bioRxivPlant Biology
Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential
G. Molero, R. Joynson, F. J. Pinera-Chavez, L.-J. Gardiner, C. Rivera-Amado, A. Hall, M. P. Reynolds
Abstract
One of the major challenges for plant scientists is increasing wheat (Triticum aestivum) yield potential (YP). A significant bottleneck for increasing YP is achieving increased biomass through optimization of Radiation Use Efficiency (RUE) along the crop cycle. Exotic material such as landraces and synthetic wheat has been incorporated into breeding programs in an attempt to alleviate this, however their contribution to YP is still unclear. To understand the genetic basis of biomass accumulation and RUE we applied genome-wide association study (GWAS) to a panel of 150 elite spring wheat genotypes including many landrace and synthetically derived lines. The panel was evaluated for 31traits over two years under optimal growing conditions and genotyped using the 35K Wheat Breeders array. Marker-trait-association identified 94 SNPs significantly associated with yield, agronomic and phenology related traits along with RUE and biomass at various growth stages that explained 7-17 % of phenotypic variation. Common SNP markers were identified for grain yield, final biomass and RUE on chromosomes 5A and 7A. Additionally we show that landrace and synthetic derivative lines showed higher thousand grain weight (TGW), biomass and RUE but lower grain number (GNO) and harvest index (HI). Our work demonstrates the use of exotic material as a valuable resource to increase YP. It also provides markers for use in marker assisted breeding to systematically increase biomass, RUE and TGW and avoid the TGW/GNO and BM/HI trade-off. Thus, achieving greater genetic gains in elite germplasm while also highlighting genomic regions and candidate genes for further study.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- research focus: gene regulatory network inference
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
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