Having given my thoughts on Haidt’s generally excellent The Righteous Mind in my last post, I want to turn to Haidt’s use of group selection in the last third of his book. The central themes of his book don’t rest on group selection (in my opinion), but Haidt is at the centre of the reemergence of group selection in the social sciences and his points are worth discussing.

Haidt uses the more modern phrase “multilevel selection” in addition to “group selection” through the book. Multilevel selection refers to a method of accounting for selection at the different levels (e.g. the gene, individual, group etc.), while the older concept of group selection usually refers to natural selection between groups and the evolution of group-level adaptations. Multilevel selection also involves what are called “trait groups”, which may briefly form and break up, compared to the rigid reproducing groups of the old group selection.

It seems that Haidt has a reasonable grasp on these distinctions but his use of the term multilevel selection is often confusing. For example, he keeps using the phrase “product of multilevel selection”. But multilevel selection is, as the name suggests, selection at different levels. You can look at a scenario through a multilevel selection framework and conclude that all the selection occurs at the level of the gene or individual. Multilevel selection and inclusive fitness are just different accounting methods (or languages). It is not a case that one happens and the other doesn’t. What Haidt is implying, and the way he should state it, is that selection is occurring predominantly at the level of the group. Based on some passages of the book, it is clear that Haidt understands this, but at other times his language is loose.

When Haidt argues for the importance of selection at the level of the group (I’ll refer to it as group selection for rest of this post), he offers four lines of evidence: the role of group selection in the major evolutionary transitions; the shared intentionality of humans; gene-culture evolution; and the potential for fast evolution.

The major evolutionary transitions, such as the emergence of eukaryotic cells from the combination of bacteria, are one of the few areas where many evolutionary biologists will agree that group selection occurred. Haidt characterises the major evolutionary transitions as times where methods to control freeriding evolved at one level, allowing superorganisms to arise at the next. Haidt then follows in the footsteps of biologists such as David Sloan Wilson and E. O. Wilson and argues that the evolution of “ultrasociality” in humans is a similar transition.

I don’t want to rehash this argument in-depth, but it goes back to the classic group selection debate. In the evolutionary transitions of the past, a reproductive bottleneck was present. Once two bacteria are combined in a cell, the only way they can reproduce is if the “group” reproduces. But that bottleneck does not exist in human groups, so there is opportunity for freeriding. We then get to the old debate about the level of freeriding and whether the level of group extinction and the degree of gene flow between groups allows group selection to outweigh this freeriding.

While Haidt follows in others’ footsteps in referring to the major evolutionary transitions, his other arguments are more his own. On shared intentionality, Haidt argues that the ability to share intentions between people allows collaboration, the division of labour and shared norms. While Haidt claims this is group selection, this is a case where the multilevel selection framework should be properly applied. How much benefit does one get as an individual from understanding what someone else is thinking, versus the benefit you get from pairing with someone who also has that ability and working together to succeed as a group? While having more people in your group who are able to share intentions will help you defeat other groups, shared intentionality is clearly beneficial to an individual. Being in a group of mind readers when you have no idea what is going on is suboptimal. Which level the selection predominantly operates at needs to be analysed (and will depend upon assumptions about what makes up a group). This is the type of scenario that I have argued before is simpler to analyse in an inclusive fitness framework.

Haidt’s third line of evidence is a somewhat confusing take on gene-culture evolution. Haidt argues that cultural group selection supported “prototribalism”, which led to an environment that then supported genetic evolution. However, Haidt’s examples do not sound like group selection. For example, Haidt writes:

[I]ndividuals who found it harder to play along, to restrain their antisocial impulses, and to conform to the most important collective norms would not have been anyone’s top choice when it came time to choose partners for hunting, foraging, or mating. In particular, people who were violent would have been shunned, punished, or in extreme cases killed.

This sounds like individual level selection against violent, non-conformist individuals. I am not sure why Haidt was so keen to covert Boyd and Richerson’s arguments on cultural group selection into genetic group selection, but try he did.

