misunderstood what was meant when their candidate was referred to as a General Groper.

These officers are and were undoubtedly very patriotic. Even if they were closet racists, their honor was presumably more important. I don't have any reason to think, much less suggest that these officers lie, cheat, or steal.  But to me, honor codes are not something to be shuttled to the sideline for convenience. They're not cover for dishonor.  Somehow these officers have willfully and I would say shamefully, cast a question about what their own honor is or means to them, or even their own patriotism.  In endorsing someone who is widely, objectively, publicly, and daily shown to have lived a life of lying, cheating, and, in double-dealing also essentially stealing, as a way of life, these officers seem dishonorably to have forgotten that their honor code had a second phrase, that was not about their personal behavior, but the broader sense of the honor they once swore to uphold:

                                                    ". . . . nor tolerate those who do."

Friday, October 7, 2016

Science journals: Anything for a headline

Well, this week's sensational result is reported in the Oct 5 Nature in a paper about limits to the human lifespan. The unsensational nature of this paper shows yet again how Nature and the other 'science' journals will take any paper that they can use for a cheap headline.  This paper claims that the human life span cannot exceed 115 (though the cover picture in a commentary in the same issue is a woman-- mentioned in the paper itself--who lived to be substantially older than that!).  The Nature issue has all the exciting details of this novel finding, which of course have been trumpeted by the story-hungry 'news' media.

In essence the authors argue that maximum longevity on a population basis has been increasing only very slowly or not at all over recent decades.  It is, one might say, approaching an asymptote of strong determination. They suggest that there is, as a result of many complex contributing factors-of-decline, essentially a limit to how long we can live, at least as a natural species without all sorts of genetic engineering.  In that sense, dreams of hugely extended life, even as a maximum (that is, if not for everyone), are just that: dreams.

This analysis raises several important issues, but largely ignores others.  First, however, it is important to note that virtually nothing in this paper, except some more recent data, is novel in any way.  The same issues were discussed at very great length long ago, as I know from my own experience.  I was involved in various aspects of the demography and genetics of aging, as far back as the 1970s.  There was a very active research community looking at issues such as species-specific 'maximum lifespan potential', with causal or correlated factors ranging from the effects of basic metabolism, or body or brain size.  Here's a figure from 1978 that I used in a 1989 paper

There was experimental research on this including life-extension studies (e.g., dietary restriction) as well as comparison of data over time, much as (for its time) the new paper.  The idea that there was an effective limit to human lifespan (and likewise for any species) was completely standard at that time, and how much this could be changed by modern technologies and health care etc. was debated. In 1975, for example (and that was over 40 years ago!), Richard Cutler argued in PNAS that various factors constrained maximum lifespan in a species-related way.  The idea, and one I also wrote a lot about in the long-ago past, is that longevity is related to surviving the plethora of biological decay processes, including mutation, and that would lead to a statistical asymptote in lifespan.  That is, that lifespan was largely a statistical result rather than a deterministically specified value.  The mortality results related to lifespan were not about 'lifespan' causation per se, but were just the array of diseases (diabetes, cancer, heart disease, etc.) that arose as a result of the various decays that led to risk increasing with duration of exposure, wear and tear, and so on, and hence were correlated with age.  Survival to a given age was the probability of not succumbing to any of these causes by that age.

This paper of mine (mentioned above) was about the nature of arguments for a causally rather that statistically determined lifespan limit.  If that were so, then all the known diseases, like heart disease, diabetes, cancer, and so on, were irrelevant to our supposed built-in lifespan limit!  That makes no evolutionary sense, since evolution would not be able to work on such a limit (nobody's still reproducing anywhere near that old).  It would make no other kind of sense, either.  What would determine such a limit and how could it have evolved?  On the other hand, if diseases--the real causes that end individual lives--were, together, responsible for the distribution of lifespan lengths, then a statistical rather than deterministic end is what's real.  The new paper doesn't deal with these, but by arguing that there is some sort of asymptotic limit, it implicitly invokes some sort of causal, evolutionarily determined value, and that seems implausible.

Indeed, evolutionary biologists have long argued that evolution would produce 'negative pleiotropy', in which genomes would confer greater survival at young ages, even if the result was at the expense of greater mortality later on.  That way, the species' members could live to reproduce (at least, if they survived developmentally-related infant mortality), and they were dispensable at older ages so that there was no evolutionary pressure to live longer.   But that would leave old-age longevity to statistical decay processes, not some built-in limit.

Of course, with very large data sets and mortality a multicausal statistical process, rare outliers would be seen, so that more data meant longer maximum survival 'potential' (assuming everyone in a species somehow had that potential, clearly a fiction given genetic diseases and the like that affect individuals differently).  There were many problems with these views, and many have since tried to find single-cause lifespan-determining factors (like telomere decay, in our chromosomes), an active area of research (more on that below).  We still hunger for the Fountain of Youth--the single cause or cure that will immortalize us!

The point here is that the new paper is at most a capable but modest update of what was already known long ago.  It doesn't really address the more substantive issues, like those I mention above.  It is not a major finding, and its claims are also in a sense naive, since future improvements in health and lifestyles that we don't have now but that applied to our whole population could extend life expectancy--the average age at death--and hence the maximum to which anyone would survive. After all, when we had huge infectious disease loads, hardly anybody lived to 115, and in the old days of research, to which the authors seem oblivious, something like 90-100 was assumed to be our deadline.

