Population admixture associated with disease prevalence in the Boston Puerto Rican health study

Older Puerto Ricans living in the continental U.S. suffer from higher rates of diabetes, obesity, cardiovascular disease and depression compared to non-Hispanic White populations. Complex diseases, such as these, are likely due to multiple, potentially interacting, genetic, environmental and social risk factors. Presumably, many of these environmental and genetic risk factors are contextual. We reasoned that racial background may modify some of these risk factors and be associated with health disparities among Puerto Ricans. The contemporary Puerto Rican population is genetically heterogeneous and originated from three ancestral populations: European settlers, native Taíno Indians, and West Africans. This rich-mixed ancestry of Puerto Ricans provides the intrinsic variability needed to untangle complex gene–environment interactions in disease susceptibility and severity. Herein, we determined whether a specific ancestral background was associated with either of four major disease outcomes (diabetes, obesity, cardiovascular disease, and depression). We estimated the genetic ancestry of 1,129 subjects from the Boston Puerto Rican Health Study based on genotypes of 100 ancestry informative markers (AIMs). We examined the effects of ancestry on tests of association between single AIMs and disease traits. The ancestral composition of this population was 57.2% European, 27.4% African, and 15.4% Native American. African ancestry was negatively associated with type 2 diabetes and cardiovascular disease, and positively correlated with hypertension. It is likely that the high prevalence rate of diabetes in Africans, Hispanics, and Native Americans is not due to genetic variation alone, but to the combined effects of genetic variation interacting with environmental and social factors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Disentangling spatio‐temporal processes in a hierarchical system: a case study in fisheries discards

In the last decade, various spatial and temporal methodologies were developed to investigate the processes that drive ecological and evolutionary patterns. However, these methods frequently fail to acknowledge that the observed patterns result from the overlap of different underlying processes. In order to understand how the patterns are formed, we must have recourse to methods that allow us to disentangle these simultaneous processes. Here we develop a hierarchical spatial predictive process (PP) combined with a separable temporal PP to disentangle and describe those overlapping processes in one very frequent setting in ecology and evolution: multilevel spatio‐temporally indexed data. We present our methodology through a case study of fisheries discards and investigate for example whether the inclusion of the hierarchical structure and the temporal processes of the system alter the observed spatial patterns. Recently it is recognized that understanding the processes driving discards is essential to sustainably manage and conserve marine resources. The results show that consideration of multiple underlying processes dramatically changes the pattern and characteristics of the discards hot‐ and coldspots. In the Irish Sea, the inclusion of the hierarchical structure of the system leads to the reduction of the hot‐ and coldspots. Simultaneously, our model identifies key bi‐annual fluctuations in the temporal process which, together with the variance associated at the level of individual fishing trips in the hierarchical structure of the data explained most of the variance driving discards. Whether the hierarchical, spatial and temporal processes are considered together or not can profoundly alter our understanding of what constitutes an appropriate mitigation measure. Misidentification of hotspots can culminate in inappropriate mitigation practices which can sometimes be irreversible. As the proposed method offers a unified approach for understanding the processes that drive observed patterns, many areas in ecology such as conservation and epidemiological studies can benefit from its use, increasing the effectiveness of management plans.     

VirtualPlant: A Software Platform to Support Systems Biology Research

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org.Today, experimental biology laboratories usually investigate the molecular mechanisms underlying a physiological or developmental response by identifying the genes involved using a genomic platform, such as microarray (or, soon, deep sequencing) technology. Such a platform might identify genes regulated during a physiological or developmental response. Once the relevant gene sets are identified, biologists next analyze their functional relationships (e.g. whether they belong to the same metabolic pathway) and analyze their properties in the context of known biological pathways (DeRisi et al., 1997). Performing these tasks can be cumbersome because the biologist has to use several different tools to accomplish them. In addition, the difficulty is often increased because the different tools do not read and write the same data formats, forcing the biologist to obtain data conversion software.Aside from the challenge of integrating the vast amount of knowledge accumulated in the literature about the relevant genes, the genomic data available in the public domain have been obtained with a large number of experimental approaches and an even larger number of laboratories. Moreover, the information is stored in numerous databases, and it is encoded in diverse formats and database schemas. Bioinformatics faces a major challenge integrating this large-scale, heterogeneous information into architectures that support biological research. Different approaches that have been employed include hypertext navigation on the World Wide Web, data warehousing, and client-side integration (for example, see Ritter, 1994; Karp, 1996; Siepel et al., 2001; Philippi, 2004; Wilkinson et al., 2005). Once data from distinct database sources are coherently integrated, tools and computer models can be used to enable one to visualize and analyze this biological data from a systems perspective (Ideker et al., 2001). Several environments have been developed to support data integration and modeling (Kahlem and Birney, 2007). Some software allows detailed mathematical representation of cellular processes (e.g. Gepasi [Mendes, 1997] and Virtual Cell [Loew and Schaff, 2001]), while other software permits qualitative representations of cellular components and their interactions (e.g. Cytoscape [Shannon et al., 2003], Osprey [Breitkreutz et al., 2003], and N-Browse [Kao and Gunsalus, 2008]). Generally, quantitative models build detailed mathematical abstractions of specific cellular process. Quantitative models are powerful because they describe a system in detail (Endy and Brent, 2001), but they require a detailed understanding of the system. Unfortunately, this information is available for only a few biological processes. In fact, there are still many gaps in our qualitative understanding of biological systems, even for model organisms. For example, most of the genes in Arabidopsis (Arabidopsis thaliana) have not yet been experimentally characterized. Thus, while quantitative computer models can provide powerful, detailed representations of biological systems, not enough is known about Arabidopsis and other plants to construct such models of them or their major components. Therefore, we have focused on building software that facilitates analysis of the systems and statistical and interaction relationships between their genes and gene products.Today''s most widely available measure of gene function is the level of gene expression provided by a microarray analysis. Many approaches and tools support analysis of expression data. A now classic approach, for example, is to identify genes that are coregulated in their expression patterns across selected experimental conditions (e.g. Eisen et al., 1998). An extensive review of the different software tools that are available for studying gene coexpression is available (Usadel et al., 2009). To identify genes that are differentially expressed between two experimental conditions, statistical methods such as Rank Products can be used (Breitling et al., 2004; Hong et al., 2006). Several tools are available as packages in BioConductor, a project largely composed of tools written in the statistical language R (Gentleman et al., 2004). To determine the biological significance of differentially or coexpressed genes, biologists often evaluate the frequency of occurrence of functional attributes provided by structured functional annotations, such as Gene Ontology (GO; Ashburner et al., 2000). Several software packages to automate this type of analysis now exist (e.g. Onto-Express [Khatri et al., 2002], GoMiner [Zeeberg et al., 2003], GOSurfer [Zhong et al., 2004], and FatiGO [Al-Shahrour et al., 2004]). While advanced data analysis tools for exploiting genomic data are rapidly emerging (for review, see Brady and Provart, 2009), the narrow specialization of most current software tools forces geneticists to employ many tools to analyze the data in a single biological study. This cumbersome and inefficient process greatly hinders biologists following a systems approach of iterative in silico exploration and experimentation.VirtualPlant addresses these problems by integrating selected genomic data and analysis tools into a single Web-accessible software platform. The goal of our work is to help biologists discover new insights by synthesizing multiple data sources. VirtualPlant provides access to a database storing selected information about Arabidopsis and rice (Oryza sativa) experiments, genes, gene products, and their properties. VirtualPlant''s software architecture and data model have been designed and created in a generic, species-independent manner to ease the addition of new organisms and tools in the future. The VirtualPlant database also includes a high-level representation of plant cellular components and interactions that allow users to create molecular networks “on the fly.” These molecular networks provide a framework for analyzing experimental measurements. VirtualPlant also includes novel data visualization and data analysis techniques that allow seamless information exploration across many data sets with the help of a shopping cart in which gene sets from experiments and/or analyses can be stored and then used as inputs to other tools to enable iterative analysis. For concreteness, we present an example of how we have used VirtualPlant to identify gene networks and putative regulatory hubs that control seed development. We have previously demonstrated the use of VirtualPlant and specific tools embodied in the VirtualPlant system to generate hypotheses that were validated experimentally (Wang et al., 2004; Gutiérrez et al., 2007b, 2008; Gifford et al., 2008; Thum et al., 2008).     

Pollination Ecology and Seed Production of Rhododendron Ponticum in Native and Exotic Habitats

Alien plants may be reproductively limited in exotic habitats because of a lack of mutualistic pollinators. However, if plants are adequately served by generalist pollinators, successful reproduction, naturalisation and expansion into exotic habitats may occur. Rhododendron ponticum is very successful, ecologically damaging invasive plant in Britain and Ireland, but is in decline in its native Iberian habitat. It spreads locally by sending out lateral branches, but for longer distance dispersal it relies on sexually produced seeds. Little is known about R. ponticum's pollination ecology and breeding biology in invaded habitats. We examined the flower-visiting communities and maternal reproductive success of R. ponticum in native populations in southern Spain and in exotic ones in Ireland. R. ponticum in flowers are visited by various generalist (polylectic) pollinator species in both native and exotic habitats. Although different species visited flowers in Ireland and Spain, the flower visitation rate was not significantly different. Insects foraging on R. ponticum in Spain carried less R. ponticum pollen than their Irish counterparts, and carried fewer pollen types. Fruit production per inflorescence varied greatly within all populations but was significantly correlated with visitation at the population level. Nectar was significantly depleted by insects in some exotic populations, suggesting that this invasive species is providing a floral resource for native insects in some parts of Ireland. The generality of the pollination system may be factor contributing to R.ponticum's success in exotic habitats.