Population Genetics Theory Research Articles

Forward and backward modeling of cultural evolutionary processes

Modern cultural evolution theory adopts a variety of concepts and methods developed in mathematical biology, in particular population genetics theory. In addition to forward-looking approaches such as two-locus models, backward-looking approaches such as coalescent theory, which describe ancestral states of the current population, have played an important role in population genetics. Here, we show how forward and backward approaches can be applied to two examples in cultural evolution. The first example deals with the number of cultural traits, which can be analyzed by a backward approach. Cultural coalescent theory illustrates many unique aspects of cultural processes such as information transfer (i.e., social learning) from many donors (“cultural parents”), including or excluding the biological parents. Theory predicts that many cultural traits of intermediate (or higher) frequency (popularity) can exist that are surprisingly old. Many unsolved issues remain, however, such as how to incorporate social structure as well as natural and/or cultural selection, which we believe to be an urgent agenda. The second example is the punishment of sibling incest, a problem to which cultural coalescent theory cannot currently be usefully applied. By analyzing forward recursions, we show that punishment is ineffective in suppressing incest, unless the incestuous inclination is diminished by the presence of punishers. Based on these examples, we discuss the merits and demerits of forward and backward approaches.

Two fitness inference schemes compared using allele frequencies from 1,068,391 sequences sampled in the UK during the COVID-19 pandemic.

Throughout the course of the SARS-CoV-2 pandemic, genetic variation has contributed to the spread and persistence of the virus. For example, various mutations have allowed SARS-CoV-2 to escape antibody neutralization or to bind more strongly to the receptors that it uses to enter human cells. Here, we compared two methods that estimate the fitness effects of viral mutations using the abundant sequence data gathered over the course of the pandemic. Both approaches are grounded in population genetics theory but with different assumptions. One approach, tQLE, features an epistatic fitness landscape and assumes that alleles are nearly in linkage equilibrium. Another approach, MPL, assumes a simple, additive fitness landscape, but allows for any level of correlation between alleles. We characterized differences in the distributions of fitness values inferred by each approach and in the ranks of fitness values that they assign to sequences across time. We find that in a large fraction of weeks the two methods are in good agreement as to their top-ranked sequences, \textit{i.e.} as to which sequences observed that week are most fit. We also find that agreement between the ranking of sequences varies with genetic unimodality in the population in a given week.&#xD.

Genetic diversity loss in the Anthropocene will continue long after habitat destruction ends.

Genetic diversity within species is the basis for evolutionary adaptive capacity and has recently been included as a target for protection in the United Nations' Global Biodiversity Framework (GBF). However, there is a lack of reliable large-scale predictive frameworks to quantify how much genetic diversity has already been lost, let alone to quantitatively predict future losses under different conservation scenarios in the 21st century. Combining spatio-temporal population genetic theory with population genomic data of 18 plant and animal species, we studied the dynamics of genetic diversity after habitat area losses. We show genetic diversity reacts slowly to habitat area and population declines, but lagged losses will continue for many decades even after habitats are fully protected. To understand the magnitude of this problem, we combined our predictive method with species' habitat area and population monitoring reported in the Living Planet Index, the Red List, and new GBF indicators. We then project genetic diversity loss in 13,808 species with a short-term genetic diversity loss of 13 - 22% and long-term loss of 42 - 48% with substantial deviations depending on the level of habitat fragmentation. These results highlight that protection of only current habitats is insufficient to ensure the genetic health of species and that continuous genetic monitoring alone likely underestimates long term impacts. We provide an area-based spatio-temporal predictive framework to develop quantitative scenarios of global genetic biodiversity.

Understanding admixture fractions: theory and estimation of gene-flow.

