High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

Zhang, Xuehai; Huang, Chenglong; Wu, Di; Qiao, Feng; Li, Wenqiang; Duan, Lingfeng; Wang, Ke; Xiao, Yingjie; Chen, Guoxing; Liu, Qian; Xiong, Lizhong; Yang, Wanneng; Yan, Jianbing

doi:10.1104/pp.16.01516

Cited by 183 publications

(163 citation statements)

References 46 publications

(57 reference statements)

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“…As an example, the "smart canopy" ideotype considers leaf position and morphology, proposing plants with erect leaves at the top of the canopy as a means to increase photosynthetic efficiency, in combination with biochemical traits (Innes and Blackwell 1983;Araus et al 1993;Richards and Lukacs 2002;Ort et al 2015). Several studies support the importance of leaf angle manipulation in different cereal crops (e.g., Gardener 1966; Zhang et al 2017).…”

Section: Erect Leaves and Canopy Architecturementioning

confidence: 99%

Genetics of barley tiller and leaf development

Shaaf

Bretani

Biswas

et al. 2019

JIPB

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In cereals, tillering and leaf development are key factors in the concept of crop ideotype, introduced in the 1960s to enhance crop yield, via manipulation of plant architecture. In the present review, we discuss advances in genetic analysis of barley shoot architecture, focusing on tillering, leaf size and angle. We also discuss novel phenotyping techniques, such as 2D and 3D imaging, that have been introduced in the era of phenomics, facilitating reliable trait measurement. We discuss the identification of genes and pathways that are involved in barley tillering and leaf development, highlighting key hormones involved in the control of plant architecture in barley and rice. Knowledge on genetic control of traits related to plant architecture provides useful resources for designing ideotypes for enhanced barley yield and performance.

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Section: Erect Leaves and Canopy Architecturementioning

confidence: 99%

Genetics of barley tiller and leaf development

Shaaf

Bretani

Biswas

et al. 2019

JIPB

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“…Unlike single time point measurements, time series trait data enables the quantification of the dynamics of plant growth and development. Biomass data collected from maize RIL and association populations have demonstrated that different genetic loci are identified using data from different time points in development 2,3 . However statistical approaches for both dealing with the particular complexities of time series phenotypic measurements and extracting as much information as possible from repeated phenotypic measurements remains an ongoing area of development within plant biology and quantitative genetics.…”

Section: Introductionmentioning

confidence: 99%

Functional Modeling of Plant Growth Dynamics

Xu¹,

Qiu²,

Schnable³

2017

Preprint

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Recent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach -functional ANOVA -which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves -e.g. growth rates -simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.

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“…In practice, plant phenomics for genetic studies is often resolved as breaking down universal, whole‐plant phenotypes into separate ones – in space or time, or both – following the logic that each ‘sub’ trait is being controlled by a smaller number of genes (Figure ). Functional variation within such traits, which is only controlled by a subset of genes, is expected to show stronger trait associations in genetic analysis than when more general phenotypes are recorded, thus improving phenotypic resolution (Tian et al ., ; Yang et al ., ; Crowell et al ., ; Zhang et al ., ; Prado et al ., ). This was conceptually verified by Crowell et al .…”

Section: Towards High‐throughput Phenotyping To Study Natural Variatimentioning

confidence: 99%

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

et al. 2019

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SummaryIn recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High‐throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis‐related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio‐engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype‐to‐gene identification pipeline.

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High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

Cited by 183 publications

References 46 publications

Genetics of barley tiller and leaf development

Genetics of barley tiller and leaf development

Functional Modeling of Plant Growth Dynamics

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

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