Reactionary fringe meets mutation-biased adaptation. 2. Some objections addressed.

This is the third in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 2. Some objections addressed.
by Arlin Stoltzfus
In the previous post Part 1, we reviewed evidence from 8 analyses suggesting that modest several-fold biases in mutation may impose modest several-fold biases on the spectrum of changes involved in adaptation, including some legendary cases of natural adaptation.

Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardIs the evidence strong enough already to conclude in favor of a bold new idea? The authors of the hatchet piece at TREE believe that nothing has been shown, arguing that the proposed effect is theoretically unlikely and is probably due to selection.

The focus of this post is on alternative hypotheses (theoretical arguments will be addressed later). For the sake of brevity, I will address just 2 of the many spurious objections offered by these authors in their quest to exemplify the Dunning-Kruger effect. For instance, they write "we stress that parallel genetic change underlying phenotypic convergence is not sufficient evidence for mutation bias being important in causing such convergence."

This is an inversion of the argument, common in the parallelism literature (see Bailey, et al. 2015), that the recurrence of exactly the same change is by itself evidence of selection.

In fact, the case for mutation-biased adaptation does not depend on such weak inferences. In the 8 analyses we reviewed, no change is designated as adaptive solely based on a pattern of recurrence. Instead, each mutational path has either (1) a genetic association with fitness or resistance, or (2) an experimentally verified molecular effect consistent with the adaptive story. Once adaptive changes have been identified, statistical tests are applied to detect an excess of changes of the mutationally favored class.

As another example, TREE's hatchet piece refers to selection as an independent force of adaptation, then attacks the strawman theory of mutation bias as an independent force of adaptation. To ensure that the reader is deceived about mutation-biased adaptation, and ill disposed toward this line of research, this strawman is repeated 5 times on the first page (figure).

Both arguments illustrate how reactionary minds fail to grasp new ideas, and see only perversions or inversions of cherished old ideas.

Now, let us set aside strawman arguments, to focus on genuine alternatives.

For instance, the authors suggest that transitions could be favored "owing to selection on genomic base composition," citing work on GC content. This hypothesis can not work. If the effect of selection is to conserve GC content, this can not explain a bias toward transitions, because the universe of GC-conserving mutations has a transition:transversion ratio of 0. Likewise, if the effect of selection is to change GC content, this can not explain the observed degree of bias, because the universe of GC-changing amino acid replacement mutations has roughly a 1:1 transition:transversion ratio, not large enough to explain results of Payne, et al. (2019) or Stoltzfus and McCandlish (2017).

A more plausible alternative raised by the authors, following Stoltzfus and Norris (2016), is that the observed evolutionary bias could be caused by a bias in protein-level fitness effects that happens to align with the mutation bias, e.g., they suggest that "selectively beneficial transitions and selectively beneficial transversions could also have different distributions of fitness effects."

Let us consider, for the 8 analyses addressed previously, the hypothesis that observed evolutionary biases are not due to mutation bias at all, but to a cryptic fitness bias that happens to align with the mutation bias.

First, in the studies by MacLean, et al. (2010), Sackman, et al. (2017) and Liu, et al. (2019), the authors measure fitness (or resistance). The data from MacLean, et al. (2010) reveal no correlation of mutation rate with fitness (figure).

In their model of effects in drug-resistant tumors, Liu, et al. (2019) find that the mutational factor (estimated mutation rate) explains more variance than the fitness-related factor (measured drug resistance). Results of one-step adaptation from Sackman, et al. (2017) are shown in the figure (left: transitions are in light gray, transversions are in dark gray; upper scale is selection coefficient, lower scale is number of evolved lineages out of 20). Here the mean selection coefficients for transitions and transversions are 0.37 (CI 0.053) and 0.40 (CI 0.18), respectively, i.e., transversions are insignificantly better (data from their Table 1).

Next, consider the experimental study by Couce, et al. (2015) shown in the figure below (courtesy of Alex Couce). Among resistant mutants in PBP3, the resistant mutT isolates (blue) overwhelmingly have the kind of mutations favored by mutT (left box), and the resistant mutH isolates (red) overwhelmingly have the kind of mutations favored by mutH (center box; other types of mutations are in the right box, which includes most of the black isolates indicating a wild-type parent).

The only way to explain this as a fitness effect would be to argue that (1) the mutT and mutH genotypes have widespread, strong, and utterly distinct epistatic effects on the fitness landscape for PBP3, i.e., each mut genotype induces a distinct set of beneficial alleles, and (2) the corresponding mutations for those alleles just happen to be (overwhelmingly) the same type of mutation favored by the mutator.  This is wildly implausible because it implies that the blue-red segregation of columns in the figure above is accidental.

What about the meta-analyses of transition-transversion bias? Could there be a fitness advantage of transitions that explains this effect?

Stoltzfus and Norris (2016) analyzed data on 544 transitions and 695 transversions with experimentally measured fitness effects. Comparing various binary predictors, they considered the chance that a nominally conservative mutation is more fit than a nominally radical one, aka the AUC, which ranges from 0 to 1, with a null expectation of 0.5. Transition-transversion class is a weak predictor (AUC = 0.53, figure), out-performed by most biochemical factors, all 200 of which are out-performed by a conservative-radical distinction based on Tang's U (AUC = 0.64), an empirical measure of relative fixation probability computed from a large set of sequence alignments. Yet, the conservative-radical distinction from Tang's U corresponds to a mere 2.7-fold fixation bias in evolution. Using this relationship, Stoltzfus and Norris (2016) estimate that the transition:transversion distinction corresponds to a 1.3-fold fixation bias, with a confidence interval from 1.0 (no effect) to 1.6.

