Genetic Covariance of Environments in the Potato National Chip Processing Trial. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Schmitz Carley, Cari A.; Coombs, Joseph J.; Clough, Mark E.; De Jong, Walter S.; Douches, David S.; Haynes, Kathleen G.; Higgins, Charles R.; Holm, David G.; Miller, J. Creighton; Navarro, Felix M.; Novy, Richard G.; Palta, Jiwan P.; Parish, David L.; Porter, Gregory A.; Sathuvalli, Vidyasagar R.; Thompson, Asunta L.; Yencho, G. Craig; Zotarelli, Lincoln; Endelman, Jeffrey B.
• Is Part Of: Crop science. Volume 59:Issue 1(2019)
• Page Start: 107
• Page End: 114
• Publication Date: 2019-01-01
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.2135/cropsci2018.05.0314
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract : The National Chip Processing Trial is a collaborative effort between public breeding programs and the potato ( Solanum tuberosum L.) industry to identify new clones with broad adaptation. The objective of this study was to investigate the genetic covariance of trial locations, based on 337 clones evaluated in 10 states from 2011 to 2016. Three models were considered: (I) assuming a uniform genetic correlation between locations within a year, (II) using a factor‐analytic (FA) model of the total genetic covariance of environments (location–year combinations), and (III) using a FA model of the additive genetic covariance based on 5278 single‐nucleotide polymorphism (SNP) markers. With Model I, the genetic correlation between locations was 0.50 for vine maturity, 0.54 for tuber yield, and 0.72 for specific gravity. The Akaike information criterion decreased as model complexity increased, from Models I to II to III, for maturity and yield but not specific gravity. Of the 10 states in the dataset, Florida stood out for having environments with substantial (up to 88%) genetic variance unexplained by the latent factors. Linear discriminants (LD) of the factor loadings were used to visualize the genetic correlation between locations. For vine maturity, LD1 separated Florida from the other locations, and LD2 separated the remaining southern locations from the northern ones. For yield, LD1 separated Texas from the other locations. This study has created a foundation for the design of more efficient trialing and selection programs for the US potato community.