Asthma, atopy, and related phenotypes are heterogeneous complex traits, with both genetic and environmental risk factors. Extensive research has been conducted and over hundred genes have been associated with asthma and atopy phenotypes, but many of these findings have failed to replicate in subsequent studies. To separate true associations from false positives, candidate genes need to be examined in large well-characterized samples, using standardized designs (genotyping, phenotyping and analysis). In an attempt to replicate previous associations we amalgamated the power and resources of four studies and genotyped 5,565 individuals to conduct a genetic association study of 93 previously associated candidate genes for asthma and related phenotypes using the same set of 861 single-nucleotide polymorphisms (SNPs), a common genotyping platform, and relatively harmonized phenotypes. We tested for association between SNPs and the dichotomous outcomes of asthma, atopy, atopic asthma, and airway hyperresponsiveness using a general allelic D. Daley and M. Lemire contributed equally to this manuscript. Electronic supplementary materialThe online version of this article

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We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotelysensed 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 and comparing 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 forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were underestimated by 1-4% in mid-season and overestimated 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.

Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics. Exact inference for logistic regression can be problematic when data sets are large and the support of the conditional distribution cannot be represented in memory. Additionally, these methods are not widely implemented except in commercial software packages such as LogXact and SAS. Therefore, we have developed elrm, software for R implementing (approximate) exact inference for binomial regression models from large data sets. We provide a description of the underlying statistical methods and illustrate the use of elrm with examples. We also evaluate elrm by comparing results with those obtained using other methods.

BACKGROUND: Young Indigenous people, particularly those involved in the child welfare system, those entrenched in sub stance use and those living with HIV or hepatitis C, are dying prematurely. We report mortality rates among young Indigenous people who use drugs in Brit ish Columbia and explore predictors of mortality over time. METHODS:We analyzed data collected every 6 months between 2003 and 2014 by the Cedar Project, a prospective cohort study involving young Indigenous people who use illicit drugs in Vancouver and Prince George, BC. We calculated agestandardized mortality ratios using Indigenous and Canadian reference popu lations. We identified predictors of mortality using timedependent Cox pro portional hazard regression. RESULTS:Among 610 participants, 40 died between 2003 and 2014, yielding a mortality rate of 670 per 100 000 personyears. Young Indigenous people who used drugs were 12.9 (95% confi dence interval [CI] 9.2-17.5) times more likely to die than all Canadians the same age and were 7.8 (95% CI 5.6-10.6) times more likely to die than Indigenous people with Status in BC. Young women and those using drugs by injection were most affected. The lead ing causes of death were overdose (n = 15 [38%]), illness (n = 11 [28%]) and sui cide (n = 5 [12%]). Predictors of mortal ity included having hepatitis C at base line (adjusted hazard ratio [HR] 2.76, 95% CI 1.47-5.16), previous attempted suicide (adjusted HR 1.88, 95% CI 1.01-3.50) and recent overdose (adjusted HR 2.85, 95% CI 1.00-8.09). INTERPRETATION:Young Indigenous people using drugs in BC are dying at an alarming rate, particularly young women and those using injection drugs. These deaths likely reflect complex intersec tions of historical and presentday injus tices, substance use and barriers to care.