Dissecting Genetic Architecture Underlying Seed Traits in Multiple Environments

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits.     

Mapping quantitative trait Loci interactions from the maternal and offspring genomes

The expression of most developmental or behavioral traits involves complex interactions between quantitative trait loci (QTL) from the maternal and offspring genomes. The maternal-offspring interactions play a pivotal role in shaping the direction and rate of evolution in terms of their substantial contribution to quantitative genetic (co)variation. To study the genetics and evolution of maternal-offspring interactions, a unifying statistical framework that embraces both the direct and indirect genetic effects of maternal and offspring QTL on any complex trait is developed. This model is derived for a simple backcross design within the maximum-likelihood context, implemented with the EM algorithm. Results from extensive simulations suggest that this model can provide reasonable estimation of additive and dominant effects of the QTL at different generations and their interaction effects derived from the maternal and offspring genomes. Although our model is framed to characterize the actions and interactions of maternal and offspring QTL affecting offspring traits, the idea can be readily extended to decipher the genetic machinery of maternal traits, such as maternal care. Our model provides a powerful means for studying the evolutionary significance of indirect genetic effects in any sexually reproductive organisms.     

Bayesian analysis of genetic architecture of quantitative trait using data of crosses of multiple inbred lines

Using the data of crosses of multiple of inbred lines for mapping QTL can increase QTL detecting power compared with only cross of two inbred lines. Although many fixed-effect model methods have been proposed to analyze such data, they are largely based on one-QTL model or main effect model, and the interaction effects between QTL are always neglected. However, effectively separating the interaction effects from the residual error can increase the statistical power. In this article, we both extended the novel Bayesian model selection method and Bayesian shrinkage estimation approaches to multiple inbred line crosses. With two extensions, interacting QTL are effectively detected with high solution; in addition, the posterior variances for both main effects and interaction effects are also subjected to full Bayesian estimate, which is more optimal than two step approach involved in maximum-likelihood. A series of simulation experiments have been conducted to demonstrate the performance of the methods. The computer program written in FORTRAN language is freely available on request.     

Genes involved in host–parasite interactions can be revealed by their correlated expression

Molecular interactions between a parasite and its host are key to the ability of the parasite to enter the host and persist. Our understanding of the genes and proteins involved in these interactions is limited. To better understand these processes it would be advantageous to have a range of methods to predict pairs of genes involved in such interactions. Correlated gene expression profiles can be used to identify molecular interactions within a species. Here we have extended the concept to different species, showing that genes with correlated expression are more likely to encode proteins, which directly or indirectly participate in host–parasite interaction. We go on to examine our predictions of molecular interactions between the malaria parasite and both its mammalian host and insect vector. Our approach could be applied to study any interaction between species, for example, between a host and its parasites or pathogens, but also symbiotic and commensal pairings.     

QTL mapping and the genetic basis of adaptation: recent developments

Quantitative trait loci (QTL) mapping has been used in a number of evolutionary studies to study the genetic basis of adaptation by mapping individual QTL that explain the differences between differentiated populations and also estimating their effects and interaction in the mapping population. This analysis can provide clues about the evolutionary history of populations and causes of the population differentiation. QTL mapping analysis methods and associated computer programs provide us tools for such an inference on the genetic basis and architecture of quantitative trait variation in a mapping population. Current methods have the capability to separate and localize multiple QTL and estimate their effects and interaction on a quantitative trait. More recent methods have been targeted to provide a comprehensive inference on the overall genetic architecture of multiple traits in a number of environments. This development is important for evolutionary studies on the genetic basis of multiple trait variation, genotype by environment interaction, host–parasite interaction, and also microarray gene expression QTL analysis.     