Haidt’s biggest reach, however, comes with his argument that the potential for fast evolution supports group selection. Haidt notes that gene-culture evolution reached fever pitch in the last 12,000 years, and that is an assessment I would agree with. He refers to the group selection experiments conducted by William Muir, in which Muir rapidly improved egg laying by selecting groups of successful chickens (achieved, of course, through the effective creation of a reproductive bottleneck in the experimental design). Haidt then pushes the rapid evolution argument to the limits when he seeks to implicate group selection in the emergence of religion. As large-scale religion only emerged since the dawn of agriculture, Haidt suggests the rapid recent evolution identified by the likes of John Hawks, Greg Cochrane and Henry Harpending provides scope for recent group selection. He writes:

[G]roup selection can work very quickly (as in the case of those group-selected hens that became more peaceful in just a few generations). Ten thousand years is plenty of time for gene-culture coevolution, including some genetic changes, to have occurred. And 50,000 years is more than plenty of time for genes, brains, groups, and religions to have coevolved into a very tight embrace.

The problem is that group extinction and reproduction generally occurs more slowly than individual level selection. At the individual level, we see large differences in fertility every generation. For many people, it is the end of the genetic line. To the extent heritable traits underlie this variation, we can see rapid changes in genotype. In contrast, studies of rates of group extinction suggest it is slow. Further, groups tend not to be simply wiped out, but the “loser” groups tend to merge into the victor, bringing their genes with them.

Having said all this, we might be able to build a multilevel selection model in which we allow temporary religious or other “trait groups” to form and break up in short periods and divide the degree of selection between the various levels. However, I still doubt we will see significant selection at the group level for most of these examples and I don’t feel that this was the sort of group selection Haidt was interested in. Further, I expect the inclusive fitness framework would give a clearer picture. If this trait group approach could have provided a stronger argument, Haidt might have used it.

I am going to give my thoughts on Jonathan Haidt’s The Righteous Mind: Why Good People Are Divided by Politics and Religion over two posts as I want to split the good from the bad (second post here). The first two-thirds and the conclusion of the book are excellent. However, slotted in the last third is Haidt’s take on group selection. His group selection argument deserves attention, but I don’t want to derail this post with a group selection critique, particularly when Haidt’s broader arguments do not rest on it.

Haidt’s goal is to explain why people are divided by politics and religion. He has three major explanations for this division: we are primarily guided by our intuitions (not reason); there’s more to morality than harm or fairness; and morality binds and blinds.

Part 1 of the book is based on the concept that intuition comes first, strategic reasoning comes second. When presented with a new situation, we tend not to reason to our moral response. Rather, our instincts offer a moral response, and we then use our power of reasoning to justify it. Haidt asks us to picture our reasoning as a rider on an intuitive elephant. The elephant leans in response to a situation, and the rider rationalises why they are going in that direction. It takes a real effort to turn the elephant.

Much of the material through Part 1 is the fodder of popular accounts of decision-making, ranging from material on confirmation bias to Philip Tetlock’s work on expert political judgment. Haidt’s elephant and the rider also draws comparisons with Daniel Kahneman’s System 1 and System 2. However, the application of this framework to moral psychology is interesting, particularly as the nature of the elephant changes between people – as Haidt highlights in Part 2.

Haidt starts Part 2 with a story about an experiment in which he exposes subjects to a novel moral dilemma and makes them justify their moral judgement. One dilemma involves a man who buys a chicken from the supermarket (already dead) and has sex with it before he cooks and eats it. As no-one is harmed, someone rationalizing the story under the scrutiny of an interviewer might ultimately decide that there was no moral transgression.

When Haidt moved beyond his usual WEIRD (Western, educated, industrialised, rich and democratic) experimental subjects to people entering a suburban McDonald’s, he found that there was astonishment at the interviewer’s questions as to whether the action was wrong. Why do you even need to ask? From this picture, Haidt argues that there is more to morality than fairness and harm, the staples of liberal morality (liberal in the sense the term is used in the United States – and how I will use it for the rest of this post). Instead, there are six foundations to morality – care/harm, liberty/oppression, fairness/cheating, loyalty/betrayal, authority/subversion, and sanctity/degradation.  An extra wrinkle is that fairness contains equality and proportionality elements.

