High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

Wang, Xu; Xuan, Hong; Evers, Byron; Shrestha, Sandesh; Pless, Robert; Poland, Jesse

doi:10.1101/527911

Cited by 31 publications

(34 citation statements)

References 30 publications

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“…These genes are known to influence photoperiod sensitivity, and therefore transition to flowering and HD (Welsh et al 1973;Law et al 1978;Scarth and Law 1983). Certain allele pairs at these genes have been shown to exhibit a high degree of epistasis (Wang et al 2019) in a biparental family. It is unclear why no interaction was observed in this population.…”

Section: Resultsmentioning

confidence: 99%

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

2019

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Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions.

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Section: Resultsmentioning

confidence: 99%

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

2019

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“…Recent studies have intensively investigated deep learning-based solutions to flower detection and counting for field crops such as wheat [12][13][14][15][16], corn [17], sorghum [18], rice [19], and cotton [20]. Based on the counting strategies, these methods can be classified into three categories: regression-based, classification-based, and detection-based counting [21].…”

Section: Introductionmentioning

confidence: 99%

“…The two counting strategies considerably reduce the training complexity and the cost of data annotation. Experiments showed that they can provide fairly good counting accuracies (up to 90%) [13,[15][16][17]. However, the regression-and classification-based methods may suffer from overfitting problems because the CNN models are generally much more complex than training objectives (a numeric value or several classes).…”

Section: Introductionmentioning

confidence: 99%

DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field

et al. 2020

Plant Methods

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Background Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant. Results A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2–3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes. Conclusions The developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.

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“…For example, hyperspectral reflectance phenotypes, which record spectral reflectance at a large range of wavelengths, were shown to increase prediction accuracy for GY when compared to VIs (Montesinos-López et al, 2017). Beyond spectral reflectance-related traits, deep learning algorithms have been developed to determine DTHD from proximal imagery of wheat canopies (Wang et al, 2019), UAV imagery has been used to estimate lodging in wheat as a function of changes in plant height throughout the growing season (Singh et al, 2019), and convolutional neural networks have been trained to identify foliar diseases in maize from aerial imagery (Wu et al, 2019). Looking ahead, these comprehensive suites of traits may allow confounding effects such as those observed in this study to be adequately addressed through integrative selection approaches that account for genetic correlations between traits.…”

Section: Discussionmentioning

confidence: 99%

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

Krause

Mondal

Crossa

et al. 2020

Preprint

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Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

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High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

Cited by 31 publications

References 30 publications

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

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