This webinar addresses the current status and future directions for massively high-throughput genomics for plant and animal breeding and research.
A major drawback to sequencing-based agriculture studies has been the cost. Arrays and reduced representation sequencing methods are common alternatives for genotyping, but each of these methods has significant limitations associated with it.
In this webinar, Charles Johnson, founder of the Texas A&M AgriLife Genomics and Bioinformatics Service, shares how his team developed a new approach, called AgSeq, to address these shortcomings.
AgSeq is a novel agriculture-focused genotyping pipeline that uses optimized laboratory processing, massive sample multiplexing, and machine learning to obtain highly accurate genotype information from low-coverage sequencing data. The reduced cost of whole-genome sequencing afforded by AgSeq allows for a substantial increase in individuals genotyped per study. AgSeq is powered by optimized library prep, automation, and high-throughput sequencing coupled with a reduction in the amount of data needed per individual. Data from individual samples is used to accurately impute gaps resulting from reduced coverage, allowing for accurate genotyping of large populations for plant and animal studies.