Duck (Anas platyrhynchos) linkage mapping by AFLP fingerprinting

Amplified fragment length polymorphism (AFLP) with multicolored fluorescent molecular markers was used to analyze duck (Anas platyrhynchos) genomic DNA and to construct the first AFLP genetic linkage map. These markers were developed and genotyped in 766 F2 individuals from six families from a cross between two different selected duck lines, brown Tsaiya and Pekin. Two hundred and ninety-six polymorphic bands (64% of all bands) were detected using 18 pairs of fluorescent TaqI/EcoRI primer combinations. Each primer set produced a range of 7 to 29 fragments in the reactions, and generated on average 16.4 polymorphic bands. The AFLP linkage map included 260 co-dominant markers distributed in 32 linkage groups. Twenty-one co-dominant markers were not linked with any other marker. Each linkage group contained three to 63 molecular markers and their size ranged between 19.0 cM and 171.9 cM. This AFLP linkage map provides important information for establishing a duck chromosome map, for mapping quantitative trait loci (QTL mapping) and for breeding applications

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