Combining phenotypic and genomic data to improve prediction of binary traits.

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here 'main traits') of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or 'phenotypes') that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any phenotypic variables. We develop a new statistical technique that ensures appropriate representation from both the secondary traits and the genotypic variables for optimal prediction. When two data types (markers and secondary traits) are available, we achieve improved prediction of a binary trait by two steps that are designed to ensure that a significant intrinsic effect of a phenotype is incorporated in the relation before accounting for extra effects of genotypes. First, we sparsely regress the secondary traits on the markers and replace the secondary traits by their residuals to obtain the effects of phenotypic variables as adjusted by the genotypic variables. Then, we develop a sparse logistic classifier using the markers and residuals so that the adjusted phenotypes may be selected first to avoid being overwhelmed by the genotypic variables due to their numerical advantage. This classifier uses forward selection aided by a penalty term and can be computed effectively by a technique called the one-pass method. It compares favorably with other classifiers on simulated and real data.

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines' performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha(-1) across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.

Guías para la atención de las principales emergencias obstétricas

[Introducción] El presente documento pretende ser una herramienta accesible y práctica para el abordaje de las situaciones de emergencia y urgencia obstétricas más frecuentes. Trata las principales causas de mortalidad materna, en el entendido que el correcto diagnóstico y manejo de las mismas puede evitar la muerte de la mujer gestante...Este manual esta dirigido a establecer un diagnóstico oportuno y acciones adecuadas, por parte de los profesionales de la salud de los diferentes niveles de atención, ante una embarazada con complicaciones capaces de llevarla a la muerte. Estas incluyen las infecciones obstétricas graves, las hemorragias del embarazo, parto y puerperio, las complicaciones graves de los estados hipertensivos del embarazo y un capítulo referido a la asistencia en caso de paro cardiorrespiratorio en la embarazada. Su abordaje adecuado puede significar la diferencia entre la vida y la muerte, tanto para la madre como el niño, siendo su impacto más significativo en los países con alta morbimortalidad materna. Es por ello que los programas para reducir la MM en los países con recursos limitados, deben estar enfocados en el manejo adecuado de las complicaciones obstétricas.

FIGO Save the Mothers Initiative: the Central America and USA collaboration.

The American College of Obstetricians and Gynecologists (ACOG) and the Central American Federation of Associations and Societies of Obstetrics and Gynecology (FECASOG), as a part of the FIGO Save the Mothers Initiative, undertook a pilot project to improve provision of basic emergency obstetric care in selected departments in four Central American countries. This article describes the process of the development and implementation of the project. Preliminary results suggest that the capacity to provide this care has been improved by the training of healthcare personnel.