Turnover and post‐bottleneck genetic structure in a recovering population of Peregrine Falcons Falco peregrinus

Dispersal is a process that increases genetic diversity and genetic connectivity of populations. We studied the turnover rate of breeding adults and genetic population structure to estimate dispersal in Peregrine Falcons in Finland. We used relatedness estimates among Finnish Peregrine Falcons over a 5‐year period, genotyping over 500 nestlings with 10 microsatellite loci to reveal the rate of turnover. Our results reveal a high turnover rate (21.7%) that does not seem to be correlated with the breeding success of the previous year. The extent of population genetic structure and diversity, and possible signs of the population crash during the 1970s, was assessed with a reduced dataset, excluding relatives. We found genetic diversity to be similar to previously studied falcon populations (expected heterozygosity of 0.581) and no population genetic structuring among our sampled populations. We did not find a genetic imprint of the past population bottleneck that the Finnish Peregrine population experienced. We conclude that high dispersal rates are likely to have contributed to maintaining genetic diversity across the landscape, by mixing individuals within the species’ distribution in Finland and thus preventing genetic structuring and negative effects associated with the population decline in the 1970s.     

The effect of non-additive genetic interactions on selection in multi-locus genetic models

Additive genetic variance might usually be expected to decrease in a finite population because of genetic drift. However, both theoretical and empirical studies have shown that the additive genetic variance of a population could, in some cases, actually increase owing to the action of genetic drift in presence of non-additive effects. We used Monte-Carlo simulations to address a less-well-studied issue: the effects of directional truncation selection on a trait affected by non-additive genetic variation. We investigated the effects on genetic variance and the response to selection. We compared two different genetic models, representing various numbers of loci. We found that the additive genetic variance could also increase in the case of truncation selection, when dominance and epistasis was present. Additive-by-additive epistatic effects generally gave a higher increase in additive variance compared to dominance. However, the magnitude of the increase differed depending on the particular model and on the number of loci.     

Fluctuating environments and the role of mutation in maintaining quantitative genetic variation

We study a class of genetic models in which a quantitative trait determined by several additive loci is subject to temporally fluctuating selection. Selection on the trait is assumed to be stabilizing but with an optimum that varies periodically and might be perturbed stochastically. The population mates at random, is infinitely large and has discrete generations. We pursue a statistical and numerical approach, covering a wide range of ecological and genetic parameters, to determine the potential of fluctuating environments to maintain quantitative genetic variation. Whereas, in contrast to some recent claims, this potential seems to be rather limited in the absence of recurrent mutation, fluctuating environments might, in combination with it, often generate high levels of additive genetic variation. We investigate how the genetic variation maintained depends on the ecological parameters and on the underlying genetics.     

Synthetic biology and genetic causation

Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems.     

Maximum likelihood estimation of oncogenetic tree models

We present a new approach for modelling the dependences between genetic changes in human tumours. In solid tumours, data on genetic alterations are usually only available at a single point in time, allowing no direct insight into the sequential order of genetic events. In our approach, genetic tumour development and progression is assumed to follow a probabilistic tree model. We show how maximum likelihood estimation can be used to reconstruct a tree model for the dependences between genetic alterations in a given tumour type. We illustrate the use of the proposed method by applying it to cytogenetic data from 173 cases of clear cell renal cell carcinoma, arriving at a model for the karyotypic evolution of this tumour.     

Endangered species in small habitat patches can possess high genetic diversity: the case of the Tana River red colobus and mangabey

We used mtDNA sequence data from the Tana River red colobus and mangabey to determine how their population genetic structure was influenced by dispersal and habitat fragmentation. The colobus and mangabey are critically endangered primates endemic to gallery forests in eastern Kenya. The forests are a Pliocene–Pleistocene refugium that has recently undergone significant habitat loss and fragmentation due to human activities. We expected both primates to exhibit low levels of genetic diversity due to elevated genetic drift in their small populations, and to show a strong correspondence between genetic and geographic distance due to disruption of gene flow between forests by habitat fragmentation. Additionally, because mangabey females are philopatric, we expected their mtDNA variation to be homogeneous within forest patches but to be heterogeneous between patches. In contrast, colobus have a female-biased dispersal and so we expected their mtDNA variation to be homogeneous within and between forest patches. We found high levels of haplotype and nucleotide diversity as well as high levels of sequence divergence between haplotype groups in both species. The red colobus had significantly higher genetic variation than the mangabey did. Most of the genetic variation in both primates was found within forest fragments. Although both species showed strong inter-forest patch genetic structure we found no correspondence between genetic and geographic distances for the two primates. We attributed the high genetic diversity to recent high effective population size, and high sequence divergence and strong genetic structures to long-term habitat changes in the landscape.     

