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Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology.

iScience · 10 Jul 2021 · 10.1016/j.isci.2021.102846

Abstract

The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.

Plant phenotyping relevance

植物の生理形質を取得・解析するFPP-FM統合フレームワークを開発し、トマト集団の蒸散測定で実証しており、表現型取得と解析手法が中心である。

abstractHere, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM).
abstractThis integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype.
abstractAs a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework

Code and data availability

保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。

Codepublic

We have coded all statistical algorithms that build our framework into a user-friendly R package for public use ( https://github.com/FFP-FM/Version1 ).

Open resource ↗FFP-FM/Version1 · lines:232-250

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