Costly resistance to parasitism: evidence from simultaneous quantitative trait loci mapping for resistance and fitness in Tribolium castaneum

Information on the molecular basis of resistance and the evolution of resistance is crucial to an understanding of the appearance, spread, and distribution of resistance genes and of the mechanisms of host adaptation in natural populations. One potential important genetic constraint for the evolution of resistance is fitness cost associated with resistance. To determine whether host resistance to parasite infection is associated with fitness costs, we conducted simultaneous quantitative trait loci (QTL) mapping of resistance to parasite infection and fitness traits using the red flour beetle (Tribolium castaneum) and the tapeworm parasite (Hymenolepis diminuta) system in two independent segregating populations. A genome-wide QTL scan using amplified fragment length polymorphism (AFLP) markers revealed three QTL for beetle resistance to tapeworm infection. These three QTL account for 44-58% variance in beetle infection intensity. We identified five QTL for fecundity and five QTL for egg-to-adult viability, which accounted for 36-57% and 36-49%, respectively, of the phenotypic variance in fecundity and egg-to-adult viability. The three QTL conferring resistance were colocalized with the QTL affecting beetle fitness. The genome regions that contain the QTL for parasite resistance explained the majority of the variance in fecundity and egg-to-adult viability in the mapping populations. Colocalization of QTL conferring resistance to parasite infection and beetle fitness may result from the pleiotropic effects of the resistance genes on host fitness or from tight linkages between resistance genes and adverse deleterious mutations. Therefore, our results provide evidence that the genome regions conferring resistance to tapeworm infection are partially responsible for fitness costs in the resistant beetle populations.     

Novel nuclear-cytoplasmic interaction in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis>) induces vigorous plants

Interspecific hybridization can be considered an accelerator of evolution, otherwise a slow process, solely dependent on mutation and recombination. Upon interspecific hybridization, several novel interactions between nuclear and cytoplasmic genomes emerge which provide additional sources of diversity. The magnitude and essence of intergenomic interactions between nuclear and cytoplasmic genomes remain unknown due to the direction of many crosses. This study was conducted to address the role of nuclear-cytoplasmic interactions as a source of variation upon hybridization. Wheat (Triticum aestivum) alloplasmic lines carrying the cytoplasm of Aegilops mutica along with an integrated approach utilizing comparative quantitative trait locus (QTL) and epigenome analysis were used to dissect this interaction. The results indicate that cytoplasmic genomes can modify the magnitude of QTL controlling certain physiological traits such as dry matter weight. Furthermore, methylation profiling analysis detected eight polymorphic regions affected by the cytoplasm type. In general, these results indicate that novel nuclear-cytoplasmic interactions can potentially trigger an epigenetic modification cascade in nuclear genes which eventually change the genetic network controlling physiological traits. These modified genetic networks can serve as new sources of variation to accelerate the evolutionary process. Furthermore, this variation can synthetically be produced by breeders in their programs to develop epigenomic-segregating lines.     

The socially parasitic ant Polyergus mexicanus has host‐associated genetic population structure and related neighbouring colonies

The genetic structure of populations can be both a cause and a consequence of ecological interactions. For parasites, genetic structure may be a consequence of preferences for host species or of mating behaviour. Conversely, genetic structure can influence where conspecific interactions among parasites lay on a spectrum from cooperation to conflict. We used microsatellite loci to characterize the genetic structure of a population of the socially parasitic dulotic (aka “slave‐making”) ant (Polyergus mexicanus), which is known for its host‐specificity and conspecific aggression. First, we assessed whether the pattern of host species use by the parasite has influenced parasite population structure. We found that host species use was correlated with subpopulation structure, but this correlation was imperfect: some subpopulations used one host species nearly exclusively, while others used several. Second, we examined the viscosity of the parasite population by measuring the relatedness of pairs of neighbouring parasitic ant colonies at varying distances from each other. Although natural history observations of local dispersal by queens suggested the potential for viscosity, there was no strong correlation between relatedness and distance between colonies. However, 35% of colonies had a closely related neighbouring colony, indicating that kinship could potentially affect the nature of some interactions between colonies of this social parasite. Our findings confirm that ecological forces like host species selection can shape the genetic structure of parasite populations, and that such genetic structure has the potential to influence parasite‐parasite interactions in social parasites via inclusive fitness.     

