Biology subjects

Silva-Junior, O. B.

Publications and source records attributed to Silva-Junior, O. B..

3 recordsLinked to original sources

bioRxiv2022

Genetic associations with resistance to Meloidogyne enterolobii in guava (Psidium sp.) using cross-genera SNPs and comparative genomics to Eucalyptus highlight evolutionary conservation across the Myrtaceae

Tropical fruit tree species constitute a yet untapped supply of outstanding diversity of taste and nutritional value, barely developed from the genetics standpoint, with scarce or no genomic resources to tackle the challenges arising in modern breeding practice. We generated a de novo genome assembly of the Psidium guajava, the super fruit "apple of the tropics", and successfully transferred 14,268 SNP probesets from Eucalyptus to Psidium at the nucleotide level, to detect genomic loci linked to resistance to the root knot nematode (RKN) Meloidogyne enterolobii derived from the wild relative P. guineense. Significantly associated loci with resistance across alternative analytical frameworks, were detected at two SNPs on chromosome 3 in a pseudo-assembly of Psidium guajava genome built using a syntenic path approach with the Eucalyptus grandis genome to determine the order and orientation of the contigs. The P. guineense-derived resistance response to RKN and disease onset is conceivably triggered by mineral nutrients and phytohormone homeostasis or signaling with the involvement of the miRNA pathway. Hotspots of mapped resistance quantitative trait loci and functional annotation in the same genomic region of Eucalyptus provide further indirect support to our results, highlighting the evolutionary conservation of genomes across genera of Myrtaceae in the adaptation to pathogens. Marker assisted introgression of the resistance loci mapped should accelerate the development of improved guava cultivars and hybrid rootstocks.

genetics↗

bioRxiv2020

A 3K Axiom(R) SNP array from a transcriptome-wide SNP resource sheds new light on the genetic diversity and structure of the iconic subtropical conifer tree Araucaria angustifolia (Bert.) Kuntze

High-throughput SNP genotyping has become a precondition to move to higher precision and wider genome coverage genetic analysis of natural and breeding populations of non-model species. We developed a 44,318 annotated SNP catalog for Araucaria angustifolia, a grandiose subtropical conifer tree, one of the only two native Brazilian gymnosperms, critically endangered due to its valuable wood and seeds. Following transcriptome assembly and annotation, SNPs were discovered from RNA-seq and pooled RAD-seq data. From the SNP catalog, an Axiom(R) SNP array with 3,038 validated SNPs was developed and used to provide a comprehensive look at the genetic diversity and structure of 15 populations across the natural range of the species. RNA-seq was a far superior source of SNPs when compared to RAD-seq in terms of conversion rate to polymorphic markers on the array, likely due to the more efficient complexity reduction of the huge conifer genome. By matching microsatellite and SNP data on the same set of A. angustifolia individuals, we show that SNPs reflect more precisely the actual genome-wide patterns of genetic diversity and structure, challenging previous microsatellite-based assessments. Moreover, SNPs corroborated the known major north-south genetic cline, but allowed a more accurate attribution to regional versus among-population differentiation, indicating the potential to select ancestry-informative markers. The availability of a public, user-friendly 3K SNP array for A. angustifolia and a catalog of 44,318 SNPs predicted to provide [~]29,000 informative SNPs across [~]20,000 loci across the genome, will allow tackling still unsettled questions on its evolutionary history, toward a more comprehensive picture of the origin, past dynamics and future trend of the species genetic resources. Additionally, but not less importantly, the SNP array described, unlocks the potential to adopt genomic prediction methods to accelerate the still very timid efforts of systematic tree breeding of A. angustifolia.

genetics↗

bioRxiv2019

Enviromics in breeding: applications and perspectives on envirotypic-assisted selection

Genotype by Environment interaction (G x E) studies have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent Geographic Information System (GIS) techniques have opened new frontiers for understanding and dealing with G x E. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term Enviromics under an envirotypic-assisted breeding framework and propose the GIS-GE method, i.e. a geospatial tool to maximize genetic gains by predicting the phenotypic performance of unobserved genotypes using \"enviromic markers\". In summary, a particular site represents a set of envirotypes, each one representing a set of environmental factors that interact with the genetic background of genotypes, thus resulting in informative re-rankings to make decisions over different environments. Based on a simulated case study, we show that GIS-GE allows accurate (i) matching of genotypes to their most appropriate sites; (ii) definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (iii) indication of the best sites to carry out experiments for further analysis based on environments that maximize heritability. Envirotyping techniques provide a new class of markers for genetic studies, which are inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of the Enviromics approach into breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity. Environmental scenarios can also be improved using information from strategic plans for biodiversity and genetic resources management, especially in the current perspective of dynamic climate change.\n\nKey messageWe propose the application of Enviromics to breeding practice, by which the similarity among sites assessed on an \"omics\" scale of environmental attributes drives the prediction of unobserved genotypes.

plant biology↗