ABSTRACT
Because of its wide distribution, high yield potential, and short cycle, the potato has become essential for global food security. However, the complexity of tetrasomic inheritance, the high level of heterozygosity of the parents, the low multiplication rate of tubers, and the genotype-by-environment interactions impose severe challenges on tetraploid potato-breeding programs. The initial stages of selection take place in experiments with low selection accuracy for many of the quantitative traits of interest, for example, tuber yield. The goal of this study was to investigate the contribution of incorporating a family effect in the estimation of the total genotypic effect and selection of clones in the initial stage of a potato-breeding program. The evaluation included single trials (STs) and multi-environment trials (METs). A total of 1,280 clones from 67 full-sib families from the potato-breeding program at Universidade Federal de Lavras were evaluated for the traits total tuber yield and specific gravity. These clones were distributed in six evaluated trials that varied according to the heat stress level: without heat stress, moderate heat stress, and high heat stress. To verify the importance of the family effect, models with and without the family effect were compared for the analysis of ST and MET data for both traits. The models that included the family effect were better adjusted in the ST and MET data analyses for both traits, except when the family effect was not significant. Furthermore, the inclusion of the family effect increased the selective efficiency of clones in both ST and MET analyses via an increase in the accuracy of the total genotypic value. These same models also allowed the prediction of clone effects more realistically, as the variance components associated with family and clone effects within a family were not confounded. Thus, clonal selection based on the total genotypic value, combining the effects of family and clones within a family, proved to be a good alternative for potato-breeding programs that can accommodate the logistic and data tracking required in the breeding program.
ABSTRACT
The largest family of disease resistance genes in plants are nucleotide-binding site leucine-rich repeat genes (NLRs). The products of these genes are responsible for recognizing avirulence proteins (Avr) of phytopathogens and triggering specific defense responses. Identifying NLRs in plant genomes with standard gene annotation software is challenging due to their multidomain nature, sequence diversity, and clustered genomic distribution. We present the results of a genome-wide scan and comparative analysis of NLR loci in three coffee species (Coffea canephora, Coffea eugenioides and their interspecific hybrid Coffea arabica). A total of 1311 non-redundant NLR loci were identified in C. arabica, 927 in C. canephora, and 1079 in C. eugenioides, of which 809, 562, and 695 are complete loci, respectively. The NLR-Annotator tool used in this study showed extremely high sensitivities and specificities (over 99%) and increased the detection of putative NLRs in the reference coffee genomes. The NLRs loci in coffee are distributed among all chromosomes and are organized mostly in clusters. The C. arabica genome presented a smaller number of NLR loci when compared to the sum of the parental genomes (C. canephora, and C. eugenioides). There are orthologous NLRs (orthogroups) shared between coffee, tomato, potato, and reference NLRs and those that are shared only among coffee species, which provides clues about the functionality and evolutionary history of these orthogroups. Phylogenetic analysis demonstrated orthologous NLRs shared between C. arabica and the parental genomes and those that were possibly lost. The NLR family members in coffee are subdivided into two main groups: TIR-NLR (TNL) and non-TNL. The non-TNLs seem to represent a repertoire of resistance genes that are important in coffee. These results will support functional studies and contribute to a more precise use of these genes for breeding disease-resistant coffee cultivars.
ABSTRACT
BACKGROUND: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). RESULTS: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. CONCLUSIONS: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (-2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