But these results use the entire distribution of mutations, including the worst ones that (in nature) would be removed by selection. Therefore, Stoltzfus and Norris (2016) truncated the data to see if a stronger benefit would emerge among benign mutations. Instead of getting stronger, the effect disappeared (their Fig. 1).

Next, Stoltzfus and Norris (2016) set aside the above data, and looked at an independent set of data from 4 studies of laboratory adaptation implicating 111 beneficial mutations with measured fitness effects. In the table below, the AUC value in the penultimate column is the chance that a transition is ranked higher than a randomly chosen transversion: the values are all < 0.5. That is, beneficial transitions rank slightly lower than beneficial transversions. The later study by Sackman, et al. (2017) (above) represents a 5th independent case in which beneficial transitions rank slightly lower than beneficial transversions.

Thus, available data, reflecting multiple lines of evidence, indicate that transitions simply do not have a fitness advantage that could explain a several-fold effect on amino acid changes in evolution.

Finally, note that Payne, et al. (2019) report evolutionary biases that cannot be explained by protein-level selection, including transition bias in non-coding changes, and the excess of Met-to-Ile transitions over Met-to-Ile transversions (which are twice as likely without mutation bias).

To summarize, in our evaluation of the cryptic-fitness-difference hypothesis, we find that: in 3 cases, the fitness effects were measured, with results that do not support the hypothesis; in 3 cases (counting 2 meta-analyses in Stoltzfus and McCandlish, 2017), the evidence indicates that the mutationally favored class (transitions) does not have a sufficient fitness advantage; in 1 case, the hypothesis is wildly implausible (Couce, et al., 2015); and in 1 remaining case, Storz, et al. (2019) invoke a mutational effect without any clear justification for assuming an absence of differential fitness effects.

Concluding thoughts

In recent years, systematic data have begun to accumulate on molecular changes implicated in phenotypic adaptation. The pattern emerging from these data is that the molecular changes implicated in adaptation are enriched for the kinds of changes that are favored by mutation, and this enrichment cannot be explained by a cryptic fitness bias that happens to align with the mutation bias.

We could treat this merely as a pattern, as a new and useful empirical generalization.

But there is much more to the story. Mutation-biased adaptation was predicted under a theory that contrasts sharply with classical thinking, which holds that internal tendencies of variation cannot cause evolutionary trends or biases, because mutation rates are too small: in order for mutation biases to be important, mutation rates must be very large, or the opposing pressure of selection must be absent, i.e., effects of biases in ordinary mutations will be limited to neutral evolution.

Yampolsky and Stoltzfus (2001) argued that this view, which derives from the mutation-selection balance model of Fisher and Haldane, assumes that evolution can be treated as a short-term process of shifting the frequencies of pre-existing alleles, without considering the (potentially biased) introduction of new alleles. Using a simple model, they showed that the efficacy of biases in introduction does not require absolute constraints, neutral evolution, or high mutation rates. They argued that this conclusion applies to developmental biases as well as mutation biases.

Thus, it is time to understand this theory, what it implies, and why it differs from classical thinking-- the topic of the next post in the series.

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Bailey SF, Blanquart F, Bataillon T, Kassen R. (2017). What drives parallel evolution?: How population size and mutational variation contribute to repeated evolution. Bioessays 39:1-9.[doi.org/10.1002/bies.201600176]

Couce A., RodrÃ-guez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645] 

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019). Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation. Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

Stoltzfus A, Norris RW. (2016). On the Causes of Evolutionary Transition:Transversion Bias. Mol Biol Evol 33:595-602. [doi.org/10.1093/molbev/msv274]

Storz J.F., Natarajan C., Signore A.V., Witt C.C., McCandlish D.M. and Stoltzfus A. (2019) The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Phil. Trans. R. Soc. B [doi: 10.1098/rstb.2018.0238]

Reactionary fringe meets mutation-biased adaptation. 1. The empirical case

This is the second in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution. The first post was: Reactionary fringe meets mutation-biased adaptation: Introduction.

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Reactionary fringe meets mutation-biased adaptation. 1. The empirical case
by Arlin Stoltzfus
Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardAs noted in the intro to this series, the appearance of an opinion piece on how mutation bias affects adaptation in Trends in Ecology and Evolution (TREE) appears to be a milestone for understanding this interesting new topic. The authors misrepresent a position on dual causation stated consistently in the literature for 20 years, ignore or misinterpret important new empirical work, and blithely repeat a self-serving view of history that my colleagues and I spent years debunking with careful scholarship.

In other words, as a colleague once said, "This is what victory looks like-- everyone butchering your ideas."

But it gets better. Scientists often invoke the cliche that new truths are first ridiculed as impossible, then opposed as unlikely, then claimed as traditional.  QuoteInvestigator has a piece on this "stages of truth" meme, with the following from Dr. J. Marion Sims, 1868:

For it is ever so with any great truth. It must first be opposed, then ridiculed, after a while accepted, and then comes the time to prove that it is not new, and that the credit of it belongs to some one else.

The idea that the course of evolution reflects internal biases in variation-- or, stated differently, that the generation of variation plays a dispositional role in evolution-- has been (1) ridiculed as an appeal to "the vagueness of inherent tendencies, vital urges, or cosmic goals, without known mechanism" (Simpson, 1967), and (2) ruled out by the famous "opposing pressures" argument from Fisher and Haldane that we will address later.

So, it was exciting that the TREE authors, who represent the reactionary fringe of evolutionary biology-- dedicating to shifting the "Synthesis" goal-posts to maintain the illusion that nothing is new--, not only butcher the idea of mutation-biased adaptation, try to minimize its importance, and misrepresent the evidence: they also want to claim it! Yes, they want to appropriate this unoriginal, unimportant, unsubstantiated idea for the legacy of Ronald Fisher, the closeted mutationist!

