Austin Leland Hughes (1949–2015) taught in the Department of Biological Sciences at the University of South Carolina. Hughes studied the evolution of altruistic behavior in human beings and adaptive molecular evolution, subjects to which he made significant contributions.1

Originally proposed by Motoo Kimura, Jack King, and Thomas Jukes, the neutral theory of molecular evolution is inherently non-Darwinian.2 Darwinism asserts that natural selection is the driving force of evolutionary change. It is the claim of the neutral theory, on the other hand, that the majority of evolutionary change is due to chance.

Each individual in a typical mammal population has two copies of its genome in almost every cell. The exact DNA sequences they contain may differ as the result of mutations, random copying errors in which one nucleotide letter is replaced by another. Other changes can also occur, such as the deletion or duplication of larger DNA segments. The result is genetic variation, and it is estimated that, for human beings, each child acquires 100 new mutations—50 in each genome copy—that were not present in its parents’ DNA.3 Genetic variation means no more than spelling differences in the DNA sequences carried by different individuals in a population. When a new DNA spelling is generated, an allele is born. This alternative form of the original gene may or may not lead to changes in the organism’s physical characteristics.

Evolution involves the substitution of one allele for another in a population. Having come about by chance, a new allele becomes increasingly common, and finally replaces the old allele. An evolutionary substitution has occurred.4

When the technology enabling the study of molecular polymorphisms—variations in the sequences of genes and proteins—first arose, a great deal more variability was discovered in natural populations than most evolutionary biologists had expected under natural selection.5 The neutral theory made the bold claim that these polymorphisms become prevalent through chance alone. It sees polymorphism and long-term evolutionary change as two aspects of the same phenomenon: random changes in the frequencies of alleles. While the neutral theory does not deny that natural selection may be important in adaptive evolutionary change, it does claim that natural selection accounts for a very small fraction of genetic evolution.

A dramatic consequence now follows. Most evolutionary change at the genetic level is not adaptive.

It is difficult to imagine random changes accomplishing so much. But random genetic drift is now widely recognized as one of the most important mechanisms of evolution. Together with J. B. S. Haldane and Ronald Fisher, Sewall Wright was one of the founders of mathematical population genetics. Genetic drift, Wright reasoned, is inevitable in populations having a finite size.6 This is because natural populations are subject to all the uncertainties of random sampling. If a fair coin is tossed three times, the probability that it will land on heads, tails, and then heads (H-T-H) is one in eight.7 This is the probability to which these coins will converge in the long run. However, in the short run, it is impossible to predict exactly how many times H-T-H will occur. The sequence might not appear at all, or else appear surprisingly often. In keeping with the laws of large numbers, the error—the difference between the proportion of H-T-H observed in our samples and the true probability—will shrink toward zero as our number of trials increases.

When organisms mate, only about half of maternal or paternal genetic material is given to its offspring. Unless a pair of mates has an incredibly large number of children, some of their alleles must be randomly lost. When one allele dies out, others will take its place. Quite apart from the issue of random sampling, different breeding pairs will leave different numbers of offspring. If one family has four children while a second has five, the second family’s alleles will have a higher frequency in the next generation.

Random events make possible many evolutionary substitutions. This is genetic drift, a force than can easily become more powerful than natural selection. Differences in differential reproduction caused by natural selection can be slight, easily dwarfed by random differences.

The neutral theory is not necessarily incompatible with the occurrence of evolutionary substitutions by Darwinian evolution. However, to the extent that evolutionary change is neutral, selection is rendered superfluous. In other words, the majority of evolution may be neutral, but natural selection can be invoked to explain some key, albeit rare, changes that are adaptive.8

The Neutral Theory

Austin Hughes viewed Motoo Kimura, the primary developer and advocate of the neutral theory, as a figure as important as Charles Darwin in evolutionary biology.9 Law-like change had been a familiar concept since, at least, the Stoics, but Kimura’s neutral theory, together with Werner Heisenberg’s uncertainty principle and Kurt Gödel’s incompleteness theorem, suggested that the universe is, at its core, non-deterministic.

Adaptive evolution, Hughes noted, was very often treated as if it were itself a null hypothesis, remarking that:

This was an extraordinary proposal—surely unique in the history of science. For it is the usual practice in science that the null hypothesis is the hypothesis of no effect, which is accepted unless we are able to show that the data deviate significantly from what would be expected under the null hypothesis.10

The neutral theory would make predictions that “go beyond a mere null model,” especially in the work of Kimura’s student and colleague Tomoko Ohta.11

Hughes spent much of his career testing these predictions.

Kimura first proposed the neutral theory because he thought selection could not explain the speed of evolution observed in animal proteins. In his first paper on the subject, he compared the amino acid sequences of hemoglobin protein molecules from several species and estimated that one amino acid substitution had occurred in every 1.8 years of mammalian evolution. This was shockingly fast. Imagine a mutation conferring some trait so advantageous that it allows its carrier’s offspring to replace the entire population in under two years. For organisms such as human beings, such change is biologically unfeasible, in part because it would require profound differences in reproduction rates that exceed the physiological limits of our species.12

The real picture is even worse. Only a small fraction of an animal’s DNA actually encodes proteins—linear chains whose building blocks are small molecules known as amino acids. Hemoglobin is one example. Twenty amino acids are used by biological organisms. Some of these can be encoded by several different DNA triplets, known as codons, making the genetic code partially redundant. Only some DNA changes will result in a change to a protein. For this reason, DNA sequences are said to contain synonymous and non-synonymous sites.13 At non-synonymous DNA sites, a change in the nucleotide changes the encoded amino acid. For example, changing G to A in ATG will result in ATA, switching the amino acid from methionine to isoleucine.14 On the other hand, changes at synonymous sites will not result in an amino acid change. Switching G in CTG to any other nucleotide will not alter the amino acid—CTA, CTG, CTC, and CTT all encode leucine.15

Because synonymous mutations do not change an amino acid, the resulting protein variant usually remains invisible to natural selection.16 Synonymous changes are free to accumulate by genetic drift. On the other hand, since the proteins encoded by DNA usually work well, non-synonymous mutations tend to disrupt their function and lower the reproductive rate of their carriers. The result is negative or purifying selection, which acts to eliminate harmful mutations from a population. Such deleterious mutations are quite common.17 Although they are usually deleterious, non-synonymous mutations may sometimes be actually beneficial, meaning that the lucky organism contributes an increased number of offspring to future generations. This is positive, or Darwinian, selection. Positive selection may then drive the evolutionary substitution of a beneficial mutation until it reaches fixation. Because beneficial mutations are rare, positive selection is rare as well.

It is possible to detect selection by comparing evolutionary rates at non-synonymous and synonymous sites. About 75% of the sites in a typical protein-coding DNA sequence are non-synonymous.18 This means that about three times as many will change the amino acid as not. Simply tallying the numbers of each type of change is not a fair comparison. One must divide the actual number of changes that have occurred by the possible number of changes of each type.

Imagine two DNA sequences from different individuals. Let nN be the average number of non-synonymous sites in the sequences, nS the average number of synonymous sites in the sequences, and mN and mS the number of non-synonymous and synonymous mutational differences between them. In this case, the corrected number of non-synonymous and synonymous differences (dN and dS, respectively) are:19

dN=mNnN

and

dS=mSnS .

