Interactive Graphics and Local Statistics

Genetic transformation: a powerful tool for dissection of adaptive traits in trees

Plant transformation and regeneration systems have become indispensable parts of gene discovery and functional characterization over the last two decades. Adoption of transformation methods in studies of plant adaptation to natural environments has been slow. This is a result of poor genomic knowledge and inefficient transformation systems for species dominating terrestrial ecosystems, and logistical difficulties in conducting field tests of genetically engineered organisms. In trees, where long generation cycles, high background polymorphism, large sizes and outcrossing systems of mating make production of near-isogenic lines and large experiments difficult, transformation is an attractive alternative for establishing direct linkages between genes and adaptively significant phenotypes. Here, we outline the capabilities, challenges, and prospects for transformation to become a significant tool for studying the ecophysiological adaptation of trees to the environment. Focusing on poplars (genus Populus) as model system, we describe how transformation-based approaches can provide insights into the genes that control adaptive traits. The availability of the poplar genome sequence, along with its large expressed sequences tag (EST) databanks, facile transformation and rapid growth, enable reverse genetic approaches to be used to test virtually any hypothesis of gene function.

Spatial statistical methods in health

The study of the geographical distribution of disease incidence and its relationship to potential risk factors (referred to here as "geographical epidemiology") has provided, and continues to provide, rich ground for the application and development of statistical methods and models. In recent years increasingly powerful and versatile statistical tools have been developed in this application area. This paper discusses the general classes of problem in geographical epidemiology and reviews the key statistical methods now being employed in each of the application areas identified. The paper does not attempt to exhaustively cover all possible methods and models, but extensive references are provided to further details and to additional approaches. The overall aim is to provide a picture of the "current state of the art" in the use of spatial statistical methods in epidemiological and public health research. Following the review of methods, the main software environments which are available to implement such methods are discussed. The paper concludes with some brief general reflections on the epidemiological and public health implications of the use of spatial statistical methods in health and on associated benefits and problems.