Seriously, you could not make this stuff up!

To dig out from under this mess will take some time. Let's begin by reviewing the evidence that the changes that occur during adaptation are enriched for mutationally likely changes.

The case for mutation-biased adaptation

First, consider 4 analyses of experimental evolution.

In the first stage of the compound study by Sackman, et al. (2017), Rokyta, et al. (2005) measured fitness for the beneficial changes-- 9 of them-- implicated in 20 replicate episodes of adaptation of phiX174, aka ID11. Because the fittest variant was found only once, yet the 4th most-fit was found 6 times, the authors explored a model of mutational effects, including transition bias and the multiplicity of mutational paths to an alternative amino acid (e.g., an ATG Met codon can mutate to Ile in 3 different ways, but an Ile codon such as ATC has only one mutational path to Met). An origin-fixation model with mutation bias fit the data better than the mutational landscape model of Orr, which considers only fixation probability. Sackman, et al. (2017) carried out this same 20-replicate protocol with 3 closely related phages: the combined results how a strong bias favoring transitions, 29:5 for paths, 74:6 for events (figure below).

Note the terminology. A "path" specifies the starting and ending genotype, e.g., "gene F, site 3665, C → T", and an event is an occurrence of change along a path-- in this case, a replicate culture in which a phage genome changes. Events along the same paths are parallel events.

MacLean, et al. (2010) repeatedly evolved Rifampicin-resistant Pseudomonas aeruginosa, with results showing a significant correlation between the chance of evolving and the measured mutation rate for 11 changes in rpoB (center panel below). All of these changes are nucleotide substitutions with mutation rates that differ due to unexamined context effects. The lack of correlation in the left panel is not too surprising, given that the calculated probability of fixation ranges only from 0.47 to 0.84, because s is so large (meanwhile, the mutation rate varies 50-fold).

Couce, et al. (2015) evolved cefotaxime resistance repeatedly in 2 different Escherichia coli mutator strains with distinctly different mutation spectra, resulting in two distinct distributions of changes among resistant strains, each with a strong correspondence to the respective parental mutation spectrum.

McCandlish and Stoltzfus (2017) gathered a large set of published cases of laboratory parallel adaptation due to recurrent amino acid replacements (389 events on 63 paths), and found that these data exhibit a substantial excess of transitions, relative to the null expectation for no mutation bias (a 1:2 ratio). Note that this study integrates data from MacLean, et al. (2010) and Rokyta, et al. (2005), but (1) this is a minority of the data (22 %), and (2) the test for transition bias is an independent result from the correlation shown earlier in data from MacLean, et al. (2010).

Next, consider 4 analyses of natural evolution. McCandlish and Stoltzfus (2017), in the same paper just mentioned, gathered data on natural parallelisms from 10 different study systems (231 events on 55 paths), and showed the same kind of transition bias. The summary table below shows that they draw from some famous adaptive stories in molecular evolution, including spectral tuning, resistance to cardiac glycosides (e.g., bird vs. monarch vs. milkweed), foregut fermentation, and echolocation.

The table above represents natural cases from Stoltzfus and McCandlish (2017). Cases 1, 4, 8 and 10 represent recent local adaptation of sub-populations (total 11:10 paths, 69:48 events), while the others represent species divergence (17:17 paths, 63:51 events).

Payne, et al. (2019) examined effects of transition bias in two different curated databases of causative mutations in antibiotic-resistant isolates of Mycobacterium tuberculosis, finding an excess of transition mutations. For instance, they take advantage of the unusual case of Met-to-Ile replacements, which can take place by 1 transition (ATG to ATA) or 2 different transversions (ATG to ATT or ATC). Instead of this 1:2 ratio of possibilities, they see a ratio of 88:49 (Basel dataset) or 96:39 (Manson dataset), roughly 4-fold above null expectations. Because all the replacements are the same type, the bias can not be due to selection preferring some replacements over others.

Storz, et al. (2019) examined changes in hemoglobins using 35 phylogenetically independent comparisons of low- and high-altitude bird populations. They identified adaptive changes in 20 comparisons, implicating 10 different paths and 22 events. (See Table below: Asterisks indicate CpG mutations.) The observation of 6 paths and 10 events associated with CpG mutations was about 6-fold over the null expectation, a statistically significant excess.

Finally, in a completely different type of study, Liu, et al. (2019) explore the emergence of imatinib resistance in leukemia patients, combining clinical data on the frequency of various resistant mutants (of the BCR-ABL oncogene) across 4 continents over 17 years, with laboratory characterization of engineered mutants. In a model for clinical frequency based on drug resistance (measured) and mutation biases (inferred from comparative data), they found that both factors were important, but the mutational factor was more important.

I mention Liu, et al. (2019) to draw attention to a fascinating, biomedically important study. However, I won't include it in future discussions about biological significance, because typically we do not include resistant tumor outgrowths in the category of natural adaptation (and I don't want to confuse people or invite distracting criticisms).

To summarize, various recent studies suggest that the changes that occur during adaptation are enriched for the kinds of changes that are mutationally likely. The spectrum of adaptive changes shows modest biases with the same orientation and magnitude as modest mutation biases (either known or suspected) that range in magnitude from a few-fold (transition bias) to 10-fold (CpG bias) to as large as 50-fold (the range of mutation rates measured by MacLean, et al., 2010).

Going further

Considered on their own, these results are perhaps uninteresting. Mutation biases are secretly influencing the details underlying adaptation. Perhaps this was not expected on classical grounds, but why should we care?

