Pooled Association Tests for Rare Genetic Variants: A Review and Some New Results

In the search for genetic factors that are associated with complex heritable human traits, considerable attention is now being focused on rare variants that individually have small effects. In response, numerous recent papers have proposed testing strategies to assess association between a group of rare variants and a trait, with competing claims about the performance of various tests. The power of a given test in fact depends on the nature of any association and on the rareness of the variants in question. We review such tests within a general framework that covers a wide range of genetic models and types of data. We study the performance of specific tests through exact or asymptotic power formulas and through novel simulation studies of over 10,000 different models. The tests considered are also applied to real sequence data from the 1000 Genomes project and provided by the GAW17. We recommend a testing strategy, but our results show that power to detect association in plausible genetic scenarios is low for studies of medium size unless a high proportion of the chosen variants are causal. Consequently, considerable attention must be given to relevant biological information that can guide the selection of variants for testing.Comment: Published in at http://dx.doi.org/10.1214/13-STS456 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

An original deconvolution approach for oil production allocation based on geochemical fingerprinting

We tackle oil commingling scenarios and develop an original deconvolution approach for geochemical production allocation. This yields robust assessment of the proportions of oils forming a mixture originating from commingling oils associated with diverse reservoirs or, wells. Our study starts from considering that production allocation performed by means of geochemical fingerprinting is relevant in the context of modern and sustainable use of georesources, with the added benefit of favoring shared facilities and production equipment. A geochemical production allocation workflow is typically structured according to two steps: (i) determination of the chromatograms associated with the mixture (and eventually with each of the End Members, EMs, constituting the fluids in the mixture), and (ii) the use of a deconvolution algorithm to estimate the mass fraction of each EM. Concerning the latter step, we introduce an original approach and the ensuing deconvolution algorithm (hereafter termed PGM) that does not require additional laboratory efforts in comparison with traditional approaches. We also present extensions of widely used deconvolution algorithms, which we frame in a (stochastic) Monte Carlo context to improve their robustness and reliability. The new PGM approach is assessed jointly with a suite of typically used approaches and algorithms against new laboratory-based commingling scenarios. The latter are based on the design and introduction of a novel and low-cost experimental method. The results of the study (i) constitute a unique and rigorous comparison of the traditionally employed production allocation deconvolution algorithms, (ii) document the critical importance of the number of features of the chromatograms used during a quantitative deconvolution, and (iii) suggest that our new PGM approach is very robust and accurate compared to existing approaches

Scale effects on genomic modelling and prediction

In dieser Arbeit wird eine neue Methode für den skalenunabhängigen Vergleich von LD-Strukturen in unterschiedlichen genomischen Regionen vorgeschlagen. Verschiedene Aspekte durch Skalen verursachter Probleme – von der Präzision der Schätzung der Marke-reffekte bis zur Genauigkeit der Vorhersage für neue Individuen - wurden untersucht. Darüber hinaus, basierend auf den Leistungsvergleichen von unterschiedlichen statistischen Methoden, wurden Empfehlungen für die Verwendungen der untersuchten Methoden gege-ben.In dieser Arbeit wird eine neue Methode für den skalenunabhängigen Vergleich von LD-Strukturen in unterschiedlichen genomischen Regionen vorgeschlagen. Verschiedene Aspekte durch Skalen verursachter Probleme – von der Präzision der Schätzung der Marke-reffekte bis zur Genauigkeit der Vorhersage für neue Individuen - wurden untersucht. Darüber hinaus, basierend auf den Leistungsvergleichen von unterschiedlichen statistischen Methoden, wurden Empfehlungen für die Verwendungen der untersuchten Methoden gegebenIn this thesis, a novel method for scale corrected comparisons of LD structure in different genomic regions is suggested. Several aspects of scale-caused problems – from precision of marker effect estimates to accuracy of predictions for new individuals - are investigated. Furthermore, based on a comparison of the performance of different approaches, recommendations on the application of examined methods are given

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future