bioRxivPlant BiologyPreprint
The first nested association mapping (NAM) population for outbreeding Italian ryegrass reveals candidate genes for seed shattering and related traits
Jenny KiesbaueriD, Christoph GriederiD, Meril Sindelar, Linda Helene Schlatter, Daniel Ariza-SuáreziD, Steven Andrew YatesiD, Ingrid Stoffel-Studer, Dario CopettiiD, Bruno StuderiD, Roland KöllikeriD
Abstract
Nested association mapping (NAM) populations are a powerful tool for investigating the genetic control of agronomically important traits and have been successfully used in many inbreeding crops. Here, we present the first NAM population established in an outcrossing forage grass species, Italian ryegrass (Lolium multiflorum Lam.), to dissect the genetic control of seed shattering. The NAM population was based on 23 diverse and one common founder plants and consisted of 708 F2 individuals. Reduced-representation sequencing (ddRAD) of the 708 F2 individuals, combined with whole genome sequencing data of the 24 founder plants, yielded a total of 3,199,253 SNPs that were used for population structure analysis, parentship analysis and genome-wide association studies. Phenotypic data for seed shattering and seed yield-related traits, collected in three year x location environments, showed high phenotypic variance within the NAM population. A total of seven QTL were identified for seed shattering, seed yield, spike length, flag leaf length and flowering time. Within these QTL regions, one candidate gene for seed shattering and three candidate genes for flowering time were identified. For seed shattering, the significant SNP association within the gene chr7.26897, known to be in involved in ripening-related pathways, explained 10.03% of the phenotypic variance. These candidate genes identified provide valuable targets for functional validation and demonstrate the effectiveness of NAM populations for elucidating the genetic architecture of complex traits in outcrossing forage grasses.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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