Estimation of admixture proportions has become one of the most commonly used computational tools in population genomics. However, there is remarkably little population genetic theory on statistical properties of these variables. We develop theoretical results that can accurately predict means and variances of admixture proportions within a population using models with recombination and genetic drift. Based on established theory on measures of multilocus disequilibrium, we show that there is a set of recurrence relations that can be used to derive expectations for higher moments of the admixture proportions distribution. We obtain closed form solutions for some special cases. Using these results, we develop a method for estimating admixture parameters from estimated admixture proportions obtained from programs such as Structure or Admixture. We apply this method to HapMap 3 data and find that the population history of African Americans, as expected, is not best explained by a single admixture event between people of European and African ancestry. The model of constant gene flow starting at 8 generations and ending at 2 generations before present gives the best fit.

Predictability and evolutionary determinism — the search for quantitative explanation

Physics describes reality through the understanding and elucidation of physical laws. From these laws, the universe is predictable. It has indeed been suggested that explanation of observable phenomena comes from our ability to predict these events, through our understanding of physical laws and causal processes. Biological entities operate, of course, through physical and chemical laws. But there exist biological processes that have emergent properties, notably the evolution of adaptation through natural selection, which add an extra dimension to the concept of the understanding of biological phenotypes. The population genetic theory of evolution is mathematically formulated, and contains elements of determinism, typified by the action of natural selection, and elements of randomness, typified by the action of genetic drift and by the randomness of mutation. But is the goal-directed property of natural selection sufficient to conclude that the deterministic elements of the evolutionary process make evolutionary change predictable? And to what extent can we expect 'adaptation' to function as an explanation of biological phenotypes?

PopDMS infers mutation effects from deep mutational scanning data.

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions. popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.

Differentiating mechanism from outcome for ancestry-assortative mating in admixed human populations.

Population genetic theory, and the empirical methods built upon it, often assume that individuals pair randomly for reproduction. However, natural populations frequently violate this assumption, which may potentially confound genome-wide association studies, selection scans, and demographic inference. Within several recently admixed human populations, empirical genetic studies have reported a correlation in global ancestry proportion between spouses, referred to as ancestry-assortative mating. Here, we use forward genomic simulations to link correlations in ancestry between mates to the underlying mechanistic mate-choice process. We consider the impacts of two types of mate-choice model, using either ancestry-based preferences or social groups as the basis for mate pairing. We find that multiple mate-choice models can produce the same correlations in ancestry proportion between spouses; however, we also highlight alternative analytic approaches and circumstances in which these models may be distinguished. With this work, we seek to highlight potential pitfalls when interpreting correlations in empirical data as evidence for a particular model of human mating practices, as well as to offer suggestions toward development of new best practices for analysis of human ancestry-assortative mating.

Insertion-Deletion Events Are Depleted in Protein Regions with Predicted Secondary Structure.

A fundamental goal in evolutionary biology and population genetics is to understand how selection shapes the fate of new mutations. Here, we test the null hypothesis that insertion-deletion (indel) events in protein-coding regions occur randomly with respect to secondary structures. We identified indels across 11,444 sequence alignments in mouse, rat, human, chimp, and dog genomes and then quantified their overlap with four different types of secondary structure-alpha helices, beta strands, protein bends, and protein turns-predicted by deep-learning methods of AlphaFold2. Indels overlapped secondary structures 54% as much as expected and were especially underrepresented over beta strands, which tend to form internal, stable regions of proteins. In contrast, indels were enriched by 155% over regions without any predicted secondary structures. These skews were stronger in the rodent lineages compared to the primate lineages, consistent with population genetic theory predicting that natural selection will be more efficient in species with larger effective population sizes. Nonsynonymous substitutions were also less common in regions of protein secondary structure, although not as strongly reduced as in indels. In a complementary analysis of thousands of human genomes, we showed that indels overlapping secondary structure segregated at significantly lower frequency than indels outside of secondary structure. Taken together, our study shows that indels are selected against if they overlap secondary structure, presumably because they disrupt the tertiary structure and function of a protein.

Ecological characteristics explain neutral genetic variation of three coastal sparrow species.