The new paper has been criticized by a few investigators, as seen in reports in the news media coverage.  But the paper's authors probably are right that nothing foreseeable will make a truly huge change in maximum survival, nor will many survive to such an extended age.  Nor--importantly--does this mean that those who do luck out are actually very lucky: the last few years or decades of decrepitude may not be worth it to most who last to the purported limit. To think of this as more than a statistical result is a mistake.  Not everyone can live to any particular age, obviously.

The main fault in the paper in my view is the claim in essence to portray the result as a new finding, and the publication in a purportedly major journal, with the typical media ballyhoo suggesting that.

On the other hand....
On the other hand, investigators who were interviewed about this study (to give it 'balance'!) denigrated it, saying that novel medical or other (genetic?) interventions could make major changes in human longevity.  This has of course happened in the past century or two.  More medical intervention, antibiotics and vaccines and so on have greatly increased average lifespan and, in so doing in large populations, increased the maximum survival that we observe.  This latter is a statistical result of the probabilistic nature of degenerative processes like accumulating wear and tear or mutations, as I mentioned earlier.  There is no automatic reason that major changes in life-extending technologies are in the offing, but of course it can't be denied as a possibility either. Similarly, if, say, antibiotic resistance becomes so widespread that infectious diseases are once again a major cause of death in rich countries, our 'maximum lifespan' will start to look younger.

Those who argue against this paper's assertions of a limit must be viewed just as critically as they judged the new paper.  The US National Institute on Aging, among other agencies, spends quite a lot of your money on aging, including decades (I know because I had some of it) on lifespan determination.  If someone quoted as dissing the new 'finding' is heavily engaged in the funding from NIA and elsewhere, one must ask whether s/he is defending a funding trough: if it's hopeless to think we'll make major longevity differences, why not close down their labs and instead spend the funding on something that's actually useful for society?

There are still many curious aspects of lifespan distributions, such as why rodents have small bodies that should be less vulnerable per-year to cancer or telomere degradation etc. that relate to the number of at-risk cells, yet only live a few years.  Why hasn't evolution led us to be in prime health for decades longer than we are?  There are potential answers to such questions, but mechanisms are not well understood, and the whole concept of a fixed lifespan (rather than a statistical one) is poorly constructed.

Still, everything suggests that, without major new interventions that probably will, at best, be for the rich only, there are rough limits to how long anyone can statistically avoid the range of independent risk our various organ systems face, not to  mention surviving in a sea of decrepitude.

One thing that does seem to be getting rather old, is the relentless hyperbole of the media including pop-culture journals like Nature and Science, selling non-stories as revolutionary new findings.  If we want to make life better for everyone, not just researchers and journals, we could spend our resources more equitably on quality of life, and our research resources on devastating diseases that strike early in the lives we already are fortunate to have.

Thursday, September 22, 2016

Chain-ring genetics

If you're a bike rider, as I am, you know that there is a huge market out there trying to lure you into a really, really fancy bike.  Bike prices can easily get well into 4 digits, amazingly, and apparently there are enthusiasts who are willing to pay for them--maybe the thrill of the purchase is itself enough!
In a way, fancy bikes serve as an analogy for broader aspects of our society, as I'll try to illustrate.

I live in a pretty hilly area, and even though I just to bike-path or street-and-sidewalk riding, it's a pretty dramatic range of effort one needs in order to navigate the changing ups and downs.  And my bike, shown in this amateur photo, is a Trek Navigator hybrid, with a 3 x 7 gear cog setup: 3 chain rings in the front, and a 7-cog rear gear set.  That's 21 different gears, and I was happy to buy a bike with such a wide range of pedaling-efficiency options.

The next figure shows the gear ratio range schematically.  For each front chainring (Low, Middle, High), the corresponding line shows the relative gear ratio across the 7 rear cogs:So, in the extreme, if you go up a steep hill you want a front-1/rear-1 choice (the easiest combination, with more pedal rotations per rear wheel rotation, making each rotation easier even if you go slower), and downhill you'd want 3-7 for the opposite effects.
High, Mid, Low gear ratio range  for the 3-front, 7-rear cogs (schematic)

This plethora of gears was an attractive selling point when I bought this bike, which is a good one, but now that it's a few years old, I decided to shop around to see what's on offer these days.  I notice some  3 x 8, 3 x 9, and 2 x 9 front/back cog numbers.  The more expensive bikes tend to have more gears, though one had only 2 chainrings in front--and I wondered about that.  If the rear cogs had the right ratios, there is less weight in the front only having two chainrings, and the shifting will be easier and the shifting mechanism may act more quickly.  But the overall range was less, meaning it might not suit all riders as easily. In any case, there's a lot of techie glitter and salesmanship going on to get you to pony up the $K's for the fancier bikes.  They weigh a few pounds less, too, and so on, as the price goes up.