These results are important because they demonstrate a principle not previously accepted: modest quantitative biases in the generation of variation may impose predictable biases on evolution, without a requirement for absolute constraints, neutral evolution or high mutation rates, contradicting the classic logic of the opposing-pressures argument.

If this new principle is general, it would apply to other kinds of mutational biases, as well as to other types of biases, e.g., developmental biases induced by the structure of a genotype-phenotype map. That is, these results provide proof-of-principle for ideas long discussed in evo-devo. As argued by Stoltzfus (2019), the same results add plausibility to key ideas in the self-organization literature, regarding what Cowperthwaite and Ancel (2007) call "the large-scale patterns of mutational connectivity within genotype spaces."

Before exploring these implications in future posts, we need to take a more critical look at the evidence. The authors at TREE claim that nothing has been shown, and that the results are more likely to be due to selection. What is the status of this alternative hypothesis?

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Cowperthwaite, M.C., Meyers, L.A. (2007) How Mutational Networks Shape Evolution: Lessons from RNA Models. Annual Review of Ecology, Evolution, and Systematics 38:203-230.[doi.org/10.1146/annurev.ecolsys.38.091206.095507]

Couce A., Rodríguez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645]

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019) Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Rokyta DR, Joyce P, Caudle SB, Wichman HA. (2005) An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nat Genet 37:441-444. [https://doi.org/10.1038/ng1535]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Simpson GG. (1967) The Meaning of Evolution. New Haven, Conn.: Yale University Press.

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation, Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

Stoltzfus, A. (2019) Understanding bias in the introduction of variation as an evolutionary cause. [https://arxiv.org/abs/1805.06067]

Storz J.F., Natarajan C., Signore A.V., Witt C.C., McCandlish D.M. and Stoltzfus A. (2019) The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Phil. Trans. R. Soc. B [doi: 10.1098/rstb.2018.0238]

Reactionary fringe meets mutation-biased adaptation: Introduction

This is the first of a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation: Introduction
by Arlin Stoltzfus
Theoreticians often formulate mathematical or computational models with the aim of exploring (or justifying) behavior anticipated from pre-existing verbal theories.  Yet, the resulting formalisms may exhibit behavior that was not expected.  Indeed, sometimes the model breaks the theory.

Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardIn the 1970s, deterministic chaos was recognized in a number of dynamic models, including the Lotka-Volterra model used by ecologists for decades to illustrate notions of control via feedbacks between predator and prey abundance.  Depending on the delay in feedback, the system either oscillates predictably, crashes, or becomes chaotic.

Before the chaotic realm was named, characterized, and publicized, surely many researchers stumbled upon it, either when looking at data, or while working out numeric examples.  However, this did not elicit the phrase "Eureka!  I have discovered deterministic chaos!"  Chaotic dynamics did not fit with ideas about "control" or "feedback."  It existed in the world of nature but not in the world of science, even though it involves no new underlying processes.

In evolutionary genetics, the breeder's equation for change in a quantitative trait under selection once justified neo-Darwinism: invoking selection as the creative principle and source of direction in evolution had a rigorous mathematical basis, given abundant infinitesimal variation, and assuming that everything is a continuous trait (see Gould's excellent, well documented analysis in Ch. 4 of Ever Since Darwin, or ca. p. 140 of his 2002 book The Structure of Evolutionary Theory).

What Darwinism means for Ernst Mayr (Mayr and Provine 1980 p. 3).



Yet, after Lande and Arnold (1983) derived the multivariate generalization of quantitative genetics for the simultaneous change in multiple traits, Δz = Gβ, quantitative geneticists began to acknowledge that, in the words of Steppan, Phillips and Houle (2002) "Together with natural selection (the adaptive landscape), [the G matrix] determines the direction and rate of evolution." This new verbal theory, in which the rate and direction of evolution are jointly attributed to selection and standing variation, does not correspond to the old verbal theory, even though quantitative genetics is the branch of mathematical theory most closely aligned with neo-Darwinism, literally assuming abundant infinitesimal variation in every trait.

That is, Darwin's verbal theory led to Fisher's mathematical theory, then further developments along with empirical results (e.g., Schluter, 1996)) led to conflicts with the original verbal theory. As a result, quantitative genetics now tells us something different: the verbal theory of selection as a governing principle or independent shaping force, invoked by generations of neo-Darwinians, is mathematically impossible.

This series of posts relates to another unexpected twist that arises from another archaic ruling-principle theory: the view that the course of evolution largely reflects innate tendencies that shape variation, with selection playing only a minor role, traditionally known as orthogenesis.

A classic argument from Fisher and Haldane (based on their mutation-selection balance equation) says that variation-induced trends cannot be a cause of direction: for mutation to overcome the opposing force of selection would require abnormally high mutation rates. Mutation biases can influence neutral evolution, but otherwise, the only kind of bias that could possibly be influential is an absolute constraint distinguishing possible from impossible variants.

Yet Yampolsky and Stoltzfus (2001) used computer simulations of a simple 2-locus model, along with mathematical formulas based on origin-fixation dynamics, to show how parallelisms and trends may arise from mutational and developmental biases in variation, without requiring neutral evolution, absolute constraints, or high mutation rates.

We will delve into this theory later. For now, the important thing to note is the crucial prediction that the changes involved in adaptation may show the effects of modest quantitative biases in mutations with ordinary rates (e.g., transition-transversion bias in nucleotide substitution mutations).