Eco-phylogeographic approaches to comparative population genetic analyses allow for the inclusion of intrinsic influences as drivers of intraspecific genetic structure. This insight into microevolutionary processes, including changes within a species or lineage, provides better mechanistic understanding of species-specific interactions and enables predictions of evolutionary responses to environmental change. In this study, we used single nucleotide polymorphisms (SNPs) identified from reduced representation sequencing to compare neutral population structure, isolation by distance (IBD), genetic diversity and effective population size (Ne) across three closely related and co-distributed saltmarsh sparrow species differing along a specialization gradient-Nelson's (Ammospiza nelsoni subvirgata), saltmarsh (A. caudacuta) and seaside sparrows (A. maritima maritima). Using an eco-phylogeographic lens within a conservation management context, we tested predictions about species' degree of evolutionary history and ecological specialization to tidal marshes, habitat, current distribution and population status on population genetic metrics. Population structure differed among the species consistent with their current distribution and habitat factors, rather than degree of ecological specialization: seaside sparrows were panmictic, saltmarsh sparrows showed hierarchical structure and Nelson's sparrows were differentiated into multiple, genetically distinct populations. Neutral population genetic theory and demographic/evolutionary history predicted patterns of genetic diversity and Ne rather than degree of ecological specialization. Patterns of population variation and evolutionary distinctiveness (Shapely metric) suggest different conservation measures for long-term persistence and evolutionary potential in each species. Our findings contribute to a broader understanding of the complex factors influencing genetic variation, beyond specialist-generalist status and support the role of an eco-phylogeographic approach in population and conservation genetics.

The fundamental theorem of natural selection: the end of a story.

The direction of research in population genetics theory is currently, and correctly, retrospective, that is directed toward the past. What events in the past have led to the presently observed genetic constitution of a population? This direction is inspired, first, by the large volumes of genomic data now available and, second, by the success of the classical prospective theory in validating the Darwinian theory in terms of Mendelian genetics. However, the prospective theory should not be forgotten, and in that theory, perhaps the most interesting and certainly the most controversial, is Fisher's so-called "Fundamental Theorem of Natural Selection." This article describes the history and the current status of that theorem.

A Community-Based Framework Integrates Interspecific Interactions into Forest Genetic Conservation

Forest genetic conservation is typically species-specific and does not integrate interspecific interaction and community structure. It mainly focuses on the theories of population and quantitative genetics. This approach depicts the intraspecific patterns of population genetic structure derived from genetic markers and the genetic differentiation of adaptive quantitative traits in provenance trials. However, it neglects possible interspecific interaction in natural forests and overlooks natural hybridization or subspeciation. We propose that the genetic diversity of a given species in a forest community is shaped by both intraspecific population and interspecific community evolutionary processes, and expand the traditional forest genetic conservation concept under the community ecology framework. We show that a community-specific phylogeny derived from molecular markers would allow us to explore the genetic mechanisms of a tree species interacting with other resident species. It would also facilitate the exploration of a species’ ecological role in forest community assembly and the taxonomic relationship of the species with other species specific to its resident forest community. Phylogenetic β-diversity would assess the similarities and differences of a tree species across communities regarding ecological function, the strength of selection pressure, and the nature and extent of its interaction with other species. Our forest genetic conservation proposal that integrates intraspecific population and interspecific community genetic variations is suitable for conserving a taxonomic species complex and maintaining its evolutionary potential in natural forests. This provides complementary information to conventional population and quantitative genetics-based conservation strategies.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution.