The bike-tech web sites basically warn you to avoid cross-chaining, which is to set the front chainring to a side of the cluster opposite to the cog set in the rear.  Instead, common advice says, shift to front-rear combinations for which the connecting chain is as close to parallel with the frame as possible.
But if you read a bit more carefully, you can see that some of the cross-chaining evidence, for modern bikes, is not very well established: you may not damage the chain, cog teeth, or be detectably less efficient, after all.  And some of the combinations--where the ranges in the above figure overlap--would never really be used.

So I wondered why one would not just stick with the middle front chainring all the time.  If you do that, the full range of rear cogs can be used without cross-chaining issues.  You don't get all of the bike's range, but you do get most of it.  What would the same ride feel like using only the middle chainring?

I've now tried that by taking my ride today without using the high or low chainring (stupidly, I never had tried that before!).  In going up the very steepest hill, I knew I could find it a tad easier to use the easiest front-rear combination.  Going downhill, I could muster up a bit more speed with the opposite extreme front-rear combination. But basically, the ride was the same.  It was also a bit simpler and involved a lot less coordinated shifting.

I decided I don't need a fancy new bike, after all!

So what does this have to do with genetics?
I was led to write this brief reflection when I thought about how many not really avid bikers have been led by cycle makers to get the most extensive, fanciest gearing (among other options), forking over very much more money, for very little gain, in the process.  Yes, performance is a bit better, but it doesn't really match up to the hype, especially not at the cost, unless you are a bike-racer or off-road biker, or have a yen for the latest-and-greatest and lots of 1%er money to invest in ego toys. The marginal gain per unit cost is minimal.

We're getting a lot of similar marketing for gearing up, so to speak, in our biomedical research and its application.  We're being told how marvelous having lots of chainrings and large rear cog-sets will yield miraculously better health than our old-fashioned ways have done so far.  It's called by flashy impressive or intimidating names like 'next-gen sequencing' or 'Big Data' or 'exome profiling', or 'precision genomic medicine', and that's the analog of Big Gearing (though a lot more costly).  Big Data is for the research community as carbon-fiber frames are for the bicycle industry.  Scientist and general public alike are suckers for slogans promising unbelievably more in the health-research industry from gearing up, much as we are for slogans promising unbelievably better biking.

The promotions are always shifting, so to speak, as the science rolls on.

But genetics can be important to our very lives!
The line we are fed by NIH and the research establishment always stresses the vital importance of our Big Data investment.  That is, after all, what 'precision genomic medicine' and wars on cancer and so on suggest they are promising.  It is true that under some circumstances, for some people, large-scale genomic database research may soon, or eventually, lead to more effective treatments of disease. There are already some examples, though how many really required massive genomewide association studies and the like is open to discussion.

As we've noted here many times, there are tons of more clearly genetic, or otherwise-caused, disorders for which the same monetary investment might yield much greater benefit. Most advances still, generally, seem to come from focused research on known, substantial causes.  Lifestyle changes, if our epidemiological data are worth their own huge cost, could much more massively reduce or defer common adult-onset diseases.  And there are a large number of clearly genetic diseases, pediatric and otherwise, for which the actual gene or genes are known.  They often strike at birth or in childhood, and are life-long debilitating,or life-shortening conditions.  They have, in my view, a much stronger and more legitimate claim on research resources.

Nobody wants a disease, genetic or otherwise, not even if it only strikes late in life.  But we should use the gears we have to get up those hills, rather than constantly being promised miracles if we only add another chainring, and then another, and then.....

Friday, August 26, 2016

Is life itself a simulation of life?

It often happens in science that our theory of some area of reality is very precise, but the reality is too complex to work out precisely, or analytically.  This can be when we decide to use computer simulation of that reality to get at least a close approximation to the truth.  When a phenomenon is determined by a precise process, then if we increase the complexity of our simulation, and if the simulation really is simulating the underlying reality, then the more computer power we apply, the closer we get to the truth--that is, our results approach that truth asymptotically.

For example, if you want to predict the rotation of galaxies in space relative to each other, and of the stars within the galaxies, the theories of physics will do the job, in principle. But solving the equations directly the way one does in algebra or calculus is not possible with so many variables.  However, you can use a computer to simulate the movement and get a very good approximation (we've discussed this here, among other places).  Thus, at each time interval, you take the position and motion of each object you want to follow, and those measures of nearby objects, and use Newton's law of gravity to predict the position of the objects one time interval later.

If the motion you simulate doesn't match what you can observe, you suspect you've got something wrong with the theory you are using. In the case of cosmology, one such factor is known as 'dark matter'.  That can be built into models of galactic motion, to get better predictions.  In this way, simulation can tell you something you didn't already know, and because the equations can't be directly solved, simulation is an approach of choice.

In many situations, even if you think that the underlying causal process is deterministic, measurements are imperfect, and you may need to add a random 'noise' factor to each iteration of your simulation.  Each simulation will be slightly 'off' because of this, but you run the same simulation thousands of times, so the effect of the noise evens out, and the average result represents what you are trying to model.

Is life a simulation of life?
Just like other processes that we attempt to simulate, life is a complex reality.  We try to explain it with the very general theory of evolution, and we use genetics to try to explain how complex traits evolve, but there are far too many variables to predict future directions and the like analytically.   This is more than just because of biological complexity however, in part because the fundamental processes of life seem, as far as we can tell, inherently probabilistic (not just a matter of measurement error).  This adds an additional twist that makes life itself seem to be a simulation of its underlying processes.

