David S. Zamar

A thymic stromal lymphopoietin gene variant is associated with asthma and airway hyperresponsiveness.

BACKGROUND The epithelial cell-derived protein thymic stromal lymphopoietin stimulates dendritic and mast cells to promote proallergic T(H)2 responses. Studies of transgenic expression of thymic stromal lymphopoietin and its receptor knockout mice have emphasized its critical role in the development of allergic inflammation. Association of genetic variation in thymic stromal lymphopoietin with IgE levels has been reported for human subjects. OBJECTIVE The aim of this study was to evaluate the relationship between variants of thymic stromal lymphopoietin and asthma and related phenotypes. METHODS We selected 6 single nucleotide polymorphisms in thymic stromal lymphopoietin and genotyped 5565 individuals from 4 independent asthma studies and tested for association with asthma, atopy, atopic asthma, and airway hyperresponsiveness by using a general allelic likelihood ratio test. P values were corrected for the effective number of independent single nucleotide polymorphisms and phenotypes. RESULTS The A allele of rs1837253, which is 5.7 kb upstream of the transcription start site of the gene, was associated with protection from asthma, atopic asthma, and airway hyperresponsiveness, with the odds ratios and corrected P values for each being 0.79 and 0.0058; 0.75 and 0.0074; and 0.76 and 0.0094, respectively. Associations between thymic stromal lymphopoietin and asthma-related phenotypes were the most statistically significant observations in our study, which has to date examined 98 candidate genes. Full results are available online at http://genapha.icapture.ubc.ca/. CONCLUSIONS A genetic variant in the region of the thymic stromal lymphopoietin gene is associated with the phenotypes of asthma and airway hyperresponsiveness.

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.

Path: a tool to facilitate pathway-based genetic association analysis

Summary: Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP–SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB). Availability: The software, example datasets and tutorials are freely available from http://genapha.icapture.ubc.ca/PathTutorial. Contact: ddaley@mrl.ubc.ca

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

Abstract Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This paper develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. The proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.