Consider an example: if there are 100 sites in each of two sequences, an average of 75 might be non-synonymous, leaving 25 that are synonymous. If we observe three non-synonymous differences and one synonymous difference between them, dN = 3/75 = 0.04 and dS = 1/25 = 0.04. Because dN = dS, the rates of evolution at non-synonymous and synonymous sites are in fact equal. In this case, the evidence suggests that evolution has been dominated by genetic drift, since selection did not change dN—the rate of evolution at non-synonymous sites—relative to dS. In other cases, purifying selection can lead to dN < dS by acting against non-synonymous mutations that disrupt protein structure. Finally, positive selection can lead to the opposite pattern of dN > dS by promoting multiple non-synonymous changes.

Hughes and his doctoral student Meredith Yeager recognized that, because Kimura’s estimate of the rate of evolutionary substitutions was based on amino acid sequences alone, his analysis missed synonymous DNA changes.20 When they estimated evolutionary rates by comparing mouse and rat genomes, their results suggested a much faster rate of 8.14 substitutions per year, or one substitution every 44 days in protein-coding regions of DNA.21 Even allowing that the rate of evolution may be up to 1.4 times higher in rodents than in humans and monkeys, this is a rate far too swift to be explained by positive selection.22

These facts are explained by the neutral theory.

Huge numbers of selectively neutral mutations, the neutral theory insists, coexist in a population at any given time. The billions of alleles in a human genome are linked in a specific order. When mutations occur, they are scattered throughout the DNA, but some will be quite close together. Now imagine that, by genetic drift, one allele reproduces enough to replace another in the population. When it does so, any mutations in its neighboring regions will tag along. This is a strategy that allows neutral changes to accumulate very rapidly, but it is one not generally available to beneficial mutations.

In a population of size N, there are 2N copies of DNA, with each organism possessing one copy from both parents. Let K be the rate at which evolutionary substitutions occur. Now suppose that a new neutral mutation arises. This new mutation will be present in just one copy in one individual, at a frequency of 1/(2N). Since all copies have an equal chance of undergoing substitution, 1/(2N) is also the new mutation’s probability of being the eventual winner—the one that fixes. If the number of mutations occurring in each DNA copy per generation is u, then a population consisting of 2N copies will acquire 2Nu new mutations in every generation.

This leads to a remarkable result for the rate of substitution, and a hallmark of the neutral theory:

K=2Nu × 12N=u .

The 2Ns cancel and we are left with the mutation rate alone. Thus, the rate of substitution under neutral evolution is equal to the mutation rate u, and does not depend whatsoever on the population size N.23

Hughes described the situation as follows.24 If uT is the total number of mutations that occur each generation, and f0 is the fraction (ranging from 0 to 100%) of those mutations that are neutral, then the rate K0 at which neutral mutations complete an evolutionary substitution each generation is

K0=uTf0 .

Suppose that each individual experiences uT = 100 mutations per generation, f0 = 90% of which are neutral. In this case, 100 × 90% = 90 neutral mutations will occur every generation, and the same number will complete the process of substitution by reaching fixation.

Since mutations at non-synonymous sites are more likely to disrupt function, f0 is closer to 0% at these sites. On the other hand, f0 will be nearer to 100% at synonymous sites, where mutations are much more likely to be neutral.

This leads to an important prediction of the neutral theory: because f0 is lower at non-synonymous sites, non-synonymous evolution should generally be slower than synonymous evolution.25 This is another way of saying that dN should be less than dS, or that purifying selection is more common than positive selection.

Few contributed as much as Hughes in showing that, for almost all genes of all species, dN is a great deal lower than dS.26 In a comparison of 42 genes shared by mice and rats, he calculated that dN was about 1/3 the value of dS.27 In a 2003 study of the human genome, he demonstrated not only that dN was less than dS, but also that dN decreases for sites having more extreme amino acid changes.28 It is entirely possible for dN > dS, but this occurs only on rare, though important, occasions. Purifying selection also constrains the evolution of non-protein-coding DNA in many species29 and even dominates most regions of human immune genes, otherwise known as excellent examples of positive selection.30

Hughes tested various other predictions of the neutral theory as well. Using Fumio Tajima’s D statistic, he showed that purifying selection dominates the evolution of bacteria, verifying the neutral theory's prediction that slightly deleterious mutations will be widespread in large populations.31

The ubiquity of purifying selection also revealed something important about how functionally important gene regions behave over the course of evolution; they rarely change. In bioinformatics, the alignment between DNA and protein sequences of different species is routinely used to identify regions with high levels of similarity between species. Sequences have been preserved between species because they play important functional roles, and purifying selection has eliminated many mutations disrupting them.32 Researchers are able to predict gene function and infer protein structure based on sequence similarity alone. This stands in sharp contrast to the original Darwinian view, which held that most evolutionary change is driven by positive selection. Were that so, most evolutionary change would occur at functionally important sites in the genome—the only sites capable of incurring mutations that alter fitness—and such sites would instead be the least similar between species.

In 2013, the ENCODE (Encyclopedia of DNA Elements) Project published results suggesting that eighty per cent of the human genome serves some function. This was considered a rebuttal to the widely held view that a large part of the genome was junk, debris collected over the course of evolution. Hughes sided with his friend Dan Graur in rejecting this point of view. Their argument was simple. Only ten per cent of the human genome shows signs of purifying selection, as opposed to neutrality.33

When purifying selection on a protein is relaxed, mutations accumulate by drift at all sites, leading to dN = dS. It may seem surprising that proteins can tolerate amino acid changes, but data show a strong correlation between the mutation rate and the rate of non-synonymous substitutions.34 A great many of these changes, it would seem, roughly preserve functionality. Going further, if positive selection intervenes to favor multiple amino acid substitutions in a protein region, this can even lead to dN > dS. Although such selection is “a relative rarity,” Hughes wrote, it is “of great interest, precisely because it represents a departure from the norm.”35

Natural Selection and Immune Genes

Hughes was the world’s expert on the role of positive selection in shaping the human leukocyte antigen (HLA) genes—the genes of the immune system.36 Encoding the major histocompatibility complex (MHC) receptors on the surface of cells,37 they have long been known as the most polymorphic genes in vertebrates, with variability of around eighty per cent.38 It was not always clear why. Some researchers suggested that there might be a higher mutation rate in the HLA genes.39 Struck by the facts of self-incompatibility genes in plants, other researchers thought that maternal antibodies against fetal MHC molecules in humans benefited offspring by promoting genetic diversity.40 The tendency in mice to choose mates with different MHC receptors has seemed a pertinent fact to some scientists.41 Hughes had another answer.

In all cells that have a nucleus, MHC molecules bind to peptide fragments randomly sliced from a sample of the proteins present inside the cell. Once a fragment is MHC-bound, it is transported to the surface of the cell, where passing immune system cells recognize and bind the MHC-peptide complex.42 If the peptide-presenting cell happens to be infected with a pathogen such as a virus, the peptide fragment it presents may have originated from the foreign invader. In that case, the immune cell may, after binding the MHC-peptide complex, initiate the destruction of the infected host cell.

