PT  - JOURNAL ARTICLE
AU  - Valérian Méline
AU  - Denise L. Caldwell
AU  - Bong-Suk Kim
AU  - Sriram Baireddy
AU  - Changye Yang
AU  - Erin E. Sparks
AU  - Edward J. Delp
AU  - Anjali S. Iyer-Pascuzzi
TI  - Image-based assessment of plant disease progression identifies new genetic loci for resistance
AID  - 10.1101/2021.07.13.452064
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.07.13.452064
4099  - http://biorxiv.org/content/early/2021/07/22/2021.07.13.452064.short
4100  - http://biorxiv.org/content/early/2021/07/22/2021.07.13.452064.full
AB  - A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based, non-destructive quantification of disease phenotypes would enable the rapid identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based, non- destructive phenotyping allowed both earlier detection and identified new genetic components of resistance.Competing Interest StatementThe authors have declared no competing interest.