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New Phytol. 2015 Oct;208(1):257-68. doi: 10.1111/nph.13509. Epub 2015 Jun 17.

Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape.

Author information

1
Department of Botany, University of Wyoming, Laramie, WY, 82071, USA.
2
Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA.
3
Department of Plant Biology, University of California, Davis, CA, 95616, USA.
4
Center for Conservation and Sustainable Development, Missouri Botanical Garden, St Louis, MO, 63166, USA.
5
Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA.

Abstract

Improved predictions of fitness and yield may be obtained by characterizing the genetic controls and environmental dependencies of organismal ontogeny. Elucidating the shape of growth curves may reveal novel genetic controls that single-time-point (STP) analyses do not because, in theory, infinite numbers of growth curves can result in the same final measurement. We measured leaf lengths and widths in Brassica rapa recombinant inbred lines (RILs) throughout ontogeny. We modeled leaf growth and allometry as function valued traits (FVT), and examined genetic correlations between these traits and aspects of phenology, physiology, circadian rhythms and fitness. We used RNA-seq to construct a SNP linkage map and mapped trait quantitative trait loci (QTL). We found genetic trade-offs between leaf size and growth rate FVT and uncovered differences in genotypic and QTL correlations involving FVT vs STPs. We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development. Leaf shape is associated with venation features that affect desiccation resistance. The genetic independence of leaf shape from other leaf traits may therefore enable crop optimization in leaf shape without negative effects on traits such as size, growth rate, duration or gas exchange.

KEYWORDS:

Brassica rapa; allometry; function valued traits; growth modeling; leaf development; modularity; quantitative genetics; quantitative trait locus (QTL) mapping

PMID:
26083847
DOI:
10.1111/nph.13509
[Indexed for MEDLINE]
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