Much of our knowledge of speciation genetics stems from quantitative trait locus (QTL) studies. However, interpretations of the size and distribution of QTL underlying species differences are complicated by differences in the way QTL magnitudes are estimated. Also, many studies fail to exploit information about QTL directions or to compare inter- and intraspecific QTL variation. Here, we comprehensively analyze an extensive QTL data set for an interspecific backcross between two wild annual sunflowers, Helianthus annuus and H. petiolaris, interpret different estimates of QTL magnitudes, identify trait groups that have diverged through selection, and compare inter- and intraspecific QTL magnitudes. Our results indicate that even minor QTL (in terms of backcross variance) may be surprisingly large compared to levels of standing variation in the parental species or phenotypic differences between them. Morphological traits, particularly flower morphology, were more strongly or consistently selected than life history or physiological traits. Also, intraspecific QTL were generally smaller than interspecific ones, consistent with the prediction that larger QTL are more likely to spread to fixation across a subdivided population. Our results inform the genetics of species differences in Helianthus and suggest an approach for the simultaneous mapping of inter- and intraspecific QTL.
Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g....
Genetics is assuming an increasingly important role in medicine. As a result, the teaching of genetics should also be increased proportionally to ensure that future physicians will be able to take advantage of the new genetic technology, and to understand the associated ethical, legal and social issues. At the University of Rochester School of Medicine and Dentistry, we have been able to incorporate genetic education into a four-year medical curriculum in a fully integrated fashion. This model may serve as a template for other medical curriculum still in development.
Synovial sarcomas are highly aggressive mesenchymal cancers that show modest response to conventional cytotoxic chemotherapy, suggesting a definite need for improved biotargeted agents. Progress has been hampered by the lack of insight into pathogenesis of this deadly disease. The presence of a specific diagnostic t(X;18) translocation leading to expression of the unique SYT-SSX fusion protein in effectively all cases of synovial sarcoma suggests a role in the etiology. Other nonspecific anomalies such as overexpression of Bcl-2, HER-2/neu, and EGFR have been reported, but their role in the pathogenesis remains unclear. Using gene targeting, we recently generated mice conditionally expressing the human SYT-SSX2 fusion gene from mouse endogenous ROSA26 promoter in chosen tissue types in the presence of Cre recombinase. These mice develop synovial sarcoma when SYT-SSX2 is expressed within myoblasts, thereby identifying a source of this enigmatic tumor and establishing a mouse model of this disease that recapitulates the clinical, histologic, immunohistochemical, and transcriptional profile of human synovial sarcomas. We review the genetics of synovial sarcoma and discuss the usefulness of genetics-based mouse models as a valuable research tool in the hunt for key molecular determinants of this lethal disease as well as a preclinical platform for designing and evaluating novel treatment strategies.
Genetic analysis of parasitic nematodes has been a neglected area of research and the basic genetics of this important group of pathogens are poorly understood. Haemonchus contortus is one of the most economically significant livestock parasites worldwide and is a key experimental model for the strongylid nematode group that includes many important human and animal pathogens. We have undertaken a study of the genetics and the mode of mating of this parasite using microsatellite markers. Inheritance studies with autosomal markers demonstrated obligate dioecious sexual reproduction and polyandrous mating that are reported here for the first time in a parasitic helminth and provide the parasite with a mechanism of increasing genetic diversity. The karyotype of the H. contortus, MHco3(ISE) isolate was determined as 2n = 11 or 12. We have developed a panel of microsatellite markers that are tightly linked on the X chromosome and have used them to determine the sex chromosomal karyotype as XO male and XX female. Haplotype analysis using the X-chromosomal markers also demonstrated polyandry, independent of the autosomal marker analysis, and enabled a more direct estimate of the number of male parental genotypes contributing to each brood. This work provides a basis for future forward genetic analysis on H. contortus and related parasitic nematodes.
Biology is characterized by complex interactions between phenotypes, such as recursive and simultaneous relationships between substrates and enzymes in biochemical systems. Structural equation models (SEMs) can be used to study such relationships in multivariate analyses, e.g., with multiple traits in a quantitative genetics context. Nonetheless, the number of different recursive causal structures that can be used for fitting a SEM to multivariate data can be huge, even when only a few traits are considered. In recent applications of SEMs in mixed-model quantitative genetics settings, causal structures were preselected on the basis of prior biological knowledge alone. Therefore, the wide range of possible causal structures has not been properly explored. Alternatively, causal structure spaces can be explored using algorithms that, using data-driven evidence, can search for structures that are compatible with the joint distribution of the variables under study. However, the search cannot be performed directly on the joint distribution of the phenotypes as it is possibly confounded by genetic covariance among traits. In this article we propose to search for recursive causal structures among phenotypes using the inductive causation (IC) algorithm after adjusting the data for genetic effects. A standard multiple-trait model is fitted using Bayesian methods to obtain a posterior covariance matrix of phenotypes conditional to unobservable additive genetic effects...
