Molecular mapping of water-stress responsive genomic loci in lettuce (Lactuca spp.) using kinetics chlorophyll fluorescence, hyperspectral imaging and machine learning

Authors: Kumar, Pawan; Eriksen, Renee; Simko, Ivan; Mou, Beiquan

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/29/2021
Publication Date: 2/18/2021
Citation: Kumar, P., Eriksen, R.L., Simko, I., Mou, B. 2021. Molecular mapping of water-stress responsive genomic loci in lettuce (Lactuca spp.) using kinetics chlorophyll fluorescence, hyperspectral imaging and machine learning. Frontiers in Genetics. 12. Article 634554. https://doi.org/10.3389/fgene.2021.634554.
DOI: https://doi.org/10.3389/fgene.2021.634554

Interpretive Summary: Lettuce production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency as the water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. A better understanding of genetic architecture of water-stress tolerance is critical for developing water-stress-tolerant lettuce cultivars. We identified 25 regions in the lettuce genome that control three horticultural traits (fresh weight, dry weight, water content) under control (well-watered) and water-stress conditions. Breeding for water-stress tolerance is often limited by precise phenotyping of the water-stress related traits. We therefore, evaluated the utility of two non-destructive image-based phenotyping methodsfor identifying genomic regions affected during water-stress progression: chlorophyll fluorescence and hyperspectral imaging. Sixty such genomic regions were distributed on eight lettuce chromosomes. Genomic regions controlling horticultural traits and image-based phenotyping traits existed in clusters. Non-destructive image-based phenotyping methods can reliably be used in lettuce breeding programs for early screening of water-stress-tolerant plants.

Technical Abstract: Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively ascertain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a Lactuca sativa × Lactuca serriola recombinant inbred line (RIL) population. We identified 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits, and 34 QTL for spectral vegetation indices (VI). The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5 %, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26 %, and PV explained by the VI QTL ranged from 7.2 to 17.19 %. We identified eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI on six lettuce chromosomes.