Genetic diversity of the forage peanut in the Jequitinhonha, São Francisco, and Paranã River valleys of Brazil.

Arachis pintoi and A. repens are legumes with a high forage value that are used to feed ruminants in consortium systems. Not only do they increase the persistence and quality of pastures, they are also used for ornamental and green cover. The objective of this study was to analyze microsatellite markers in order to access the genetic diversity of 65 forage peanut germplasm accessions in the section Caulorrhizae of the genus Arachis in the Jequitinhonha, São Francisco and Paranã River valleys of Brazil. Fifty-seven accessions of A. pintoi and eight of A. repens were analyzed using 17 microsatellites, and the observed heterozygosity (HO), expected heterozygosity (HE), number of alleles per locus, discriminatory power, and polymorphism information content were all estimated. Ten loci (58.8%) were polymorphic, and 125 alleles were found in total. The HE ranged from 0.30 to 0.94, and HO values ranged from 0.03 to 0.88. By using Bayesian analysis, the accessions were genetically differentiated into three gene pools. Neither the unweighted pair group method with arithmetic mean nor a neighbor-joining analysis clustered samples into species, origin, or collection area. These results reveal a very weak genetic structure that does not form defined clusters, and that there is a high degree of similarity between the two species.

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.