Selection of cowpea progenies with enhanced drought-tolerance traits using principal component analysis.

Vigna unguiculata (L.) Walp (cowpea) is a food crop with high nutritional value that is cultivated throughout tropical and subtropical regions of the world. The main constraint on high productivity of cowpea is water deficit, caused by the long periods of drought that occur in these regions. The aim of the present study was to select elite cowpea genotypes with enhanced drought tolerance, by applying principal component analysis to 219 first-cycle progenies obtained in a recurrent selection program. The experimental design comprised a simple 15 x 15 lattice with 450 plots, each of two rows of 10 plants. Plants were grown under water-deficit conditions by applying a water depth of 205 mm representing one-half of that required by cowpea. Variables assessed were flowering, maturation, pod length, number and mass of beans/pod, mass of 100 beans, and productivity/plot. Ten elite cowpea genotypes were selected, in which principal components 1 and 2 encompassed variables related to yield (pod length, beans/pod, and productivity/plot) and life precocity (flowering and maturation), respectively.

Diallel analysis in cowpea aiming at selection for extra-earliness.

In Brazil, the production of cowpea is concentrated in the Northeast and North; however, in recent years, its cultivation has expanded to the Cerrado biome of the Brazilian Midwest region, where it is incorporated into production arrangements in the form of off-season

Selection indices and multivariate analysis show similar results in the evaluation of growth and carcass traits in beef cattle.

2016

Genome selection in fruit breeding: application to table grapes.

Genomic selection (GS) has recently been proposed as a new selection strategy which represents an innovative paradigm in crop improvement, now widely adopted in animal breeding. Genomic selection relies on phenotyping and high-density genotyping of a sufficiently large and representative sample of the target breeding population, so that the majority of loci that regulate a quantitative trait are in linkage disequilibrium with one or more molecular markers and can thus be captured by selection. In this study we address genomic selection in a practical fruit breeding context applying it to a breeding population of table grape obtained from a cross between the hybrid genotype D8909-15 (Vitis rupestris × Vitis arizonica/girdiana), which is resistant to dagger nematode and Pierce?s disease (PD), and ?B90-116?, a susceptible Vitis vinifera cultivar with desirable fruit characteristics. Our aim was to enhance the knowledge on the genomic variation of agronomical traits in table grape populations for future use in marker-assisted selection (MAS) and GS, by discovering a set of molecular markers associated with genomic regions involved in this variation. A number of Quantitative Trait Loci (QTL) were discovered but this method is inaccurate and the genetic architecture of the studied population was better captured by the BLasso method of genomic selection, which allowed for efficient inference about the genetic contribution of the various marker loci. The technology of genomic selection afforded greater efficiency than QTL analysis and can be very useful in speeding up the selection procedures for agronomic traits in table grapes.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Marker-assisted selection integrated to the embrapa common bean breeding program.

The establishment of a specific Marker-Assisted Selection Facility at the Embrapa Rice and Beans Biotechnology Laboratory, in 2014, has better supported the routine analysis with molecular markers demanded by the Embrapa Common Bean Breeding Program. In addition, it has also supported other Embrapa plant breeding programs, such as rice and cotton.