Flexible modelling of spatial variation in agricultural field trials with the R package INLA

The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ( AR1⊗AR1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA

Secondary malnutrition and overweight in a pediatric referral hospital: Associated factors

OBJECTIVES:: To establish the prevalence and identify the clinical and sociodemographic factors associated with malnutrition and overweight in a pediatric referral hospital. PATIENTS AND METHODS:: We studied a cross-sectional, random sample from a pediatric hospital. Malnutrition was defined as acute when the z score of weight/height was less than-2.0 and as chronic if in addition the height/age z score was less than-2.0. Overweight risk was defined as a body mass index percentile between 85 and 94, and overweight as a body mass index percentile of 95 or higher. RESULTS:: The study included 641 patients, with mean age 7.1 4.9 years (56% male). The overall prevalence of acute malnutrition was 8% and chronic malnutrition 17.0%. Overweight risk was present in 15.4% and overweight in 12.2%. Acute malnutrition was predicted by conditions on admission (hospitalization: odds ratio [OR] 2.3, confidence interval [CI] 1.3-4.3; nonsurgical subspecialty: OR 2.1, CI 1.0-4.3) and number of siblings (1 child, single mother: OR 2.6, CI 1.3-5.0). Chronic malnutrition was predicted by age (infants vs preschoolers: OR 2.0, CI 1.1-3.6; infants vs school children: OR 3.1, CI 1.8-5.5) and illness duration (>30 days: OR 2, CI 1.1-3.7). Overweight risk was associated with age (>36 months: OR 2.0, CI 1.6-3.4) and the father's educational level (college and university: OR 2.3, CI 1.3-4.3). Overweight was predicted by sex (boys: OR 2.0, CI 1.0-3.6) and age (>36 months: OR 1.7, CI 1.0-2.8). CONCLUSIONS:: Overweight was as prevalent as malnutrition. Malnutrition was associated with clinical condition, age, family size, and illness duration, whereas overweight was related to age, sex, and father's education. Overweight appears as a novel finding in the nutritional profile of pediatric referral hospitals in Mexico. 2009 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition