Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs

Mapa genético saturado de microssatélite: caminhando para uma cobertura genômica em uma população de milho tropical (Zea mays L.)

Dense molecular genetic maps are used for an efficient quantitative trait loci (QTL) mapping and in the marker-assisted selection programs. A dense genetic map was generated with 139 microsatellite markers using 256 F2 plants generated by the crossing of two tropical maize inbred lines (L-02-03D and L-20-01F). This map presented 1,858.61 cM in length, where 10 linkage groups were found spanned, with an average interval of 13.47 cM between adjacent markers. Seventy seven percent of the maize genetic mapping bins were covered, which means an increase of 14% coverage in relation to the previous tropical maize maps. The results provide a more detailed and informative genetic map in a tropical maize population representing the first step to make possible the studies of genetic architecture to identify and map QTL and estimate their effects on the variation of quantitative traits, thus allowing the manipulation and use in tropical maize breeding programs323499508Mapas genéticos saturados são utilizados para um eficiente mapeamento de caracteres de interesse agronômico (QTL) e nos programas de seleção assistida. Este trabalho gerou um mapa genético saturado utilizando 139 marcadores moleculares do tipo microssatélites em 256 plantas F2 geradas pelo cruzamento de duas linhagens de milho tropical (L-02-03D e L-20-01F). O mapa obtido teve uma extensão total de 1.858,61 cM, ao longo de 10 grupos de ligação, com intervalo médio entre os marcadores de 13,47 cM. Setenta e nove percento dos "bins" do mapa genético de milho foram cobertos, com um acréscimo de 14% de cobertura em relação aos mapas de milho publicados. Os resultados mostram um mapa genético mais detalhado e informativo em uma população de milho tropical representando uma primeira etapa que possibilitará desenvolver estudos da arquitetura genética para a identificação e mapeamento de QTL e a estimativa de seus efeitos sobre a variação de um caráter quantitativo, permitindo assim a sua manipulação e utilização em programas de melhoramento do milh

De Novo Transcriptome Assembly For The Tropical Grass Panicum Maximum Jacq.

Guinea grass (Panicum maximum Jacq.) is a tropical African grass often used to feed beef cattle, which is an important economic activity in Brazil. Brazil is the leader in global meat exportation because of its exclusively pasture-raised bovine herds. Guinea grass also has potential uses in bioenergy production due to its elevated biomass generation through the C4 photosynthesis pathway. We generated approximately 13 Gb of data from Illumina sequencing of P. maximum leaves. Four different genotypes were sequenced, and the combined reads were assembled de novo into 38,192 unigenes and annotated; approximately 63% of the unigenes had homology to other proteins in the NCBI non-redundant protein database. Functional classification through COG (Clusters of Orthologous Groups), GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses showed that the unigenes from Guinea grass leaves are involved in a wide range of biological processes and metabolic pathways, including C4 photosynthesis and lignocellulose generation, which are important for cattle grazing and bioenergy production. The most abundant transcripts were involved in carbon fixation, photosynthesis, RNA translation and heavy metal cellular homeostasis. Finally, we identified a number of potential molecular markers, including 5,035 microsatellites (SSRs) and 346,456 single nucleotide polymorphisms (SNPs). To the best of our knowledge, this is the first study to characterize the complete leaf transcriptome of P. maximum using high-throughput sequencing. The biological information provided here will aid in gene expression studies and marker-assisted selection-based breeding research in tropical grasses.8e7078

Reciprocal recurrent selection effects on the genetic structure of tropical maize populations assessed at microsatellite loci

A modified reciprocal recurrent selection (RRS) method, which employed one cycle of high-intensity selection, was applied to two tropical maize (Zea mays L.) populations, BR-105 and BR-106, originating the improved synthetics IG-3 and IG-4, respectively. In the present study the effects of this kind of selection on the genetic structure of these populations and their synthetics were investigated at 30 microsatellite (SSR) loci. A total of 125 alleles were revealed. A reduction in the number of alleles was observed after selection, as well as changes in allele frequencies. In nearly 13% (BR-105) and 7% (BR-106) of the loci evaluated, the changes in allele frequencies were not explained, exclusively due to the effects of genetic drift. The effective population sizes estimated for the synthetics using 30 SSR loci were similar to those theoretically expected after selection. The genetic differentiation (G ST) between the synthetics increased to 77% compared with the original populations. The estimated R ST values, a genetic differentiation measure proper for microsatellite data, were similar to those obtained for G ST. Despite the high level of selection applied, the total gene diversity found in the synthetics allows them to be used in a new RRS cycle.355364Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L)

The objective of this study was to evaluate whether different similarity coefficients used with dominant markers can influence the results of cluster analysis, using eighteen inbred lines of maize from two different populations, BR-105 and BR-106. These were analyzed by AFLP and RAPD markers and eight similarity coefficients were calculated: Jaccard, Sorensen-Dice, Anderberg, Ochiai, Simple-matching, Rogers and Tanimoto, Ochiai II and Russel and Rao. The similarity matrices obtained were compared by the Spearman correlation, cluster analysis with dendrograms (UPGMA, WPGMA, Single Linkage, Complete Linkage and Neighbour-Joining methods), the consensus fork index between all pairs of dendrograms, groups obtained through the Tocher optimization procedure and projection efficiency in a two-dimensional space. The results showed that for almost all methodologies and marker systems, the Jaccard, Sorensen-Dice, Anderberg and Ochiai coefficient showed close results, due to the fact that all of them exclude negative co-occurrences. Significant alterations in the results for the Simple Matching, Rogers and Tanimoto, and Ochiai II coefficients were not observed either, probably due to the fact that they all include negative co-occurrences. The Russel and Rao coefficient presented very different results from the others in almost all the cases studied and should not be used, because it excludes the negative co-occurrences in the numerator and includes them in the denominator of their expression. Due to the fact that the negative co-occurrences do not necessarily mean that the regions of the DNA are identical, the use of coefficients that do not include negative co-occurrences was suggested.8391Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES