Evolutionary genetics as a field emerged in the early 20th century. There were some upsides to this. R. A. Fisher was alive, so there were some incredibly brilliant theoretical minds who could focus upon the project of formalizing evolutionary process and fusing it with Mendelian genetics. And, frankly there are situations where data-free theorizing is best because that sort of theorizing at least is blind to what the solutions should be. But there were also many downsides to this early flowering of theoretical evolutionary biology. The reality that biologists were not clear as to the nature of the biomolecular substrate of inheritance, DNA, was not a hindrance for most of the high level abstraction. But to trace patterns of transmission of characters, and implicitly genotypes, within populations researchers relied upon classical phenotypic markers. This means that the theoretical speculation advanced rapidly into confusing and tendentious terrain, while the empirical data sets to test the questions at issue were simply not sufficient to resolve the debates. The emergence of molecular markers in the 1960s, and the maturation of genomics in the 2000s, has revolutionized the empirical domain of evolutionary genetics. To use a rough analogy the large data sets of the present offer up raw material for the machinery of theory to sift, process, and refine.

A new paper in Nature is a perfect illustration of this, Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations:

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The Philadelphia Inquirer has a long piece which reports on the reality that the ‘crack baby epidemic’ of mentally retarded or unstable individuals turned out to be unfounded. The experimental design is as simple as can be: compare individuals of similar socioeconomic background, and track them over their lives. The past generation since the 1980s crack wave has been an unfortunate, yet illuminating, ‘natural experiment.’ The researchers found that contrary to expectations there are basically no statistically significant differences between ‘crack babies’ and control individuals.* This is not a surprising result, as there were hints of this in the child development literature even in the 1990s. The author of the study focused on a sample of lower class black Americans, and noted that despite the lack of statistical differences in outcomes due to exposure to crack in utero, these individuals have been exposed to a lifetime of an underclass milieu, which is likely not conducive to human flourishing. In other words, the reality is much more banal and unsurprising when viewed in a broader light.

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Definitely summer….

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My personal preference would have been that they somehow extend the plot line of The Chronicles of Riddick. As Brian Switek suggested this seems like Pitch Black, but with much better special effects. Though how many they have left after this trailer, I don’t know.

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You have probably heard or read that most genetic variation is within races, not between races. This assertion has led, in my opinion, to unwarranted inferences. Often bracketed under “Lewontin’s Fallacy”, the basic intuition is that if most variation is within races, then races as a taxonomic unit are without utility or substantive basis. This is disputable. In plain English, though most genetic variation may be within races (i.e., not diagnostic of racial identity), the variation across races is quite systematic because that variation reflects deep population history. In this way of thinking population or racial substructure are simply reflections of the tips of the tree which has been shaped by history.

But these discussions are ultimately predicated upon a statistic, F_ST. F_ST is generally considered one of the fixation indices pioneered by the American evolutionary geneticist Sewall Wright. What Wright’s F_ST aims to capture is the relative amount of genetic variance which is due population substructure. In regards to human races out of the total genetic variation ~15% of it can be inferred simply by looking at population substructure (F_ST ~0.15), with the balance not being due to population structure. But this is an average value. At rs1426654 in SLC24A5 when comparing Europeans and Africans almost all of the variation is between the populations, because the allele frequencies are disjoint. But what if I told you that Wright’s F_ST is quite a bit woollier than you might think?

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While 60 percent of Americans support the legality of 1st trimester abortions, only 30 percent support the legality of those in the 2nd, and it is in that trimester that the abortion of a fetus with a trisomy abnormality now occurs because the various prenatal tests are at this stage. Mind you, I understand that despite what the public says a larger share of parents who receive “positive” results in that trimester abort the pregnancy than might be expected based on surveys about the legality of the practice. But, there is probably considerable room on the margin for the class of those who would opt for this if the results arrived in the 1st trimester.

And of course trisomy tests are just the beginning. Whole genome sequencing of 2nd trimester fetuses is now possible, and it seems very likely that in the next few years they’ll move all the way to the 1st trimester. At that point the genetic analysis of 1st trimester fetuses will be routinized and be a simple consumption good. The ultimate question is what are we going to do with all that information? This is not hypothetical, speculative, or blue sky. It’s almost a reality.

Two weeks ago I saw the film Mud. It’s one of the few “serious” movies I’ve watched over the past twoyears. I can’t tell you what Pacific Rim was really about despite having viewed it five days ago (aside from the striking fact that of the protagonists two were played by bizarrely similar looking actors). And yet aspects of Mud have stuck with me. Why? It’s not because of the plot, which was laughably implausible. Or the development of the characters, which I found a bit overwrought or cliche in most (though not all) cases. Rather, I am still reflecting upon the depiction of the main young protagonist, a fourteen year old played by Tye Sheridan, and the landscape upon which he is “coming of age,” the central theme of the film. The specific details of the concerns of a teenage boy navigating new found feelings toward the opposite sex, an unstable family life, and a pedestrian rural milieu, are not novel. Rather, it was the whole portrait which I think warrants further exploration here in 2013.

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Thanks to the efforts of geneticists the story of the extinction of the Spanish Habsburgs is now well known. They are in short a case study in the disastrous consequences of an inbred pedigree. The downsides of inbreeding are to some extent intuitively understood by all, especially consanguineous relations between first order relatives. Though I’m willing to bet that all things equal inbred individuals are not as attractive or intelligent as outbred individuals, the literature in this area for humans is surprisingly thin. A major problem is controlling for confounds; all things are often not equal (e.g., imagine if inbreeding is more common in marginal isolated communities, which is often true in the West. See Consanguinity, Inbreeding, and Genetic Drift in Italy, where it is obvious that the less developed areas of Italy had elevated rates of marriage between relatives despite Catholic discouragement of the practice). But the case that inbreeding results in the expression of deleterious recessive diseases is more straightforward. The rarer the disease, the higher the proportion of individuals who are affected who are the consequence of inbreeding. This is due to the logical fact that very rare alleles tend not to come back together in homozygote form due to the character of the Hardy-Weinberg equilibrium. If the recessive trait is caused by a minor allele with a frequency of p, p² can converge upon zero very rapidly as p decreases in frequency. At p = 0.1 the recessive trait will express in 1% of the population (so p/p² = 10). At p = 0.01 the recessive trait will express in 0.01% of the population (so p/p²= 100). And so forth.

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