Current Status and Future Prospects of Next-Generation Data Management and Analytical Decision Support Tools for Enhancing Genetic Gains in Crops

Agricultural disciplines are becoming data intensive and the agricultural research data generation technologies are becoming sophisticated and high throughput. On the one hand, high-throughput genotyping is generating petabytes of data; on the other hand, high-throughput phenotyping platforms are also generating data of similar magnitude. Under modern integrated crop breeding, scientists are working together by integrating genomic and phenomic data sets of huge data volumes on a routine basis. To manage such huge research data sets and use them appropriately in decision making, Data Management Analysis & Decision Support Tools (DMASTs) are a prerequisite. DMASTs are required for a range of operations including generating the correct breeding experiments, maintaining pedigrees, managing phenotypic data, storing and retrieving high-throughput genotypic data, performing analytics, including trial analysis, spatial adjustments, identifications of MTAs, predicting Genomic Breeding Values (GEBVs), and various selection indices. DMASTs are also a prerequisite for understanding trait dynamics, gene action, interactions, biology, GxE, and various other factors contributing to crop improvement programs by integrating data generated from various science streams. These tools have simplified scientists’ lives and empowered them in terms of data storage, data retrieval, data analytics, data visualization, and sharing with other researchers and collaborators. This chapter focuses on availability, uses, and gaps in present-day DMASTs

Molecular mapping of grapevine genes

In this chapter, we review the history of grapevine genetics and gene mapping. Genetic markers are introduced considering both sequence-based and sequence-independent approaches used for variant discovery. We provide a survey of genotyping tools, from low- to high-throughput platforms. We describe general principles of map building and implementation, highlighting specificities for outbred species such as the grapevine. Then, we review the different approaches applied for QTL identification according to the genetic material, from bi-parental progenies, pedigree-supported segregating populations, to germplasm collection. In particular, our emphasis is on the relevance of such studies for the dissection of a complex trait. We describe the difficult process of identifying genes responsible for QTLs and the few cases of QTL cloning. Many years have passed from the first grapevine marker isolation, the development of genetic and physical maps, until the deciphering of the genome sequence. With such a wealth of detailed information on wild and cultivated grapevines, we discuss how data sharing and multidisciplinary data integration are the current challenges that the scientific community faces to effectively translate knowledge into practic