Liberal morality tends to rest on the care/harm and to a lesser extent on the fairness/cheating (equality) and liberty/oppression dimensions. Conservative morality tends to rely on all six, with an emphasis on proportionality for the fairness/cheating dimension.  The libertarian moral framework rests almost entirely on the liberty/oppression dimension (with a small dose of fairness/cheating thrown in). Haidt suggests that the broader moral foundation of conservatives gives them an edge in understanding the concerns of the full political spectrum. It is not that conservatives don’t care about harm. They simply weight it differently. When conservatives and liberals undertake an ideological Turing test, where they had to answer questions as though they were the other, conservatives and moderates do better than liberals. Haidt does not delve into the consequences of the narrow libertarian moral foundations in detail, but it raises the question of libertarian’s ability to understand and communicate with other audiences.

The moral framework test at YourMorals.org that informs much of Haidt’s book suggests that I have a liberal framework, although with more weighting to proportionality than equality in the fairness/cheating dimension and a stronger tendency towards liberty (my scores are the green bars). I lean libertarian, but this is more due to my beliefs about how to reduce harm than a foundation built on freedom from interference, so the assessment may make sense given my bleeding heart libertarian tendencies.

Haidt applies little substantive judgement to the merit of these moral foundations. In the conclusion, he supports moral pluralism, not relativism – but you get little of that flavor in the rest of the book. In part, Haidt’s swing towards conservatism makes him disinclined to critique any of the conservative foundations. However, as a description of moral frameworks, the discussion is excellent.

In Part 3, Haidt notes the grouping instincts of humans. In times of crisis, such as after 9/11, people act less selfish and pull together as a group. This groupish behaviour can act as a barrier to understanding others and is parochial, but Haidt argues that there are ways to increase group cohesiveness in ways that are not necessarily harmful to outgroups. We should be looking for ways to trigger this cohesiveness.

To illustrate this, Haidt  dedicates a chapter to religion, the ultimate in groupish behaviour. He argues that religion is an evolved cultural trait, not a maladaptive meme, as religion binds people into groups, suppresses freeriding and supports cooperation (he even goes as far as putting religion into the group selection basket, but I will also save that issue for my second post).  It is not an argument that will win fans among the new atheists.

Haidt closes the book with some suggestions to answer the opening question of the book: “Can we all get along?” Haidt is slightly naive in his hope that understanding someone else’s moral foundation will reduce conflict, but some of his other throw away ideas, such as having the families of legislators live in the same neighbourhoods to build civility, are interesting – although as Haidt suggests, we might be too far gone for that. If nothing else, his framework might help meet Haidt’s initial goal of understanding conservative morality and allow the Democrats write some better speeches with broad appeal.

My second post on Haidt’s book is here.

One common explanation for fertility declines over the last 200 years is that parents have shifted to investing in quality of children, rather than quantity. What is often not made clear is that this quality-quantity trade-off has two dimensions. The first trade-off relates to socioeconomic status (SES), with greater numbers of children resulting in less investment in education and resource dilution. The second trade-off relates to fitness, as a short-term increase in children may reduce fertility in future generations.

These two dimensions may not line up in modern settings. If an increased investment in quality increases both SES and fitness, there must be some point at which that investment pays off through more children. If not, despite the socioeconomic gains, the trade-off will not be delivering increased fitness.

A new article in the Proceedings of the Royal Society B: Biological Sciences by Goodman and colleagues presents some empirical evidence on this issue. Analysing a cohort of 14,000 Swedes born between 1915 and 1929 and their descendants, they showed that low fertility and high SES for parents increased child SES (as measured by school marks, university attendance and income) for as much as four generations into the future. However, this SES did not translate into higher fitness.

This result lends support to models which suggest that fertility is reduced for socioeconomic gains, but puts into doubt biologically based models in which reduced fertility is an adaptive trade-off for long-run fitness. The high SES people in the study had lower fertility than optimal, so the strategy pursued by these people is maladaptive.