Much of population genetics is based on the diffusion limit of the Wright–Fisher model, which assumes a fixed population size. This assumption is violated in most natural populations, particularly for microbes. Here we study a more realistic model that decouples birth and death events and allows for a stochastically varying population size. Under this model, classical quantities such as the probability of and time before fixation of a mutant allele can differ dramatically from their Wright–Fisher expectations. Moreover, inferences about natural selection based on Wright–Fisher assumptions can yield erroneous and even contradictory conclusions: at small population densities one allele will appear superior, whereas at large densities the other allele will dominate. Consequently, competition assays in laboratory conditions may not reflect the outcome of long-term evolution in the field. These results highlight the importance of incorporating demographic stochasticity into basic models of population genetics.
The United States and the world face serious societal challenges in the areas of food, environment, energy, and health. Historically, advances in plant genetics have provided new knowledge and technologies needed to address these challenges. Plant genetics remains a key component of global food security, peace, and prosperity for the foreseeable future. Millions of lives depend upon the extent to which crop genetic improvement can keep pace with the growing global population, changing climate, and shrinking environmental resources. While there is still much to be learned about the biology of plant–environment interactions, the fundamental technologies of plant genetic improvement, including crop genetic engineering, are in place, and are expected to play crucial roles in meeting the chronic demands of global food security. However, genetically improved seed is only part of the solution. Such seed must be integrated into ecologically based farming systems and evaluated in light of their environmental, economic, and social impacts—the three pillars of sustainable agriculture. In this review, I describe some lessons learned, over the last decade, of how genetically engineered crops have been integrated into agricultural practices around the world and discuss their current and future contribution to sustainable agricultural systems.
Research in population genetics theory has two main strands. The first is deterministic theory, where random changes in allelic frequencies are ignored and attention focuses on the evolutionary effects of selection and mutation. The second strand, stochastic theory, takes account of these random changes and thus is more complete than deterministic theory. This essay is one in the series of Perspectives and Reviews honoring James F. Crow on the occasion of his 95th birthday. It concerns his contributions to, and involvement with, the stochastic theory of evolutionary population genetics.
R. C. Punnett, the codiscoverer of linkage with W. Bateson in 1904, had the good fortune to be invited to be the first Arthur Balfour Professor of Genetics at Cambridge University, United Kingdom, in 1912 when Bateson, for whom it had been intended, declined to leave his new appointment as first Director of the John Innes Horticultural Institute. We here celebrate the centenary of the first professorship dedicated to genetics, outlining Punnett’s career and his scientific contributions, with special reference to the discovery of “partial coupling” in the sweet pea (later “linkage”) and to the diagram known as Punnett’s square. His seeming reluctance as coauthor with Bateson to promote the reduplication hypothesis to explain the statistical evidence for linkage is stressed, as is his relationship with his successor as Arthur Balfour Professor, R. A. Fisher. The background to the establishment of the Professorship is also described.
The Systems Genetics Resource (SGR) (http://systems.genetics.ucla.edu) is a new open-access web application and database that contains genotypes and clinical and intermediate phenotypes from both human and mouse studies. The mouse data include studies using crosses between specific inbred strains and studies using the Hybrid Mouse Diversity Panel. SGR is designed to assist researchers studying genes and pathways contributing to complex disease traits, including obesity, diabetes, atherosclerosis, heart failure, osteoporosis, and lipoprotein metabolism. Over the next few years, we hope to add data relevant to deafness, addiction, hepatic steatosis, toxin responses, and vascular injury. The intermediate phenotypes include expression array data for a variety of tissues and cultured cells, metabolite levels, and protein levels. Pre-computed tables of genetic loci controlling intermediate and clinical phenotypes, as well as phenotype correlations, are accessed via a user-friendly web interface. The web site includes detailed protocols for all of the studies. Data from published studies are freely available; unpublished studies have restricted access during their embargo period.
Robert Heath Lock (1879–1915), a Cambridge botanist associated with William Bateson and R. C. Punnett, published his book Recent Progress in the Study of Variation, Heredity, and Evolution in 1906. This was a remarkable textbook of genetics for one appearing so early in the Mendelian era. It covered not only Mendelism but evolution, natural selection, biometry, mutation, and cytology. It ran to five editions but was, despite its success, largely forgotten following Lock’s early death in 1915. Nevertheless it was the book that inspired H. J. Muller to do genetics and was remembered by A. H. Sturtevant as the source of the earliest suggestion that linkage might be related to the exchange of parts between homologous chromosomes. Here we also put forward evidence that it had a major influence on the statistician and geneticist R. A. Fisher at the time he was a mathematics student at Cambridge.