Genetic variation in a host-parasite association: potential for coevolution and frequency-dependent selection

Abstract.— Models of host‐parasite coevolution assume the presence of genetic variation for host resistance and parasite infectivity, as well as genotype‐specific interactions. We used the freshwater crustacean Daphnia magna and its bacterial microparasite Pasteuria ramosa to study genetic variation for host susceptibility and parasite infectivity within each of two populations. We sought to answer the following questions: Do host clones differ in their susceptibility to parasite isolates? Do parasite isolates differ in their ability to infect different host clones? Are there host clone‐parasite isolate interactions? The analysis revealed considerable variation in both host resistance and parasite infectivity. There were significant host clone‐parasite isolate interactions, such that there was no single host clone that was superior to all other clones in the resistance to every parasite isolate. Likewise, there was no parasite isolate that was superior to all other isolates in infectivity to every host clone. This form of host clone‐parasite isolate interaction indicates the potential for coevolution based on frequency‐dependent selection. Infection success of original host clone‐parasite isolate combinations (i.e., those combinations that were isolated together) was significantly higher than infection success of novel host clone‐parasite isolate combinations (i.e., those combinations that were created in the laboratory). This finding is consistent with the idea that parasites track specific host genotypes under natural conditions. In addition, correspondence analysis revealed that some host clones, although distinguishable with neutral genetic markers, were susceptible to the same set of parasite isolates and thus probably shared resistance genes.     

Host and parasite life history interplay to yield divergent population genetic structures in two ectoparasites living on the same bat species

Host–parasite interactions are ubiquitous in nature. However, how parasite population genetic structure is shaped by the interaction between host and parasite life history remains understudied. Studies comparing multiple parasites infecting a single host can be used to investigate how different parasite life history traits interplay with host behaviour and life history. In this study, we used 10 newly developed microsatellite loci to investigate the genetic structure of a parasitic bat fly (Basilia nana). Its host, the Bechstein's bat (Myotis bechsteinii), has a social system and roosting behaviour that restrict opportunities for parasite transmission. We compared fly genetic structure to that of the host and another parasite, the wing‐mite, Spinturnix bechsteini. We found little spatial or temporal genetic structure in B. nana, suggesting a large, stable population with frequent genetic exchange between fly populations from different bat colonies. This contrasts sharply with the genetic structure of the wing‐mite, which is highly substructured between the same bat colonies as well as temporally unstable. Our results suggest that although host and parasite life history interact to yield similar transmission patterns in both parasite species, the level of gene flow and eventual spatiotemporal genetic stability is differentially affected. This can be explained by the differences in generation time and winter survival between the flies and wing‐mites. Our study thus exemplifies that the population genetic structure of parasites on a single host can vary strongly as a result of how their individual life history characteristics interact with host behaviour and life history traits.     

A unified statistical model for functional mapping of environment-dependent genetic expression and genotype x environment interactions for ontogenetic development

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.     

Within‐host interactions shape virulence‐related traits of trematode genotypes

Within‐host interactions between co‐infecting parasites can significantly influence the evolution of key parasite traits, such as virulence (pathogenicity of infection). The type of interaction is expected to predict the direction of selection, with antagonistic interactions favouring more virulent genotypes and synergistic interactions less virulent genotypes. Recently, it has been suggested that virulence can further be affected by the genetic identity of co‐infecting partners (G × G interactions), complicating predictions on disease dynamics. Here, we used a natural host–parasite system including a fish host and a trematode parasite to study the effects of G × G interactions on infection virulence. We exposed rainbow trout (Oncorhynchus mykiss) either to single genotypes or to mixtures of two genotypes of the eye fluke Diplostomum pseudospathaceum and estimated parasite infectivity (linearly related to pathogenicity of infection, measured as coverage of eye cataracts) and relative cataract coverage (controlled for infectivity). We found that both traits were associated with complex G × G interactions, including both increases and decreases from single infection to co‐infection, depending on the genotype combination. In particular, combinations where both genotypes had low average infectivity and relative cataract coverage in single infections benefited from co‐infection, while the pattern was opposite for genotypes with higher performance. Together, our results show that infection outcomes vary considerably between single and co‐infections and with the genetic identity of the co‐infecting parasites. This can result in variation in parasite fitness and consequently impact evolutionary dynamics of host–parasite interactions.     

