Dan Graur has written a good summary of genetic load. It’s an important concept in population genetics, and everyone should be familiar with it…and this is a nice 2½ page summary with only a little math in it.
I’ll try to summarize the summary in two paragraphs and even less math … but you should read the whole thing.
Genetic load is the cost of natural selection. You all understand natural selection (my usual problem is trying to explain that there’s more to evolution than just selection), and so you know that you can’t have selection without imposing a loss of fitness on individuals that lack the trait in question. As it turns out, when you do the math, the only parameter that matters is the mutation rate, µ, and the mean fitness of a population, w, is (1-µ)n, where n is the number of loci, or genes, in the genome. What w is, basically, is the cost to the population of carrying suboptimal variants.
Notice that (1-µ) is taken to the nth power — that tells you right away that the number of genes has a significant effect on the cost to a population. As Graur shows by example, using a reasonable estimate of the number of genes and the mutation rate, the human genetic load is easily bearable — if each couple has about 2½ children, losses due to selection overall will be easily compensated for, and the population size will be stable. But if n is significantly greater than 20-30,000 genes, because of that exponent, the cost becomes excessive. If the genome was 80% functional, he estimates we’d each have to have 7 x 1045 children just to maintain our current population.
What all this means is that there is an upper bound to the number of genes we can possibly carry, and it happens to be in the neighborhood of the number of genes estimated in the human genome project. We can’t have significantly more, or the likelihood of genes breaking down with our current mutation rate would mean that most of our children would be born dead of lethal genetic errors, or the burden of a swarm of small deficits to their fitness.
What Graur doesn’t mention is that this is old news. The concept was worked out in the 1930s by Haldane; it was dubbed “genetic load” in 1950 by Muller; Dobzhansky and Crow wrote papers on the topic in the 50s and 60s. I learned it as an undergraduate biology student in the 1970s. I have an expectation that more advanced and active researchers in the field will have this concept well in hand, and are completely familiar with it. It’s just part of the quantitative foundation of evolutionary biology.
And this is why some of us go all spluttery and cross-eyed at any mention of the ENCODE project. They just blithely postulated orders of magnitude more functioning elements in the genome than could be tolerated by any calculation of the genetic load — it quickly became clear that these people had no understanding of the foundation of modern evolutionary biology.
It was embarrassing. It was like seeing a grown-up reveal that he didn’t know how to use fractions. It’s as if NASA engineers plotted a moon launch while forgetting the exponent “2” in F= gm1m2/r2. Oops.
When well-established, 80 year old scientific principles set an upper bound on the number of genes in your data set, and you go sailing off beyond that, at the very least you don’t get to just ignore the fact that you’re flouting all that science. You’d better be able to explain how you can break that limit.
Just in case you wanted to replicate the experience of a struggling undergraduate, here’s a paper I wrestled with when I was 21 and at the University of Washington: James Crow, Some Possibilities for Measuring Selection Intensity in Man (Woman, you’re off the hook). It’s got more math than Graur’s explanation. If you don’t get it, or are not already familiar with the topic from other educational sources, then that’s OK — you just shouldn’t be getting million-dollar grants to study functional elements in the human genome.