bioRxivGeneticsPreprint
Strong genotype by environment interactions in the rice Global MAGIC population across seedling stage drought
Brook T. MoyersiD, Amelia HenryiD, Chitra Raghavan, Hein ZawiD, Annarita MarranoiD, Hei Leung, John McKayiD
Abstract
Crop adaptation is required to sustainably increase the rate of yield gains to meet projected future needs under the challenging conditions of climate change and competition for resources. Future adaptation will likely need to harness both highly polygenic traits and genotype-by-environment interactions (GxE), the study of which is aided by complex recombinant populations. We used the diverse Oryza sativa Global Multiparent Advanced Generation InterCross (MAGIC) population to study the genetic architecture and contributions of seedling emergence, establishment, and development to yield components under dry-direct seeding and seedling stage drought across three growing seasons. Dry-direct seeding is an establishment practice that has the potential to reduce methane emissions, water use, and labor demands for rice farmers, but increases the possibility of early-season drought conditions. We found substantial evidence for large roles of environmental variation and GxE in controlling trait variation. Maintenance of shoot growth during seedling stage drought was positively correlated with crown root number and both directly and indirectly influenced agronomic traits. Other than the major green revolution locus sd1, most allelic effects were conditionally neutral (affecting trait values in some environments but not others) and most alleles had their strongest effect in an environmental extreme. This discovery is both a challenge and a potential opportunity: with variable genetic architecture, selection in any one environment may not result in stable trait gains across environments. However, conditionally neutral GxE is a potential route to sustainable yield stability through allele pyramiding.
- organism: plant (other)
- research focus: genome assembly
- research focus: GWAS / association mapping
- research focus: polygenic risk
- approach: statistical model
- Citations
- 2
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