In 1974, Rolf Zinkernagel and Peter Doherty demonstrated that MHC molecules made from different HLA alleles differ in the peptide fragments they can bind.43 They went on to suggest that the high levels of genetic variability observed at the HLA loci may result from an evolutionary phenomenon called heterozygote advantage.44 Heterozygotes are organisms whose two copies of a gene differ in sequence; they possess two different alleles. Instead of driving a beneficial mutation to fixation, overdominant selection promotes variation. In sickle-cell anemia, heterozygotes are able to resist malaria. In the case of HLA, organisms having multiple HLA alleles bind a wider range of peptide fragments, making them better able to fight infections.

A few years earlier, Takeo Maruyama and Masatoshi Nei had published theoretical work predicting that heterozygote advantage should speed up the rate of amino acid substitution at the sites under selection.45 Hughes and Nei then predicted that the non-synonymous substitution rate should be increased in codons for the MHC’s peptide-binding region (PBR).46 This would lead to an increase in dN at just those sites, but not in dS (since synonymous mutations do not change the amino acid), resulting in dN > dS. Given the recently published structure of the MHC, Hughes and Nei knew right where to look, and they had twelve DNA sequences with which to do it.

The prediction of heterozygote advantage was spectacularly vindicated. Every single comparison between DNA sequences showed that dN > dS in the PBR codons. Outside PBR codons, almost all comparisons exhibited the opposite pattern, and purifying selection reigned. Together with Tatsuya Ota, Hughes and Nei showed that these non-synonymous changes are disproportionately concentrated in just those twenty-nine amino acids of the PBR that are most likely to be involved in peptide-binding. Changes altering the electric charge of the amino acid are the ones most favored.47 The more sequence data that poured in, the clearer these trends became.48

These results falsified some of the competing hypotheses. If the gene regions encoding the PBR truly experienced a higher mutation rate, an increase should have been observed in dS as well as dN. But it turned out that dS was no higher in the PBR than the rest of the genome. Hughes also rejected the idea that animals choose mates with different MHC molecules, earlier research with mice notwithstanding. Heterozygote advantage is the best explanation for the uniquely high levels of variability in PBR.

Hughes later asked the same question from the point of view of the infecting pathogen. Because viruses and the hosts they infect are constantly competing to identify and evade each other, selection produces multiple non-synonymous changes in this case as well. Hughes and his collaborators focused especially on malaria, human immunodeficiency virus (HIV), and simian immunodeficiency virus (SIV).49 More recently, he and others developed an approach to perform this sort of analysis on the latest viral genetic data.50 They were building on earlier theoretical work by Nei and Takashi Gojobori. Although it is too early to tell how useful this approach will be for vaccine development, studies with influenza, HIV, SIV, and various monkey viruses have already yielded valuable insights into how viruses evolve within their hosts.51

The methods used by Hughes and Nei soon took on a life of their own. A host of researchers set out to discover Darwinism at the nucleotide level. They were apparently unaware that dN > dS was a prediction only in cases of heterozygote advantage.52 Their approach was utterly misguided.

Hughes and Nei knew what to look for. The inequality dN > dS does not imply heterozygote advantage, let alone other forms of positive selection.53 It can occur just by chance.54 In fact, dN > dS is caused by positive selection only in the rarest of cases. In a typical account of positive selection, a new beneficial mutation arises, increases in frequency, and goes on to fixation.55 Before the beneficial mutation occurs, there is no signature of positive selection; after fixation, there remains no signature. All diversity is purged when an evolutionary substitution reaches completion. There’s only a brief window of time during which one might observe that dN > dS.

Dismayed at the “vast outpouring of pseudo-Darwinian hype,” Hughes did much to correct this trend in the literature.56 In the provocatively-titled “Looking for Darwin in All the Wrong Places: The Misguided Quest for Positive Selection at the Nucleotide Level,”57 Hughes argued not only that many of the supposed cases of positive selection were incorrect, but further that they identified the relaxation of purifying selection. If most mutations in protein-coding DNA are slightly deleterious, then purifying selection will depress their frequencies.58 Purifying selection having been suspended, as occurs when populations become small or form a new species, slightly deleterious mutations increase in frequency or fix by chance alone, increasing dN.59

The importance of this point cannot be overstated.

When researchers identify DNA regions that they think are under positive selection, they often infer that these regions are important to the function of the organism. The gene for microcephalin in the human brain is an example.60 Medical research might then proceed on these grounds. If these cases actually result from the relaxation of purifying selection, the opposite is true; these are unimportant regions where slightly deleterious mutations flourish. Functionally-important regions are those with a very low frequency of non-synonymous mutations. They have a low frequency because purifying selection is acting to conserve the original function.61 It was for this reason that Hughes described the vast majority of studies identifying positive selection as worthless.62

His concerns have been vindicated. The Graur group has shown that misapplying dN vs. dS falsely identifies gene regions that have sequencing errors or misalignments.63 Other studies have shown that these methods consistently miss those codons that are already known to be under positive selection.64 One striking example involves a study of a protein used in vision, in which all functional differences were determined experimentally and mapped to twelve codons. None of these important codons were identified by methods used for detecting selection; experimentally-induced changes in the eight codons that were identified by these methods had no effect on the protein’s function.65

Other studies typically operate by computing some measure of genetic variation and then identifying as positively selected those genes having the very highest values, say, the top 1%.66 Hughes would frequently compare this approach to lining up all humans by height and declaring that the top one percent are Martians.67 He concluded the matter thus:

The so-called “codon-based” methods of testing for positive selection are derived from an unwarranted generalization of the MHC case. … Contrary to a widespread impression, natural selection does not leave any unambiguous “signature” on the genome, certainly not one that is still detectable after tens or hundreds of millions of years. To biologists schooled in Neo-Darwinian thought processes, it is virtually axiomatic that any adaptive change must have been fixed as a result of natural selection. But it is important to remember that reality can be more complicated than simplistic textbook scenarios.68

That being said, positive selection does sometimes fix adaptations. Hughes led the effort to chronicle well-established cases in Adaptive Evolution of Genes and Genomes.69 He noted that most recent examples involve loss-of-function mutations. It is relatively easy to damage unneeded proteins.70 The number of convincing cases has barely increased since the 1970s.71

Hughes also explored the role of natural selection in the evolution of altruism. Evolution and Human Kinship is his most mathematical work.72 It is also a philosophical work, and contains a discussion of the scientific method, adaptation, and the distinction between social anthropology and sociobiology.73 The book celebrates evolutionary theory as an instrument capable of addressing the social sciences.

In the 1960s, William D. Hamilton argued that altruism could be efficiently explained in purely Darwinian terms if the altruist’s costs were outweighed by recipient’s benefits when discounted by a measure of genetic similarity. Hughes goes further than Hamilton in predicting that individuals will be more likely to forgive one another when their genetic relatedness and the likelihood of future reciprocity (based on reputation) are high; on the other hand, a scarcity of resources will prevent even close kin from helping one another.74 Hughes also predicted that community leaders will tend to be bien branché.75 Although none of these concepts is surprising, Hughes did formulate them mathematically.

