AUG 25, 2021 7:00 AM PDT

Automation-aided targeted genotyping with Allegro for maize breeding: a case study of QTL analysis with MAGIC mapping populations

Sponsored by: Tecan
Speakers

Abstract
Date:  August 25, 2021
Time: 7:00am PDT, 10:00am EDT, 4:00pm CEST
 
 
Multi-parental populations (MPPs) have been proposed as outstanding genetic resources for mapping QTL in crops. MPPs are based on the intercrossing of genetically divergent parental lines to produce a set of recombinant inbred lines (RILs) whose genome is a mosaic of the original parental lines. This design allows higher definition in QTL mapping owing to low population substructure, high recombination density, and high diversity. Among MPP designs, Multi-parent Advanced Generation Inter-Cross (MAGIC) were successfully used in animals and plants to map highly complex quantitative traits.
 
The high diversity of MAGIC RILs requires high-density and informative molecular markers to accurately reconstruct pedigree relationships among lines and consequently assign each recombination block to each of the donor parental along the genome. To this purpose, the Allegro genotyping system has proven to be an unprecedented tool to retrieve high-density genetic information through targeted and untargeted SNP typing of approximately 50k selected loci utilizing a single primer enrichment technology (SPET). Moreover, the implementation of the Allegro protocol on the DreamPrep liquid handler enabled the preparation of hundreds of barcoded libraries with a completely hands-off workflow, shortening the sample-to-data lead time.
 
Here we show the application of Allegro genotyping on a panel of MAGIC maize RILs derived from the intercrossing of eight founder lines. We show how these markers may be used to study the diversity of the RIL lines and help identify agronomically relevant QTLs.
 
 
 
Learning objectives:
 
  • Discover how MAGIC populations as a powerful tool for dissection of quantitative traits in maize.
  • Learn how targeted genotyping as a cost-effective solution to retrieve the most relevant genetic information in complex genomes.
  • Acquire valuable knowledge of how tailored automation can bring moderately complex NGS workflows to production scale.
 
 
 
 
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