One way in which SES affected fertility was the time between generations. While most of the low-SES people in the sample had four generations of children since the first study members were born, the higher SES members often had only three. In a growing population, you can have the same number of children per generation but lower fitness, as the shorter generation time of competitors allows them to increase in population more quickly.

The study did not analyse in-depth whether SES transmission is due to genetic inheritance, parental decisions to invest in quality over quantity, or resource transfers such as inheritance. As I am skeptical about the returns to investments in quality above a basic threshold (particularly in a developed country such as Sweden), I lean towards the genetic and resource transfer explanations.

Videos of the presentations at the Biological Basis of Preferences and Behaviour conference have been put online. Many are worth watching. I hope to write more detailed posts about a few of the presentations soon, but the three presentations I got the most out of were:

1. “Social Networks and Cooperation in Hunter-Gatherers” by Coren Apicella. Presentations such as this always remind me that I should be reading far more work by anthropologists.
2. “The Genetic Architecture of Economic and Political Preferences” by David Cesarini. A good introduction to the growing field of genoeconomics.
3. “Cognitive Trade-Offs in Chimpanzee Versus Human Mixed Strategy Play” by Colin Camerer. Don’t play poker against chimpanzees. I’ve posted on these chimps before.

My earlier posts on the conference include my general impressions and some comments on the presentation by Balazs Szentes.

Technology wants increasing efficiency, opportunity, emergence, complexity, diversity, specialisation, ubiquity, freedom, mutualism, beauty, sentience, structure and evolvability. As Kevin Kelly argues in What Technology Wants, these are the same things that life wants. Technology extends evolution’s four billion year path.

Whether you buy Kelly’s central thesis or not (in general, I don’t) and if you ignore some of Kelly’s near-religious fervour (particularly in the opening and closing chapters), Kelly provides a strong argument that the growth in technology is primarily beneficial, with the major benefit being increased choice. Technology provides the basis for achievement. The technology of vibrating strings provided the opportunity for virtuoso violin players. The technology of film allowed cinematic talents to blossom. And consider the technologies of writing and mathematics.

Kelly is not blind to the potential negative effects of technology. Shipping technology allowed mass slavery. The chemical industry spawns toxins. All technologies have unintended consequences. But in response to those negative elements, Kelly argues that prohibition is pointless. Prohibitions don’t work, don’t last and when they are put in place, are usually gone in the next technological cycle. Rather, we should use new technologies to offer solutions to old technologies, and use our knowledge of the path of technology to understand how to control the negative consequences. When new technologies emerge, they should not be banned but rather tested and actively assessed. Where harms occur, they should be rectified and where problems emerge, the technology should be redirected. The path of technology is inevitable and we cannot stop it.

The inevitability of technology is a central plank of Kelly’s argument. If the clock of time was rewound and started again, even with  different initial conditions, we would still end up with a similar path of development and resulting inventions. Kelly points to the examples of similar inventions occurring independently on different continents, such as agriculture. He points to the more recent phenomenon of multiple inventors of the same invention, such as lightbulbs or calculus. These technologies are inevitable, as is the rough order that they appear, as one builds on the other.

Kelly builds on this argument of inevitability by pointing to the (widely disputed) inevitability of biological evolution. Convergent evolution is a similar phenomenon to concurrent invention. Eyes and lactose tolerance evolved on multiple occasions.

I am not convinced that Kelly’s examples of concurrent invention or convergent evolution provide a strong case of the inevitability of invention or evolution, primarily because we don’t know what the fitness landscape of these technologies or traits looks like. If there is a single, clear peak for fitness, all paths will converge to it. If there is a complex multi-modal fitness landscape with a complex topography, we won’t see many of the possibilities. Within our own little world we will see convergence to a local peak, giving the impression of inevitability, but we might be missing the big picture. There may be an array of possibilities that we cannot get to.

Another issue is that we can find examples of one-off inventions or evolutionary solutions. As pointed out in a review by Jerry Coyne, the wheel only appeared in North America when brought by Eurasians. Similarly, bones, feathers and the human brain have only appeared once. How different would Africa or Australia’s path have been, and for how long, if they had been isolated from industrialising Europe?

