Breedbase: a digital ecosystem for modern plant breeding.

Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.

Genetic dissection of Al tolerance QTLs in the maize genome by high density SNP scan.

BACKGROUND: Aluminum (Al) toxicity is an important limitation to food security in tropical and subtropical regions. High Al saturation on acid soils limits root development, reducing water and nutrient uptake. In addition to naturally occurring acid soils, agricultural practices may decrease soil pH, leading to yield losses due to Al toxicity. Elucidating the genetic and molecular mechanisms underlying maize Al tolerance is expected to accelerate the development of Al-tolerant cultivars. RESULTS: Five genomic regions were significantly associated with Al tolerance, using 54,455 SNP markers in a recombinant inbred line population derived from Cateto Al237. Candidate genes co-localized with Al tolerance QTLs were further investigated. Near-isogenic lines (NILs) developed for ZmMATE2 were as Al-sensitive as the recurrent line, indicating that this candidate gene was not responsible for the Al tolerance QTL on chromosome 5, qALT5. However, ZmNrat1, a maize homolog to OsNrat1, which encodes an Al(3+) specific transporter previously implicated in rice Al tolerance, was mapped at ~40 Mbp from qALT5. We demonstrate for the first time that ZmNrat1 is preferentially expressed in maize root tips and is up-regulated by Al, similarly to OsNrat1 in rice, suggesting a role of this gene in maize Al tolerance. The strongest-effect QTL was mapped on chromosome 6 (qALT6), within a 0.5 Mbp region where three copies of the Al tolerance gene, ZmMATE1, were found in tandem configuration. qALT6 was shown to increase Al tolerance in maize; the qALT6-NILs carrying three copies of ZmMATE1 exhibited a two-fold increase in Al tolerance, and higher expression of ZmMATE1 compared to the Al sensitive recurrent parent. Interestingly, a new source of Al tolerance via ZmMATE1 was identified in a Brazilian elite line that showed high expression of ZmMATE1 but carries a single copy of ZmMATE1. CONCLUSIONS: High ZmMATE1 expression, controlled either by three copies of the target gene or by an unknown molecular mechanism, is responsible for Al tolerance mediated by qALT6. As Al tolerant alleles at qALT6 are rare in maize, marker-assisted introgression of this QTL is an important strategy to improve maize adaptation to acid soils worldwide.