Why the future won't be genetically homogeneous

While reading The Founders of Evolutionary Genetics I encountered a chapter where the late James F. Crow admitted that he had a new insight every time he reread R. A. Fisher's The Genetical Theory of Natural Selection. This prompted me to put down The Founders of Evolutionary Genetics after finishing Crow's chapter and pick up my copy of The Genetical Theory of Natural Selection. I've read it before, but this is as good a time as any to give it another crack. Almost immediately Fisher aims at one of the major conundrums of 19th century theory of Darwinian evolution: how was variation maintained? The logic and conclusions strike you like a hammer. Charles Darwin and most of his contemporaries held to a blending model of inheritance, where offspring reflect a synthesis of their parental values. As it happens this aligns well with human intuition. Across their traits offspring are a synthesis of their parents. But blending presents a major problem for Darwin's theory of adaptation via natural selection, because it erodes the variation which is the raw material upon which selection must act. It is a famously peculiar fact that the abstraction of the gene was formulated over 50 years before the concrete physical embodiment of the gene, DNA, was ascertained with any confidence. In the first chapter of The Genetical Theory R. A. Fisher suggests that the logical reality of persistent copious heritable variation all around us should have forced scholars to the inference that inheritance proceeded via particulate and discrete means, as these processes do not diminish variation indefinitely in the manner which is entailed by blending. More formally the genetic variance decreases by a factor of 1/2 every generation in a blending model. This is easy enough to understand. But I wanted to illustrate it myself, so I slapped together a short simulation script. The specifications are as follows: 1) Fixed population size, in this case 100 individuals 2) 100 generations 3) All individuals have 2 offspring, and mating is random (no consideration of sex) 4) The offspring trait value is the mid-parent value of the parents, though I also including a "noise" parameter in some of the runs, so that the outcome is deviated somewhat in a random fashion from expected parental values In terms of the data structure the ultimate outcome is a 100 ✕ 100 matrix, with rows corresponding to generations, and each cell an individual in that generation. The values in each cell span the range from 0 to 1. In the first generation I imagine the combining of two populations with totally different phenotypic values; 50 individuals coded 1 and 50 individuals coded 0. If a 1 and 1 mate, the produce only 1's. Likewise with 0's. On the other hand a 0 and a 1 produce a 0.5. And so forth. The mating is random in each generation.