The title above basically describes the message of evolutionary biologist Mike Lynch from what I can gather. His basic argument is outlined in long form in The Origins of Genome Architecture, though the outline of the thesis is evident over 10 years back (see Preservation of Duplicate Genes by Complementary, Degenerative Mutations). Verbally I think the easiest way to explain Lynch’s framework is that in species with small effective population sizes the creativity of stochastic forces in generating non-adaptive structure and complexity tends to overwhelm the power of natural selection to prune this tendency toward baroque. I reviewed a paper last year which argued that Lynch’s observation of an inverse relation between effective population and genome size was an artifact, that once you controlled for phylogenetic history it disappeared. Suffice it to say this is an area of dispute and active research, so we shouldn’t take any individual’s word for it. This is science on the broadest canvas. Extraordinary general claims need to backed by a generation of publication I’d think.
Lynch is now a co-author on a new letter to Nature (which is open access, so read it!), Non-adaptive origins of interactome complexity. Imagine if you took biochemistry, specifically the nearly impenetrable language of protein interactions, and crossed it with evolutionary genomics. This is what you’d get.
Over the past decade evolutionary geneticist Mike Lynch has been articulating a model of genome complexity which relies on stochastic factors as the primary motive force by which genome size increases. The argument is articulated in a 2003 paper, and further elaborated in his book The Origins of Genome Architecture. There are several moving parts in the thesis, some of which require a rather fine-grained understanding of the biophysical structural complexity of the genome, the nature of Mendelian inheritance as a process, and finally, population genetics. But the core of the model is simple: there is an inverse relationship between long term effective population size and genome complexity. Low individual numbers ~ large values in terms of base pairs and counts of genetic elements such as introns.