Natural variation in the genetic architecture of a host-parasite interaction in the bumblebee Bombus terrestris

The genetic architecture of fitness-relevant traits in natural populations is a topic that has remained almost untouched by quantitative genetics. Given the importance of parasitism for the host's fitness, we used QTL mapping to study the genetic architecture of traits relevant for host-parasite interactions in the trypanosome parasite, Crithidia bombi and its host, Bombus terrestris. The three traits analysed were the parasite's infection intensity, the strength of the general immune response (measured as the encapsulation of a novel antigen) and body size. The genetic architecture of these traits was examined in three natural, unmanipulated mapping populations of B. terrestris. Our results indicate that the intracolonial phenotypic variation of all three traits is based on a network of QTLs and epistatic interactions. While these networks are similar between mapping populations in complexity and number of QTLs, as well as in their epistatic interactions, the variability in the position of QTL and the interacting loci was high. Only one QTL for body size was plausibly found in at least two populations. QTLs for encapsulation and Crithidia infection intensity were located on the same linkage groups.     

Mapping genome-genome epistasis: a high-dimensional model

MOTIVATION: The proper development of any organ or tissue requires the coordinated expression of its underlying genes that can be located on different genomes present in an organism. For instance, each step in the development of seed for a higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes. RESULTS: We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporating two important aspects of seed development in plants-QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has a high dimensionality, is constructed within the maximum-likelihood context based on a finite mixture model. The implementation of the expectation-maximization algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness.Conclusions: Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and to animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research. AVAILABILITY: A package of software will be provided from the corresponding author upon request.     

Coevolutionary interactions between host and parasite genotypes

More than 20 years after Dawkins introduced the concept of "extended phenotype" (i.e. phenotypes of hosts and parasites result from interactions between the two genomes) and although this idea has now reached contemporary textbooks of evolutionary biology, most studies of the evolution of host-parasite systems still focus solely on either the host or the parasite, neglecting the role of the other partner. It is important to consider that host and parasite genotypes share control of the epidemiological parameters of their relationship. Moreover, not only the traits of the infection but also the genetic correlations among these and other traits that determine fitness might be controlled by interactions between host and parasite genotypes.     

Genetic architecture of Arabidopsis thaliana response to infection by Pseudomonas syringae

Plant pathogens can severely reduce host yield and fitness. Thus, investigating the genetic basis of plant response to pathogens is important to further understand plant-pathogen coevolution and to improve crop production. The interaction between Arabidopsis thaliana and Pseudomonas syringae is an important model for studying the genetic basis of plant-pathogen interactions. Studies in this model have led to the discovery of many genes that differentiate a resistant from a susceptible plant. However, little is known about the genetic basis of quantitative variation in response to P. syringae. In this study, we investigate the genetic basis of three aspects of A. thaliana's response to P. syringae: symptom severity, bacterial population size and fruit production using a quantitative trait loci (QTL) analysis. We found two QTL for symptom severity and two for fruit production (possible candidate genes for observed QTL are discussed). We also found significant two-locus epistatic effect on symptom severity and fruit production. Although bacterial population size and symptom severity were strongly phenotypically correlated, we did not detect any QTL for bacterial population size. Despite the detected genetic variation observed for susceptibility, we found only a weak overall relationship between susceptibility traits and fitness, suggesting that these traits may not respond to selection.     

