Home » Cornell Conference, Natural selection » Open Mike: Cornell OBI Conference Chapter 13—“Selection Threshold Severely Constrains Capture of Beneficial Mutations”—Concluding Comments excerpt

Open Mike: Cornell OBI Conference Chapter 13—“Selection Threshold Severely Constrains Capture of Beneficial Mutations”—Concluding Comments excerpt

Biological Information

To facilitate discussion, we are publishing the abstracts and conclusions/summaries/Introduction excerpts of the 24 papers from the Cornell Conference on the Origin of Biological Information here at Uncommon Descent, with cumulative links to previous papers at the bottom of each page. You can get from anywhere to anywhere in the system.

Note: A blow-by-blow account of the difficulties that the authors experienced from Darwin lobby attempts to censor the book by denying it publication with Springer are detailed here. Fortunately, the uproar resulted in an opportunity for readers like yourself to read the book online. That said, the hard cover version is now shipping.

The Concluding Comments of “Selection Threshold Severely Constrains Capture of Beneficial Mutations” by John C. Sanford, John R. Baumgardner, Wesley H. Brewer:

Concluding comments: Our findings raise a very interesting theoretical problem — in a large genome, how do the millions of low-impact (yet functional) nucleotides arise? It is universally agreed that selection works very well for high-impact mutations. However, unless some new and as yet undiscovered process is operating in nature, there should be selection breakdown for the great majority of mutations that have small impact on fitness. We have now shown that this applies equally to both beneficial and deleterious mutations, and we have shown that selection interference is especially important when there are high-impact beneficial mutations. We conclude that only a very small fraction of all non-neutral mutations are selectable within large genomes. Our results reinforce and extend the findings of earlier studies [1–13], which in general employed many simplifying assumptions and rarely included more than a single source of biological noise. We show that selection breakdown is not just a simple function of population size, but is seriously impacted by other factors, especially selection interference. We are convinced that our formulation and methodology (i.e., genetic accounting) provide the most
biologically-realistic analysis of selection breakdown to date.

Methods: For both the companion paper [14] and this paper, our basic approach has been to develop and employ the computer program Mendel’s Accountant (henceforth “Mendel” for short) to simulate genetic change over time. Mendel’s numerical approach introduces a discrete set of new mutations into the population every generation and then tracks each mutation through the processes of mating, recombination, gamete formation, and transmission to the new offspring in all successive generations. Our method tracks which individuals survive to reproduce after selection and records the transmission of each surviving mutation every generation. This allows a detailed mechanistic accounting of each mutation that enters and leaves the population over the course of many generations. We term this type of analysis genetic accounting, as reflected in the name of the program, Mendel’s accountant [28,29]. Its inner workings are described in great detail elsewhere [28].

Mendel is designed to mimic Mendelian heredity as we currently understand it. It acts as a meticulous accounting program to record and track huge numbers of
discrete genetic events over time. This discrete approach contrasts sharply with the traditional approach that has been used by population geneticists for the past nine decades that has sought to represent the processes solely in terms of analytical equations and then to solve these equations by hand. Like any accounting program, Mendel’s primarily limitation is the requirement that the inputs’ parameter values be clearly and honestly stated, so they properly characterizes the particular biological circumstance the user wants to investigate.

Although Mendel is designed with the ability to model a broad spectrum of haploid and diploid organisms, for the sake of simplicity we have limited our consideration in this paper to sexual diploid organisms with large genomes. We use parameters appropriate for human populations because more is generally known about the relevant values. We start with a genetically uniform population, approximating the relative genetic uniformity that follows a significant population bottleneck, and we initially assign each individual a fitness of 1. In the experiments reported here, we keep all parameters constant, except for the following: 1) mutation rate, 2) environmental variance, 3) fraction of beneficial mutations, 4) selection mode, 5) population size, and 6) number of generations. Mendel’s calculations use a mutation’s fitness effect, rather than its selection coefficient, in order to disentangle the genetic impact of a mutation on biological function from the selection process itself. In much of the population genetic literature, the selection coefficient and the influence of a given mutation on genetic fitness (fitness effect) have been equated by definition, which is true only when probability selection is combined with the multiplicative model of mutational effects and no other confounding factors occur. However, with other forms of selection and with the inclusion of other factors, a complex relationship emerges between a mutation’s impact on functional fitness, its predicted selection coefficient, and its actual selectability [50, 51]. Functional fitness is a concept integrating every element that influences survival and reproduction. We believe that the term “functional fitness” is both easily understood and conceptually useful. Our investigations show that numerous factors confound the correlation between a mutation’s effect on functional fitness and its selectability.

