keys (25.2 MA, a group that includes everything from gibbons to humans), human-gorilla (9.4 MA), and chimp-bonobo (2–2.5 MA). Furthermore, they were able to estimate the timing of specific historical events in human history, e.g., the settling of the Canary Islands and Remote Oceania, and the post-Ice Age resettlement of Europe. It is possible, however, that more missense mutations can be found among newer mutations because genetic systems are breaking down due to the effects of entropy and the Curse. As far as timing the rise of the great ape lineages, all they are doing is uncovering the differences God built into their initial genomes. One of the basic premises of creation thought is that God designed hierarchically (Cserhati and Carter 2020), so some pairs of created groups are necessarily more similar than others and uncovering those differences does nothing to address the creation-evolution debate. Also, archaeological timing estimates can swing wildly. For example, some are arguing that people were in the Americas long before 15 kya. Consider also the claim by Carter et al. (2008) that the mitochondrial sequence data in this era were problematic. Finally, their rate estimate is divorced from any consideration of real-world biological mutation rates. After assuming a human-chimp split time in the millions of years range, their mutation rate was biased downward by several orders of magnitude. For these reasons, and more, attempts to peg archaeology to genetics are clearly fraught with difficulty. Genetic Drift A second work-around evolutionists sometimes employ is an appeal to genetic drift. Most new mutations are lost from the population quickly. If the probability of fixation is proportional to allele frequency (f), then the probability of loss is proportional to 1 – f. Any new mutation, therefore, has a very high likelihood of being lost. Rupe and Sanford (2013) estimated that something like 99.99% of all new mutations that enter a human-like model population are lost. The rate, however, does depend on population size, yet this can also be modeled. If most mutations are lost, does this not indicate that the short-term rate should be much faster than the long-term rate? No, for even if the probability of any one mutation being lost is high, the sheer number of new mutations entering the population guarantees that some will survive. Consider that you inherited only one-half of the mutations each of your parents carry. You also inherited only one-quarter of those in your grandparents, and one-eighth of those in your great-grandparents. Looking forward in time, mutations disappear quickly. Looking backward, however, one realizes that each individual has two parents. Even though they only passed down one-half of their mutations, 2 x 0.5 = 1. Likewise, 4 x 0.25 = 1 and 8 x 0.125 = 1. Thus, the mutation load of any given individual is identical to the mutation load of prior generations. Since each individual adds more mutations to the population, drift does nothing to slow down the rate of mutation accumulation. Even so, natural selection should have some effect. It will be removing some alleles and so there should be a difference between the genealogical and phylogenetic mutation rate. The question is, “How much?” What follows is an attempt to quantify the difference between the two mutation rate estimates. II. METHODS For full-genome analysis, multiple human-like populations were modeled with the online version of Mendel’s Accountant (see Carter 2019a for a comprehensive assessment of this program). Default parameters were used for most settings, including a 3-billion-bp genome with 989 linkage subunits. The mutation rate was held to 50 per person. Mutation effects were assigned according to a Weibull distribution with a beneficial/deleterious ratio of 0.0001 and a 50/50 ratio of dominant to recessive alleles. Five population sizes (100, 500, 1,000, 5,000 and 10,000) were modeled for 10,000 generations with eleven proportions of neutral alleles (ranging from 0 to 100%). The ending fitness, the number of fixed alleles, the number (or projected number) of generations to population extinction, the percent of mutations retained, and the average number of mutations per individual were tracked and recorded. Due to the high number of runs, each model was run only once. However, initial prototyping showed that repeated model runs produced highly similar results. Additional Mendel runs were performed to estimate the rate of mutation accumulation in mitochondria. Parameters were similar to those listed above, except that a single chromosome with a single linkage block composed of 16,569 nucleotides was used, and the recombination rate and the fraction of recessive mutations was set to zero. One set of models was designed to reproduce the mutation accumulation curves of other studies. Models were run for 100,000 generations using a variety of mutation rates. Another set of models attempted to produce a modern-looking mutation accumulation in a biblical time frame. The models started with 3 individuals, a reproduction rate (prior to selection) of 2, a growth rate of 1.2 (to allow for plenty of selection during the population growth phase), and a maximum population size of 100,000. The models were run for 250 generations with varying mutation rates. All other parameters were as above. f(neut) Pop Size 100 500 1000 5000 10000 0.00 0.000 0.000 0.000 0.102 0.115 0.25 0.000 0.000 0.066 0.148 0.165 0.50 0.000 0.064 0.142 0.245 0.261 0.60 0.000 0.103 0.194 0.305 0.322 0.70 0.000 0.177 0.271 0.390 0.399 0.80 0.008 0.274 0.315 0.517 0.528 0.85 0.020 0.373 0.510 0.603 0.610 0.90 0.275 0.529 0.613 0.699 0.707 0.95 0.436 0.752 0.792 0.840 0.842 0.99 0.903 0.940 0.955 0.965 0.966 0.9999 1.000 1.000 1.000 1.000 1.000 1.0 1.000 1.000 1.000 1.000 1.000 Table 1. Ending fitness in the modeled populations vs the frequency of neutral mutations (first column). Black: population went extinct prior to the 10,000th generation. Gray: population was trending toward extinction but survived to the end of the model run. White: population survived with no fitness loss or the fitness had stabilized. CARTER Genealogical vs. phylogenetic mutation rates 2023 ICC 172
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