Analysis of rice panicle traits and detection of QTLs using an image analyzing method

Ikeda, Mayuko; Hirose, Yoshitsugu; Takashi, Tomonori; Shibata, Yosuke; Yamamura, Takuya; Komura, Toshiro; Doi, Kouki; Ashikari, Motoyuki; Matsuoka, Makoto; Kitano, Hidemi

doi:10.1270/jsbbs.60.55

Cited by 57 publications

(45 citation statements)

References 41 publications

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“…Additionally, we identified several regions of the genome that show pleiotropic clustering of panicle QTLs and confirmed the overlap of PANorama QTLs with two independently measured reproductive traits (DTH and PN). Both of these findings are consistent with previous studies in rice (Kobayashi et al, 2003;Ando et al, 2008;Liu et al, 2008;Ikeda et al, 2010), adding further biological validity to our QTL analysis. Taken together, these results attest to both the efficiency and accuracy of the PANorama pipeline.…”

Section: Quantitative Panicle Phenotypes Are Necessary To Leverage Hisupporting

confidence: 93%

“…The concept of pleiotropy in panicle development has been confirmed numerous times within other QTL studies (Kobayashi et al, 2003;Ando et al, 2008;Liu et al, 2008;Ikeda et al, 2010). However, limited genotype and phenotype resolution has always prevented the dissection of these complex loci, which could be composed of single large-effect genes or arrays of linked, small-effect genes, as we suggest exist on chromosome 1 (Fig.…”

Section: Rice Panicle Architecture Varies Across Different Genetic Basupporting

confidence: 60%

“…The traditional breeding phenotype PL not only ignores meristem transitions but also lumps in the length of the rachis axis with that of the terminal primary branch ( Fig. 1; Ikeda et al, 2004Ikeda et al, , 2010. Following this logic, measuring rachis length (RL) using the IM abortion point is also not ideal, as the rachis can be considered a compound phenotype.…”

Section: Development and Implementation Of The Phenotyping Platform Pmentioning

confidence: 99%

“…Two software packages have previously been developed to measure panicle-related traits, PASTAR/PASTA Viewer and P-TRAP (Ikeda et al, 2010;AL-Tam et al, 2013). PASTAR/PASTA Viewer extracts length and count traits from panicle images by identifying and connecting the top and bottom termini of branches (Ikeda et al, 2010).…”

mentioning

confidence: 99%

“…PASTAR/PASTA Viewer extracts length and count traits from panicle images by identifying and connecting the top and bottom termini of branches (Ikeda et al, 2010). This platform was tested in a quantitative trait locus (QTL) mapping study (Ikeda et al, 2010), yet it is under license and not freely available. The second platform, P-TRAP, is open source, yet it has not been validated in a genetic mapping study and was released after being tested on only 26 images (AL-Tam et al, 2013).…”

mentioning

confidence: 99%

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High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

Crowell¹,

Falcão

Shah

et al. 2014

Plant Physiology

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Variation in inflorescence development is an important target of selection for numerous crop species, including many members of the Poaceae (grasses). In Asian rice (Oryza sativa), inflorescence (panicle) architecture is correlated with yield and grain-quality traits. However, many rice breeders continue to use composite phenotypes in selection pipelines, because measuring complex, branched panicles requires a significant investment of resources. We developed an open-source phenotyping platform, PANorama, which measures multiple architectural and branching phenotypes from images simultaneously. PANorama automatically extracts skeletons from images, allows users to subdivide axes into individual internodes, and thresholds away structures, such as awns, that normally interfere with accurate panicle phenotyping. PANorama represents an improvement in both efficiency and accuracy over existing panicle imaging platforms, and flexible implementation makes PANorama capable of measuring a range of organs from other plant species. Using high-resolution phenotypes, a mapping population of recombinant inbred lines, and a dense singlenucleotide polymorphism data set, we identify, to our knowledge, the largest number of quantitative trait loci (QTLs) for panicle traits ever reported in a single study. Several areas of the genome show pleiotropic clusters of panicle QTLs, including a region near the rice Green Revolution gene SEMIDWARF1. We also confirm that multiple panicle phenotypes are distinctly different among a small collection of diverse rice varieties. Taken together, these results suggest that clusters of small-effect QTLs may be responsible for varietal or subpopulation-specific panicle traits, representing a significant opportunity for rice breeders selecting for yield performance across different genetic backgrounds.

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Section: Quantitative Panicle Phenotypes Are Necessary To Leverage Hisupporting

confidence: 93%

Section: Rice Panicle Architecture Varies Across Different Genetic Basupporting

confidence: 60%

Section: Development and Implementation Of The Phenotyping Platform Pmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

Crowell¹,

Falcão

Shah

et al. 2014

Plant Physiology

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High‐Throughput Phenotyping: Potential Tool for Genomics

Barbadikar

Balakrishnan

Gireesh

et al. 2019

OMICS‐Based Approaches in Plant Biotechnology

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An image‐based methodology to evaluate oat panicle architecture

2023

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Panicle size and architecture are traits of major relevance for oat production as they play a key role in conditioning the potential number of grains per spikelet and in consequence grain shape, size, and distribution. The aim of this research was to propose an image-based strategy and methodology to evaluate oat panicles. Specifically, the goals were to create and deploy the foundations for a high-throughput methodology to phenotype oat panicles and to determine the optimum number of panicles needed to detect differences between genotypic means for oat panicle traits with high power. A total of 48 genotypes were evaluated in four field experiments with three true replications in two locations and 2 years. Ten panicles per experimental unit were harvested and evaluated with image analysis using MATLAB. Panicle length, width, aspect ratio, compactness, and digital biomass were obtained from the images. The heritability of all panicle traits was high between 0.76 and 0.93. A high power of 0.80 to detect significant differences at an alpha of 0.05 among genotypic means for all panicle architecture traits was reached with the use of six panicles per replication. Our results showed a highly efficient and powerful strategy to study panicle architecture in oat. This methodology could be scaled-up to efficiently phenotype large populations and provide the basis for understanding panicle architecture and grain morphology in oat.

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Analysis of rice panicle traits and detection of QTLs using an image analyzing method

Cited by 57 publications

References 41 publications

High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

High‐Throughput Phenotyping: Potential Tool for Genomics

An image‐based methodology to evaluate oat panicle architecture

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