Scanning the rice Global MAGIC population for dynamic genetic control of seed traits under vegetative drought

Abstract

Grain size and weight are important yield components in rice (Oryza sativa L.). There is still uncertainty about the genetic control of these traits under drought stress, the most pressing emerging issue in many rice cultivation areas. To address this lack of knowledge, we investigated the genetic architecture of seed size, shape, and weight using the rice Global Multi-parent Advanced Generation Intercross (MAGIC) population, grown under well-watered and vegetative drought conditions. We measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV). Besides being affordable, rapid, and accurate, our method captured the phenotypic divergence between drought and well-watered samples, expressed as 12 different traits that include traditional size metrics and new grain shape measures. Overall, under water deficit, the MAGIC lines produced smaller and shorter seeds. We identified ten MAGIC lines with traits that make them good candidates for the release of rice cultivars with high yield potential under vegetative drought stress. We ran a marker-trait association analysis for the measured seed-related traits. Most of the identified marker-trait associations showed strong genotype-by-environment interactions (GxE), with most allele effects being conditionally neutral. These results suggest dynamic genetic control of seed size, shape, and weight under vegetative drought stress in rice, highlighting the importance of understanding the contribution of GxE interactions on trait variation to develop resilient and high-yielding rice varieties. Our study confirms that combining low-cost and high-throughput phenotyping strategies with a diverse genetic material suited for multi-environmental trial provides solutions for adapting rice cultivation to current and future environmental adversities.

An image‐based methodology to evaluate oat panicle architecture

“…In addition, new and emerging traits can be obtained from images that would otherwise be extremely challenging to measure by hand or traditional techniques (Yang et al, 2020). In the last two decades, successful image analysis algorithms were generated to characterize different plant structures and organs, including grains (Duan et al, 2011;Hughes et al, 2017;Marrano & Moyers, 2020), panicles (AL-Tam et al, 2013;Crowell et al, 2014;Ikeda et al, 2010), tassels (Gage et al, 2017), leaves , and roots (Le Marié et al, 2016;Mathieu et al, 2015;Narisetti et al, 2019). However, although these strategies have been deployed in several species, to our knowledge, no study has characterized oat panicle architecture with this methodology.…”