The hypothesis of natural selection makes the tacit but critical assumption that mutations affecting a trait of interest actually exist in the population.76 For example, unless an appropriate escape mutation actually occurs in an HIV virus, it cannot evade the host’s immune system. If the variation that causes selfless behavior never arises, of what use selection? Even in adaptive evolution, mutation is king.77 Hughes pioneered several ideas about how such evolution might work.

Adaptive Evolution by Other Means

Hughes was skeptical that positive selection can successfully explain much of adaptive evolution. His incredulity ran deeper than many may realize. In an interview with Heredity about positive selection, he declared that “there really isn’t all that much evidence that it actually happens to the extent to which it would be needed to explain all of the adaptive traits of organisms.”78

What were his reservations about positive selection? For one thing, important genes do not change much. But there was a deeper reason. In a 2004 study with Robert Friedman, Hughes used evolutionary tree-building techniques to determine the relationships between worms, insects, fish, and humans based on the presence or absence of related genes.79 The results were surprising. Instead of suggesting the gradual addition of new genes in different lineages, the trees implied that the ancestor of all these animals had the ultimate gene set, which was then whittled away as different animals evolved.

The primary mode of evolution is gene loss, not gene gain.

This finding led to a new idea about adaptive evolution.80 Hughes called the mechanism plasticity-relaxation-mutation (PRM). We know that purifying selection is ubiquitous, and that this selection is sometimes relaxed, allowing the fixation of slightly deleterious mutations. We also know that plasticity—the ability of organisms to change their phenotype without changing their genes—can play a role in evolution.81 When these concepts are combined, it is possible to account for the evolutionary substitution of adaptive traits without selection.

Imagine a species able to change its behavior to avoid two different predators. Any mutation disrupting the ability to avoid predator 1 or predator 2 would likely be eliminated by purifying selection, thus maintaining plasticity. Suppose predator 2 disappears from the environment. Purifying selection would no longer maintain the response it elicits. Mutations would accumulate in the gene encoding the unused response to predator 2, deactivating it, and drift might then fix these new adaptive mutations. The result would be the fixation of only one gene state: the ability to evade predator 1.

Hughes noted evidence for PRM in the evolutionary literature, and argued that this non-Darwinian mechanism may play a role even in the adaptive evolution of such celebrated examples as that of the Galápagos Islands finch beak.82 His work also emphasized an important distinction not always appreciated by evolutionary biologists; knowing that a trait is adaptive is not the same thing as knowing how it arose.83

Hughes also produced important work on the role of gene duplication in evolution. Beginning in the late 1960s, Susumu Ohno proposed that the duplication of entire genes plays a central role in the evolution of new protein functions.84 A gene duplicates, but having both copies is redundant. Purifying selection is therefore relaxed, allowing one of the copies to mutate and lose its function.85 In some cases, a random set of mutations might occur which produces a new function.

Hughes might have described this as nonadaptive storytelling.86 The Darwinian explanation had been able to account for seemingly designed aspects of biological organisms by appealing to material causes that went beyond blind chance.87 Work with frogs uncovered convincing evidence that duplicated gene copies are, in fact, usually not freed from purifying selection.88

This led Hughes to advocate a radically different perspective.89 New functions might be present before and not after gene duplication. A single gene in ducks encodes both an eye protein (crystalline) and a glycolysis enzyme (enolase).90 The gene is duplicated. Mutations might then disable one of the two functions in each gene copy. If the two functions are A and B, then function B might be disabled in gene copy 1, and function A in gene copy 2. There are now two genes with two different functions. Call this subfunctionalization.

Positive selection might then improve each function. Since the original gene had been forced to maintain two functions concurrently, it may not have been able to maximize either. Freed from bifunctionality, it can.91 Either positive selection or genetic drift could fix these changes.

A great deal of evidence supports this scenario. One example from colobine monkeys involves two genes that enable the digestion of bacterial genomes as a source of nitrogen.92 A recent duplication event seems to have been followed by a change of nine amino acids between the two genes. Experimental analyses suggest that these differences have allowed one copy to become specialized for digestion in the small intestine, while the other copy is specialized for the pancreas.93

Some evolutionary biologists objected to Hughes’s radical views on adaptive evolution.94 Surely the genome could not accumulate genetic information only by losing it; one cannot build up a savings account by constantly withdrawing.

It is my view that Hughes considered this an interesting question, but not essentially a scientific one. Science should be concerned with theories that are empirically adequate, that is, theories that can explain our observations and make accurate predictions.95

His hypotheses for adaptive evolution did just that.

