12 research outputs found
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QTLs for shelf life in lettuce co-locate with those for leaf biophysical properties but not with those for leaf developmental traits
Developmental and biophysical leaf characteristics that influence post-harvest shelf life in lettuce, an important leafy crop, have been examined. The traits were studied using 60 informative F-9 recombinant inbed lines (RILs) derived from a cross between cultivated lettuce (Lactuca sativa cv. Salinas) and wild lettuce (L. serriola acc. UC96US23). Quantitative trait loci (QTLs) for shelf life co-located most closely with those for leaf biophysical properties such as plasticity, elasticity, and breakstrength, suggesting that these are appropriate targets for molecular breeding for improved shelf life. Significant correlations were found between shelf life and leaf size, leaf weight, leaf chlorophyll content, leaf stomatal index, and epidermal cell number per leaf, indicating that these pre-harvest leaf development traits confer post-harvest properties. By studying the population in two contrasting environments in northern and southern Europe, the genotype by environment interaction effects of the QTLs relevant to leaf development and shelf life were assessed. In total, 107 QTLs, distributed on all nine linkage groups, were detected from the 29 traits. Only five QTLs were common in both environments. Several areas where many QTLs co-located (hotspots) on the genome were identified, with relatively little overlap between developmental hotspots and those relating to shelf life. However, QTLs for leaf biophysical properties (breakstrength, plasticity, and elasticity) and cell area correlated well with shelf life, confirming that the ideal ideotype lettuce should have small cells with strong cell walls. The identification of QTLs for leaf development, strength, and longevity will lead to a better understanding of processability at a genetic and cellular level, and allow the improvement of salad leaf quality through marker-assisted breeding
A high-density, integrated genetic linkage map of lettuce (Lactuca spp.)
An integrated map for lettuce comprising of 2,744 markers was developed from seven intra- and inter-specific mapping populations. A total of 560 markers that segregated in two or more populations were used to align the individual maps. 2,073 AFLP, 152 RFLP, 130 SSR, and 360 RAPD as well as 29 other markers were assigned to nine chromosomal linkage groups that spanned a total of 1,505 cM and ranged from 136 to 238 cM. The maximum interval between markers in the integrated map is 43 cM and the mean interval is 0.7 cM. The majority of markers segregated close to Mendelian expectations in the intra-specific crosses. In the two L. saligna Ă— L. sativa inter-specific crosses, a total of 155 and 116 markers in 13 regions exhibited significant segregation distortion. Data visualization tools were developed to curate, display and query the data. The integrated map provides a framework for mapping ESTs in one core mapping population relative to phenotypes that segregate in other populations. It also provides large numbers of markers for marker assisted selection, candidate gene identification, and studies of genome evolution in the Composita
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PhytoOracle: Scalable, modular phenomics data processing pipelines
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area). Copyright © 2023 Gonzalez, Zarei, Hendler, Simmons, Zarei, Demieville, Strand, Rozzi, Calleja, Ellingson, Cosi, Davey, Lavelle, Truco, Swetnam, Merchant, Michelmore, Lyons and Pauli.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]