When this theory was proposed, data on molecular changes involved in adaptation were rare—not sufficient to support statistical hypothesis-testing. In recent years, the data have become much more abundant, and we are seeing the predicted effect in both experimental adaptation (e.g. MacLean, et al. (2010), Couce, et al. (2015), Sackman, et al. (2017)), and more importantly, in retrospective analyses of natural adaptation (e.g., Payne, et al. (2019), Storz, et al. (2019), Liu, et al. (2019), and Stoltzfus and McCandlish (2017)).

That is, we are witnessing the establishment of a fundamental new principle of evolution, a principle that was not just unexpected, but rejected by the architects of the Modern Synthesis.

Thus, it was an odd choice for Trends in Ecology and Evolution (TREE) to publish a deeply deceptive article that attacks this new idea, from a pair of authors so unfamiliar with the topic that they literally mis-define "mutation bias." According to the authors, there are no new principles here, only "standard evolutionary theory" from Fisher, Haldane and Kimura. The appearance of mutation-biased adaptation is illusory, they argue, claiming that the evolutionary biases are due to selection.

What motivated such a gratuitous attack? A colleague who described the paper as "an abomination" assigned it to one of his advisees to study as an example of what a really bad paper looks like. How did it get published? Why didn't the editor get critical reviews?

The answers relate to a dispute between advocates of an "Extended Evolutionary Synthesis" or EES, and defenders of a re-branded version of the Modern Synthesis called SET (Standard Evolutionary Theory). The authors of the hatchet piece are members of a fringe movement dedicated to arguing that (1) SET automagically updates itself to include valid new thinking, and (2) there is no valid new thinking, because anything that seems new actually traces back to important dead people. Thus, these guardians of orthodoxy were obliged to undermine the novelty and importance of mutation-biased adaptation, while at the same time claiming it for SET.

This peculiar set of circumstances—an unorthodox theory, powerful new results, and the backlash from reactionaries—defines a series of posts that Larry has offered to host here on SandWalk. The plan for the series is as follows:

1. The empirical case. Results from adaptation in the lab, and retrospective analyses of adaptation in nature, show that the changes involved in adaptation are enriched for mutationally-favored changes.
2. Some objections addressed. The evidence now available rules out the possibility that the observed evolutionary biases are due to a cryptic bias in fitness that happens to align with the mutational bias.
3. The causes and consequences of biases in the introduction process. The theory of Yampolsky and Stoltzfus (2001) addresses mutation biases, developmental biases, and effects of connectivity of genetic networks (invoked in the self-organization literature)
4. What makes this theory new. For a very long time, mutation-biased adaptation was not anticipated, due to the "gene pool" assumption that evolution begins with standing variation.
5. A diversion into the EES-SET culture war. Issues relevant to navigating high-level disputes in evolutionary biology are addressed in a series of 4 posts.
   5.1. Thinking about theories.
   5.2. The Modern Synthesis (1959 - 1969).
   5.3. The abuse of history.
   5.4. Synthesis sophistry.
6. What "limits" adaptation? What makes adaptation something other than an ideal process in which the best genotype arises in infinite time after all possibilities have been tested?
7. Future directions. Abundant opportunities exist to build a broader empirical and theoretical understanding of the evolutionary role of biases in the introduction of variation, and to leverage this role in evolutionary inference.

The posts will be released every few days, and will be linked in to this Introduction page (so you can bookmark this).

So, please join in the discussion, and invite your colleagues to do the same.

Next post: Reactionary fringe meets mutation-biased adaptation. 1. The empirical case.

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Couce A., Rodríguez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Lande, R., and Arnold, S.J. (1983) The measurement of selection on correlated characters. Evolution, 37:1210-1226. [doi: 10.1111/j.1558-5646.1983.tb00236.x]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645]

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019) Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Schluter, D. (1996) Adaptive radiation along genetic lines of least resistance. Evolution, 50:1766-1774. [doi: 10.1111/j.1558-5646.1996.tb03563.x]

Steppan, S.J., Phillips, P.C., and Houle, D. (2002) Comparative quantitative genetics: evolution of the G matrix. Trends in Ecology & Evolution, 17:320-327. [doi: 10.1016/S0169-5347(02)02505-3]

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation, Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

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My father on D-Day: 75 years ago

Today is the 75th anniversary of D-Day—the day British, Canadian, and American troops landed on the beaches of Normandy.¹

For us baby boomers it always meant a day of special significance for our parents. In my case, it was my father who took part in the invasions. That's him on the right as he looked in 1944. He was an RAF pilot flying rocket-firing typhoons in close support of the ground troops. His missions were limited to quick strikes and reconnaissance during the first few days of the invasion because Normandy was at the limit of their range from southern England. During the second week of the invasion (June 14th) his squadron landed in Crepon, Normandy and things became very hectic from then on with several close support missions every day [see Hawker Hurricanes and Typhoons in World War II].

The frequency of splicing errors reflects the balance between selection and drift

Splice variants are very common in eukaryotes. We know that it's possible to detect dozens of different splice variants for each gene with multiple introns. In the past, these variants were thought to be examples of differential regulation by alternative spicing but we now know that most of them are due to splicing errors. Most of the variants have been removed from the sequence databases but many remain and they are annotated as examples of alternative splicing, which implies that they have a biological function.

I have blogged about splice variants many times, noting that alternative splicing is a very real phenomenon but it's probably restricted to just a small percentage of genes. Most of splice variants that remain in the databases are probably due to splicing errors. They are junk RNA [The persistent myth of alternative splicing].

The ongoing controversy over the origin of splice variants is beginning to attract attention in the scientific literature although it's fair to say that most scientists are still unaware of the controversy. They continue to believe that abundant alternative splicing is a real phenomenon and they don't realize that the data is more compatible with abundant splicing errors.