  1. For an appreciation of Austin Hughes, see my essay “The Humble Scientist” in the Fall 2015 issue of The New Atlantis; for memories from his colleagues, see Chase Nelson, “Remembering Austin L. Hughes,” Infection, Genetics and Evolution 40 (2016): 262–65. 
  2. See Motoo Kimura, “Evolutionary rate at the molecular level,” Nature 217, no. 5129 (1968): 624–26; Motoo Kimura, “The neutral theory of molecular evolution,” Scientific American 241, no. 5 (1979): 98–126; Motoo Kimura, The Neutral Theory of Molecular Evolution (Cambridge, UK: Cambridge University Press, 1983); Jack King and Thomas Jukes, “Non-Darwinian evolution,” Science 164, no. 3881 (1969): 788–98. 
  3. See reference 61 in Chase Nelson, “Haldane’s Dilemma,” Inference: International Review of Science 1, no. 3 (2015). 
  4. An evolutionary substitution occurs when an allele’s frequency increases until it is present in all genome copies of a population. This is not to be confused with a mutational substitution in which one nucleotide letter (say, A) is replaced with another (say, G) in a single genome copy of one individual—what we have called, simply, a mutation. 
  5. See John Hubby and Richard Lewontin, “A molecular approach to the study of genic heterozygosity in natural populations. I. The number of alleles at different loci in Drosophila pseudoobscura,” Genetics 54, no. 2 (1966): 577–94; Richard Lewontin and John Hubby, “A molecular approach to the study of genic heterozygosity in natural populations. II. Amount of variation and degree of heterozygosity in natural populations of Drosophila pseudoobscura,” Genetics 54, no. 2 (1966): 595–609. One reason was the cost of natural selection; see Chase Nelson, “Haldane’s Dilemma,” Inference: International Review of Science 1, no. 3 (2015). 
  6. For example, see Sewall Wright, “Evolution in Mendelian populations,” Genetics 16, no. 2 (1931): 106–7. 
  7. That is, the probability of the sequence heads-tails-heads is prob(heads) × prob(tails) × prob(heads) = ½ × ½ × ½ = 0.125. 
  8. Both Kimura and Hughes emphasized this view; for example, see Motoo Kimura, “How genes evolve: a population geneticist’s view,” Annales de génétique 19, no. 3 (1976): 153–68; and Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 51. 
  9. Hughes said so in the first draft of a paper on the neutral theory (Austin Hughes, “Near-neutrality: the leading edge of the neutral theory of molecular evolution,” Annals of the New York Academy of Sciences 1133 (2008): 162–79). One reviewer apparently commented, “Yikes!” and the final version instead reads: “Given the importance of his contribution, it is not an exaggeration to say that Kimura was the most important evolutionary biologist since Darwin.” 
  10. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 11–12. 
  11. See Austin Hughes, “Near-neutrality: the leading edge of the neutral theory of molecular evolution,” Annals of the New York Academy of Sciences 1133 (2008): 162–79; Tomoko Ohta, “Slightly deleterious mutant substitutions in evolution,” Nature 246, no. 5428 (1973): 96–98; Tomoko Ohta, “Role of very slightly deleterious mutations in molecular evolution and polymorphism,” Theoretical Population Biology 10, no. 3 (1976): 254–75. 
  12. See Aylwyn Scally and Richard Durbin, “Revising the human mutation rate: implications for understanding human evolution,” Nature Reviews Genetics 13 (2012): 745–53. The change us unfeasible, that is, unless unrealistically huge numbers of adaptations substitute together at the same time; for a detailed explanation, see Chase Nelson, “Haldane’s Dilemma,” Inference: International Review of Science 1, no. 3 (2015). 
  13. Hughes and others always place them in this order: synonymous followed by non-synonymous. No doubt there is some reasonable precedent for doing this, but I shall hereafter keep non-synonymous in front, as it comes first both alphabetically, and in terms of biological importance. 
  14. Specifically, in the codon ATG, which encodes the amino acid methioinine, all three sites are non-synonymous because any change to any site will result in an amino acid difference (ATA, ATC, and ATT will instead encode isoleucine; AAG will instead encode lysine; and so on). 
  15. Specifically, in the codon CTG, which encodes leucine, the third site is synonymous because all three of the possible changes (CTA, CTC, or CTT) still encode leucine. These examples make it sound easy, but counting non-synonymous and synonymous sites and differences can be surprisingly complicated; see Masatoshi Nei and Takashi Gojobori, “Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions,” Molecular Biology and Evolution 3, no. 5 (1986): 418–26. 
  16. Technically, synonymous mutations can sometimes influence protein folding because some codons are used more frequently than others in the cell, leading to differences in the speed of amino acid incorporation that result in a different protein configuration. However, such a possibility would cause selection to act against synonymous mutations too, making purifying selection more difficult to detect—yet it is still widely observed: Ryan Hunt et al., “Silent (synonymous) SNPs: should we care about them?” Methods in Molecular Biology 578 (2009): 23–39; Austin Hughes et al., “Widespread purifying selection at polymorphic sites in human protein-coding loci,” Proceedings of the National Academy of Sciences USA 100, no. 26 (2003): 15,754–57. 
  17. Adam Eyre-Walker and Peter Keightley, “The distribution of fitness effects of new mutations,” Nature Reviews Genetics 8, no. 8 (2007): 610–18; Laurence Loewe and William Hill, “The population genetics of mutations: good, bad and indifferent,” Philosophical Transactions of the Royal Society B 365, no. 1544 (2010): 1,153–67. 
  18. Masatoshi Nei, Molecular Population Genetics and Evolution, vol. 40 of Frontiers of Biology, ed. A. Neuberger and E. L. Tatum (Amsterdam: North-Holland Publishing Company, 1975), 24-25; Dan Graur and Wen-Hsiung Li, Fundamentals of Molecular Evolution (Sunderland, MA: Sinauer Associates, Inc., 2001), 28. 
  19. In practice, these measures are increased slightly to account for the possibility of past mutations that remain unobservable, but we will proceed without such a correction in the present case.

    For example, the same mutation might have occurred since the common ancestor of both sequences, leaving no observable differences where mutations have in fact taken place. One common way to account for this is by using the Jukes–Cantor correction, which defines these measures as –3/4 ln[1 – 4x/3], where x is dN or dS as defined in this text; Thomas Jukes and Charles Cantor, “Evolution of protein molecules,” in Mammalian Protein Metabolism, vol. III, ed. H. N. Munro (New York, NY: Academic Press, 1969), 21–132. 
  20. Austin Hughes and Meredith Yeager, “Comparative evolutionary rates of introns and exons in murine rodents,” Journal of Molecular Evolution 45, no. 2 (1997): 125–30; Austin Hughes and Meredith Yeager, “Erratum: Comparative evolutionary rates of introns and exons in murine rodents,” Journal of Molecular Evolution 46, no. 4 (1998): 497. 
  21. Hughes assumed 70,000 protein-coding genes at the time, but updating the number of genes to the more recent estimate of 20,000 changes this number only negligibly, to 8.22 substitutions per year. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 41. 
  22. Wen-Hsiung Li, Molecular Evolution (Sunderland, MA: Sinauer, 1997), 220–24. 
  23. At one extreme, imagine a population consisting of only one individual. Any mutations occurring in that organism’s DNA are by definition substituted in the population. If this one individual experiences u = 90 neutral mutations per generation, then 90 mutations will occur (and substitute) every generation. If we increase the population size, then mutations have the additional hurdle that they must not only occur, but also increase in frequency until they reach fixation. However, in perfect counterbalance to this impediment, huge numbers of neutral mutations will always exist at intermediate frequencies—being present in more than 0% but less than 100% of the DNA copies—at any given time. Because of this build-up of neutral mutations in the population, the overall rate of substitution in the whole population stays exactly the same, regardless of the population’s size. 
  24. He called this the most important equation in molecular evolution: “If you understand this, then you understand how evolution works.” 
  25. Motoo Kimura and Tomoko Ohta, “On some principles governing molecular evolution,” Proceedings of the National Academy of Sciences USA 71, no. 7 (1974): 2,848–52; Motoo Kimura, “Preponderance of synonymous changes as evidence for the neutral theory of molecular evolution,” Nature 267, no. 5608 (1977): 275–76. 
  26. Regarding vindication, see, for example, Wen-Hsiung Li, Chung-I Wu, and Chi-Cheng Luo, “A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relatively likelihood of nucleotide and codon changes,” Molecular Biology and Evolution 2, no. 2 (1985): 150–74; Masatoshi Nei, Yoshiyuki Suzuki, and Masafumi Nozawa, “The neutral theory of molecular evolution in the genomic era,” Annual Review of Genomics and Human Genetics 11 (2010): 265–89. For Hughes’s contribution see Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999). 
  27. Austin Hughes and Meredith Yeager, “Comparative evolutionary rates of introns and exons in murine rodents,” Journal of Molecular Evolution 45, no. 2 (1997): 125–30; Austin Hughes and Meredith Yeager, “Erratum: Comparative evolutionary rates of introns and exons in murine rodents,” Journal of Molecular Evolution 46, no. 4 (1998): 497. 
  28. Austin Hughes et al., “Widespread purifying selection at polymorphic sites in human protein-coding loci,” Proceedings of the National Academy of Sciences USA 100, no. 26 (2003): 15,754–57. 
  29. See Austin Hughes et al., “Effects of natural selection on interpopulation divergence at polymorphic sites in human protein-coding loci,” Genetics 170, no. 3 (2005): 1,181–87; Austin Hughes and Robert Friedman, “Variation in the pattern of synonymous and nonsynonymous difference between two fungal genomes,” Molecular Biology and Evolution 22, no. 5 (2005): 1,320–24; Austin Hughes, Mary Ann Hughes, and Robert Friedman, “Variable intensity of purifying selection on cytotoxic T-lymphocyte epitopes in Hepatitis C virus,” Virus Research 123, no. 2 (2007): 147–53; Austin Hughes, “Micro-scale signature of purifying selection in Marburg virus genomes,” Gene 392, no. 1–2 (2007): 266–72; Austin Hughes and Mary Ann Hughes, “Patterns of nucleotide difference in overlapping and non-overapping reading frames of papillomavirus genomes,” Virus Research 113, no. 2 (2005): 81–88; Helen Piontkivska and Austin Hughes, “Patterns of sequence evolution at epitopes for host antibodies and cytotoxic T-lymphocytes in human immunodeficiency virus type 1,” Virus Research 116, nos. 1-2 (2006): 98–105; Austin Hughes et al., “Simultaneous positive and purifying selection on overlapping reading frames of the tat and vpr genes of simian immunodeficiency virus,” Journal of Virology 75, no. 17 (2001): 7,966–72; Adam Bailey et al., “High genetic diversity and adaptive potential of two simian hemorrhagic fever viruses in a wild primate population,” PLoS One 9, no. 3 (2014): e90714; Adam Bailey et al., “Arterivurses, pegiviruses, and lentiviruses are the predominant plasma-borned RNA viruses of Cercopithecoid nonhuman primates,” pre-publication; Haiwei Luo et al., “Ongoing purifying selection on intergenic spacers in group A streptococcus,” Infection, Genetics and Evolution 11, no. 2 (2011): 343–48; Austin Hughes and Jeffrey French, “Homologous recombination and the pattern of nucleotide substitution in Ehrlichia ruminantium,” Gene 387, no. 1-2 (2007): 31–37; Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 49-50; Austin Hughes and Robert Friedman, “Patterns of sequence divergence in 5’ intergenic spacers and linked coding regions in 10 species of pathogenic bacteria reveal distinct recombinational histories,” Genetics 168, no. 4 (2004): 1,795-803; Austin Hughes, “Birth-and-death evolution of protein-coding regions and concerted evolution of non-coding regions in the multi-component genomes of nanoviruses,” Molecular Phylogenetics and Evolution 30, no. 2 (2004): 287–94. 
  30. Austin Hughes et al., “High level of functional polymorphism indicates a unique role of natural selection at human immune loci,” Immunogenetics 57, no. 11 (2005): 821–27. 
  31. Austin Hughes, “Evidence for abundant slightly deleterious polymorphisms in bacterial populations,” Genetics 169, no. 2 (2005): 533–38. 
  32. For example, see Austin Hughes, Heredity Podcast, November 2011; Austin Hughes, “Near-neutrality: the leading edge of the neutral theory of molecular evolution,” Annals of the New York Academy of Sciences 1133 (2008): 162–79. 
  33. Dan Graur et al., “On the immortality of television sets: ‘function’ in the human genome according to the evolution-free gospel of ENCODE,” Genome Biology and Evolution 5, no. 3 (2013): 578–90. 
  34. Wen-Hsiung Li, Molecular Evolution (Sunderland, MA: Sinauer, 1997), 421. 
  35. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 53. 
  36. Hughes’s future work with the founders of Histogenetics LLC was poised to augment his contributions in this area considerably.