Life evolves by parents transmitting genes to offspring.  For those genes to be transmitted to the next generation, the offspring have to live long enough, must be able to acquire mates, and must be able to reproduce. Genes vary because mutations arise.  For simplicity's sake, let's say that successful mating requires not falling victim to natural selection before offspring are produced, and that that depends on an organism's traits, and that genes are causally responsible for those traits.  In reality, there are other process to be considered, but these will illustrate our point.

Mutation and surviving natural selection seem to be probabilistic processes.  If we want to simulate life, we have to specify the probability of a mutation along some simulated genome, and the probability that a bearer of the mutation survives and reproduces.  Populations contain thousands of individuals, genomes incur thousands of mutations each generation, and reproductive success involves those same individuals.  This is far too hard to write tractable equations for in most interesting situations, unless we make almost uselessly simplifying assumptions.  So we simulate these phenomena.

How, basically, do we do this?  Here, generically and simplified, but illustrating the issues, is the typical way (and the way taken by my own elaborate simulation program, called ForSim which is freely available):

For each individual in a simulated population, each generation, we draw a random number based on an assumed mutation rate, and add the resulting number and location of mutations to the genotype of the individual.  Then for each resulting simulated genotype, we draw a random number from the probability that such a genotype reproduces, and either remove or keep the individual depending on the result.  We keep doing this for thousands of generations, and see what happens.  As an example, the box lists some of the parameter values one specifies for a program like ForSim.

Sometimes, if the simulation is accurate enough, the probability and other values we assume look like what ecologists or geneticists believe is going on in their field site or laboratory.  In the case of humans, however, we have little such data, so we make a guess at what we think might have been the case during our evolution.  Often these things are empirically estimated one at a time, but their real values affect each other in  many ways.  This is, of course, very far from the situation in physics, described above!  Still, we at least have a computer-based way to approximate our idea of evolutionary and genetic processes.

We run this for many, usually many thousand generations, and see the trait and genomic causal pattern that results (we've blogged about some of these issues here, among other posts).  This is a simulation since it seems to follow the principles we think are responsible for evolution and genetic function.  However, there is a major difference.

Unlike simulations in astronomy, life really does seem to involve random draws for probabilistic processes.  In that sense, life looks like it is, itself, a simulation of these processes.  The random draws it makes are not just practical estimates of some underlying phenomenon, but manifestation of the actual probabilistic nature of the phenomenon.

This is important, because when we simulate a process, we know that its probabilistic component can lead to different results each time through.  And yet, life itself is a one-time run of those processes. In that sense, life is a simulation but we can only guess at the underlying causal values (like mutation and survival rates) from the single set of data: what actually happened its one time through.  Of course, we can test various examples, like looking at mutation rates in bacteria or in some samples of people, but these involve many problems and are at best general estimates from samples, often artificial or simplified samples.

But wait!  Is life a simulation after all?  If not, what is life?
I don't want us to be bogged down in pure semantics here, but I think the answer is that in a very profound way, life is not a simulation in the sense we're discussing.  For the relevant variables, life is not based on an underlying theoretical process in the usual sense, of whose parameters we use random numbers to approximate in simulations.

For example, we evaluate biological data in terms of 'the' mutation rate in genomes from parent to offspring.  But in fact, we know there is no such thing as 'the' mutation rate, one that applies to each nucleotide as it is replicated from one generation to the next, and from which each actual mutation is a random draw.  The observed rate of mutation at a given location in a given sample of a given species' genomes depends among other things on the sex, the particular nucleotides surrounding the site in question (and hence all sites along the DNA string), and the nature of the mutation-detection proteins coded by that individual's genome, and mutagen levels in the environment.  In our theory, and in our simulations, we assume an average rate, and that the variation from that average will, so to speak, 'average out' in our simulations.

But I think that is fundamentally wrong. In life, every condition today is a branch-point for the future. The functional implications of a mutation here and now, depend on the local circumstances, and that is built into the production of the future local generations.  Life in fact does not 'average' over the genome and over individuals does not in fact generate what life does, but in a sense the opposite.  Each event has its own local dynamics and contingencies, but the effect of those conditions affects the rates of events in the future.  Everywhere it's different, and we have no theory about how different, especially over evolutionary time.

Indeed, one might say that the most fundamental single characteristic of life is that the variation generated here today is screened here today and not anyplace else or any time else.  In that sense, each mutation is not drawn from the same distribution.  The underlying causal properties vary everywhere and all the time.  Sometimes the difference may be slight, but we can't count on that being true and, importantly, we have no way of knowing when and to what extent it's true.

The same applies to foxes and rabbits. Every time a fox chases a rabbit, the conditions (including the genotypes of the fox and rabbit) differ. The chance aspect of whether it's caught or not are not the same each time, the success 'rate' is not drawn from a single, fixed distribution.  In reality, each chase is unique.

After the fact, we can look back at net results, and it's all too tempting to think of what we see as a steady, deterministic process with a bit of random noise thrown in.  But that's not an accurate way to think, because we don't know how inaccurate it is, when each event is to some (un-prespecified) extent unique.  Overall, life is not, in fact, drawing from an underlying distribution.  It is ad hoc by its very nature and that's what makes life different from other physical phenomena.