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

The quantitative genetics of human disease: 1. Foundations

In this the first of an anticipated four paper series, fundamental results of quantitative genetics are presented from a first principles approach. While none of these results are in any sense new, they are presented in extended detail to precisely distinguish between definition and assumption, with a further emphasis on distinguishing quantities from their usual approximations. Terminology frequently encountered in the field of human genetic disease studies will be defined in terms of their quantitive genetics form. Methods for estimation of both quantitative genetics and the related human genetics quantities will be demonstrated. The principal target audience for this work is trainees reasonably familiar with population genetics theory, but with less experience in its application to human disease studies. We introduce much of this formalism because in later papers in this series, we demonstrate that common areas of confusion in human disease studies can be resolved be appealing directly to these formal definitions. The second paper in this series will discuss polygenic risk scores. The third paper will concern the question of “missing” heritability and the role interactions may play. The fourth paper will discuss sexually dimorphic disease and the potential role of the X chromosome. Background: With over a hundred years of history, most fundamental results in quantitative genetics are well known to most population genetics students, yet there is often considerable confusion concerning precise definitions and assumptions, particularly when interactions may exist. The connections between quantitative genetics and human disease genetics can be obscure to many. Methods: Fundamental quantitative genetics quantities are derived as conditional expectations of phenotype. Genetic, environmental, additive, dominance and interaction effects and their associated variances are defined, with key results explicitly derived. The effects of linkage disequilibrium and methods to account for it are examined. Methods to estimate and interpret heritability are discussed. Results: Application of quantitative genetics quantities are extended to binary traits with special emphasis on translation between commonly estimated human disease genetics quantities and their corresponding quantitative genetics representations. Conclusions: The distinction between modeling definitions and assumptions is made clear. Methods to unite human disease genetics and quantitative genetics are elucidated. Methods to account for linkage disequilibrium and other forms of interaction are described.

How chromosomal inversions reorient the evolutionary process.

Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce the effective rate of recombination in the heterozygous state. They play a major role in adaptation, as well as in other evolutionary processes such as speciation. Although inversions have been studied since the 1920s, they remain difficult to investigate because the reduced recombination conferred by them strengthens the effects of drift and hitchhiking, which in turn can obscure signatures of selection. Nonetheless, numerous inversions have been found to be under selection. Given recent advances in population genetic theory and empirical study, here we review how different mechanisms of selection affect the evolution of inversions. A key difference between inversions and other mutations, such as single nucleotide variants, is that the fitness of an inversion may be affected by a larger number of frequently interacting processes. This considerably complicates the analysis of the causes underlying the evolution of inversions. We discuss the extent to which these mechanisms can be disentangled, and by which approach.

Modelling speciation: Problems and implications.

Darwin's and Wallace's 1859 explanation that novel speciation resulted from natural variants that had been subjected to selection was refined over the next 150 years as genetic inheritance and the importance of mutation-induced change were discovered, the quantitative theory of evolutionary population genetics was produced, the speed of genetic change in small populations became apparent and the ramifications of the DNA revolution became clear. This paper first discusses the modern view of speciation in its historical context. It then uses systems-biology approaches to consider the many complex processes that underpin the production of a new species; these extend in scale from genes to populations with the processes of variation, selection and speciation being affected by factors that range from mutation to climate change. Here, events at a particular scale level (e.g. protein network activity) are activated by the output of the level immediately below (i.e. gene expression) and generate a new output that activates the layer above (e.g. embryological development), with this change often being modulated by feedback from higher and lower levels. The analysis shows that activity at each level in the evolution of a new species is marked by stochastic activity, with mutation of course being the key step for variation. The paper examines events at each of these scale levels and particularly considers how the pathway by which mutation leads to phenotypic variants and the wide range of factors that drive selection can be investigated computationally. It concludes that, such is the complexity of speciation, most steps in the process are currently difficult to model and that predictions about future speciation will, apart from a few special cases, be hard to make. The corollary is that opportunities for novel variants to form are maximised.

Rethinking convergence in plant parasitism through the lens of molecular and population genetic processes.

The autotrophic lifestyle of photosynthetic plants has profoundly shaped their body plan, physiology, and gene repertoire. Shifts to parasitism and heterotrophy have evolved at least 12 times in more than 4000 species, and this transition has consequently left major evolutionary footprints among these parasitic lineages. Features that are otherwise rare at the molecular level and beyond have evolved repetitively, including reduced vegetative bodies, carrion-mimicking during reproduction, and the incorporation of alien genetic material. Here, I propose an integrated conceptual model, referred to as the funnel model, to define the general evolutionary trajectory of parasitic plants and provide a mechanistic explanation for their convergent evolution. This model connects our empirical understanding of gene regulatory networks in flowering plants with classical theories of molecular and population genetics. It emphasizes that the cascading effects brought about by the loss of photosynthesis may be a major force constraining the physiological capacity of parasitic plants and shaping their genomic landscapes. Here I review recent studies on the anatomy, physiology, and genetics of parasitic plants that lend support to this photosynthesis-centered funnel model. Focusing on nonphotosynthetic holoparasites, I elucidate how they may inevitably reach an evolutionary terminal status (i.e., extinction) and highlight the utility of a general, explicitly described and falsifiable model for future studies of parasitic plants.