    HLA, also called H-2 in mice, actually has several gene regions, including HLA-A, -B, and -C, but a discussion is beyond the scope of this review; see David Lawlor et al., “Evolution of class-I MHC genes and proteins: from natural selection to thymic selection,” Annual Review of Immunology 8 (1990): 23–63. 
  37. The structure of the MHC molecule was published in 1987 by Pamela Bjorkman and colleagues, the first year of Hughes’s postdoctoral work with Masatoshi Nei at the University of Texas at Houston. See: Pamela Bjorkman et al., “Structure of the human class I histocompatibility antigen, HLA-A2,” Nature 329, no. 6139 (1987): 506–12; Pamela Bjorkman et al., “The foreign antigen binding site and T cell recognition regions of class I histocompatibility antigens,” Nature 329, no. 6139 (1987): 512–18. 
  38. Two types of MHC molecules exist in humans, MHC class I and MHC class II, but a discussion is beyond the scope of this review.

    Regarding the percentage figure: specifically, heterozygosity levels of 80%. In other words, roughly five different alleles might be expected to be present at equal frequencies in a given population. 
  39. D. W. Bailey and H. I. Kohn, “Inherited histocompatibility changes in progeny of irradiated and unirradiated inbred mice,” Genetical Research 6, no. 3 (1965): 330–40. 
  40. Lewis Thomas, “Biological signals for self-identification,” in Progress in Immunology II, ed. Leslie Brent and John Holborrow (Amsterdam: North-Holland, 1974), 239–347; Bryan Clarke and D. R. S. Kirby, “Maintenance of histocompatibility polymorphisms,” Nature 211, no. 5052 (1966): 999–1,000. 
  41. Lewis Thomas, “Biological signals for self-identification,” in Progress in Immunology II, ed. Leslie Brent and John Holborrow (Amsterdam: North-Holland, 1974), 239–347; K. Yamazaki et al., “Control of mating preferences in mice by genes in the major histocompatibility complex,” The Journal of Experimental Medicine 144, no. 5 (1976): 1,324–35. 
  42. Specifically, cyto(cell)-toxic T cells, also called cytotoxic T lymphocytes (CTL). 
  43. Rolf Zinkernagel and Peter Doherty, “Immunological surveillance against altered self components by sensitised T lymphocytes in lymphocytic choriomeningitis,” Nature 251, no. 5475 (1974): 547–48. 
  44. Peter Doherty and Rolf Zinkernagel, “Enhanced immunological surveillance in mice heterozygous at the H-2 gene complex,” Nature 256, no. 5512 (1975): 50–52. 
  45. Takeo Maruyama and Masatoshi Nei, “Genetic variability maintained by mutation and overdominant selection in finite populations,” Genetics 98, no. 2 (1981): 441–59. 
  46. Austin Hughes and Masatoshi Nei, “Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection,” Nature 335, no. 6186 (1988): 167–70. 
  47. Austin Hughes, Tatsuya Ota, and Masatoshi Nei, “Positive Darwinian selection promotes charge profile diversity in the antigen-binding cleft of class I major-histocompatibility-complex molecules,” Molecular Biology and Evolution 7, no. 6 (1990): 515–24. 
  48. Austin Hughes and Meredith Yeager, “Natural selection at major histocompatibility complex loci of vertebrates,” Annual Review of Genetics 32 (1998): 415–35. 
  49. See Austin Hughes, “Circumsporozoite protein genes of malaria parasites (Plasmodium spp.): evidence for positive selection on immunogenic regions,” Genetics 127, no. 2 (1991): 345–53; Marianne Hughes and Austin Hughes, “Natural selection on Plasmodium surface proteins,” Molecular and Biochemical Parasitology 71, no. 1 (1995): 99–113; Somchai Jongwutiwes, Chaturong Putaporntip, and Austin Hughes, “Bottleneck effects on vaccine-candidate antigen diversity of malaria parasites in Thailand,” Vaccine 28, no. 18 (2010): 3,112–17; Helen Piontkivska and Austin Hughes, “Patterns of sequence evolution at epitopes for host antibodies and cytotoxic T-lymphocytes in human immunodeficiency virus type 1,” Virus Research 116, no. 1-2 (2006): 98–105; Stephanie Irausquin and Austin Hughes, “Conflicting selection pressures on T-cell epitopes in HIV-1 subtype B,” Infection, Genetics and Evolution 11, no. 2 (2011): 483–88; Todd Allen et al., “Tat-specific cytotoxic T lymphocytes selection for SIV escape variants during resolution of primary viraemia,” Nature 407, no. 6802 (2000): 386-90; Austin Hughes et al., “Simultaneous positive and purifying selection on overlapping reading frames of the tat and vpr genes of simian immunodeficiency virus,” Journal of Virology 75, no. 17 (2001): 7,966–72. 
  50. See Masatoshi Nei and Takashi Gojobori, “Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions,” Molecular Biology and Evolution 3, no. 5 (1986): 418–26; Chase Nelson and Austin Hughes, “Within-host nucleotide diversity of virus populations: insights from next-generation sequencing,” Infection, Genetics and Evolution 30 (2015): 1–7; Chase Nelson, Louise Moncla, and Austin Hughes, “SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data,” Bioinformatics 31, no. 22 (2015): 3,709–11. 
  51. See Peter Wilker et al., “Selection on haemagglutinin imposes a bottleneck during mammalian transmission of reassortant H5N1 influenza viruses,” Nature Communications 4 (2013): 2,636; Louise Moncla et al., “Selective bottlenecks shape evolutionary pathways taken during mammalian adaptation of a 1918-like avian influenza virus,” Cell Host & Microbe, pre-publication; for HIV, a paper is in preparation with Nell Bond and Nick Maness; Dane Gellerup et al., “Conditional immune escape during chronic SIV infection,” Journal of Virology 90, no. 1 (2015): 545–52; Adam Bailey et al., “High genetic diversity and adaptive potential of two simian hemorrhagic fever viruses in a wild primate population,” PLoS One 9, no. 3 (2014): e90714; Adam Bailey et al., “Arterivurses, pegiviruses, and lentiviruses are the predominant plasma-borned RNA viruses of Cercopithecoid nonhuman primates,” pre-publication. 
  52. Austin Hughes, “Natural selection and the genome,” in Science, Engineering, and Biology Informatics – Vol. 7: Advances in Genomic Sequence Analysis and Pattern Discovery, ed. Laura Elnitski, Helen Piontkivska, and Lonnie Welch (World Scientific, 2011), 209–20. 
  53. Philosophically speaking, these researchers have mistaken an implication for a biconditional: “A implies B” does not mean “B implies A.” Scientifically speaking, multiple amino-acid changes are expected because multiple amino acids are involved in peptide-peptide recognition between host and pathogen. When these two coevolve in a competition to detect and escape one another, a constant turnover of non-synonymous changes is expected at just those codons underlying the amino acids involved in recognition. The same is not expected in most cases not involving coevolution. 
  54. Austin Hughes and Robert Friedman, “Variation in the pattern of synonymous and nonsynonymous difference between two fungal genomes,” Molecular Biology and Evolution 22, no. 5 (2005): 1,320–24; Austin Hughes and Robert Friedman, “Codon-based tests of positive selection, branch lengths, and the evolution of mammalian immune system genes,” Immunogenetics 60, no. 9 (2008): 495–506. 
  55. For example, the case of a single non-synonymous mutation which changed coat color and went to fixation in beach mice: Hopi Hoekstra et al., “A single amino acid mutation contributes to adaptive beach mouse color pattern,” Science 313, no. 5783 (2006): 101–104. 
