Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as An image segmentation‐based method using the software I The segmentation‐based method was error‐prone (correlation between true and predicted seed counts, Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.
Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. Here, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.
- NSF-PAR ID:
- 10456364
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 226
- Issue:
- 6
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1873-1885
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Arabidopsis thaliana .mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts.r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. -
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