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

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

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

Genetic Mapping With Allele Dosage Information in Tetraploid Urochloa decumbens (Stapf) R. D. Webster Reveals Insights Into Spittlebug (Notozulia entreriana Berg) Resistance

Urochloa decumbens (Stapf) R. D. Webster is one of the most important African forage grasses in Brazilian beef production. Currently available genetic-genomic resources for this species are restricted mainly due to polyploidy and apomixis. Therefore, crucial genomic-molecular studies such as the construction of genetic maps and the mapping of quantitative trait loci (QTLs) are very challenging and consequently affect the advancement of molecular breeding. The objectives of this work were to (i) construct an integrated U. decumbens genetic map for a full-sibling progeny using GBS-based markers with allele dosage information, (ii) detect QTLs for spittlebug (Notozulia entreriana) resistance, and (iii) seek putative candidate genes involved in defense against biotic stresses. We used the Setaria viridis genome a reference to align GBS reads and selected 4,240 high-quality SNP markers with allele dosage information. Of these markers, 1,000 were distributed throughout nine homologous groups with a cumulative map length of 1,335.09 cM and an average marker density of 1.33 cM. We detected QTLs for resistance to spittlebug, an important pasture insect pest, that explained between 4.66 and 6.24% of the phenotypic variation. These QTLs are in regions containing putative candidate genes related to defense against biotic stresses. Because this is the first genetic map with SNP autotetraploid dosage data and QTL detection in U. decumbens, it will be useful for future evolutionary studies, genome assembly, and other QTL analyses in Urochloa spp. Moreover, the results might facilitate the isolation of spittlebug-related candidate genes and help clarify the mechanism of spittlebug resistance. These approaches will improve selection efficiency and accuracy in U. decumbens molecular breeding and shorten the breeding cycle

Deep expression analysis reveals distinct cold-response strategies in rubber tree (hevea brasiliensis)

Natural rubber, an indispensable commodity used in approximately 40,000 products, is fundamental to the tire industry. The rubber tree species Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg., which is native the Amazon rainforest, is the major producer of latex worldwide. Rubber tree breeding is time consuming, expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. In this work, transcriptome sequencing was used to identify a full set of transcripts and to evaluate the gene expression involved in the different cold-response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes.ResultsWe built a comprehensive transcriptome using multiple database sources, which resulted in 104,738 transcripts clustered in 49,304 genes. The RNA-seq data from the leaf tissues sampled at four different times for each genotype were used to perform a gene-level expression analysis. Differentially expressed genes (DEGs) were identified through pairwise comparisons between the two genotypes for each time series of cold treatments.DEG annotation revealed that RRIM600 and GT1 exhibit different chilling tolerance strategies. To cope with cold stress, the RRIM600 clone upregulates genes promoting stomata closure, photosynthesis inhibition and a more efficient reactive oxygen species (ROS) scavenging system. The transcriptome was also searched for putative molecular markers (single nucleotide polymorphisms (SNPs) and microsatellites) in each genotype. and a total of 27,111 microsatellites and 202,949 (GT1) and 156,395 (RRIM600) SNPs were identified in GT1 and RRIM600. Furthermore, a search for alternative splicing (AS) events identified a total of 20,279 events.ConclusionsThe elucidation of genes involved in different chilling tolerance strategies associated with molecular markers and information regarding AS events provides a powerful tool for further genetic and genomic analyses of rubber tree breeding20CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP478701/2012–8; 402954/2012Sem informação2007/50392–1; 2012/50491–8; 2014/18755–0; 2015/24346–

De Novo Assembly And Transcriptome Analysis Of The Rubber Tree (hevea Brasiliensis) And Snp Markers Development For Rubber Biosynthesis Pathways.

Hevea brasiliensis (Willd. Ex Adr. Juss.) Muell.-Arg. is the primary source of natural rubber that is native to the Amazon rainforest. The singular properties of natural rubber make it superior to and competitive with synthetic rubber for use in several applications. Here, we performed RNA sequencing (RNA-seq) of H. brasiliensis bark on the Illumina GAIIx platform, which generated 179,326,804 raw reads on the Illumina GAIIx platform. A total of 50,384 contigs that were over 400 bp in size were obtained and subjected to further analyses. A similarity search against the non-redundant (nr) protein database returned 32,018 (63%) positive BLASTx hits. The transcriptome analysis was annotated using the clusters of orthologous groups (COG), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam databases. A search for putative molecular marker was performed to identify simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). In total, 17,927 SSRs and 404,114 SNPs were detected. Finally, we selected sequences that were identified as belonging to the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, which are involved in rubber biosynthesis, to validate the SNP markers. A total of 78 SNPs were validated in 36 genotypes of H. brasiliensis. This new dataset represents a powerful information source for rubber tree bark genes and will be an important tool for the development of microsatellites and SNP markers for use in future genetic analyses such as genetic linkage mapping, quantitative trait loci identification, investigations of linkage disequilibrium and marker-assisted selection.9e10266