  56. Austin Hughes, “The origin of adaptive phenotypes,” Proceedings of the National Academy of Sciences USA 105, no. 36 (2008): 13,194. 
  57. Austin Hughes, “Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level,” Heredity 99, no. 4 (2007): 364–73. 
  58. DNA is digital information which, like English text or computer code, is more easily destroyed by random changes than it is created or improved. Beyond such common-sense reasoning, this is supported by the majority of studies. See Adam Eyre-Walker and Peter Keightley, “The distribution of fitness effects of new mutations,” Nature Reviews Genetics 8, no. 8 (2007): 610–18; Laurence Loewe and William Hill, “The population genetics of mutations: good, bad and indifferent,” Philosophical Transactions of the Royal Society B 365, no. 1544 (2010): 1,153–67. 
  59. Such as occurred in human history: Henry Harpending et al., “Genetic traces of ancient demography,” Proceedings of the National Academy of Sciences USA 95, no. 4 (1998): 1,961–67. 
  60. Yin-Qiu Wang and Bing Su, “Molecular evolution of microcephalin, a gene determining human brain size,” Human Molecular Genetics 13, no. 11 (2004): 1,131–37. 
  61. Austin Hughes et al., “Widespread purifying selection at polymorphic sites in human protein-coding loci,” Proceedings of the National Academy of Sciences USA 100, no. 26 (2003): 15,754–57; Lev Yampolsky, Fyodor Kondrashov, and Alexey Kondrashov, “Distribution of the strength of selection against amino acid replacements in human proteins,” Human Molecular Genetics 14, no. 21 (2005): 3,191–201. 
  62. Austin Hughes, Heredity Podcast, November 2011. 
  63. See Adrian Schneider et al., “Estimates of positive Darwinian selection are inflated by errors in sequencing, annotation, and alignment,” Genome Biology and Evolution 1 (2008): 114–18; Karen Wong, Marc Suchard, and John Huelsenbeck, “Alignment uncertainty and genomic analysis,” Science 319, no. 5862 (2008): 473–76. 
  64. Austin Hughes and Robert Friedman, “Codon-based tests of positive selection, branch lengths, and the evolution of mammalian immune system genes,” Immunogenetics 60, no. 9 (2008): 495–506. 
  65. Shozo Yokoyama et al., “Elucidation of phenotypic adaptations: molecular analyses of dim-light vision proteins in vertebrates,” Proceedings of the National Academy of Sciences USA 105, no. 36 (2008): 13,480–85. 
  66. See, for example, Benjamin Voight et al., “A map of recent positive selection in the human genome,” PLoS Biology 4, no. 3 (2006): e72; Pardis Sabeti et al., “Genome-wide detection and characterization of positive selection in human populations,” Nature 449, no. 7164 (2007): 913–18; Joseph Pickrell et al., “Signals of recent positive selection in a worldwide sample of human populations,” Genome Research 19, no. 5 (2009): 826–37; Yali Xue et al., “Population differentiation as an indicator of recent positive selection in humans: an empirical evaluation,” Genetics 183, no. 3 (2009): 1,065–77; Daniela Zanetti et al., “Potential signals of natural selection in the top risk loci for coronary artery disease: 9p21 and 10q11,” PLoS One 10, no. 8 (2015): e0134840. 
  67. This type of procedure is not a statistical test. All distributions—say, the distribution of human height—have a top 1%. What these studies do is effectively to claim that the top 1% of observations comes from a different distribution. Many other egregious errors could be named, such as uses of the McDonald–Kreitman test: John McDonald and Martin Kreitman, “Adaptive protein evolution at the Adh locus in Drosophila,” Nature 351, no. 6328 (1991): 652–54. See Austin Hughes, “Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level,” Heredity 99, no. 4 (2007): 364–73. 
  68. Austin Hughes, “The origin of adaptive phenotypes,” Proceedings of the National Academy of Sciences USA 105, no. 36 (2008): 13,193–94. 
  69. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 119–42. 
  70. See Austin Hughes, “Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level,” Heredity 99, no. 4 (2007): 364–73. Also explored in Austin Hughes, “Evolution of adaptive phenotypic traits without positive Darwinian selection,” Heredity 108, no. 4 (2012): 347–53; and formalized in Michael Behe, “Experimental evolution, loss-of-function mutations, and ‘the first rule of adaptive evolution’,” The Quarterly Review of Biology 85, no. 4 (2010): 419–45. 
  71. Austin Hughes, “Evolution of adaptive phenotypic traits without positive Darwinian selection,” Heredity 108, no. 4 (2012): 347–53. 
  72. Austin Hughes, Evolution and Human Kinship (New York: Oxford University Press, 1988). 
  73. Probably aware it was a touchy subject, he made the following caveat: “A scientific approach to social behavior should be materialist in the sense that all science is materialist. It seeks explanations in terms of factors that are material—including the genetic constitution of the individuals involved and the evolutionary history that shaped it—and the physical, biotic, and social environments. Scientific materialism need not be accompanied, it seems to me, by an ontological commitment to the belief that only material entities exist or can be objects of reference; but it appeals only to material entities and processes as termini of explanation”; Austin Hughes, Evolution and Human Kinship (New York: Oxford University Press, 1988), 13–14. 
  74. Specifically, this occurs because the correlation between an individual’s resources and their fitness is likely to be negatively allometric—it levels off; Austin Hughes, Evolution and Human Kinship (New York: Oxford University Press, 1988), 34–36, 44–47. 
  75. Austin Hughes, Evolution and Human Kinship (New York: Oxford University Press, 1988), 89. 
  76. In a podcast interview with Heredity, Hughes said: “And the thing that is funny if you look at a lot of the evolutionary literature, and popular literature, too, about this process, is that the mutational step is kind of left out. So people will say, ‘well, this evolved in response to this or that environmental change,’ but of course, if the mutation didn’t happen, then there wouldn’t be any response, you know; there would be extinction, you know, and that would be the end of the story.” Austin Hughes, Heredity Podcast, November 2011. 
  77. Wen-Hsiung Li cites several examples in which adaptive evolutionary changes require several mutations at the molecular level, meaning that the occurrence of the mutation, a random phenomenon, determined the rate of adaptive evolution—not natural selection. Such examples reveal “the inadequacy of neo-Darwinism even for explaining adaptive molecular evolution. This is not surprising because neo-Darwinism was developed long before the development of modern genetics and molecular biology. A good understanding of an evolutionary process or the emergence of a new system requires not only the theory of evolution and population genetics but also knowledge of the molecular steps involved”; Wen-Hsiung Li, Molecular Evolution (Sunderland, MA: Sinauer, 1997), 432. 
  78. Austin Hughes, Heredity Podcast, November 2011. 
  79. See Austin Hughes and Robert Friedman, “Shedding genomic ballast: extensive parallel loss of ancestral gene families in animals,” Journal of Molecular Evolution 59, no. 6 (2004): 827–33.