Detecting maternal-effect loci by statistical cross-fostering

Great progress has been made in understanding the genetic architecture of phenotypic variation, but it is almost entirely focused on how the genotype of an individual affects the phenotype of that same individual. However, in many species the genotype of the mother is a major determinant of the phenotype of her offspring. Therefore, a complete picture of genetic architecture must include these maternal genetic effects, but they can be difficult to identify because maternal and offspring genotypes are correlated and therefore, partially confounded. We present a conceptual framework that overcomes this challenge to separate direct and maternal effects in intact families through an analysis that we call "statistical cross-fostering." Our approach combines genotype data from mothers and their offspring to remove the confounding effects of the offspring's own genotype on measures of maternal genetic effects. We formalize our approach in an orthogonal model and apply this model to an experimental population of mice. We identify a set of six maternal genetic effect loci that explain a substantial portion of variation in body size at all ages. This variation would be missed in an approach focused solely on direct genetic effects, but is clearly a major component of genetic architecture. Our approach can easily be adapted to examine maternal effects in different systems, and because it does not require experimental manipulation, it provides a framework that can be used to understand the contribution of maternal genetic effects in both natural and experimental populations.     

Evidence of spatial genetic structure in a California bunchgrass population

We investigated the scale of genetic variation of purple needlegrass (Nassella pulchra), a species commonly used in California for grassland restoration. Common garden and field data revealed evidence of genetic differentiation between two intermixed microhabitats characterized by differences in soil depth and community composition. We assessed the genetic variation within a single population using randomly amplified polymorphic DNA (RAPD) data collected from clusters of five individuals in 40 locations. We found no evidence for genetic structure at the whole population level. At smaller spatial scales, however, we found strong evidence that genetic subdivision of the population occurs at the level of the maternal neighborhood. We suggest that the interaction between widespread pollen dispersal and restricted seed dispersal may be the primary factor generating these results; panmictic pollen dispersal will make detection of genetic patterning difficult at larger spatial scales while limited seed dispersal will generate local genetic structure. As a result, the detection of population genetic structure will depend on the spatial scale of analysis. Local selection gradients related to topography and soil depth are also likely to play a role in structuring local genetic variation. Since N. pulchra is widely used in California in grassland and woodland habitat restoration, we suggest that, as a general rule, care should be exercised in transferring germplasm for the purposes of conservation when little is known about the within-population genetic subdivision of a plant species. Received: 23 December 1996 / Accepted: 20 May 1997     

Representing genetic variation as continuous surfaces: an approach for identifying spatial dependency in landscape genetic studies

Landscape genetics, an emerging field integrating landscape ecology and population genetics, has great potential to influence our understanding of habitat connectivity and distribution of organisms. Whereas typical population genetics studies summarize gene flow as pairwise measures between sampling localities, landscape characteristics that influence population genetic connectivity are often continuously distributed in space. Thus, there are currently gaps in both the ability to analyze genotypic data in a continuous spatial context and our knowledge of expected of landscape genetic structure under varying conditions. We present a framework for generating continuous “genetic surfaces”, evaluate their statistical properties, and quantify statistical behavior of landscape genetic structure in a simple landscape. We simulated microsatellite genotypes under varying parameters (time since vicariance, migration, effective population size) and used ancestry (q) values from STRUCTURE to interpolate a genetic surface. Using a spatially adjusted Pearson's correlation coefficient to test the significance of landscape variable(s) on genetic structure we were able to detect landscape genetic structure on a contemporary time scale (≥5 generations post vicariance, migration probability ≤0.10) even when population differentiation was minimal (F_ST≥0.00015). We show that genetic variation can be significantly correlated with geographic distance even when genetic structure is due to landscape variable(s), demonstrating the importance of testing landscape influence on genetic structure. Finally, we apply genetic surfacing to analyze an empirical dataset of black bears from northern Idaho USA. We find black bear genetic variation is a function of distance (autocorrelation) and habitat patch (spatial dependency), consistent with previous results indicating genetic variation was influenced by landscape by resistance. These results suggest genetic surfaces can be used to test competing hypotheses of the influence of landscape characteristics on genetic structure without delineation of categorical groups.     

INTERACTING PHENOTYPES AND THE EVOLUTIONARY PROCESS: I. DIRECT AND INDIRECT GENETIC EFFECTS OF SOCIAL INTERACTIONS

Interacting phenotypes are traits whose expression is affected by interactions with conspecifics. Commonly-studied interacting phenotypes include aggression, courtship, and communication. More extreme examples of interacting phenotypes—traits that exist exclusively as a product of interactions—include social dominance, intraspecific competitive ability, and mating systems. We adopt a quantitative genetic approach to assess genetic influences on interacting phenotypes. We partition genetic and environmental effects so that traits in conspecifics that influence the expression of interacting phenotypes are a component of the environment. When the trait having the effect is heritable, the environmental influence arising from the interaction has a genetic basis and can be incorporated as an indirect genetic effect. However, because it has a genetic basis, this environmental component can evolve. Therefore, to consider the evolution of interacting phenotypes we simultaneously consider changes in the direct genetic contributions to a trait (as a standard quantitative genetic approach would evaluate) as well as changes in the environmental (indirect genetic) contribution to the phenotype. We then explore the ramifications of this model of inheritance on the evolution of interacting phenotypes. The relative rate of evolution in interacting phenotypes can be quite different from that predicted by a standard quantitative genetic analysis. Phenotypic evolution is greatly enhanced or inhibited depending on the nature of the direct and indirect genetic effects. Further, unlike most models of phenotypic evolution, a lack of variation in direct genetic effects does not preclude evolution if there is genetic variance in the indirect genetic contributions. The available empirical evidence regarding the evolution of behavior expressed in interactions, although limited, supports the predictions of our model.     

Hypervariability, suppressed recombination and the genetics of individuality

We define 'genetic individuality' as intraspecies variation that has substantial heritability and involves traits that are sufficiently common that they can be observed in any modest-sized sampling of individuals. We propose that genetic individuality is largely shaped by the combinatory shuffling of a modest number of genes, each of which exists as a family of functionally and structurally diverged alleles. Unequivocal examples of such allele families are found at the O-antigen-biosynthetic locus in Pseudomonas aeruginosa and the human leucocyte antigen locus in humans. We examine characteristic features of these allele families and explore the possibility that genetic loci with similar characteristics can be recognized in a whole-genome scan of human genetic variation.     

Neutral Genetic Patterns for Expanding Populations with Nonoverlapping Generations

We investigate the inside dynamics of solutions to integrodifference equations to understand the genetic consequences of a population with nonoverlapping generations undergoing range expansion. To obtain the inside dynamics, we decompose the solution into neutral genetic components. The inside dynamics are given by the spatiotemporal evolution of the neutral genetic components. We consider thin-tailed dispersal kernels and a variety of per capita growth rate functions to classify the traveling wave solutions as either pushed or pulled fronts. We find that pulled fronts are synonymous with the founder effect in population genetics. Adding overcompensation to the dynamics of these fronts has no impact on genetic diversity in the expanding population. However, growth functions with a strong Allee effect cause the traveling wave solution to be a pushed front preserving the genetic variation in the population. In this case, the contribution of each neutral fraction can be computed by a simple formula dependent on the initial distribution of the neutral fractions, the traveling wave solution, and the asymptotic spreading speed.     

Interpreting population differentiation in terms of drift and selection

Summary Many empirical studies demonstrate some degree of genetic differentiation among populations of the same species. Understanding the relative importance of the processes causing this genetic differentiation has proven to be a difficult task. In particular, population differentiation can be influenced primarily by selection, genetic drift, and migration. We review the effect of drift and migration on patterns of genetic variation, with special reference to the conditions necessary for population differentiation. Conceptually, selection may be implicated in cases of population differentiation if the effect of drift and migration can be shown to be insufficient to cause the observed patterns. We examine some of the pitfalls of this approach when used with allozyme data, and revise a previous conclusion concerning the relative importance of selection in poulations of scale insects.     

Examining genetic structure in a bogus yucca moth: a sequential approach to phylogeography

Understanding the phylogeography of a species requires not only elucidating patterns of genetic structure among populations, but also identifying the possible evolutionary events creating that structure. The use of a single phylogeographic test or analysis, however, usually provides a picture of genetic structure without revealing the possible underlying evolutionary causes. We used current analytical techniques in a sequential approach to examine genetic structure and its underlying causes in the bogus yucca moth Prodoxus decipiens (Lepidoptera: Prodoxidae). Both historical biogeography and recent human transplantations of the moth's host plants provided a priori expectations of the pattern of genetic structure and its underlying causes. We evaluated these expectations by using a progression of phylogenetic, demographic, and population genetic analyses of mtDNA sequence data from 476 individuals distributed across 25 populations that encompassed the range of P. decipiens. The combination of these analyses revealed that much of the genetic structure has evolved more recently than suggested by historical biogeography, has been influenced by changes in demography, and can be best explained by long distance dispersal and isolation by distance. We suggest that performing a suite of analyses that focus on different temporal scales may be an effective approach to investigating the patterns and causes of genetic structure within species.     