Genomic footprints of bottleneck in landlocked salmon population

At the end of the last ice age, several Atlantic salmon populations got caught up in the lakes and ponds of the Northern Hemisphere. Occasionally, the populations also got locked when the flow of rivers terminated from reaching the sea due to land upheaval. Therefore, the pattern of evolution shaping the landlocked salmon populations is different from the other anadromous salmons, which migrate between the sea and rivers. According to the theories of population genetics, the effect of genetic drift is expected to be more pronounced in the former compared to the latter. Here we examined this using the whole genome data of landlocked and anadromous salmon populations of Norway. Our results showed a 50–80% reduction in the genomic heterozygosity in the landlocked compared to anadromous salmon populations. The number and total size of the runs of homozygosity (RoH) segments of landlocked salmons were two to eightfold higher than those of their anadromous counterparts. We found the former had a higher ratio of nonsynonymous-to-synonymous diversities than the latter. The investigation also revealed a significant elevation of homozygous deleterious Single Nucleotide Variants (SNVs) in the landlocked salmon compared to the anadromous populations. All these results point to a significant reduction in the population size of the landlocked salmons. This process of reduction might have started recently as the phylogeny revealed a recent separation of the landlocked from the anadromous population. Previous studies on terrestrial vertebrates observed similar signatures of a bottleneck when the populations from Island and the mainland were compared. Since landlocked waterbody such as ponds and lakes are geographically analogous to Islands for fish populations, the findings of this study suggest the similarity in the patterns of evolution between the two.

The paradox of adaptive trait clines with nonclinal patterns in the underlying genes

Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Phenotypic clines evolved with nonmonotonic (i.e., nonclinal) patterns in allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Such conditions resulted from interactions among landscape, demography, pleiotropy, and genetic architecture. GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios due to first principles (clinal patterns did not evolve) or statistical issues (clinal patterns evolved but were not detected due to overcorrection for structure). Despite the limitations of GEAs, this study shows that a back-transformation of multivariate ordination can accurately predict individual multivariate traits from genotype and environmental data regardless of whether inference from GEAs was accurate. In addition, frameworks are introduced that can be used by empiricists to quantify the importance of clinal alleles in adaptation. This research highlights that multivariate trait prediction from genotype and environmental data can lead to accurate inference regardless of whether the underlying loci display clinal or nonmonotonic patterns.

Fragmented habitat compensates for the adverse effects of genetic bottleneck

In the face of the human-caused biodiversity crisis, understanding the theoretical basis of conservation efforts of endangered species and populations has become increasingly important. According to population genetics theory, population subdivision helps organisms retain genetic diversity, crucial for adaptation in a changing environment. Habitat topography is thought to be important for generating and maintaining population subdivision, but empirical cases are needed to test this assumption. We studied Saimaa ringed seals, landlocked in a labyrinthine lake and recovering from a drastic bottleneck, with additional samples from three other ringed seal subspecies. Using whole-genome sequences of 145 seals, we analyzed the distribution of variation and genetic relatedness among the individuals in relation to the habitat shape. Despite a severe history of genetic bottlenecks with prevalent homozygosity in Saimaa ringed seals, we found evidence for the population structure mirroring the subregions of the lake. Our genome-wide analyses showed that the subpopulations had retained unique variation and largely complementary patterns of homozygosity, highlighting the significance of habitat connectivity in conservation biology and the power of genomic tools in understanding its impact. The central role of the population substructure in preserving genetic diversity at the metapopulation level was confirmed by simulations. Integration of genetic analyses in conservation decisions gives hope to Saimaa ringed seals and other endangered species in fragmented habitats.