    Technically, gene families—groups of closely related genes present in the same genome that are descended from a common ancestral gene. 
  80. Austin Hughes, “Evolution of adaptive phenotypic traits without positive Darwinian selection,” Heredity 108, no. 4 (2012): 347–53. 
  81. Mary Jane West-Eberhard, Developmental Plasticity and Evolution (New York, NY: Oxford University Press, 2003). 
  82. “What was their effective population size? Like, two?” he once joked. 
  83. Also see, for example, the arguments in Michael Lynch, The Origins of Genome Architecture (Sunderland, MA: Sinauer Associates, Inc., 2007); James Shapiro, Evolution: A View from the 21st Century (Upper Saddle River, NJ: FT Press Science, 2011); Masatoshi Nei, Mutation-Driven Evolution (Oxford, UK: Oxford University Press, 2013). 
  84. For example, see Susumu Ohno, Evolution by Gene Duplication (New York, NY: Springer-Verlag, 1970); Susumu Ohno, “Ancient linkage groups and frozen accidents,” Nature 244, no. 5414 (1973): 259–62. 
  85. Hughes documented this for duplicates of the so-called “nonclassical” MHC genes in Austin Hughes and Masatoshi Nei, “Evolution of the major histocompatibility complex: independent origin of nonclassical class I genes in different groups of mammals,” Molecular Biology and Evolution 6, no. 6 (1989): 559–79. 
  86. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 10, 239. 
  87. Francisco Ayala, “Darwin’s greatest discovery: design without designer,” Proceedings of the National Academy of Sciences USA 104, suppl. 1 (2007): 8,567–73. 
  88. Marianne Hughes and Austin Hughes, “Evolution of duplicate genes in a tetraploid animal, Xenopus laevis,” Molecular Biology and Evolution 10, no. 6 (1993): 1,360–69. 
  89. Austin Hughes, “The evolution of functionally novel proteins after gene duplication,” Proceedings of the Royal Society London B 256, no. 1346 (1994): 119–24. 
  90. Joram Piatigorsky and Graeme Wistow, “The recruitment of crystallins: new functions precede gene duplication,” Science 252, no. 5009 (1991): 1,078–79. 
  91. Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999), 173. 
  92. Austin Hughes, “Adaptive evolution after gene duplication,” TRENDS in Genetics 18, no. 9 (2002), 433–34. 
  93. Jianzhi Zhang, Ya-Ping Zhang, and Helene Rosenberg, “Adaptive evolution of a duplicated pancreatic ribonuclease gene in a leaf-eating monkey,” Nature Genetics 30, no. 4 (2002): 411–15. 
  94. See John Brookfield, “Dangers of ‘adaptation’,” Heredity 108, no. 4 (2012): 460; Luis-Miguel Chevin and Andrew Beckerman, “From adaptation to molecular evolution,” Heredity 108, no. 4 (2012): 457–59; Austin Hughes, “Evolution of duplicated gene clusters in archael genomes,” Recent Research Developments in Bacteriology 2 (2005): 1–15; Austin Hughes and Robert Friedman, “Poxvirus genome evolution by gene gain and loss,” Molecular Phylogenetics and Evolution 35, no. 1 (2005): 186–95; codon usage bias is reviewed in chapter 8 of Austin Hughes, Adaptive Evolution of Genes and Genomes (New York: Oxford University Press, 1999); Austin Hughes, “Sexual selection and mate choice: insights from a neutralist perspective,” Evolutionary Biology 42, no. 3 (2015): 366–78. 
  95. The term “empirically adequate” is loosely borrowed from Bas van Fraasen, The Scientific Image (Oxford, UK: Oxford University Press, 1980).