Abstract

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

Highlights

  • Developing crop varieties that maintain a consistent yield across different environmental conditions is an important target for plant breeding as weather patterns become more variable due to global changes in climate

  • We evaluated the proposed method using three natural datasets across different plant species and different treatment types, including a population of recombinant inbred lines of Foxtail grass (Setaria) treated with drought stress [3], a panel of sorghum treated with nitrogen deficiency [22], and the founders of a nested association mapping population of canola Brassica napus treated with drought stress

  • The experiments presented here were performed on two NVIDIA Titan V GPUs, and the time required per experiment ranged from two to eight hours depending on the number of accessions and the number of time points in the dataset

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Summary

Introduction

Developing crop varieties that maintain a consistent yield across different environmental conditions is an important target for plant breeding as weather patterns become more variable due to global changes in climate. Treatment studies, where some individuals are subjected to different growing conditions than a control group, play an important role in uncovering the genetic potential for tolerance of stress. Such experiments include genotype-by-environment (G × E) studies, where the treatment is often abiotic stress, such as water-limited growing conditions, or genotype-by-management (G × M) studies, where the treatment is a different application of inputs, such as herbicide application to assess herbicide tolerance or nitrogen application to assess nitrogen use efficiency. A core challenge for this broad class of experiments is the ability to quantify and characterize the physical changes observed in the treated plant population relative to the control population, i.e., to phenotype a plant’s response-to-treatment. An accurate and quantitative assessment of response-to-treatment is important for genomic association studies

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