Life, and we who partake of it, are unique. The fact of local, contingent uniqueness is an important reason that the study of life eludes much of what makes modern physical science work.  The latter's methods and concepts assume replicable law-like underlying regularity. That's the kind of thing we attempt to model, or simulate, by treating phenomena like mutation as if they are draws from some basic underlying causal distribution. But life's underlying regularity is its irregularity.

This means that one of the best ways we have of dealing with complex phenomena of life, simulating them by computer, smoothes over the very underlying process that we want to understand.  In that sense, strangely, life appears to be a simulation but is even more elusive than that.  To a great extent, except by some very broad generalities that are often too broad to be very useful, life isn't the way we simulate it, and doesn't even simulate itself in that way.

What would be a better approach to understanding life?  The next generation will have to discover that.

Thursday, August 25, 2016

2016's textbook-free Intro to BioAnth course

This is an abridged syllabus for my course this fall. Apologies for any formatting issues, but copying and pasting from Word into Blogger isn't a party. For background on my textbook-free approach and overall philosophy for teaching evolution, please see this post and the links therein.  Cheers to all you learners, teachers, and professors!

Fall 2016
APG 201: Human Origins and Evolution
3 credits
Dr. Holly Dunsworth

Course Description
The biocultural evolution of humans. An investigation into humankind’s place in nature, including a review of the living primates, human genetics and development, evolutionary theory, and the human fossil record. Fulfills both the General Education outcomes A1 (STEM knowledge) and B4 (information literacy).

Required reading 
Your Inner Fish by Neil Shubin
The Incredible Unlikeliness of Being by Alice Roberts
Additional articles are linked in the syllabus, or posted on our course site on Sakai

Non-required reference
Biological Anthropology, 3rd Edition by Stanford, et al. (2013, Pearson) – standard textbook (a copy is on reserve at the library, along with Shubin and Roberts)

Quizzes 1, 2, and 3 (15% each); Research Project (15%; a two part exercise in information literacy, evolutionary thinking, and writing); Portfolio (40%; a thin folder or binder containing all the assignments in chronological order.) 

Unit 1. Observe and Explain - This view of life. Our place in nature. What is the anthropological perspective? What about the biocultural? What is the scientific approach to understanding human origins? What is a human? What are human traits? How do humans fit on the Tree of Life? What is evolution?
7-Sep       1.1-Introduction to course (reflecting on knowledge to spark semester)
9-Sep 1.2-Overview of course (syllabus, anthropology, etc...)
12-Sep 1.3-Scientific process  
14-Sep 1.4-Linnaeus and the Order Primates 
16-Sep 1.5-Overview of Primate taxonomy; Diet 
19-Sep 1.6-Primate locomotion and encephalization
21-Sep 1.7-Primate tool use and communication
23-Sep 1.8-Primate sociality
26-Sep 1.9-Evolution and Darwin's evidence
28-Sep 1.10-Phylogeny
30-Sep 1.11-no class today
3-Oct 1.12-Modern evidence Darwin wishes he had
5-Oct Quiz 1

Unit 2. Explain and Predict - Explaining the similarities and differences. How evolution works. Why are we like our parents but not exactly? Why are we like other species but not exactly? How did human traits and human variation evolve? How does evolution occur? How do we know what the last common ancestor (LCA) was like?
7-Oct 2.1-Inheritance and gene expression, 1
10-Oct n/a-Columbus Day, classes do not meet
12-Oct 2.2-Inheritance and gene expression, 2
14-Oct 2.3-Inheritance and gene expression, 3
17-Oct 2.4-Mutation and gene flow
19-Oct 2.5-Natural selection
21-Oct 2.6-More natural selection; Genetic drift
24-Oct 2.7-Malaria resistance and lactase persistence
26-Oct 2.8-Building evolutionary scenarios
28-Oct 2.9- Origins of Bipedalism; Species and speciation 
31-Oct 2.10 -Genomics, molecular clocks, and the LCA
2-Nov Quiz 2 -

Unit 3. Test and Observe - Evolving humans, past and present. Ancient evidence for our extinct hominin relatives. Modern human origins and variation. The cultural controversy over evolution.How did human traits evolve? How and why do humans vary? Should we look to our ancestors as a lifestyle guide? Are we still evolving? Is evolution racist? Why is human evolution misunderstood and why is it controversial? 
4-Nov 3.1-The LCA and the earliest hominins
7-Nov 3.2-Australopithecus
9-Nov 3.3-Paranthropus  (Research Project Part 1, due to Sakai by 9 am)
11-Nov n/a-Veteran's Day, classes do not meet 
14-Nov 3.4-earliest Homo  
16-Nov 3.5-Homo erectus
18-Nov 3.6-Neanderthals
21-Nov 3.7-Anatomically modern Homo sapiens
23-Nov 3.8-no class today (Research Project Part 2 due to portfolio)
25-Nov n/a-Thanksgiving Break, classes do not meet
28-Nov 3.9-The origins and evolution of human skin color variation
30-Nov 3.10-The origins and evolution of human skin color variation
2-Dec 3.11-The origins and evolution of human skin color variation
5-Dec 3.12-The origins and evolution of human skin color variation
7-Dec 3.13-The origins and evolution of human skin color variation
9-Dec 3.14-Race, racism and the cultural controversy over evolution
12-Dec 3.15-Conclusions (Portfolios due at the start of class today)
14-Dec Quiz 3 (During time of final exam)

