Statistics

Comparison of methods used to identify superior individuals in genomic selection in plant breeding

L. L. Bhering, Junqueira, V. S., Peixoto, L. A., Cruz, C. D., and Laviola, B. G., “Comparison of methods used to identify superior individuals in genomic selection in plant breeding”, vol. 14, pp. 10888-10896, 2015.

The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG.

Experimental strategies in carrying out VCU for tobacco crop I: plot design and size

F. H. R. B. Toledo, Ramalho, M. A. P., Pulcinelli, C. E., and Bruzi, A. T., “Experimental strategies in carrying out VCU for tobacco crop I: plot design and size”, vol. 12, pp. 3766-3774, 2013.

We aimed to establish standards for tobacco Valor de Cultivo e Uso (VCU) in Brazil. We obtained information regarding the size and design of plots of two varietal groups of tobacco (Virginia and Burley). Ten inbred lines of each varietal group were evaluated in a randomized complete block design with four replications. The plot contained 42 plants with six rows of seven columns each.

BayBoots: a model-free Bayesian tool to identify class markers from gene expression data

R. Z. N. Vêncio, Patrão, D. F. C., Baptista, C. S., Pereira, C. A. B., and Zingales, B., “BayBoots: a model-free Bayesian tool to identify class markers from gene expression data”, vol. 5, pp. 138-142, 2006.

One of the goals of gene expression experiments is the identification of differentially expressed genes among populations that could be used as markers. For this purpose, we implemented a model-free Bayesian approach in a user-friendly and freely available web-based tool called BayBoots. In spite of a common misunderstanding that Bayesian and model-free approaches are incompatible, we merged them in the BayBoots implementation using the Kernel density estimator and Rubin’s Bayesian Bootstrap.

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