Identification of in vivo induced maternal haploids in maize using seedling traits View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-08

AUTHORS

Vijay Chaikam, Luis Antonio Lopez, Leocadio Martinez, Juan Burgueño, Prasanna M. Boddupalli

ABSTRACT

In vivo haploid induction in high frequency followed by efficient identification of haploids are important components of deriving completely homozygous doubled haploid (DH) lines in maize. Several genetic marker systems were proposed and/or used for identification of in vivo maternal haploids in maize, such as R1-nj (Navajo), high oil, red root and transgenic markers. In this study, we propose a new method of haploid/diploid identification based on natural differences in seedling traits of haploids and diploids, which can be used in any induction cross independently of the genetic marker systems. Using confirmed haploids and diploids from five different populations, the study established that haploid and diploid seedlings exhibit significant differences for seedling traits, particularly radicle length (RL), coleoptile length (CL), and number of lateral seminal roots (NLSR). In six populations that exhibited complete inhibition of the commonly used R1-nj (Navajo) marker, we could effectively differentiate haploids from diploids by visual inspection of the seedling traits. In the haploid seed fraction identified based on R1-nj marker in ten populations, false positives were reduced several-fold by early identification of haploids at seedling stage using the seedling traits. We propose that seedling traits may be integrated at the haploid identification stage, especially in populations that are not amenable to use of genetic markers, and for improving the efficiency of DH line production by reducing the false positives. More... »

PAGES

177

Journal

TITLE

Euphytica

ISSUE

8

VOLUME

213

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10681-017-1968-3

DOI

http://dx.doi.org/10.1007/s10681-017-1968-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090674976


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