Some molecular evolution labs have become interested in the controversy and have devised tests of the two possibilities. I draw your attention to a paper that was published 18 months ago.

Are multiple transcription start sites functional or mistakes?

If you look in the various databases you'll see that most human genes have multiple transcription start sites. The evidence for the existence of these variants is solid—they exist—but it's not clear whether the minor start sites are truly functional or whether they are just due to mistakes in transcription initiation. They are included in the databases because annotators are unable to distinguish between these possibilities.

Let's look at the entry for the human triosephosphate isomerase gene (TPI1; Gene ID 7167).

The correct mRNA is NM_0003655, third from the top. (Trust me on this!). The three other variants have different transcription start sites: two of them are upstream and one is downstream of the major site. Are these variants functional or are they simply transcription initiation errors? This is the same problem that we dealt with when we looked at splice variants. In that case I concluded that most splice variants are due to splicing errors and true alternative splicing is rare.

What is the dominant view of junk DNA?

I think that about 90% of our genome is junk and I know lots of other scientists who feel the same way. I'm pretty sure that this view is not shared by the majority of scientists but I don't know whether they are convinced that most of our genome is functional or whether they just think the question is unanswerable at the present time. I suspect that the latter view is more common but I'd like to hear your opinion.

Yeast loses its introns

Baker’s yeast (Saccharomyces cerevisiae) is one of the best studied eukaryotes. Its genome is just slightly larger than the largest bacterial genome and it was the first eukaryotic genome to be sequenced (Mewes at al., 1997). It has about 7000 genes in total and 6,604 of these genes are protein-coding genes but only 280 of these genes contain introns.¹ The rest have lost their introns over the course of several hundred million years of evolution (Hooks et al., 2014).

We know that introns have been lost in yeast because the genes of related species have lots of introns. The common ancestor of all fungi undoubtedly had genes with multiple introns because the available evidence indicates that introns invaded eukarotic genes very early in the evolution of eukaryotes. The fact that most introns have been purged from the yeast genome suggests that introns are not essential for gene function. In other words, introns are mostly junk.²

What happens when twins get their DNA tested?

The Canadian Broadcastng Company (CBC) has a TV show called Marketplace that promotes itself as an advocate of consumers' rights. It has a history of testing the claims of advertisers and usually shows that these claims are misleading or false. Here's what they say on their website.

On air since 1972, Marketplace is Canada’s consumer watchdog. We get the goods to help you shop smarter and protect yourself from slick scams and misleading marketing claims. We investigate the products and services we all use every day and push companies and government for answers. And we expose the truth on stories that matter to you and your family.

Most popular Sandwalk posts of 2018

Blogging was light last year because I was busy with other things and because the popularity of blogs is declining rapidly. The most popular post, based on the number of views, garnered only 9229 views, which is more than the most popular post of 2017 but only half as much as the most popular post of 2016. The post with the most comments (53) has almost 10X fewer comments than posts from a few years ago but that's partly because more people are commenting on Facebook and because I'm restricting blog comments in various ways.

On the accuracy of Ancestry.com DNA predictions

I'm very impressed with the DNA test administered by Ancestry.com. They report that I have over 600 fourth cousins or closer but I have confirmed some even more distant relationships. See below for the most distant relationship that the DNA tests reveal.

In the vast majority of cases the people who share DNA markers with me have no family tree that's on Ancestry.com so it's impossible to say for sure whether we are related. There are often clues based on who else shares our haplotypes but unless the person reveals their name and some of their ancestors that's all I can do. I usually contact those people who could hep me sort out some unknown relationships but I rarely get a reply.

Most popular Sandwalk posts of 2017

I was looking at some of my posts from the past few years and wondered which ones were the most popular. I had previously identified the most popular post of 2016 but not the most popular ones from 2017 so here they are.

The one with the most views (7481) is a link to a video by Michio Kaku who tells us that humans have stopped evolving [Another physicist teaches us about evolution].

The one with the most comments (259) is a post about my attempts to teach a creationist about glycolysis and evolution [Trying to educate a creationist (Otangelo Grasso)].

The post that I'm most proud of is: Historical evolution is determined by chance events

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My DNA story

This is the latest update from Ancestry.com. Their algorithms are getting better and better. This corresponds very closely to what I know of my ancestors.

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Alternative splicing in the nematode C. elegans

The importance of alternative splicing is highly controversial. In the case of humans, the competing views are: (a) more than 90% of human protein-coding genes are alternatively spliced to produce multiple protein isoforms, and (b) less than 10% of human genes are alternatively spliced and most of the splice variants detected are due to splicing errors.

In addition to this fundamental difference in how to interpret the data, there's a controversy over the meaning and significance of abundant alternative splicing, assuming that it exists. The consensus view among the workers in the field is that alternative splicing is ubiquitous and it explains why humans are so complex when they have only the same number of genes as "lower" species like the nematode C. elegans. This was the view expressed by Gil Ast in a 2005 Scientific American article on "The Alternative Genome."

The persistent myth of alternative splicing

I'm convinced that widespread alternative splicing does not occur in humans or in any other species. It's true that the phenomenon exists but it's restricted to a small number of genes—probably fewer than 1000 genes in humans. Most of the unusual transcripts detected by modern technology are rare and unstable, which is consistent with the idea that they are due to splicing errors. Genome annotators have rejected almost all of those transcripts.

You can see links to my numerous posts on this topic at: Alternative splicing and the gene concept and Are splice variants functional or noise?.

The textbook view of alternative splicing

As most of you know, I'm interested in the problem of alternative splicing. I believe that the number of splice variants that have been detected is perfectly consistent with the known rate of splicing errors and that there's no significant evidence to support the claim that alternative splicing leading to the production of biologically relevant protein variants is widespread. In fact, there's plenty of evidence for the opposite view; namely, splicing errors (lack of conservation, low abundance, improbable protein predictions, inability to detect the predicted proteins).

My preferred explanation is definitely the minority view. What puzzles me is not the fact that the majority is wrong () but the fact that they completely ignore any other explanation of the data and consider the case for abundant alternative splicing to be settled.

Deflated egos and the G-value paradox

The Deflated Ego Problem refers to the fact that many scientists were very disappointed to learn we had less than 30,000 genes. Those scientists were expecting that the human genome would contain many more genes in line with their belief that humans must be genetically more complex than the "lower" animals. They should have known better since knowledgeable experts were predicting fewer than 30,000 genes and these same experts knew that humans don't need many more genes than other animals [see: Revisiting the deflated ego problem].

Disappointed scientists don't use the term "deflated ego;" instead they refer to their problem as the G-value paradox. This makes it seem like a real problem instead of just a mistaken view of evolution.

Michael Behe's third book

I'm looking forward to Michael Behe's third book, which is due to be published in February. As most of you probably know, Michael Behe is a biochemist and a former professor at Lehigh University in Scranton, Pennsylvania, USA. He's also a senior fellow at the Discovery Institute’s Center for Science & Culture—the most prominent organization pushing Intelligent Design Creationism.

This will be Behe's third book. The first one was Darwin's Black Box (1996) where he argued against evolution by suggesting that some cellular complexes (e.g. bacterial flagella) are irreducibly complex and could not possibly have evolved by natural means. His second book was The Edge of Evolution (2007) where the theme was that there are limits to evolution preventing it from accomplishing significant beneficial changes.

Latest Tango in Halifax

I've known Yana Eglit for many years. She frequently posts comments on this blog but you won't recognize her name because she uses a pseudonym.¹ Yana is a graduate student in the lab of Alastair Simpson at Dalhousie University in Halifax, Nova Scotia, Canada. A few years ago she saw some strange organisms dancing in a Petri dish.²

The microorganisms belong to the group Hemimastigophora. Yana found them in a clump of dirt she picked up while hiking near Halifax. They named the species Hemimastix kukwesjijk. The group only contains a few other species.

Hemimastigophora is one of those protist groups that have been difficult to classify and difficult to place relative to other protists. It's traditionally been given the status of a phylum but its position in the eukaryotic tree was ambiguous.

The Simpson lab, in a collaboration with Andrew Roger's group, sequenced a number of genes (transcripts) from H. kukwesjijk and another species that they recently identified (Spirenema). The datasets contained samples of about 300 genes of each species. Trees constructed with this dataset place the Hemimastigophora near the base of the eukrayotic tree as a sister group to Diaphoretices. The work was published in a recent issue of Nature (Lax, Egrit, et al., 2018).

The details of eukaryotic taxonomy and the various subdivisions needn't concern us but the important take-home lesson is that there are a huge number of protists forming diverse groups that separated more than a billion years ago. The authors claim that Hemimastigophora deserves supra-kingdom status equivalent to the other supra-kingdoms shown in the figure (modified from Figure 4 of the paper).

The root of the eukaryotic tree is controversial. It could be at positions a, b, or c, shown in the figure. According to the authors, position a is the most favored these days. Regardless of where the root is actually placed, the new positioning of Hemimastigophora adds a lot of information to the deepest parts of the eukaryotic tree and brings us closer to identifying the most primitive features of the eukaryotic cell.

I wonder how many other strange species can be found in Canadian dirt?

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Photo Credit: The photo of Yana Eglit at her microscope is from the Dalhousie University press office [Hidden in plain sight: Dal evolutionary biologists uncover a new branch on the Tree of Life]

1. Which she might accidentally reveal if she responds to this post!

2. The fact they were "dancing" gave me an excuse to use a corny title that refers to one of our favorite TV shows, "Last Tango in Halifax."

Lax, G., Eglit, Y., Eme, L., Bertrand, E. M., Roger, A. J., and Simpson, A. G. B. (2018) Hemimastigophora is a novel supra-kingdom-level lineage of eukaryotes. Nature. (in press) [doi: 10.1038/s41586-018-0708-8]

Revisiting the deflated ego problem

Humans are just another animal. All animals share a core set of several thousand genes and all mammals have about the same number of homologous genes (~25,000). The differences between species such as gorillas, bats and whales are due almost exclusively to differences in the timing of expression of these common genes.

This concept is not new. It was the major theme of Stephen Jay Gould's book, Ontogeny and Phylogeny, back in 1977 [Learning About EVO-Devo]. Over the next twenty years or so, the concept was confirmed repeatedly by the work of hundreds of developmental biology labs working mostly with model organisms such as Drosophila (fruit flies). The field is evolutionary developmental biology or "evo-devo" and that work has been nicely summarized in several popular books appearing in the 21st century.

Celebrating 50 years of Neutral Theory

The importance of Neutral Theory and Nearly-Neutral Theory cannot be exaggerated. It has radically transformed the way experts think about evolution, especially at the molecular level. Unfortunately, the average scientist is not aware of the revolution that took place 50 years ago and they still think of evolution as a synonym for natural selection. I suspect that 80% of biology undergraduates in North American universities are graduating without a deep understanding of the importance of Neutral Theory.¹

The journal of Molecular Biology and Evolution has published a special issue: Celebrating 50 years of the Neutral Theory. The key paper published 50 years ago was Motoo Kimura's paper on “Evolutionary rate at the molecular level” (Kimura, 1968) followed shortly after by a paper from Jack Lester King and Thomas Jukes on "Non-Darwinian Evolution" (King and Jukes, 1969).

