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. 2013:3:2442.
doi: 10.1038/srep02442.

Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation

Lucas Busemeyer  1 , Arno Ruckelshausen, Kim Möller, Albrecht E Melchinger, Katharina V Alheit, Hans Peter Maurer, Volker Hahn, Elmar A Weissmann, Jochen C Reif, Tobias Würschum
Affiliations

Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation

Lucas Busemeyer et al. Sci Rep. 2013.

Abstract

To extend agricultural productivity by knowledge-based breeding and tailor varieties adapted to specific environmental conditions, it is imperative to improve our ability to assess the dynamic changes of the phenome of crops under field conditions. To this end, we have developed a precision phenotyping platform that combines various sensors for a non-invasive, high-throughput and high-dimensional phenotyping of small grain cereals. This platform yielded high prediction accuracies and heritabilities for biomass of triticale. Genetic variation for biomass accumulation was dissected with 647 doubled haploid lines derived from four families. Employing a genome-wide association mapping approach, two major quantitative trait loci (QTL) for biomass were identified and the genetic architecture of biomass accumulation was found to be characterized by dynamic temporal patterns. Our findings highlight the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.

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Figures

Figure 1
Figure 1. Precision phenotyping platform.
(a,b) Platform with multiple sensors for non-invasive assessment of biomass under field conditions. 3D-ToF: 3D-Time-of-Flight camera; LDS: laser distance sensor; HSI: hyperspectral imaging; LCI: light curtain imaging. (c) Information captured by the different sensors in a single yield plot. (d) Technical repeatability, and (e) prediction accuracy of the platform based on sensor fusion models using data from two years. formula image and formula image denote the coefficient of determination of cross-validation and of repetition, respectively, and RMSREv and RMSREw denote the root mean squared relative error of cross-validation and of repetition, respectively.
Figure 2
Figure 2. Genetic architecture of biomass accumulation.
(a) Schematic representation of small grain cereal growth and the three developmental stages at which biomass (BM) was assessed in this study. (b) Venn diagram for markers significantly associated with BM1, BM2, BM3, and in the multivariate analysis. (c) Manhattan plots of the genome-wide association study. Significant associations are shown in green.
Figure 3
Figure 3. Epistatic interaction networks.
Epistatic QTL for biomass (BM) at the three developmental stages and their proportion of explained genotypic variance (pG).
See this image and copyright information in PMC

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