A next-generation marker genotyping platform (AmpSeq) in heterozygous crops: a case study for marker-assisted selection in grapevine. (17th February 2016) - Yang, Shanshan; Fresnedo-Ramírez, Jonathan; Wang, Minghui; Cote, Linda; Schweitzer, Peter; Barba, Paola; Takacs, Elizabeth M; Clark, Matthew; Luby, James; Manns, David C; Sacks, Gavin; Mansfield, Anna Katharine; et al.
Abstract: Marker-assisted selection (MAS) is often employed in crop breeding programs to accelerate and enhance cultivar development, via selection during the juvenile phase and parental selection prior to crossing. Next-generation sequencing and its derivative technologies have been used for genome-wide molecular marker discovery. To bridge the gap between marker development and MAS implementation, this study developed a novel practical strategy with a semi-automated pipeline that incorporates trait-associated single nucleotide polymorphism marker discovery, low-cost genotyping through amplicon sequencing (AmpSeq) and decision making. The results document the development of a MAS package derived from genotyping-by-sequencing using three traits (flower sex, disease resistance and acylated anthocyanins) in grapevine breeding. The vast majority of sequence reads (⩾99%) were from the targeted regions. Across 380 individuals and up to 31 amplicons sequenced in each lane of MiSeq data, most amplicons (83 to 87%) had <10% missing data, and read depth had a median of 220–244×. Several strengths of the AmpSeq platform that make this approach of broad interest in diverse crop species include accuracy, flexibility, speed, high-throughput, low-cost and easily automated analysis. Abstract : Plant breeding: Automated analysis speeds selection A new strategy for genetic analysis may accelerate development of improved cultivars in perennial crops such as grapevine. Breeding new varieties of slow-growing crops like trees and vines is a time-consuming process. The recent introduction of gene-based methods including marker-assisted selection (MAS) enables breeders to select promising individuals before maturity. However, this relies on identifying the genes associated with desired traits, a process that is not always straightforward. Now, a US team led by Lance Cadle-Davidson, USDA-ARS, Geneva, and Qi Sun, Cornell University, Ithaca, have developed a strategy to speed up MAS. Using modern 'next generation' sequencing methods and machine-learning, this approach bridges the gap between gene discovery and application in marker-assisted breeding programs. It has proved successful for three traits in grapevine, and could become useful across a wide range of crops.