Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

Moreira, Fabiana Freitas; Oliveira, Hinayah Rojas de; Volenec, Jeffrey J.; Rainey, Katy Martin; Brito, Luiz F.

doi:10.3389/fpls.2020.00681

Cited by 44 publications

(41 citation statements)

References 259 publications

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“…From the estimated curves and their derivatives, we calculate new phenotypic traits, attributes, that we call intermediate traits. 58 discuss the use of information obtained from HTP time-series traits for genomic selection and the detection of QTL and causal variants. Finally, while we have focused in the paper on data with a nested structure, the proposed modelling framework can easily be extended to accommodate more complex structures, such as data with crossed levels of grouping 21 (e.g., when modelling genotype-by-treatment interactions is of interest).…”

Section: Discussionmentioning

confidence: 99%

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

eacuterez-Valencia

Aacutelvarez

Boer

et al. 2021

Preprint

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High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

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Section: Discussionmentioning

confidence: 99%

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

eacuterez-Valencia

Aacutelvarez

Boer

et al. 2021

Preprint

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show abstract

“…Several analytical approaches are used to study phenotypic record for different longitudinal traits such as biomass accumulation and light interception, in various crops (Moreira et al 2020). In a recent pea breeding program, UAS-based imaging techniques were used to determine crop performance and biomass estimation (Quirós Vargas et al 2019).…”

Section: Applications Of High-throughput Phenotypingmentioning

confidence: 99%

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

et al. 2021

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Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

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“…High-throughput phenotyping using platforms such as unmanned aerial vehicles has significantly enriched data collection for secondary traits (Hassan et al 2019;Moreira et al 2020). As the rapid development of high-throughput phenotyping techniques continues, more relevant and useful phenotypes or traits will be available for MT-GS applications in plant breeding to meet different breeding needs.…”

Section: Multi-trait Gs Applications In Plant Breedingmentioning

confidence: 99%

Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

McGowan¹,

Zhang²,

Wang³

et al. 2020

Preprint

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Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

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Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

Cited by 44 publications

References 259 publications

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

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