The Sol Genomics Network (SGN)—from genotype to phenotype to breeding

The Sol Genomics Network (SGN, http://solgenomics.net) is a web portal with genomic and phenotypic data, and analysis tools for the Solanaceae family and close relatives. SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato, pepper, eggplant, tobacco and Nicotiana benthamiana. The database also stores loci and phenotype data, which researchers can upload and edit with user-friendly web interfaces. Tools such as BLAST, GBrowse and JBrowse for browsing genomes, expression and map data viewers, a locus community annotation system and a QTL analysis tools are available. A new tool was recently implemented to improve Virus-Induced Gene Silencing (VIGS) constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database, SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-specific databases

solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database

BACKGROUND: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases. DESCRIPTION: The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application. CONCLUSIONS: solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode

solGS: a webbased tool for genomic selection

Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program

High-Throughput Genomics Enhances Tomato Breeding Efficiency

Tomato (Solanum lycopersicum) is considered a model plant species for a group of economically important crops, such as potato, pepper, eggplant, since it exhibits a reduced genomic size (950 Mb), a short generation time, and routine transformation technologies. Moreover, it shares with the other Solanaceous plants the same haploid chromosome number and a high level of conserved genomic organization. Finally, many genomic and genetic resources are actually available for tomato, and the sequencing of its genome is in progress. These features make tomato an ideal species for theoretical studies and practical applications in the genomics field. The present review describes how structural genomics assist the selection of new varieties resistant to pathogens that cause damage to this crop. Many molecular markers highly linked to resistance genes and cloned resistance genes are available and could be used for a high-throughput screening of multiresistant varieties. Moreover, a new genomics-assisted breeding approach for improving fruit quality is presented and discussed. It relies on the identification of genetic mechanisms controlling the trait of interest through functional genomics tools. Following this approach, polymorphisms in major gene sequences responsible for variability in the expression of the trait under study are then exploited for tracking simultaneously favourable allele combinations in breeding programs using high-throughput genomic technologies. This aims at pyramiding in the genetic background of commercial cultivars alleles that increase their performances. In conclusion, tomato breeding strategies supported by advanced technologies are expected to target increased productivity and lower costs of improved genotypes even for complex traits

An ontology approach to comparative phenomics in plants

BACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Mapping and characterization of novel parthenocarpy QTLs in tomato

Parthenocarpy is the development of the fruit in absence of pollination and/or fertilization. In tomato, parthenocarpy is considered as an attractive trait to solve the problems of fruit setting under unfavorable conditions. We studied the genetics of parthenocarpy in two different lines, IL5-1 and IVT-line 1, both carrying Solanum habrochaites chromosome segments. Parthenocarpy in IL5-1 is under the control of two QTLs, one on chromosome 4 (pat4.1) and one on chromosome 5 (pat5.1). IVT-line 1 also contains two parthenocarpy QTLs, one on chromosome 4 (pat4.2) and one on chromosome 9 (pat9.1). In addition, we identified one stigma exsertion locus in IL5-1, located on the long arm of chromosome 5 (se5.1). It is likely that pat4.1, from IL5-1 and pat4.2, from IVT-line 1, both located near the centromere of chromosome 4 are allelic. By making use of the microsynteny between tomato and Arabidopsis in this genetic region, we identified ARF8 as a potential candidate gene for these two QTLs. ARF8 is known to act as an inhibitor for further carpel development in Arabidopsis, in absence of pollination/fertilization. Expression of an aberrant form of the ArabidopsisARF8 gene, in tomato, has been found to cause parthenocarpy. This candidate gene approach may lead to the first isolation of a parthenocarpy gene in tomato and will allow further use in several crop species

Multiple QTL for horticultural traits and quantitative resistance to Phytophthora infestans linked on Solanum habrochaites chromosome 11.

Previously, a Phytophthora infestans resistance QTL from Solanum habrochaites chromosome 11 was introgressed into cultivated tomato (S. lycopersicum). Fine mapping of this resistance QTL using near-isogenic lines (NILs) revealed some co-located QTL with undesirable effects on plant size, canopy density, and fruit size traits. Subsequently, higher-resolution mapping with sub-NILs detected multiple P. infestans resistance QTL within this 9.4-cM region of chromosome 11. In our present study, these same sub-NILs were also evaluated for 17 horticultural traits, including yield, maturity, fruit size and shape, fruit quality, and plant architecture traits in replicated field experiments over 2 years. The horticultural trait QTL originally detected by fine mapping each fractionated into two or more QTL at higher resolution. A total of 34 QTL were detected across all traits, with 14% exhibiting significant QTL × environment interactions (QTL × E). QTL for many traits were co-located, suggesting either pleiotropic effects or tight linkage among genes controlling these traits. Recombination in the pericentromeric region of the introgression between markers TG147 and At4g10050 was suppressed to approximately 29.7 Mbp per cM, relative to the genomewide average of 750 kbp per cM. The genetic architecture of many of the horticultural and P. infestans resistance traits that mapped within this chromosome 11 S. habrochaites region is complex. Complicating factors included fractionation of QTL, pleiotropy or tight linkage of QTL for multiple traits, pericentromeric chromosomal location(s), and/or QTL × E. High-resolution mapping of QTL in this region would be needed to determine which specific target QTL could be useful in breeding cultivated tomato

Evidence of cryptic introgression in tomato (Solanum lycopersicum L.) based on wild tomato species alleles

Abstract Background Many highly beneficial traits (e.g. disease or abiotic stress resistance) have been transferred into crops through crosses with their wild relatives. The 13 recognized species of tomato (Solanum section Lycopersicon) are closely related to each other and wild species genes have been extensively used for improvement of the crop, Solanum lycopersicum L. In addition, the lack of geographical barriers has permitted natural hybridization between S. lycopersicum and its closest wild relative Solanum pimpinellifolium in Ecuador, Peru and northern Chile. In order to better understand patterns of S. lycopersicum diversity, we sequenced 47 markers ranging in length from 130 to 1200 bp (total of 24 kb) in genotypes of S. lycopersicum and wild tomato species S. pimpinellifolium, Solanum arcanum, Solanum peruvianum, Solanum pennellii and Solanum habrochaites. Between six and twelve genotypes were comparatively analyzed per marker. Several of the markers had previously been hypothesized as carrying wild species alleles within S. lycopersicum, i.e., cryptic introgressions. Results Each marker was mapped with high confidence (e-30) to a single genomic location using BLASTN against tomato whole genome shotgun chromosomes (SL2.40) database. Neighbor-joining trees showed high mean bootstrap support (86.8 ± 2.34%) for distinguishing red-fruited from green-fruited taxa for 38 of the markers. Hybridization and parsimony splits networks, genomic map positions of markers relative to documented introgressions, and historical origins of accessions were used to interpret evolutionary patterns at nine markers with putatively introgressed alleles. Conclusion Of the 47 genetic markers surveyed in this study, four were involved in linkage drag on chromosome 9 during introgression breeding, while alleles at five markers apparently originated from natural hybridization with S. pimpinellifolium and were associated with primitive genotypes of S. lycopersicum. The positive identification of introgressed genes within crop species such as S. lycopersicum will help inform conservation and utilization of crop germplasm diversity, for example, facilitating the purging of undesirable linkage drag or the exploitation of novel, favorable alleles.</p