The special issue contains reprints of two classic papers published in Molecular Biology and Evolution in 1983 and 2005. In addition, there are 14 reviews and opinions written by editors of the journal and published earlier this year (see below). It's interesting that several of the editors of a leading molecular evolution journal are challenging the importance of Neutral Theory and one of them (senior editor Matthew Hahn) is downright hostile.

DNA Is Not Destiny by Steven J. Heine

DNA Is Not Destiny: The Remarkable Completely Misunderstood Relationship between You and Your Genes
by Steven J. Heine
W.W. Norton & Company, New York/London (2017)
ISBN: 978-0-393-24408-3

Steven Heine is a Professor in the Department of Psychology at the University of British Columbia (Vancouver, B.C., Canada). He has written a book about the perils of DNA testing and his main thesis is that the results of such tests are bound to make you fell unhappy because you will learn that you have a higher risk of several nasty diseases. He warns us that the science behind these predictions is not nearly as solid as the testing companies would have you believe but his main point is the psychological impact of the test results. He claims that we are not conditioned to put the results into the proper perspective because we are pre-conditioned to adopt a very fatalist view of our genetic makeup.

He had his DNA analyzed by a number of companies. Here's some of what he learned from 23 and Me.

23andMe provided me with a gripping set of predictions about my health with real concrete numbers—I learned that I have a 2.1 percent chance of developing Parkinson's disease, and this is 32 percent higher than the average person. The 23andMe experience "felt" satisfying because it provided a wealth of highly specific and personal information about my health. But then, so would the fortune-teller down the street, and at least she isn't claiming any scientific foundation to her predictions.

The role of chance in evolution

I highly recommend this brief editorial by Naruya Saitou: "Chance, Finiteness, and History" (Saitou, 2018). Saitou is a strong proponent of Neutral Theory and the importance of random genetic drift. Together these influences, along with the "random" nature of mutation, introduce a major element of chance and accident into evolution.

Saitou was a student of Masatochi Nei and he recounts how he was influenced by Nei's 1987 book "Molecular Evolutionary Genetics." I remember reading that book 30 years ago and being very impressed with Nei's case for mutationism. Dan Graur also studied with Nei and he was kind enough to introduce me to Nei a few years ago in Chicago.

I think it's very clear that the role of chance in evolution, especially in molecular evolution, is very much underappreciated by the average scientist and by almost all non-scientists who are interested in the field. I doubt they will be convinced by a short essay but at least it will alert them to a different way of thinking.

Here's an example from Saitou's essay of that way of thinking ...

This world is finite. Our earth is just a 40,000-km circumference sphere. Life evolved on this tiny planet. We have to face the finiteness of the living world when we think about evolution. Random fluctuation of DNA copies (allele frequencies in classic sense) is a logical consequence of this finiteness. Because evolution follows time, evolution is historical. And chance played an important role in evolutionary history, as already noted by Darwin (1859). This is why I often mention three words—chance, finiteness, and history—in my talks and books as well as the title for this perspective.

Saitou is using "evolution" in two different senses. First, there's the ongoing process involving changes in allele frequencies and then there's the history of life. I think it's best to avoid using the word "evolution" as a stand-in for the history of life but that's just a quibble. The idea behind the history of life is that the pathway that each extant lineage has followed over the past three billion years is very much due to chance and accident. It's like Gould's idea that the tape of life can't be replayed.

The essay contains a sentence about junk DNA ...

From direct comparison of protein or RNA coding gene regions with noncoding regions of many genomes, it became clear that the majority of intergenic regions and introns are in fact “junk” DNA, as predicted by Ohno (1972).

This is about all the comment that's needed if you're a population geneticist. From their perspective, the debate is over and junk DNA won decisively over the speculation that most of our genome is functional. I wish more scientists, journalists, and philosophers would realize that the leading experts have reached a consensus on this subject.¹

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1. Let me repeat what I've said many times before: you don't have to agree with the views of these experts but you do have to acknowledge what you are up against when you argue for function. Do not mislead your audience by ignoring the experts in order to make your own opinion seem more reasonable.

Saitou, N. (2018) Chance, finiteness, and history. Molecular Biology and Evolution, 35(6), 1556-1557. [doi: 10.1093/molbev/msy087]

John Mattick's latest attack on junk DNA

John Mattick is the most prominent defender of the idea that the human genome is full of functional sequences. In fact, he is just about the only scientist of any prominence who's on that side of the debate. His main "evidence" is the fact that genomes are pervasively transcribed and that most of the transcripts are functional. Let's look at his latest review paper to see how well this argument stands up to close scrutiny (Mattick, 2018).¹

As you read this post, keep in mind that in 2012 John Mattick was awarded a prize by the Human Genome Organization for proving his hypothesis [John Mattick Wins Chen Award for Distinguished Academic Achievement in Human Genetic and Genomic Research].

The Award Reviewing Committee commented that Professor Mattick’s “work on long non-coding RNA has dramatically changed our concept of 95% of our genome”, and that he has been a “true visionary in his field; he has demonstrated an extraordinary degree of perseverance and ingenuity in gradually proving his hypothesis over the course of 18 years.”

Mattick follows his usual format by giving us his version of history. He has argued for the past 15 years that the scientific community has been reluctant to accept the evidence of massive amounts of regulatory RNA genes because it conflicts with the standard paradigm of the supremacy of proteins. In the past he has claimed that this paradigm is based on the Central Dogma which states, according to him, that the only real function of DNA is to make proteins [How Much Junk in the Human Genome?]. As we shall see, he hasn't abandoned that argument but at least he no longer refers to the Central Dogma for support