Recent advances in high-throughput sequencing (HTS) technologies and computing capacity have produced unprecedented amounts of genomic data that have unraveled the genetics of phenotypic variability in several species. However, operating and integrating current software tools for data analysis still require important investments in highly skilled personnel. Developing accurate, efficient and user-friendly software packages for HTS data analysis will lead to a more rapid discovery of genomic elements relevant to medical, agricultural and industrial applications. We therefore developed Next-Generation Sequencing Eclipse Plug-in (NGSEP), a new software tool for integrated, efficient and user-friendly detection of single nucleotide variants (SNVs), indels and copy number variants (CNVs). NGSEP includes modules for read alignment, sorting, merging, functional annotation of variants, filtering and quality statistics. Analysis of sequencing experiments in yeast, rice and human samples shows that NGSEP has superior accuracy and efficiency, compared with currently available packages for variants detection. We also show that only a comprehensive and accurate identification of repeat regions and CNVs allows researchers to properly separate SNVs from differences between copies of repeat elements. We expect that NGSEP will become a strong support tool to empower the analysis of sequencing data in a wide range of research projects on different species.

Apomixis, asexual reproduction through seed, enables breeders to identify and faithfully propagate superior heterozygous genotypes by seed without the disadvantages of vegetative propagation or the expense and complexity of hybrid seed production. The availability of new tools such as genotyping by sequencing and bioinformatics pipelines for species lacking reference genomes now makes the construction of dense maps possible in apomictic species, despite complications including polyploidy, multisomic inheritance, self-incompatibility, and high levels of heterozygosity. In this study, we developed saturated linkage maps for the maternal and paternal genomes of an interspecific Brachiaria ruziziensis (R. Germ. and C. M. Evrard) × B. decumbens Stapf. F1 mapping population in order to identify markers linked to apomixis. High-resolution molecular karyotyping and comparative genomics with Setaria italica (L.) P. Beauv provided conclusive evidence for segmental allopolyploidy in B. decumbens, with strong preferential pairing of homologs across the genome and multisomic segregation relatively more common in chromosome 8. The apospory-specific genomic region (ASGR) was mapped to a region of reduced recombination on B. decumbens chromosome 5. The Pennisetum squamulatum (L.) R.Br. PsASGR-BABY BOOM-like (psASGR–BBML)-specific primer pair p779/p780 was in perfect linkage with the ASGR in the F1 mapping population and diagnostic for reproductive mode in a diversity panel of known sexual and apomict Brachiaria (Trin.) Griseb. and P. maximum Jacq. germplasm accessions and cultivars. These findings indicate that ASGR–BBML gene sequences are highly conserved across the Paniceae and add further support for the postulation of the ASGR–BBML as candidate genes for the apomictic function of parthenogenesis.

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

Current advances in sequencing technologies and bioinformatics revealed the genomic background of rice, a staple food for the poor people, and provided the basis to develop large genomic variation databases for thousands of cultivars. Proper analysis of this massive resource is expected to give novel insights into the structure, function, and evolution of the rice genome, and to aid the development of rice varieties through marker assisted selection or genomic selection. In this work we present sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa. We identified repetitive elements and recurrent copy number variation covering about 200 Mbp of the rice genome. Genotyping of over 18 million polymorphic locations within O. sativa allowed us to reconstruct the individual haplotype patterns shaping the genomic background of elite varieties used by farmers throughout the Americas. Based on a reconstruction of the alleles for the gene GBSSI, we could identify novel genetic markers for selection of varieties with high amylose content. We expect that both the analysis methods and the genomic information described here would be of great use for the rice research community and for other groups carrying on similar sequencing efforts in other crops.