Genetic structure in the seabuckthorn carpenter moth (Holcocerus hippophaecolus) in China: the role of outbreak events, geographical and host factors

Understanding factors responsible for structuring genetic diversity is of fundamental importance in evolutionary biology. The seabuckthorn carpenter moth (Holcocerus hippophaecolus Hua) is a native species throughout the north of China and is considered the main threat to seabuckthorn, Hippophae rhamnoides L. We assessed the influence of outbreaks, environmental factors and host species in shaping the genetic variation and structure of H. hippophaecolus by using Amplified Fragment Length Polymorphism (AFLP) markers. We rejected the hypothesis that outbreak-associated genetic divergence exist, as evidenced by genetic clusters containing a combination of populations from historical outbreak areas, as well as non-outbreak areas. Although a small number of markers (4 of 933 loci) were identified as candidates under selection in response to population densities. H. hippophaecolus also did not follow an isolation-by-distance pattern. We rejected the hypothesis that outbreak and drought events were driving the genetic structure of H. hippophaecolus. Rather, the genetic structure appears to be influenced by various confounding bio-geographical factors. There were detectable genetic differences between H. hippophaecolus occupying different host trees from within the same geographic location. Host-associated genetic divergence should be confirmed by further investigation.     

Genetic analysis reveals population structuring and a bottleneck in the black-faced lion tamarin (Leontopithecus caissara)

The ability of a population to evolve in a changing environment may be compromised by human-imposed barriers to gene flow. We investigated the population structure and the possible occurrence of a genetic bottleneck in two isolated populations of the black-faced lion tamarin (Leontopithecus caissara), a species with very reduced numbers (less than 400) in a very restricted range in the Atlantic Forest of southeast Brazil. We determined the genotypes of 52 individuals across 9 microsatellite loci. We found genetic divergence between the populations, each exhibiting low genetic diversity. Analysis revealed broad- and fine-scale population structuring. Both populations have evidently experienced population reduction and a genetic bottleneck without presenting any apparent detrimental effect. Anyway, measures should be taken to effectively protect the forests where L. caissara occurs in order to allow its populations to increase and counteract the eventual effects of genetic impoverishment.     

Costs of resistance: genetic correlations and potential trade-offs in an insect immune system

Theory predicts that natural selection will erode additive genetic variation in fitness-related traits. However, numerous studies have found considerable heritable variation in traits related to immune function, which should be closely linked to fitness. This could be due to trade-offs maintaining variation in these traits. We used the Egyptian cotton leafworm, Spodoptera littoralis, as a model system to examine the quantitative genetics of insect immune function. We estimated the heritabilities of several different measures of innate immunity and the genetic correlations between these immune traits and a number of life history traits. Our results provide the first evidence for a potential genetic trade-off within the insect immune system, with antibacterial activity (lysozyme-like) exhibiting a significant negative genetic correlation with haemocyte density, which itself is positively genetically correlated with both haemolymph phenoloxidase activity and cuticular melanization. We speculate on a potential trade-off between defence against parasites and predators, mediated by larval colour, and its role in maintaining genetic variation in traits under natural selection.     

A flexible Bayesian model for studying gene-environment interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant.     

No accident: genetic codes freeze in error-correcting patterns of the standard genetic code

The standard genetic code poses a challenge in understanding the evolution of information processing at a fundamental level of biological organization. Genetic codes are generally coadapted with, or 'frozen' by, the protein-coding genes that they translate, and so cannot easily change by natural selection. Yet the standard code has a significantly non-random pattern that corrects common errors in the transmission of information in protein-coding genes. Because of the freezing effect and for other reasons, this pattern has been proposed not to be due to selection but rather to be incidental to other evolutionary forces or even entirely accidental. We present results from a deterministic population genetic model of code-message coevolution. We explicitly represent the freezing effect of genes on genetic codes and the perturbative effect of changes in genetic codes on genes. We incorporate characteristic patterns of mutation and translational error, namely, transition bias and positional asymmetry, respectively. Repeated selection over small successive changes produces genetic codes that are substantially, but not optimally, error correcting. In particular, our model reproduces the error-correcting patterns of the standard genetic code. Aspects of our model and results may be applicable to the general problem of adaptation to error in other natural information-processing systems.     

Application of the RLGS image analysis tool (RAT) to the construction of a genetic linkage map of recombinant inbred strain SMXA

The construction of a genetic linkage map is the first, fundamental step to analyze the genetic properties of any organism. For this purpose, the restriction landmark genome scanning method (RLGS) can be used and has been shown to have high productivity in various genetic analyses. However, construction of a genetic linkage map by the RLGS method is laborious, because hundreds of spots must be scored, usually by visual observation. In order to reduce human involvement in the data processing, we developed an image analysis software, RAT (RLGS Analysis Tool). We evaluated its accuracy and feasibility by comparing the parental distribution patterns of RLGS spots obtained by RAT and by human observation, using Syrian hamster strain backcross progeny. We then used RAT to construct a genetic linkage map of the recombinant inbred strain SMXA. We were able to obtain 121 progenitor strain-specific spots that were assigned to a specific chromosome. Received: 1 December 1998 / Accepted: 29 January 1999