Predicting fusarium head blight epidemics with weather-driven pre- and post-anthesis logistic regression models.

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

Admixture in Hispanic Americans: its impact on ITGAM association and implications for admixture mapping in SLE.

Systemic Lupus Erythematosus (SLE) disproportionately affects minorities, such as Hispanic Americans (HA). Prevalence of SLE is 3-5 times higher in HA than in European-derived populations and have more active disease at the time of diagnosis, with more serious organ system involvement. HA is an admixed population, it is possible that there is an effect of admixture on the relative risk of the disease. This admixture can create substantial increase of linkage disequilibrium (LD) in both magnitude and range, which can provide a unique opportunity for admixture mapping. The main objectives of this study are to (a) estimate hidden population structure in HA individuals; (b) estimate individual ancestry proportions and its impact on SLE risk; (c) assess impact of admixture on ITGAM association, a recently identified SLE susceptibility gene; and (d) estimate power of admixture mapping in HA. Our dataset contained 1125 individuals, of whom 884 (657 SLE cases and 227 controls) were self-classified as HA. Using 107 unlinked ancestry informative markers (AIMs), we estimated hidden population structure and individual ancestry in HA. Out of 5671 possible pairwise LD, 54% were statistically significant, indicating recent population admixture. The best-fitted model for HA was a four-population model with average ancestry of European (48%), American-Indian (AI) (40%), African (8%) and a fourth population (4%) with unknown ancestry. We also identified significant higher risk associated with AI ancestry (odds ratio (OR)=4.84, P=0.0001, 95% CI (confidence interval)=2.14-10.95) on overall SLE. We showed that ITGAM is associated as a risk factor for SLE (OR=2.06, P=8.74 x 10(-5), 95% CI=1.44-2.97). This association is not affected by population substructure or admixture. We have shown that HA have great potential and are an appropriate population for admixture mapping. As expected, the case-only design is more powerful than case-control design, for any given admixture proportion or ancestry risk ratio.