Effects of plant population density on cabbage (Brassica oleracea var. capitata L.) crop

Com a finalidade de avaliar o efeito da densidade de população de plantas sobre a cultura de repolho (Brassica cleraoea var, capitata L.), foi realizado um experimento no Campo Experimental do Setor de Horticultura da Escola Superior de Agricultura "Luiz de Queiroz", Piracicaba, São Paulo, em um Latossol Roxo, Série Luiz de Queiroz, utilizando-se os espaçamentos de 0,60 mx 0,80 m; 0,60 m x 0,65 m; 0,60 m x 0,45 m; 0,60 m x 0,30 m e 0,60 m x 0,15 m. À medida em que se aumentou a densidade de população, houve as seguintes alterações na planta de repolho: mudança de for mato chato da "cabeça" para cônico, redução do numero de folhas, tamanho (peso, volume e diâmetros transversal e longitudinal) , aumento na densidade da "cabeça" (peso/volume) e aumento na porcentagem de plantas que não produziram "cabeça.In order to study the effects of plant population density on cabbage crop (Brassica oleracea var-. capitata), an experiment using different spacing (0,60 m x 0.80 m, 0.60 m x 0.65 m, 0.60mx0.45m, 0.60 m x 0.30 m, and 0.60mx 0.15m) was carried out. There were the following changes in cabbage plants due to increasing population density: plant head became conical: number and size (weight, volume and diameters) of leaves decreased; density (weight/volume) and percentage of plants forming no head increased

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)