Portfolio Assignments and Lecture Resources
Assigned Reading/viewing
·        IUB, Chapter 1: Beginnings - Roberts
Portfolio Assignment
·        In-class assignment
Additional resources
·         “Do animals know where babies come from?” by H. Dunsworth (Scientific American)- Located on Sakai

Assigned Reading/viewing
·        IUB, Chapter 2: Heads and brains – Roberts
Portfolio Assignment
·        Osteology and comparative anatomy worksheet - Located on Sakai
Additional resources
·        What is it like to be a biological anthropologist? A Field Paleontologist's Point of View – Su (Nature Education)
·        Notes from the Field: A Primatologist's Point of View – Morgan (Nature Education)
·        Expedition Rusinga (video; 8 min)  
·        The ape in the trees – Dunsworth (The Mermaid’s Tale)
·        How Do We Know When Our Ancestors Lost Their Tails? (video; 4 min)

Assigned Reading/viewing
·        How Science Works (video; 10 min):
·        Understanding science: How Science Works, pages 1-21; starts here:
·        Carl Sagan’s Rules for Critical Thinking and Nonsense Detection
·        10 Scientific Ideas That Scientists Wish You Would Stop Misusing
Portfolio Assignment
·        Scientific Process worksheet - Located on Sakai

Assigned Reading/viewing
·        Characteristics of Crown Primates – Kirk (Nature Education)
Portfolio Assignment
·        Primate Expert worksheet - Located on Sakai

·        Many primate video clips –Posted on Sakai
Portfolio Assignment
·        In a half-page or more: Write about your primate video viewing experience, for example, you might write about what you saw, at face value, or you might want to write about what defied your expectations or what surprised you, or what you would like to learn more about. Also: Without looking at any resources except for these films, come up with some categories for the different types of primate locomotion, give those categories names and definitions, and list which species in the films fall into which categories you’ve created.
Additional resources
·        Old World monkeys – Lawrence and Cords (Nature Education)

Assigned Reading/viewing
·        IUB, Chapter 3: Skulls and senses – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapter and be sure to include what it’s got to do with human evolution.
Additional resources
·        Primate locomotion – Gebo (Nature Education)

Assigned Reading/viewing
·        IUB, Chapter 4: Speech and gills - Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapter and be sure to include what it’s got to do with human evolution.
Additional resources
·        Primate Communication – Zuberbuhler (Nature Ed)

Assigned Reading/viewing
·        The Human Spark 2 (video; 55 mins)
Portfolio Assignment
·        In a half-page or more: Reflect on The Human Spark 2, highlighting something you already knew and also something you learned that was brand new to you. What is the human spark?
Additional resources
·        Peace Among Primates – Sapolsky (The Greater Good)
·        What Influences the Size of Groups in Which Primates Choose to Live? – Chapman & Teichroeb (Nature Ed)
·        Primate Sociality and Social Systems – Swedell (Nature Ed)
·        Primates in communities – Lambert (Nature Ed)

Assigned  Reading/viewing
·        Two chapters from The Autobiography of Charles Darwin: "Voyage…" (p. 71-81 ) and "An account of how several books arose" (p. 116- 135)
Portfolio Assignment
·        In a half-page or more: According to your impression of Darwin’s writings, what circumstances or experiences influenced Darwin's thinking?

Assigned Reading/viewing
·        Reading a phylogenetic tree – Baum (Nature Ed)
·        Trait Evolution on a Phylogenetic Tree – Baum (Nature Ed)
Portfolio Assignment
·        Phylogeny worksheet - Located on Sakai

Assigned  Reading/viewing
·        IUB, Chapter 5: Spine and segments – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapter and be sure to include what it’s got to do with human evolution.

Assigned Reading/viewing
·        YIF, Chapter 1: Finding Your Inner Fish - Shubin
·        YIF, Chapter 2: Getting a Grip - Shubin
Portfolio Assignment
·        In a half-page or more: What does Shubin mean by "your inner fish"? What's the connection between a fish’s fin and your hand? How could you falsify evolutionary theory?
Additional resources
·        Amazing Places, Amazing Fossils: Tiktaalik (video; 5 mins)
·        The Ancient History of the Human Hand (video; 4 mins)

Assigned Reading/viewing
·        YIF, Chapter 3: Handy Genes – Shubin
Portfolio Assignment
·        In a half-page or more: What the heck is this Sonic hedgehog thing that Shubin’s talking about?

Assigned Reading/viewing
·        YIF, Chapter 4: Teeth Everywhere – Shubin
Portfolio Assignment
·        In a half-page or more: Teeth make better fossils than bones and so they preserve more often and fill up the fossil record. If you want to do paleontology, you need to get excited about teeth. Why are teeth exciting?
Additional resources
·        The Evolution of Your Teeth (video; 3 mins)
·        Developing the Chromosome Theory – O’Connor (Nature Ed)
·        Genetic Recombination – Clancy (Nature Ed)
·        What is a Gene? Colinearity and Transcription Units – Pray (Nature Ed)
·        RNA functions – Clancy (Nature Ed)

Assigned reading/viewing
·        YIF, Chapter 5: Getting ahead – Shubin
Portfolio Assignment
·        In a half-page or more: What does Shubin mean by your "inner shark"?
Additional resources
·        Our Fishy Brain (video; 2.5 mins)
·        Hox Genes in Development: The Hox Code – Myers (Nature Ed)
·        Gregor Mendel and the Principles of Inheritance – Miko (Nature Ed)
·        Mendelian Genetics: Patterns of Inheritance and Single-Gene Disorders – Chial (Nature Ed)
·        Phenotypic Range of Gene Expression: Environmental Influence – Lobo & Shaw (Nature Ed)
·        Genetic Dominance: Genotype-Phenotype Relationships – Miko (Nature Ed)
·        Pleiotropy: One Gene Can Affect Multiple Traits – Lobo (Nature Ed)
·        Polygenic Inheritance and Gene Mapping – Chial (Nature Ed)

Assigned Reading/viewing
·        YIF, Chapter 6: The Best-Laid (Body) Plans - Shubin
·        YIF, Chapter 7: Adventures in Bodybuilding – Shubin
Portfolio Assignment
·        In a half-page or more: What are Hox genes and, according to Shubin, what do they have to do with linking a fruit fly to you? What is one benefit to being a sponge?
Additional resources
·        Evolution Is Change in the Inherited Traits of a Population through Successive Generations – Forbes and Krimmel (Nature Ed)
·        Mutations Are the Raw Materials of Evolution – Carlin (Nature Ed)

Portfolio Assignment
·        Scenario building assignment (Part 1) - Located on Sakai
Additional Resources
·        Natural selection, genetic drift and gene flow do not act in isolation in natural populations – Andrews (Nature Ed)
·        Sexual selection – Brennan (Nature Ed)

Portfolio Assignment
·        Wisdom tooth assignment - Located on Sakai
Additional Resources
·        Neutral Theory: The null hypothesis of molecular evolution – Duret (Nature Ed)
·        Negative selection – Loewe (Nature Ed)
·        On the mythology of natural selection. Part I: Introduction – Weiss (The Mermaid’s Tale)
·        On the mythology of natural selection. Part II: Classical Darwinism– Weiss (The Mermaid’s Tale)
·        Secrets of Charles Darwin’s Breakthrough -  Bauer (Salon)

Portfolio Assignment
·        Scenario building assignment (Part 2) - Located on Sakai
Additional resources
·        Natural Selection: Uncovering Mechanisms of Evolutionary Adaptation to Infectious Disease – Sabeti (Nature Ed)

Assigned reading/viewing
·        Evolution is the only natural explanation – Dunsworth (The Mermaid’s Tale)
·        The F-words of Evolution  – Dunsworth (The Mermaid’s Tale)
·        Another F-word of evolution  – Dunsworth (The Mermaid’s Tale)
Portfolio Assignment
·        Scenario building assignment (Part 3) - Located on Sakai
Additional resources
·        Mutation not natural selection drives evolution –  Tarlach (about Nei; Discover Magazine)

Assigned Reading/viewing
·        YIF, Chapter 8: Making Scents - Shubin
·        YIF, Chapter 9: Vision - Shubin
·        YIF, Chapter 10: Ears – Shubin
Portfolio Assignment
·        In a half-page or more: After reading the Shubin chapters… Is it fair to say that when you smell something, that something is touching your brain? Why is it called the eyeless gene if you can have it and still have eyes? How does hearing work? What does your ear do besides hear, and how? What does drinking lots of alcohol do to your ears?
Additional resources
·        Finding the Origins of Human Color Vision (video; 5 mins)
·        We Hear with the Bones that Reptiles Eat With (video; 4 mins)
·        Why should we care about species? – Hey (Nature Ed)
·        Speciation: The origin of new species – Safran (Nature Ed)
·        The maintenance of species diversity – Levine (Nature Ed)
·        Macroevolution: Examples from the Primate World – Clee & Gonder (Nature Ed)
·        Primate Speciation: A Case Study of African Apes – Mitchell & Gonder (Nature Ed)

Assigned Reading/viewing
·        Things Genes Can’t Do – Weiss and Buchanan (Aeon)
Portfolio Assignment
·        In a half-page or more: Reflect meaningfully on the Weiss and Buchanan article and highlight something that you already knew, but also the things that you learned that are brand new to you.
Additional resources
·        The Onion Test – Gregory (Genomicron)
·        The Molecular Clock and Estimating Species Divergence – Ho (Nature Ed)
·        Lice and Human Evolution (video; 11 mins)
·        Planet without apes? – Stanford (Huffington Post)

·        IUB, Chapter 6: Ribs, lungs and hearts– Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution.
Additional resources
·        How to Become a Primate Fossil – Dunsworth (Nature Ed)
·        Dating Rocks and Fossils Using Geologic Methods – Peppe (Nature Ed)
·        Desktop Diaries: Tim White (video; 7 mi– Posted on Sakai)
·        Ancient Human Ancestors: Walking in the woods (video; 4 mins)
·        Overview of hominin evolution – Pontzer (Nature Ed)
·        The Earliest Hominins: Sahelanthropus, Orrorin, and Ardipithecus - Su (Nature Ed):