I lean towards the view that biology is not repeatable . Small chance events have large effects. Although I am open to the idea that intelligence might be likely to evolve, a one-off example in over 4 billion years is hardly a strong case and doesn’t provide very many sample points.

If you pull that theoretical pin out of What Technology Wants, the argument that technology has direction lacks a solid thesis. As Matt Ridley did in The Rational Optimist, Kelly takes a general direction and tries to use evolution to turn it into an iron law. But it is the wrong tool to do so.

Regardless, I enjoyed the book greatly. It is full of interesting observations and ideas by an astute observer of technology. Just don’t look to it for the all encompassing theory of technology.

Over the last few months, I have heard the phrase “agriculture creates excess population” or other words to that effect from several sources. The latest is at Evolvify, where Andrew references Richard Manning and writes:

Agriculture creates excess population. The argument that we need more agriculture to support higher population fails to recognize its inherently circular nature.

While I have some sympathy to the argument about the destructiveness of agriculture for the ecosystems it supplants, I would prefer to frame the argument differently.

If we take the Malthusian model as a description of human history, for most of that history populations were at subsistence level. The constraint on population was the level of technology. Improved technology did not increase living standards as population would simply increase to match the rise in technology (making population density a crude measure of technology). Some populations managed to briefly have higher living standards by imposing society-wide checks on fertility, or through higher death rates due to disease or violence, but subsistence was the norm.

Thus, in hunter-gatherer societies, population was constrained by the technologies available to them. A technology that allowed more game to be caught may briefly raise living standards, but population would soon increase to take advantage of the additional resources. Population could also increase where new land was entered, such as the entry of humans into the Americas 12,000 years ago.

With the advent of agriculture, the new technology allowed even higher populations. However, up to the 18th century, population generally grew in line with technology and most of the population remained at subsistence levels. New land would at times be opened up to agriculture with accompanying population growth, such as with the European settlement of the Americas, but the Malthusian constraint remained.

Thus, it is not agriculture in itself that creates excess population. The very nature of the Malthusian state – which was the state of human populations for most of their history – is excess population.

Then, around the time of the Industrial Revolution, incomes started to grow faster than population. Populations where this occurred were now able to obtain incomes above subsistence. The twist in the tail was that those with higher incomes lowered their fertility, allowing per person income to grow even faster. So, although population has grown quickly since the Industrial Revolution and on the back of agriculture, it has not grown as fast as the loosened Malthusian constraints would allow. In that sense, there is not overpopulation. We could even argue by this measure that many parts of the world have never been less crowded.

One obvious response is to ask whether the current use of land for agriculture is destroying future capital. Is agricultural productivity ephemeral, as today’s income is coming at the cost of income at the future? In that scenario, it could be argued that there is excess population, but the current population is able to temporarily ward off the Malthusian constraint at the cost of future populations. Even if this were the case, however, I would prefer to frame that argument in terms of the nature of the technology than in terms of “excess population”. A state of excess population is the norm, not a particular result of agriculture or any other technology of the day.

The Edge has put up video and transcript of a great interview with Joe Henrich (the Canada Research Chair in Culture, Cognition and Evolution at UBC). The whole interview is worth watching or reading.

A couple of the more interesting snippets are below. First, on the division of labour:

One of the interesting things about the division of labor is that you’re not going to specialize in a particular trade—maybe you make steel plows—unless you know that there are other people who are specializing in other kinds of trades which you need—say food or say materials for making housing, and you have to be confident that you can trade with them or exchange with them and get the other things you need. There’s a lot of risk in developing specialization because you have to be confident that there’s a market there that you can engage with. Whereas if you’re a generalist and you do a little bit of farming, a little bit of manufacturing, then you’re much less reliant on the market. Markets require a great deal of trust and a great deal of cooperation to work. Sometimes you get the impression from economics that markets are for self-interested individuals. They’re actually the opposite. Self-interested individuals don’t specialize, and they don’t take it [to market], because there’s all this trust and fairness that are required to make markets run with impersonal others.