Gene networks in hexaploid wheat: interacting quantitative trait loci for grain protein content

In hexaploid wheat, single-locus and two-locus quantitative trait loci (QTL) analyses for grain protein content (GPC) were conducted using two different mapping populations (PI and PII). Main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL x environment interactions (QE, QQE) were detected using two-locus analyses in both the populations. Only a few QTLs were common in both the analyses, and the QTLs and the interactions detected in the two populations differed, suggesting the superiority of two-locus analysis and the need for using several mapping populations for QTL analysis. A sizable proportion of genetic variation for GPC was due to interactions (28.59% and 54.03%), rather than to M-QTL effects (7.24% and 7.22%), which are the only genetic effects often detected in the majority of QTL studies. Even E-QTLs made a marginal contribution to genetic variation (2.68% and 6.04%), thus suggesting that the major part of genetic variation is due to changes in gene networks rather than the presence or absence of specific genes. This is in sharp contrast to the genetic dissection of pre-harvest sprouting tolerance conducted by us earlier, where interaction effects were not substantial, suggesting that the nature of genetic variation also depends on the nature of the trait.     

Permutation Tests for Multiple Loci Affecting a Quantitative Character

The problem of detecting minor quantitative trait loci (QTL) responsible for genetic variation not explained by major QTL is of importance in the complete dissection of quantitative characters. Two extensions of the permutation-based method for estimating empirical threshold values are presented. These methods, the conditional empirical threshold (CET) and the residual empirical threshold (RET), yield critical values that can be used to construct tests for the presence of minor QTL effects while accounting for effects of known major QTL. The CET provides a completely nonparametric test through conditioning on markers linked to major QTL. It allows for general nonadditive interactions among QTL, but its practical application is restricted to regions of the genome that are unlinked to the major QTL. The RET assumes a structural model for the effect of major QTL, and a threshold is constructed using residuals from this structural model. The search space for minor QTL is unrestricted, and RET-based tests may be more powerful than the CET-based test when the structural model is approximately true.     

Food-environment mediates the outcome of specific interactions between a bumblebee and its trypanosome parasite

Specific host-parasite interactions, where the outcome of exposure to a parasite depends upon the genotypic identity of both parties, have implications for understanding host-parasite coevolution and patterns of genetic diversity. Thus, grasping the extent to which these interactions are mediated by environmental changes in a spatially and temporally heterogeneous world is vital. In this study, it is shown that the environment can influence specific host-parasite interactions in the well-studied system of the bumblebee Bombus terrestris and its trypanosome parasite Crithidia bombi. Naturally relevant variation in the quality of the food environment formed a three-way interaction with both host and parasite identity in determining the outcome of infection, with regard to the resistance of the host and the transmission of the parasite. The demonstration of such a host-genotype by parasite-genotype by environment interaction (G(H) x G(P) x E) shows the importance of considering environmental variation when investigating host-parasite interactions. Moreover, such interactions may to some extent explain levels of genetic diversity in natural host-parasite systems owing to the fact that they will create selection mosaics when environments are heterogeneous.     

A game-theoretic model of interspecific brood parasitism with sequential decisions

The interaction between hosts and parasites in bird populations has been studied extensively. This paper uses game-theoretic methods to model this interaction. This has been done in previous papers but has not been studied taking into account the detailed sequential nature of this game. We introduce a model allowing the host and parasite to make a number of decisions which will depend on various natural factors. The sequence of events begins with the host forming a nest and laying a number of eggs, followed by the possibility that a parasite bird will arrive at the nest; if it does it can choose to destroy some of the host eggs and lay one of its own. A sequence of events follows, which is broken down into two key stages; firstly the interaction between the host and the parasite adult, and secondly that between the host and the parasite chick. The final decision involves the host choosing whether to raise or abandon the chicks that are in the nest. There are certain natural parameters and probabilities which are central to these various decisions; in particular the host is generally uncertain whether parasitism has taken place, but can assess the likelihood of parasitism based upon certain cues (e.g. how many eggs remain in its nest). We then use this methodology to model two real-world interactions, that of the Reed Warbler with the Common Cuckoo and also the Yellow Warbler with the Brown-headed Cowbird. These parasites have different methods in the way they parasitize the nests of their hosts, and the hosts can in turn have different reactions to these parasites. Our model predictions generally match the real results well, and the model also makes predictions of the effect of changes in various key parameters on the type of parasitic interactions that should occur.