The structure of genotype-phenotype maps makes fitness landscapes navigable

Fitness landscapes are often described in terms of ‘peaks’ and ‘valleys’, implying an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high-dimensional, which results in counter-intuitive properties of genotype-phenotype maps, such as the close proximity of one phenotype to many others. Here we investigate how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes. For three biologically realistic genotype-phenotype map models—RNA secondary structure, protein tertiary structure and protein complexes—we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through a fitness valley. This in turn implies that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be utilised under evolutionary dynamics.

Heritability, polymorphism, and population dynamics: individual-based eco-evolutionary simulations

Contemporary evolution that occurs across ecologically relevant time scales, such as a few generations or decades, can not only change phenotypes but also feed back to demographic parameters and the dynamics of populations. This chapter presents a method to make phenotypic traits evolve in mechanistic individual-based simulations. The method is broadly applicable, as demonstrated through its applications to boreal forest adaptation to global warming, eco-evolutionary dynamics driven by fishing-induced selection in Atlantic cod, and the evolution of age at maturity in Atlantic salmon. The main message of this chapter is that there may be little reason to exclude phenotypic evolution in analyses of population dynamics, as these can be modified by evolutionary changes in life histories. Future challenges will be to integrate rapidly accumulating genomic knowledge and an ecosystem perspective to improve population projections and to better understand the drivers of population dynamics.

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

Widespread signatures of natural selection across human complex traits and functional genomic categories

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k–547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

A theory of evolutionary dynamics on any complex spatial structure

Understanding how the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes connected by migration corridors, symmetrical structures that most often act as well-mixed populations. Other studies use networks to model the more complex topologies of natural populations and to study the structures that suppress or amplify selection. However, they usually assume very small, regular networks, with strong constraints on the strength of selection considered. Here we build network generation algorithms, evolutionary simulations and derive general analytic approximations for probabilities of fixation in populations with complex spatial structure. By tuning network parameters and properties independent of each other, we systematically span across network families and show that both a network’s degree distribution, as well as its node mixing pattern shape the evolutionary dynamics of new mutations. We analytically write the relevant selective parameter, predictive of evolutionary dynamics, as a combination of network statistics. As one application, we use recent imaging datasets and build the cellular spatial networks of the stem cell niches of the bone marrow. Across a wide variety of parameters and regardless of the birth-death process used, we find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size decrease the suppression strength of the tissue spatial structure, hinting at a potential diminishing spatial suppression in the bone marrow tissue as individuals age.

Organelle bottlenecks facilitate evolvability by traversing heteroplasmic fitness valleys

Bioenergetic organelles – mitochondria and plastids – retain their own genomes, and these organelle DNA (oDNA) molecules are vital for eukaryotic life. Like all genomes, oDNA must be able to evolve to suit new environmental challenges. However, mixed oDNA populations can challenge cellular bioenergetics, providing a penalty to the appearance and adaptation of new mutations. Here we show that organelle ‘bottlenecks’, mechanisms increasing cell-to-cell oDNA variability during development, can overcome this mixture penalty and facilitate the adaptation of beneficial mutations. We show that oDNA heteroplasmy and bottlenecks naturally emerge in evolutionary simulations subjected to fluctuating environments, demonstrating that this evolvability is itself evolvable. Usually thought of as a mechanism to clear damaging mutations, organelle bottlenecks therefore also resolve the tension between intracellular selection for pure oDNA populations and the ‘bet-hedging’ need for evolvability and adaptation to new environments. This general theory suggests a reason for the maintenance of organelle heteroplasmy in cells, and may explain some of the observed diversity in organelle maintenance and inheritance across taxa.

Chromosome size affects sequence divergence between species through the interplay of recombination and selection

The structure of the genome, including the architecture, number, and size of its chromosomes, shapes the distribution of genetic diversity and sequence divergence. Importantly, smaller chromosomes experience higher recombination rates than larger ones. To investigate how the relationship between chromosome size and recombination rate affects sequence divergence between species, we adopted an integrative approach that combines empirical analyses and evolutionary simulations. We estimated pairwise sequence divergence among 15 species from three different Mammalian clades - Peromyscus rodents, Mus mice, and great apes - from chromosome-level genome assemblies. We found a strong significant negative correlation between chromosome size and sequence divergence in all species comparisons within the Peromyscus and great apes clades, but not the Mus clade, demonstrating that the dramatic chromosomal rearrangements among Mus species masked the ancestral genomic landscape of divergence in many comparisons. Moreover, our evolutionary simulations showed that the main factor determining differences in divergence among chromosomes of different size is the interplay of recombination rate and selection, with greater variation in larger populations than in smaller ones. In ancestral populations, shorter chromosomes harbor greater nucleotide diversity. As ancestral populations diverge and eventually speciate, diversity present at the onset of the split contributes to greater sequence divergence in shorter chromosomes among daughter species. The combination of empirical data and evolutionary simulations also revealed other factors that affect the relationship between chromosome size and divergence, including chromosomal rearrangements, demography, and divergence times, and deepen our understanding of the role of genome structure on the evolution of species divergence.

Three signatures of adaptive polymorphism exemplified by malaria-associated genes

Malaria has been one of the strongest selective pressures on our species. Many of the best-characterized cases of adaptive evolution in humans are in genes tied to malaria resistance. However, the complex evolutionary patterns at these genes are poorly captured by standard scans for non-neutral evolution. Here we present three new statistical tests for selection based on population genetic patterns that are observed more than once among key malaria resistance loci. We assess these tests using forward-time evolutionary simulations and apply them to global whole-genome sequencing data from humans, and thus we show that they are effective at distinguishing selection from neutrality. Each test captures a distinct evolutionary pattern, here called Divergent Haplotypes, Repeated Shifts, and Arrested Sweeps, associated with a particular period of human prehistory. We clarify the selective signatures at known malaria-relevant genes and identify additional genes showing similar adaptive evolutionary patterns. Among our top outliers, we see a particular enrichment for genes involved in erythropoiesis and for genes previously associated with malaria resistance, consistent with a major role for malaria in shaping these patterns of genetic diversity. Polymorphisms at these genes are likely to impact resistance to malaria infection and contribute to ongoing host-parasite coevolutionary dynamics.

Three hallmarks of malaria-induced selection in human genomes

Malaria has plausibly been the single strongest selective pressure on our species. Many of the best-characterized cases of adaptive evolution in humans are in genes tied to malaria resistance. However, the complex evolutionary patterns at these genes are poorly captured by standard scans for non-neutral evolution. Here we present three new statistical tests for selection based on population genetic patterns that are observed more than once among key malaria resistance loci. We assess these tests using forward-time evolutionary simulations and apply them to global whole-genome sequencing data from humans, and thus we show that they are effective at distinguishing selection from neutrality. Each test captures a distinct evolutionary pattern, here called Divergent Haplotypes, Repeated Shifts, and Arrested Sweeps, associated with a particular period of human prehistory. We clarify the selective signatures at known malaria-relevant genes and identify additional genes showing similar adaptive evolutionary patterns. Among our top outliers, we see a particular enrichment for genes involved in erythropoiesis and for genes previously associated with malaria resistance, consistent with a major role for malaria in shaping these patterns of genetic diversity. Polymorphisms at these genes are likely to impact resistance to malaria infection and contribute to ongoing host-parasite coevolutionary dynamics.