·        IUB, Chapter 7: Guts and yolk sacs – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution.
Additional resources
·        Lucy (video; 5 mins)
·        Trowelblazers (blog):  
·        An Unsuitable Job for a Woman (blog):
·        Lucy: A marvelous specimen – Schrein (Nature Ed)

·        By 9 am this morning, upload Research Project Part 1 to Sakai (so there is nothing to do today for your Portfolio)
Additional resources
·        The "Robust" Australopiths – Constantino (Nature Ed)

Assigned Reading/viewing
·        IUB, Chapter 8: Gonads, genitals and gestation – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution.
Additional resources
·        Ancient Hands, Ancient Tools (video; 5 mins)
·        A Primer on Paleolithic Technology – Ferraro (Nature Ed)
·        Evidence for Meat-Eating by Early Humans – Pobiner (Nature Ed)
·        Archaeologists officially declare collective sigh over “Paleo Diet”

·        IUB, Chapter 9: On the nature of limbsRoberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution.
Additional resources
·        Homo erectus - A Bigger, Smarter, Faster Hominin Lineage – Van Arsdale (Nature Ed)

·        IUB, Chapter 10: Hip to Toe – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution.
Additional resources
·        Archaic Homo sapiens – Bae (Nature Ed)
·        What happened to the Neanderthals? – Harvati (Nature Ed)
·        Neanderthal Behavior – Monnier (Nature Ed)

·        IUB, Chapter 11: Shoulders and Thumbs – Roberts
Portfolio Assignment
·        In a half-page or more:  Reflect on Roberts’ chapters and be sure to include what it’s got to do with human evolution
Additional resources
·        The Transition to Modern Behavior – Wurz (Nature Ed)
·        The Neanderthal Inside Us (video; 4 mins)
·        Anthropological genetics: Inferring the history of our species through the analysis of DNA – Hodgson & Disotell (Evolution: Education and Outreach)
·        Testing models of modern human origins with archaeology and anatomy – Tryon & Bailey (Nature Ed)
·        Human Evolutionary Tree – Adams (Nature Ed)
·        Paternity Testing: Blood Types and DNA – Adams (Nature Ed)

Portfolio Assignment
·        Print Research Project Part 2 and include it here

Assigned reading/viewing
·        Understanding Race:
Portfolio Assignment
·        Peruse the whole site then take the Human Variation Quiz at Understanding Race and record the correct answers (just the letters suffice).
(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)
Additional Resources
·        Human Skin Color Variation (NMNH):

Portfolio Assignment
·        In a half-page or more: Describe all the factors you can think of that contributed to the skin color you have today, right now. Would you be answering this question, in this course, if your skin color were different? Why or why not?
(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)

Assigned reading/viewing
·        Humans never stopped evolving – Hawks (The Scientist)
Portfolio Assignment
·        In a half-page or more: Are we still evolving? Why is this a question?
(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)
Additional resources
·        We are not the boss of natural selection – Dunsworth (io9)

Portfolio Assignment
·        In a half-page or more: After re-reading the essay you wrote in class on Day 1.1 ("What is evolution?") compose a letter to yourself highlighting what you were right about and what you were wrong about or what was incomplete about your answer based on what you learned this semester.
(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)

Portfolio Assignment
·        The complete student workbook for the Smithsonian’s “Evolution of Human Skin Color” curriculum (as much as we covered in class from days 3.9-3.13) - You should have already obtained and printed the workbook from Sakai for classroom work starting on 3.9. Here's where it lives publicly:

Assigned Reading/viewing
·        From the Belgian Congo to the Bronx Zoo (NPR)
·        A True and Faithful Account of Mr. Ota Benga the Pygmy, Written by M. Berman, Zookeeper – Mansbach
·        In the Name of Darwin – Kevles (PBS)
·         Human Races May Have Biological Meaning, But Races Mean Nothing About Humanity – Khan (Discover blogs)
·        Are humans hard-wired for racial prejudice?  - Sapolsky (LA Times)
Portfolio Assignment
·        In a half-page or more: What’s the link between racism and evolution? Is Ota Benga’s treatment justified by evolutionary theory? Is evolutionary theory racist?

3.15 – SUBMIT ENTIRE PORTFOLIO (including this assignment) AT THE START OF CLASS TODAY
·        YIF, Chapter 11: The Meaning of It All – Shubin
·        IUB, Chapter 12: The Making of Us - Roberts
·        Evolution reduces the meaning of life to survival and reproduction... Is that bad? – Dunsworth (The Mermaid’s Tale)
Portfolio Assignments
·        In a half-page or more: Briefly describe what you learned this semester and what, if anything, it means to you. Also, be sure to reflect on what you're still left wondering and describe how you could find the answers to your remaining questions.

Extra credit!!! Make a time machine then go back to the start of the semester, attend classes, take notes, read all of the things, think about all of the things, complete the assignments, and study for the quizzes.

You are a Homo sapiens. We are all Homo sapiens
And no Homo sapiens who doesn’t know their species will be given a letter grade for this course.