I don’t agree with Heinrich’s use of the word self-interested in the last sentence, as being trusting, specialising and trading has large individual benefits. However, the importance of trust is rarely emphasised enough.

Second, on monogamy:

Societies that have this are better able to maintain a harmonious population, increase trade and exchange, and have economic growth more than societies that allow polygamy, especially if you have a society with widely varying amounts of wealth, especially among males. Then you’re going to have a situation that would normally promote high levels of polygyny. The absolute levels of wealth difference of, say, between Bill Gates and Donald Trump and the billionaires of the world, and the men at the bottom end of the spectrum is much larger than it’s ever been in human history, and that includes kings and emperors and things like that in terms of total control of absolute wealth. Males will be males in the sense that they’ll try to obtain extra matings, but the billionaires are completely curbed in terms of what they would do if they could do what emperors have done throughout the ages. They have harems and stuff like that. Norms of modern society prevent that.

Otherwise, there would be massive male-male competition, and even to get into the mating and marriage market you would have to have a high level of wealth if we were to let nature take it’s course as it did in the earliest empires. It depends on what your views are about freedom versus societal level benefits.

The nature of the causative link between monogamy and economic growth is an interesting question. Monogamy promotes stability, but I suspect that populations that implement monogamy are the same populations likely to implement a range of other growth promoting institutions.

I also tend to see the tradeoff between the freedom of polygamy and the “societal level benefits” of monogamy as being an indirect tradeoff. If a few men monopolised all the women, they would quickly find their freedom curtailed by the other men.

The interview has plenty of other interesting food for thought.

Quamrul Ashraf and Oded Galor’s paper linking genetic diversity and economic development has been available as a working paper for a few years, but it has now found a home in the American Economic Review (the latest available version of the working paper that I am aware of is available here).

Science has picked up on the forthcoming publication is its editor’s choice section (unfortunately gated without subscription). Science summarises the results as follows:

Ashraf and Galor present the hypothesis that genetic diversity has exerted a long-lasting effect on economic development — which is quantified as population density in the precolonial era and as per-capita income for contemporary nations — beyond the influences of geography, institutions, and culture. They posit that intermediate levels of heterozygosity allow for a productive balance between the social costs of high diversity and the creative benefits of higher variance in cognitive skills. They show that the optimal level of diversity was approximately 0.68 in 1500 CE, and that this increased to 0.72 (which is pretty much where the United States sits) in the year 2000, with the most homogeneous country, Bolivia, placed at 0.63 and the most diverse country, Ethiopia, at 0.77.

I recommend reading the comments on Marginal Revolution where Tyler Cowen has noted the Science piece.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows. I will link to each below as I post them:

1. A summary of the paper methodology and findings
2. Does genetic diversity increase innovation?
3. Does genetic diversity increase conflict?
4. Is genetic diversity a proxy for phenotypic diversity?
5. Is population density a good measure of technological progress?
6. What are the policy implications of the effects of genetic diversity on economic development?
7. Should this paper have been published?

Other debate on this paper can also be found here, here, here and here. Be sure to read the comments.

At the Consilience Conference earlier this year I bumped into evolutionary biologist Michael Rose, whose research interests include examining ageing through the lens of evolutionary theory. In our brief conversation, Rose mentioned that he had laid out much of his thinking on the topic in 55 Theses, where Rose describes how to use the insights of evolutionary biology to improve your health.

