Improving Genetic Gain with Genomic Selection in Autotetraploid Potato. Issue 3 (1st November 2016)

• Record Type: Journal Article
• Authors: Slater, Anthony T.; Cogan, Noel O.I.; Forster, John W.; Hayes, Benjamin J.; Daetwyler, Hans D.
• Is Part Of: œplant genome. Volume 9:Issue 3(2016)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-11-01
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.3835/plantgenome2016.02.0021
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract : Potato ( Solanum tuberosum L.) breeders consider a large number of traits during cultivar development and progress in conventional breeding can be slow. There is accumulating evidence that some of these traits, such as yield, are affected by a large number of genes with small individual effects. Recently, significant efforts have been applied to the development of genomic resources to improve potato breeding, culminating in a draft genome sequence and the identification of a large number of single nucleotide polymorphisms (SNPs). The availability of these genome‐wide SNPs is a prerequisite for implementing genomic selection for improvement of polygenic traits such as yield. In this review, we investigate opportunities for the application of genomic selection to potato, including novel breeding program designs. We have considered a number of factors that will influence this process, including the autotetraploid and heterozygous genetic nature of potato, the rate of decay of linkage disequilibrium, the number of required markers, the design of a reference population, and trait heritability. Based on estimates of the effective population size derived from a potato breeding program, we have calculated the expected accuracy of genomic selection for four key traits of varying heritability and propose that it will be reasonably accurate. We compared the expected genetic gain from genomic selection with the expected gain from phenotypic and pedigree selection, and found that genetic gain can be substantially improved by using genomic selection.