In Mendel, a Poisson distribution describes the random number of new mutations assigned to each individual. Mutations obey an “infinite sites” model, and the distribution of mutational effects is a Weibull-type distribution [52], of the form d = exp(ax?). Here d is the effect of a homozygous pair of mutant alleles, a is the inverse of the functional genome size, x is a uniformly distributed random number between 0 and 1, and ? is determined by the frequency of “high-impact” mutations and their defining cut-off value. All these parameters, as well as degree of dominance and numerous other variables, can be specified by the Mendel user. The Weibull-type distribution, widely used in engineering for modeling degradation processes [52], readily accommodates the wide range of effects that we want to consider (eight or more orders of magnitude). This function is similar to a gamma distribution but allows a wider range of fitness effect. More.

See also: Origin of Biological Information conference: Its goals

Open Mike: Origin of Biological Information conference: Origin of life studies flatlined

Open Mike: Cornell OBI Conference— Can you answer these conundrums about information?

Open Mike: Cornell OBI Conference—Is a new definition of information needed for biology? (Chapter 2)

Open Mike: Cornell OBI Conference—New definition of information proposed: Universal Information (Chapter 2)

Open Mike: Cornell OBI Conference—Chapter Three, Dembski, Ewert, and Marks on the true cost of a successful search

Open Mike: Cornell OBI Conference—Chapter Three on the true cost of a successful search—Conservation of information

Open Mike: Cornell OBI Conference—Chapter Four: Pragmatic Information

Open Mike: Cornell OBI Conference—Chapter Four, Pragmatic information: Conclusion

Open Mike: Cornell OBI Conference Chapter Five Abstract

Open Mike: Cornell OBI Conference Chapter Five – Basener on limits of chaos – Conclusion

Open Mike: Cornell OBI Conference Chapter Six – Ewert et all on the Tierra evolution program – Abstract

Open Mike: Cornell OBI Conference Chapter Six – Ewert et all on the Tierra evolution program – Conclusion

Open Mike: Cornell OBI Conference Chapter 7—Probability of Beneficial Mutation— Abstract

Open Mike: Cornell OBI Conference Chapter 7—Probability of Beneficial Mutation— Conclusion

Open Mike: Cornell OBI Conference Chapter 8—Entropy, Evolution and Open Systems—Abstract

Open Mike: Cornell OBI Conference Chapter 8—Entropy, Evolution and Open Systems—Conclusion

Open Mike: Cornell OBI Conference Chapter 9—Information and Thermodynamics in Living Systems—Abstract

Open Mike: Cornell OBI Conference Chapter 9—Information and Thermodynamics in Living Systems—Conclusion

Open Mike: Cornell OBI Conference Chapter 10—Biological Information and Genetic Theory: Introductory Comments—Abstract

Open Mike: Cornell OBI Conference Chapter 10—Biological Information and Genetic Theory: Introductory Comments— Excerpt

Open Mike: Cornell OBI Conference Chapter 11—Not Junk After All—Abstract

Open Mike: Cornell OBI Conference Chapter 11—Not Junk After All—Conclusion

Open Mike: Cornell OBI Conference Chapter 12—“Can Purifying Natural Selection Preserve Biological Information?”—Abstract

Open Mike: Cornell OBI Conference Chapter 12——“Can Purifying Natural Selection Preserve Biological Information?”—Excerpt

Open Mike: Cornell OBI Conference Chapter 13—“Selection Threshold Severely Constrains Capture of Beneficial Mutations”—Abstract

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One Response to Open Mike: Cornell OBI Conference Chapter 13—“Selection Threshold Severely Constrains Capture of Beneficial Mutations”—Concluding Comments excerpt

  1. John Sanford calls this the “Princess and the Nucleotide Paradox” (a play on the Princess and the Pea story, where she is able to feel the lump of a single pea underneath multiple stacked bed mattresses)

    An easy to understand analogy is picturing the biological fitness landscape as a sea of rolling waves. The waves represent the fitness “noise” of every day life. (predator-prey interactions, foraging-mating opportunities, other common survival/reproduction factors)

    The near-neutral (low selection threshold) mutations effectively represents a tiny “ripple” within the waves of fitness noise. The mutation’s sample offset in fitness simply does not register on any consequential scale because it is dwarfed by normal fitness factors.

    This seems kind of obvious when you think about it.

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