| Autor: |
Delen Y; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA., Palali-Delen S; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA., Xu G; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA., Neji M; Crop Science Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, SI-1000 Ljubljana, Slovenia., Yang J; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA., Dweikat I; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA. |
| Abstrakt: |
The sunflower ( Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement. |
