A population of deletion mutants and an integrated mapping and exome-seq pipeline for gene discovery in maize

Shangang Jia, Aixia Li, Kyla Morton, Penny Avoles-Kianian, Shahryar F. Kianian, Chi Zhang, David Holding

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

To better understand maize endosperm filling and maturation, we used g-irradiation of the B73 maize reference line to generate mutants with opaque endosperm and reduced kernel fill phenotypes, and created a population of 1788 lines including 39 Mo17 · F2s showing stable, segregating, and viable kernel phenotypes. For molecular characterization of the mutants, we developed a novel functional genomics platform that combined bulked segregant RNA and exome sequencing (BSREx-seq) to map causative mutations and identify candidate genes within mapping intervals. To exemplify the utility of the mutants and provide proof-of-concept for the bioinformatics platform, we present detailed characterization of line 937, an opaque mutant harboring a 6203 bp in-frame deletion covering six exons within the Opaque-1 gene. In addition, we describe mutant line 146 which contains a 4.8 kb intragene deletion within the Sugary-1 gene and line 916 in which an 8.6 kb deletion knocks out a Cyclin A2 gene. The publically available algorithm developed in this work improves the identification of causative deletions and its corresponding gaps within mapping peaks. This study demonstrates the utility of γ-irradiation for forward genetics in large nondense genomes such as maize since deletions often affect single genes. Furthermore, we show how this classical mutagenesis method becomes applicable for functional genomics when combined with state-ofthe- art genomics tools.

Original languageEnglish (US)
Pages (from-to)2385-2395
Number of pages11
JournalG3: Genes, Genomes, Genetics
Volume6
Issue number8
DOIs
StatePublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 Jia et al.

Keywords

  • BSR-seq
  • Exome-seq
  • Maize functional genomics
  • Opaque causative mutation

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