The fuel of evolution

Quantitative epigenetics and evolution

Epigenetics refers to chemical modifications of chromatin or transcribed DNA that can influence gene activity and expression without changes in DNA sequence. The last 20 years have yielded breakthroughs in our understanding of epigenetic processes that impact many fields of biology. In this review, we discuss how epigenetics relates to quantitative genetics and evolution. We argue that epigenetics is important for quantitative genetics because: (1) quantitative genetics is increasingly being combined with genomics, and therefore we should expand our thinking to include cellular-level mechanisms that can account for phenotypic variance and heritability besides just those that are hard-coded in the DNA sequence; and (2) epigenetic mechanisms change how phenotypic variance is partitioned, and can thereby change the heritability of traits and how those traits are inherited. To explicate these points, we show that epigenetics can influence all aspects of the phenotypic variance formula: VP (total phenotypic variance) = VG (genetic variance) + VE (environmental variance) + VGxE (genotype-by-environment interaction) + 2COVGE (the genotype-environment covariance) + Vɛ (residual variance), requiring new strategies to account for different potential sources of epigenetic effects on phenotypic variance. We also demonstrate how each of the components of phenotypic variance not only can be influenced by epigenetics, but can also have evolutionary consequences. We argue that no sources of epigenetic effects on phenotypic variance can be easily cast aside in a quantitative genetic research program that seeks to understand evolutionary processes.

[The genetic walk of evolution]

Genetic mutations are the main fuel of evolution. In each generation, they produce new variations, which may be sorted out by natural or sexual selection. Mutations are generated by chance; yet which are the mutations actually sorted out by evolution, and why? This review presents some recent advances regarding this question. First, we gather results obtained at molecular and cellular levels, through synthetic experiments and under artificial selection paradigms. Next, we highlight studies at the multi-cellular level, especially studies of repeated evolution, whereby independent lineages acquire similar traits. Recent meta-analysis and quantifications are being presented; together they suggest that evolutionary relevant mutations accumulate around hotspots, spanning different levels of genetic organization. Pioneering work suggests that many causes, corresponding to many biological contexts, may explain the existence of these genetic hotspots. We finally discuss methodological limits, empirical challenges and a few future potential directions for this domain of research dedicated to the genetic path of evolution.

Purging deleterious mutations in conservation programmes: combining optimal contributions with inbred matings

Conservation programmes aim at minimising the loss of genetic diversity, which allows populations to adapt to potential environmental changes. This can be achieved by calculating how many offspring every individual should contribute to the next generation to minimise global coancestry. However, an undesired consequence of this strategy is that it maintains deleterious mutations, compromising the viability of the population. In order to avoid this, optimal contributions could be combined with inbred matings, to expose and eliminate recessive deleterious mutations by natural selection in a process known as purging. Although some populations that have undergone purging experienced reduced inbreeding depression, this effect is not consistent across species. Whether purging by inbred matings is efficient in conservation programmes depends on the balance between the loss of diversity, the initial decrease in fitness and the reduction in mutational load. Here we perform computer simulations to determine whether managing a population by combining optimal contributions with inbred matings improves its long-term viability while keeping reasonable levels of diversity. We compare the management based on genealogical information with management based on molecular data to calculate coancestries. In the scenarios analysed, inbred matings never led to higher fitness and usually maintained lower diversity than random or minimum coancestry matings. Replacing genealogical with molecular coancestry can maintain a larger genetic diversity but can also lead to a lower fitness. Our results are strongly dependent on the mutational model assumed for the trait under selection, the population size during management and the reproductive rate.