Reading through the archives of the Social Evolution Forum over the weekend (worth adding to your feed), I came across a post by Peter Turchin recounting a conversation he had with Rose at the same conference. Turchin writes on one element of Rose’s thinking, which concerns age-dependent traits:

We think of people having ‘traits,’ but actually we change quite dramatically as we age. … As an extreme example, consider reproductive ability, something of great interest to evolution. Humans do not reproduce until they reach a fairly advanced age of maturation (puberty). Young adults are not very good mothers or fathers, but they improve with age during their twenties. After that reproductive ability declines and eventually disappears. …

Ability to digest certain foods can also be age-dependent. I have already mentioned the ability to digest lactose, the sugar present in milk. Before we domesticated animals such as cows and sheep, only very young humans had this ability. Natural selection turned this ability off in adults because they never needed it (and it would be wasteful to continue producing the enzyme lactase that aids in the digestion of milk sugar). …

Because abilities to do something at the age of 10, 30, 50, etc. are separate (even if correlated) traits, they evolve relatively independently of each other. When grains became a large part of the diet, the ability of children to digest them (and detoxify the chemical compounds plants put into seeds to protect them against predators such as us) became critical. If you don’t have genes to help you deal with this new diet, you don’t survive to adulthood and don’t leave descendants. In other words, evolution worked very hard to adapt the young to the new diet. On the other hand, the intensity of selection on the old (e.g., 55 years old) was much less – in large part, because most people did not live to the age of 55 until very recently. …

The striking conclusion from this argument is that older people, even those coming from populations that have practiced agriculture for millennia, may suffer adverse health effects from the agricultural diet, despite having no problems when they were younger.

The conclusion that Rose draws is that young people descended from populations with a substantial history of agriculture can probably cope with an agricultural diet. However, as they age, may need to revert to a Paleo diet. Those without that agricultural history should get on a Paleo diet from the start. It is an interesting twist on the usual Paleo diet story.

The ENCODE (Encyclopedia of DNA Elements) project is an international collaboration that intends “to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active.”

The project has made a splash in the last couple of days with the publication of thirty open access papers across Nature, Genome Research and Genome Biology describing some of the results. Much of the blogosphere has been hosing down the declarations of the accompanying press releases, so don’t expect any revolutions to come out of this work just yet. Similarly, the ENCODE project is not about to spur the genoeconomics revolution (the use of molecular genetics in economics). However, the project is a reminder that there is some very cool work going on (at least for those of us not already in the loop).

One important consideration for genoeconomics is how the ENCODE project might affect genome wide association studies (GWAS). ENCODE outputs were compared with previous results of GWAS for disease, and support was found for previous results. As described on the Nature News site:

Since 2005, genome-wide association studies (GWAS) have spat out thousands of points on the genome in which a single-letter difference, or variant, seems to be associated with disease risk. But almost 90% of these variants fall outside protein-coding genes, so researchers have little clue as to how they might cause or influence disease.

The map created by ENCODE reveals that many of the disease-linked regions include enhancers or other functional sequences. And cell type is important. Kellis’s group looked at some of the variants that are strongly associated with systemic lupus erythematosus, a disease in which the immune system attacks the body’s own tissues. The team noticed that the variants identified in GWAS tended to be in regulatory regions of the genome that were active in an immune-cell line, but not necessarily in other types of cell and Kellis’s postdoc Lucas Ward has created a web portal called HaploReg, which allows researchers to screen variants identified in GWAS against ENCODE data in a systematic way. “We are now, thanks to ENCODE, able to attack much more complex diseases,” Kellis says.

The problem for the genoeconomics enterprise is that the existing GWAS on economic traits are often of questionable value. Any results that are not spurious are of such small effect that biochemical analysis is not much use. Further, converting genetic activity to outcomes such as time or risk preference is a much more difficult proposition than examining disease pathways.

So, for the moment, the genoeconomics enterprise is probably best left examining twin studies, GREML analysis or other techniques that don’t need a particular gene and trait to be nailed down. That said, despite being a long way from being able to control for genetic effects by examining someone’s genome, we are not short of information that we can use.

The more interesting part of the events of the last couple of days, as has been noted in many blogs, is the publication model adopted for this release of the ENCODE results. While not without problems (Daniel MacArthur’s mixed reaction is one example worth reading), the information available and the way it is presented is quite cool and hopefully another step towards more open access to data in the field. You can download an Ipad app which has the thirty open access papers, plus an interesting feature called “threads” which allows exploration of issues across the papers. Much of it is heavy going for someone not in the field, and it is useful to use the blogosphere to interpret the information, but there are worse ways to get up to speed with what is happening.