Traditionally, plant breeders identified chromosomal regions using genetic markers that are statistically associated with traits of interest. These genomic regions are called quantitative trait loci (QTLs). A QTL region can easily contain thousands of genes including those that negatively influence the trait of interest [5]. Therefore, detecting the causative gene for breeding is of major importance. There are three major approaches to address the challenge of candidate gene prediction in crop species: (i) the analysis of gene expression data or co-expression networks [6], (ii) comparative genomics [7], and (iii) integrate information stored in scientific literature and in molecular biology databases such as the ELXIR Core Data Resources [8] (including the European Nucleotide Archive (ENA) [9], Ensembl Plants [10] and UniProt [11]) and the Sol Genomics Network (SGN) [12]. To address the need for improved access to integrated plant data, we developed the _Solanaceae_ Linked Data platform (pbg-ld) [13] that combines QTLs from scientific literature and genome annotations from public databases using the Linked Data approach [14]. Our approach is to create a semantic web of data rather than that of hypertext (HTML) documents using Uniform Resource Identifiers (URIs) and Resource Description Framework (RDF) [15]. A URI is an HTTP-based resource identifier assigned to an entity whereas RDF is a generic graph-based data model for describing entities and their relationships. In addition, publishing data according to FAIR Data Principles [15] further increases the degree of discoverability and (re-)usability of research data. In plant sciences, several controlled vocabularies and ontologies have been developed to standardize domain-specific terms and/or represent the current knowledge of the domain in a machine-readable form. For example, the _Solanaceae_ Phenotype Ontology (SPTO) [16], Crop Ontology [17], Plant Ontology (PO) [18], Phenotypic Quality Ontology (PATO) [19] and Trait Ontology (TO) [20] are used to identify plant-specific phenotypic information while Gene Ontology (GO) [21], Sequence Ontology (SO) [22] and Feature Annotation Location Description Ontology (FALDO) [23] are used to identify genotypic information. Similarly, the Chemical Entities of Biological Interest database/ontology (ChEBI) [24] is focused on small chemical compounds. There are several plant-specific databases available that provide geno- and phenotypic data. For example, Ensembl Plants is a widely used integrated resource on plant genomes. Similarly, UniProt is a database of protein sequences, function annotations and proteomes of various species including plants. Both databases release their data in RDF-based format. The _Arabidopsis_ Information Resource (TAIR) [25] is a resource to analyze and to compare molecular, biological, and genetical data of the model species _Arabidopsis thaliana_. Further, the Sol Genomics Network (SGN) [12] provides genomic, genetic and phenotypic information for members of the _Solanaceae_ family. Plant Genome DataBase Japan (PGDBj) [26] is an integrated web resource for plant genome-related information from literature and public databases. However, the TAIR, SGN, and PGDBj do not distribute their data in a semantically interoperable (RDF) format. The Planteome [27] database provides gene annotations and phenotypes with the help of reference ontologies such as PO, TO, GO and ChEBI. Planteome is a user-friendly tool to query traits of interest, germplasm, and putative candidate genes. However, it lacks QTLs, genetic markers and links to publicly available databases such as Ensembl Plants. We present the pbg-ld platform that provides semantically integrated geno- and phenotypic data on _Solanaceae_ species such as the (wild) tomato and potato species. The resulting (linked) datasets are made available through a web interface or programmatic services (SPARQL and RESTful APIs). The use of these data access points is described in the results section.