Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance

Despite important biotic stresses to common bean, Anthracnose (ANT) and Angular Leaf Spot (ALS) can cause losses of up to 80 % and occur in more than 60 countries around the world. Genetic resistance is the most sustainable strategy to manage these diseases. Thus, we aimed to (1) identify new SNP markers associated with ALS and ANT resistance loci in elite common bean lines, and (2) provide a functional characterization of the DNA sequences containing the identified SNP markers. We evaluated 60 inbred lines, under field conditions, which represent the elite germplasm developed by the Embrapa common bean breeding program across 22 years, in terms of severity of the ALS and ANT. The lines were genotyped with 5,398 SNPs. Then, a Mixed Linear Model was run to determine the SNP-trait associations. We observed two-significant marker-trait associations reacting to ANT, both located on chromosome Pv-02. These markers explained 25 % of the phenotypic variation. For ALS, only one significant marker-trait association was observed, which is located in chromosome Pv-10 and explained 19 % of the phenotypic variation. These markers, along with others already used, will be useful to add or keep ANT and ALS resistance loci identified in this work in the new carioca and black seeded cultivars.(AU)

Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance

Despite important biotic stresses to common bean, Anthracnose (ANT) and Angular Leaf Spot (ALS) can cause losses of up to 80 % and occur in more than 60 countries around the world. Genetic resistance is the most sustainable strategy to manage these diseases. Thus, we aimed to (1) identify new SNP markers associated with ALS and ANT resistance loci in elite common bean lines, and (2) provide a functional characterization of the DNA sequences containing the identified SNP markers. We evaluated 60 inbred lines, under field conditions, which represent the elite germplasm developed by the Embrapa common bean breeding program across 22 years, in terms of severity of the ALS and ANT. The lines were genotyped with 5,398 SNPs. Then, a Mixed Linear Model was run to determine the SNP-trait associations. We observed two-significant marker-trait associations reacting to ANT, both located on chromosome Pv-02. These markers explained 25 % of the phenotypic variation. For ALS, only one significant marker-trait association was observed, which is located in chromosome Pv-10 and explained 19 % of the phenotypic variation. These markers, along with others already used, will be useful to add or keep ANT and ALS resistance loci identified in this work in the new carioca and black seeded cultivars.

Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Unfortunately, the first author name of the above-mentioned article was incorrectly published in the original publication. The complete correct name should read as follows.

Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

KEY MESSAGE: Testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.

Genomic prediction in maize breeding populations with genotyping-by-sequencing.

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.