Abstract
BACKGROUND: Efficient capture and use of resources is critical for optimal plant growth and productivity. Both shoot and root growth are essential for resource acquisition, namely light and CO(2) by the shoot and water and mineral nutrients by roots. Soybean [Glycine max (L.) Merr.], one of the most valuable crops world-wide, uses an additional strategy, symbiotic N fixation (SNF), for N acquisition. SNF relies on development of specialized root organs known as nodules, which represent a distinct C sink. The genetic diversity of C partitioning in N fixing soybean to shoots, roots, and nodules has not been previously investigated but is valuable to better understand consequences of differential C allocation and to develop genetic resources, including identification of quantitative trait loci (QTLs). RESULTS: A diversity panel of 402 soybean genotypes was phenotyped outdoors in a deep-tube system without addition of mineral N to measure allocation of biomass to the shoot, root, and nodules, as well as to determine nodule number, mean nodule biomass, and total shoot N accumulation. Wide ranges in phenotypes were observed for each of these traits, demonstrating extensive natural diversity in C partitioning and SNF in soybean. Using a set of 35,647 single nucleotide polymorphism (SNP) markers, we identified 121 SNPs tagging 103 QTLs that include both 84 novel and 19 previously identified QTLs for the eight examined traits. A candidate gene search identified 79 promising gene models in the vicinity of these QTLs. Favorable alleles of QTLs identified here may be used in breeding programs to develop elite cultivars with altered C partitioning. CONCLUSIONS: This study provides novel insights into the diversity of biomass allocation in soybean and illustrates that the traits measured here are heritable and quantitative. QTLs identified in this study can be used in genomic prediction models as well as for further investigation of candidate genes and their roles in determining partitioning of fixed C. Enhancing our understanding of C partitioning in plants may lead to elite cultivars with optimized resource use efficiencies.