Selection is a process that acts on variation in traits to determine the fitness (i.e., evolutionary success) of individuals, and is a key mechanism of evolution as long as the selected traits have a heritable basis. Selection is often split into sexual selection, which arises due to variance in mating/reproductive success, and natural selection, which is due to variance in all other aspects of fitness. One reason that we often distinguish between these two types of selection is because they can often oppose each other – so an estimate of total selection over an individual’s life might come out looking really small if sexual selection and natural selection act equally strongly but in opposite directions. It would be like one person walking up 50 stairs (50) and another walking down 50 stairs (-50) and saying that on average they climbed 0 stairs.
But selection can have trade-offs at many different points during an individual’s lifetime, not just between natural and sexual selection. Males and females are often under different selection pressures, and natural selection can also be broken down into different episodes or components. When it comes to measuring selective pressure at different episodes, Arnold & Wade (1984a,b) developed a systematic approach to comparing phenotypes of individuals to their fitnesses at a given episode of selection to estimate selection strength. This has been a very popular approach to understanding how selection works in any given system (and I used it to quantify sexual selection strength in pipefish), but it doesn’t get at the heritability part of the story. To do that, we need genetics.
I’ve written about the idea of selection components analysis before, and it is basically the genetic equivalent of comparing phenotypes and fitnesses. Instead, the frequency of different gene variants (alleles) are compared between individuals at different stages in the life cycle. This method allows us to isolate the effects of different types of selection (like sexual selection vs natural selection).
In my most recent paper, Genome-wide selection components analysis in a fish with male pregnancy, which is published in the journal Evolution, I used the selection components analysis approach in a population of pipefish to identify SNPs that have different allele frequencies in adult males and adult females (to find SNPs associated with differential viability in the sexes) and between successfully-mated females and the females in the population (to find SNPs associated with sexual selection).
To compare successfully-mated females and the total population of females, I used one of the cool features of pipefish as a model system: male pregnancy. The males who have mated are collected with their offspring in their brood pouch, so at each gene we can rule out which of the alleles in the offspring was contributed by the father and therefore deduce which allele was contributed by the mother. For example, if the father has a genotype C/C and the offspring has a genotype C/T, then we know that the mother had at least one copy of the T allele. Doing this, I was able to estimate allele frequencies in the females that had mated and compare those frequencies to those in the population.
In the population of pipefish that I studied, I found that sexual selection and differential viability selection on males and females (in other words, selection that puts different pressures on males than females or vice versa) both affect regions throughout the genome. Interestingly, some of the genetic regions under selection were significant in both the sexual selection and the males-females comparison — these regions may be experiencing the type of tradeoffs between episodes of selection I discussed above. It’s also possible that those regions are involved in traits that are under selection acting in the same direction in both episodes. One limitation of selection components analysis is that we can’t say which traits are under selection without doing more experiments. But it is a useful tool at picking apart the types of selection affecting the genome, and could have widespread uses across biological disciplines.
Note: If you would like a copy of my paper and don’t have access to it through a university library, please email me! Due to copyright restrictions I can’t post the PDF but I’d be happy to send it to you.