In this commentary, Rob Kulathinal describes two papers from the Perrimon laboratory, each describing a new online resource that can assist geneticists with the design of their RNAi experiments. Hu et al.’s “UP-TORR: online tool for accurate and up-to-date annotation of RNAi reagents” and “FlyPrimerBank: An online database for Drosophila melanogaster gene expression analysis and knockdown evaluation of RNAi reagents” are published, respectively, in this month’s issue of GENETICS and G3: Genes|Genomes|Genetics.
This review describes the current knowledge regarding genetics and epigenetics of pregnancy-associated diseases with placental origin. We discuss the effect on genetic linkage analyses when the fetal genotype determines the maternal phenotype. Secondly, the genes identified by genome-wide linkage studies to be associated with pre-eclampsia (ACVR2A, STOX1) and the HELLP-syndrome (LINC-HELLP) are discussed regarding their potential functions in the etiology of disease. Furthermore, susceptibility genes identified by candidate gene approaches (e.g., CORIN) are described. Next, we focus on the additional challenges that come when epigenetics also play a role in disease inheritance. We discuss the maternal transmission of the chromosome 10q22 pre-eclampsia linkage region containing the STOX1 gene and provide further evidence for the role of epigenetics in pre-eclampsia based on the cdkn1c mouse model of pre-eclampsia. Finally, we provide recommendations to unravel the genetics of pregnancy-associated diseases, specifically regarding clear definitions of patient groups and sufficient patient numbers, and the potential usefulness of (epi)genetic data in early non-invasive biomarker development.
Whole genome sequencing has allowed rapid progress in the application of forward genetics in model species. In this study, we demonstrated an application of next-generation sequencing for forward genetics in a complex crop genome. We sequenced an ethyl methanesulfonate-induced mutant of Sorghum bicolor defective in hydrogen cyanide release and identified the causal mutation. A workflow identified the causal polymorphism relative to the reference BTx623 genome by integrating data from single nucleotide polymorphism identification, prior information about candidate gene(s) implicated in cyanogenesis, mutation spectra, and polymorphisms likely to affect phenotypic changes. A point mutation resulting in a premature stop codon in the coding sequence of dhurrinase2, which encodes a protein involved in the dhurrin catabolic pathway, was responsible for the acyanogenic phenotype. Cyanogenic glucosides are not cyanogenic compounds but their cyanohydrins derivatives do release cyanide. The mutant accumulated the glucoside, dhurrin, but failed to efficiently release cyanide upon tissue disruption. Thus, we tested the effects of cyanide release on insect herbivory in a genetic background in which accumulation of cyanogenic glucoside is unchanged. Insect preference choice experiments and herbivory measurements demonstrate a deterrent effect of cyanide release capacity...
Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher’s infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with “genomic selection” is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.
Twin and family studies have shown that most traits are at least moderately heritable. But what are the implications of finding genetic influence for the design of intervention and prevention programs? For complex traits, heritability does not mean immutability, and research has shown that genetic influences can change with age, context, and in response to behavioral and drug interventions. The most significant implications for intervention will come when we move from observational genetics to investigating dynamic genetics, including genetically sensitive interventions. Future interventions should be designed to overcome genetic risk and draw upon genetic strengths by changing the environment.
Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover...
The Genetic Society of America’s Thomas Hunt Morgan Medal is awarded to an individual GSA member for lifetime achievement in the field of genetics. For over 40 years, 2015 recipient Brian Charlesworth has been a leader in both theoretical and empirical evolutionary genetics, making substantial contributions to our understanding of how evolution acts on genetic variation. Some of the areas in which Charlesworth’s research has been most influential are the evolution of sex chromosomes, transposable elements, deleterious mutations, sexual reproduction, and life history. He also developed the influential theory of background selection, whereby the recurrent elimination of deleterious mutations reduces variation at linked sites, providing a general explanation for the correlation between recombination rate and genetic variation.
A total of 44 wild jaguarundis were sampled throughout Mexico, Guatemala, Costa Rica, Colombia, Venezuela, Ecuador, Peru, Bolivia and Brazil and sequenced for three mitochondrial genes (ATP8, 16S rRNA, NADH5). This is the first molecular population genetics and phylogenetic study of this species and the most relevant results were as follows: 1- The gene diversity levels for the jaguarundi at the three mitochondrial genes sequenced were very elevated as it was found for other Neotropical wild cats such as the jaguar, ocelot, margay and the Pampas cat; 2 - The levels of gene heterogeneity among putative subspecies or among countries was extremely small, although this species has a broad distribution from southern USA to Argentina; 3- Additionally, the phylogenetics trees (genetic distances, maximum likelihood, maximum parsi mony and Bayesian) showed that no molecular subspecies were defined in contradiction with the morphological classifications of Allen (1919), Cabrera (1957) and de Oliveira (1998); 4-Bayesian and network procedures showed that the first haplotype divergence process in the jaguarundi began around 2.0-1.6 MYA, with a second haplotype divergence event around 1.1-0.8 MYA...